Environmental Science › Environmental Engineering

Remote Sensing and LiDAR Applications

Works Count: 70466

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Description

This cluster of papers focuses on the use of Lidar remote sensing technology for mapping and assessing various forest attributes such as carbon stocks, aboveground biomass, tree height, and canopy structure at global and regional scales. The papers cover techniques like airborne and terrestrial laser scanning, data fusion, individual tree detection, and biomass estimation, highlighting the importance of Lidar in sustainable forest management and ecological research.

Keywords

Lidar Remote Sensing; Forest Carbon Stocks; Aboveground Biomass; Tree Height Estimation; Global Forest Mapping; Airborne Laser Scanning; Terrestrial Laser Scanning; Forest Inventory; Biomass Estimation; Canopy Structure

Assessing the Accuracy of Remotely Sensed Data

1998-09-17

Russell G. Congalton , Kass Green

Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference … Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference Data Collection Basic Collection Forms Basic Analysis Techniques Non-Site Specific Assessments Site Specific Assessments Area Estimation/Correction Practicals Impact of Sample Design on Cost Recommendations for Collecting Reference Data ASources of Variation in Reference Data Photo Interpretation vs. Ground Visitation Interpreter Variability Observations vs. Measurements What is Correct? Labeling Map vs. Labeling the Reference Data Qualitative vs. Quantitative Analysis Local vs. Regional vs. Global Assessments Advanced Topics Beyond the Error Matrix Modifying the Error Matrix Fuzzy Set Theory Measuring Variability Complex Data Sets Change Detection Multi-Layer Assessments California Hardwood Rangeland Monitoring Project Case Study Balancing Statistical Validity with Practical Reality Bibliography

Smallest enclosing disks (balls and ellipsoids)

2006-01-25

Emo Welzl

The Line Intersect Method in Forest Fuel Sampling

1968-03-01

V. Wagner

The National Elevation Dataset

2002-01-01

Dean B. Gesch , Michael J. Oimoen , Susan K. Greenlee +3 more

The National Elevation Dataset (NED) is a raster product that provides elevation data coverage of the entire United States and its island territories in a seamless format with consistent projection, … The National Elevation Dataset (NED) is a raster product that provides elevation data coverage of the entire United States and its island territories in a seamless format with consistent projection, resolution, elevation units and horizontal and vertical datums. It is the result of the maturation of the U.S. Geological Survey elevation production program, in which national coverage of quadrangle-based digital elevation models has been completed. Specifications and production techniques for the NED are given. The NED fulfills many of the concepts of framework geospatial data as envisioned for the National Spatial Data Infrastructure, allowing users to focus on analysis rather than data preparation. The NED provides basic elevation data for many geographic information system applications and is maintained and updated regularly. The USGS is making several seamless datasets including NED available through the Internet.

Topographic structure from motion: a new development in photogrammetric measurement

2012-11-09

Mark A. Fonstad , J. Dietrich , Brittany C. Courville +2 more

ABSTRACT The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods … ABSTRACT The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware and/or software. However, image‐based methods such as digital photogrammetry have been decreasing in costs. Developed for the purpose of rapid, inexpensive and easy three‐dimensional surveys of buildings or small objects, the ‘structure from motion’ photogrammetric approach (SfM) is an image‐based method which could deliver a methodological leap if transferred to geomorphic applications, requires little training and is extremely inexpensive. Using an online SfM program, we created high‐resolution digital elevation models of a river environment from ordinary photographs produced from a workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three‐dimensional space. The basic product of the SfM process is a point cloud of identifiable features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected in the field or from measurements of camera positions at the time of image acquisition. The georeferenced point cloud can then be used to create a variety of digital elevation products. We examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand‐held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low‐altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Copyright © 2012 John Wiley & Sons, Ltd.

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Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

2013-05-30

Dimitri Lague , Nicolas Brodu , Jérôme Leroux

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Airborne laser scanning: basic relations and formulas

1999-07-01

Emmanuel P. Baltsavias

Mapping forest canopy height globally with spaceborne lidar

2011-09-16

Marc Simard , N. Pinto , Joshua B. Fisher +1 more

[1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial … [1] Data from spaceborne light detection and ranging (lidar) opens the possibility to map forest vertical structure globally. We present a wall-to-wall, global map of canopy height at 1-km spatial resolution, using 2005 data from the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite). A challenge in the use of GLAS data for global vegetation studies is the sparse coverage of lidar shots (mean = 121 data points/degree2 for the L3C campaign). However, GLAS-derived canopy height (RH100) values were highly correlated with other, more spatially dense, ancillary variables available globally, which allowed us to model global RH100 from forest type, tree cover, elevation, and climatology maps. The difference between the model predicted RH100 and footprint level lidar-derived RH100 values showed that error increased in closed broadleaved forests such as the Amazon, underscoring the challenges in mapping tall (>40 m) canopies. The resulting map was validated with field measurements from 66 FLUXNET sites. The modeled RH100 versus in situ canopy height error (RMSE = 6.1 m, R2 = 0.5; or, RMSE = 4.4 m, R2 = 0.7 without 7 outliers) is conservative as it also includes measurement uncertainty and sub pixel variability within the 1-km pixels. Our results were compared against a recently published canopy height map. We found our values to be in general taller and more strongly correlated with FLUXNET data. Our map reveals a global latitudinal gradient in canopy height, increasing towards the equator, as well as coarse forest disturbance patterns.

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Full-waveform topographic lidar: State-of-the-art

2008-10-30

Clément Mallet , Frédéric Bretar

Overview of the ICESat Mission

2005-11-01

B. E. Schutz , H. Jay Zwally , Christopher A. Shuman +2 more

The Geoscience Laser Altimeter System (GLAS) on the NASA Ice, Cloud and land Elevation Satellite (ICESat) has provided a view of the Earth in three dimensions with unprecedented accuracy. Although … The Geoscience Laser Altimeter System (GLAS) on the NASA Ice, Cloud and land Elevation Satellite (ICESat) has provided a view of the Earth in three dimensions with unprecedented accuracy. Although the primary objectives focus on polar ice sheet mass balance, the GLAS measurements, distributed in 15 science data products, have interdisciplinary application to land topography, hydrology, vegetation canopy heights, cloud heights and atmospheric aerosol distributions. Early laser life issues have been mitigated with the adoption of 33‐day operation periods, three times per year, designed to document intra‐ and inter‐annual polar ice changes in accordance with mission requirements. A variety of calibration/validation experiments have been executed which show that the elevation products, when fully calibrated, have an accuracy that meets the science requirements. The series of papers in this special ICESat issue demonstrate the utility and quality of the ICESat data.

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Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data

2002-04-01

Erik Næsset

Determination of terrain models in wooded areas with airborne laser scanner data

1998-08-01

K. Kraus , Norbert Pfeifer

Airborne laser scanning—an introduction and overview

1999-07-01

Aloysius Wehr , Uwe Lohr

ICESat's laser measurements of polar ice, atmosphere, ocean, and land

2002-10-01

H. Jay Zwally , B. E. Schutz , W. Abdalati +7 more

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A progressive morphological filter for removing nonground measurements from airborne LIDAR data

2003-04-01

Keqi Zhang , Shu‐Ching Chen , Dean Whitman +3 more

Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution … Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.

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Processing of laser scanner data—algorithms and applications

1999-07-01

Peter Axelsson

Making Bertha Drive—An Autonomous Journey on a Historic Route

2014-01-01

Julius Ziegler , Philipp Bender , Markus Schreiber +7 more

Abstract-125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, … Abstract-125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Use of LIDAR in landslide investigations: a review

2010-10-18

Michel Jaboyedoff , Thierry Oppikofer , Antonio Abellán +4 more

This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general … This paper presents a short history of the appraisal of laser scanner technologies in geosciences used for imaging relief by high-resolution digital elevation models (HRDEMs) or 3D models. A general overview of light detection and ranging (LIDAR) techniques applied to landslides is given, followed by a review of different applications of LIDAR for landslide, rockfall and debris-flow. These applications are classified as: (1) Detection and characterization of mass movements; (2) Hazard assessment and susceptibility mapping; (3) Modelling; (4) Monitoring. This review emphasizes how LIDAR-derived HRDEMs can be used to investigate any type of landslides. It is clear that such HRDEMs are not yet a common tool for landslides investigations, but this technique has opened new domains of applications that still have to be developed.

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Monitoring and estimating tropical forest carbon stocks: making REDD a reality

2007-10-01

Holly Gibbs , Sandra Brown , John O. Niles +1 more

Reducing carbon emissions from deforestation and degradation in developing countries is of central importance in efforts to combat climate change. Key scientific challenges must be addressed to prevent any policy … Reducing carbon emissions from deforestation and degradation in developing countries is of central importance in efforts to combat climate change. Key scientific challenges must be addressed to prevent any policy roadblocks. Foremost among the challenges is quantifying nations’ carbon emissions from deforestation and forest degradation, which requires information on forest clearing and carbon storage. Here we review a range of methods available to estimate national-level forest carbon stocks in developing countries. While there are no practical methods to directly measure all forest carbon stocks across a country, both ground-based and remote-sensing measurements of forest attributes can be converted into estimates of national carbon stocks using allometric relationships. Here we synthesize, map and update prominent forest biomass carbon databases to create the first complete set of national-level forest carbon stock estimates. These forest carbon estimates expand on the default values recommended by the Intergovernmental Panel on Climate Change’s National Greenhouse Gas Inventory Guidelines and provide a range of globally consistent estimates.

A comparison between photogrammetry and laser scanning

1999-07-01

Emmanuel P. Baltsavias

Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes

1986-01-01

Farzin Mokhtarian , Alan K. Mackworth

The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary … The problem of finding a description, at varying levels of detail, for planar curves and matching two such descriptions is posed and solved in this paper. A number of necessary criteria are imposed on any candidate solution method. Path-based Gaussian smoothing techniques are applied to the curve to find zeros of curvature at varying levels of detail. The result is the ``generalized scale space'' image of a planar curve which is invariant under rotation, uniform scaling and translation of the curve. These properties make the scale space image suitable for matching. The matching algorithm is a modification of the uniform cost algorithm and finds the lowest cost match of contours in the scale space images. It is argued that this is preferable to matching in a so-called stable scale of the curve because no such scale may exist for a given curve. This technique is applied to register a Landsat satellite image of the Strait of Georgia, B.C. (manually corrected for skew) to a map containing the shorelines of an overlapping area.

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Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

2013-03-21

Joe Sexton , Xiao‐Peng Song , Min Feng +7 more

Abstract We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 … Abstract We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas. Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =8.6% in 2000 and 11.9% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE=16.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earth's tree cover available to the Earth science community.

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Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds

2004-07-22

G. Sithole , George Vosselman

Benchmark map of forest carbon stocks in tropical regions across three continents

2011-05-31

Sassan Saatchi , Nancy L. Harris , Sandra Brown +7 more

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here … Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca . ±5% and ca . ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

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Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

2006-01-28

Wolfgang Wagner , Andreas Ullrich , V. Ducic +2 more

Measuring carbon in forests: current status and future challenges

2002-03-01

Sandra Brown

LiDAR remote sensing of forest structure

2003-02-20

Kevin Lim , Paul Treitz , Michael A. Wulder +2 more

Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR … Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

Determination of water depth with high‐resolution satellite imagery over variable bottom types

2003-01-01

Richard P. Stumpf , Kristine Holderied , Mark Sinclair

A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an … A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low‐albedo features. The two algorithms—the standard linear transform and the new ratio transform— were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10–15 m. However, the linear transform does not distinguish depths .15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in >25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4–5 pixels) in water depths >15–20 m. In general, the ratio transform is more robust than the linear transform.

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Graph-based approach for airborne light detection and ranging segmentation

2017-03-10

D.L. Vilariño , José C. Cabaleiro , Jorge Martínez Sánchez +2 more

A graph-based segmentation technique has been tailored to segment airborne LiDAR points which, unlike images, are irregularly distributed. In our method, every LiDAR point is labeled as a node and … A graph-based segmentation technique has been tailored to segment airborne LiDAR points which, unlike images, are irregularly distributed. In our method, every LiDAR point is labeled as a node and interconnected as a graph extended to its neighborhood, defined in a 4-D feature space: the spatial coordinates (x,y,z) and the reflection intensity. The interconnections between pairs of neighboring nodes are weighted based on the distance in the feature space. The segmentation consists of an iterative process of classification of nodes into homogeneous groups based on their similarity. This approach is intended to be part of a complete system for the classification of structures from LiDAR point clouds in applications needing fast response times. In this sense, a study of the performance/accuracy trade-off has been performed, extracting some conclusions about the benefits of the proposed solution. In addition, an interlaced graph-based approach is proposed to increase the reliability in general purpose segmentations.

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The potential and challenge of remote sensing‐based biomass estimation

2006-04-01

Dengsheng Lu

Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based biomass estimation approaches and a discussion of existing issues influencing biomass … Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based biomass estimation approaches and a discussion of existing issues influencing biomass estimation are valuable for further improving biomass estimation performance. The literature review has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest stand structures and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research is needed to focus on the integration of optical and radar data, the use of multi‐source data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties caused by different stages of the biomass estimation procedure and devoting efforts to reduce these uncertainties are critical.

Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

2013-01-25

Pasi Raumonen , M. Kaasalainen , Markku Åkerblom +6 more

This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of … This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds.

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Detecting and measuring individual trees using an airborne laser scanner

2002-01-01

Åsa Persson , Johan Holmgren , Ulf Söderman

High-resolution airborne laser scanner data offer the possibility to detect and measure individual trees. In this study an algorithm which estimated position, height, and crown diameter of individu ... High-resolution airborne laser scanner data offer the possibility to detect and measure individual trees. In this study an algorithm which estimated position, height, and crown diameter of individu ...

Image‐based 3D Modelling: A Review

2006-08-07

Fabio Remondino , Sabry F. El-Hakim

Abstract In this paper the main problems and the available solutions are addressed for the generation of 3D models from terrestrial images. Close range photogrammetry has dealt for many years … Abstract In this paper the main problems and the available solutions are addressed for the generation of 3D models from terrestrial images. Close range photogrammetry has dealt for many years with manual or automatic image measurements for precise 3D modelling. Nowadays 3D scanners are also becoming a standard source for input data in many application areas, but image‐based modelling still remains the most complete, economical, portable, flexible and widely used approach. In this paper the full pipeline is presented for 3D modelling from terrestrial image data, considering the different approaches and analysing all the steps involved.

Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography

2013-12-24

Arko Lucieer , S.M. de Jong , Darren Turner

In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we … In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we apply a Structure from Motion (SfM) workflow to derive a 3D model of a landslide in southeast Tasmania from multi-view UAV photography. The geometric accuracy of the 3D model and resulting DEMs and orthophoto mosaics was tested with ground control points coordinated with geodetic GPS receivers. A horizontal accuracy of 7 cm and vertical accuracy of 6 cm was achieved. In the second part, two DEMs and orthophoto mosaics acquired on 16 July 2011 and 10 November 2011 were compared to study landslide dynamics. The COSI-Corr image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two hillshaded DEM layers corresponded to a visual interpretation of landslide change. Results show that the algorithm can accurately map displacements of the toes, chunks of soil, and vegetation patches on top of the landslide, but is not capable of mapping the retreat of the main scarp. The conclusion is that UAV-based imagery in combination with 3D scene reconstruction and image correlation algorithms provide flexible and effective tools to map and monitor landslide dynamics.

Passive remote sensing techniques for mapping water depth and bottom features

1978-02-01

David R. Lyzenga

Ratio processing methods are reviewed, and a new method is proposed for extracting water depth and bottom type information from passive multispectral scanner data. Limitations of each technique are discussed, … Ratio processing methods are reviewed, and a new method is proposed for extracting water depth and bottom type information from passive multispectral scanner data. Limitations of each technique are discussed, and an error analysis is performed using an analytical model for the radiance over shallow water.

Remote sensing for biodiversity science and conservation

2003-06-01

Woody Turner , Sacha Spector , Ned Gardiner +3 more

A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners

2001-05-01

Juha Hyyppä , O. Kelle , Mikko Lehikoinen +1 more

In the boreal forest zone and in many forest areas, there exist gaps between the forest crowns. For example, in Finland, more than 30% of the first pulse data of … In the boreal forest zone and in many forest areas, there exist gaps between the forest crowns. For example, in Finland, more than 30% of the first pulse data of laser scanning reflect directly from the ground without any interaction with the canopy. By increasing the number of pulses, it is possible to have samples from each individual tree and also from the gaps between the trees. Basically, this means that several laser pulses can be recorded per m/sup 2/. This allows detailed investigation of forest areas and the creation of a three-dimensional (3D) tree height model. Tree height model can be calculated from the digital terrain and crown models both obtained with the laser scanner data. By analyzing the 3D tree height model by using image vision methods, e.g., segmentation, it is possible to locate individual trees, estimate individual tree heights, crown area, and, by using that data, to derive the stem diameter, number of stems, basal area, and stem volume. The advantage of the method is the capability to measure directly physical dimensions from the trees and use that information to calculate the needed stand attributes. This paper demonstrates for the first time that it is possible to accurately estimate standwise forest attributes, especially stem volume (biomass), using high-pulse-rate laser scanners to provide data, from which individual trees can be detected and characteristics of trees such as height, location, and crown dimensions can be determined. That information can be applied to provide estimates for larger areas (stands). Using the new method, the following standard errors were demonstrated for mean height, basal area and stem volume: 1.8 m (9.9%), 2.0 m/sup 2//ha (10.2%), and 18.5 m/sup 3//ha (10.5%), respectively.

Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests

1999-12-01

M. A. Lefsky , Warren B. Cohen , Steven A. Acker +3 more

Lidar Remote Sensing for Ecosystem Studies

2002-01-01

M. A. Lefsky , Warren B. Cohen , Geoffrey G. Parker +1 more

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Terrestrial laser scanning in forest inventories

2016-02-20

Xinlian Liang , Ville Kankare , Juha Hyyppä +7 more

Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on … Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique.

LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression

1981-02-01

William S. Cleveland

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

2016-06-15

Wuming Zhang , Jianbo Qi , Peng Wan +4 more

Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms … Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

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Review of studies on tree species classification from remotely sensed data

2016-08-20

Fabian Ewald Fassnacht , Hooman Latifi , Krzysztof Stereńczak +5 more

A Spherical Densiometer For Estimating Forest Overstory Density

1956-12-01

Paul E. Lemmon

Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs

2018-06-01

Loïc Landrieu , Martin Simonovsky

We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point … We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset).

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SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

2019-10-01

Jens Behley , Martin Garbade , Andres Milioto +4 more

Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides … Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete 360-degree field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.

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The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography

2020-01-27

R. Dubayah , J. B. Blair , S. J. Goetz +7 more

Obtaining accurate and widespread measurements of the vertical structure of the Earth's forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the … Obtaining accurate and widespread measurements of the vertical structure of the Earth's forests has been a long-sought goal for the ecological community. Such observations are critical for accurately assessing the existing biomass of forests, and how changes in this biomass caused by human activities or variations in climate may impact atmospheric CO2 concentrations. Additionally, the three-dimensional structure of forests is a key component of habitat quality and biodiversity at local to regional scales. The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude. The GEDI instrument is a geodetic-class laser altimeter/waveform lidar comprised of 3 lasers that produce 8 transects of structural information. Over its two-year nominal lifetime GEDI is anticipated to provide over 10 billion waveforms at a footprint resolution of 25 m. These data will be used to derive a variety of footprint and gridded products, including canopy height, canopy foliar profiles, Leaf Area Index (LAI), sub-canopy topography and biomass. Additionally, data from GEDI are used to demonstrate the efficacy of its measurements for prognostic ecosystem modeling, habit and biodiversity studies, and for fusion using radar and other remote sensing instruments. GEDI science and technology are unique: no other space-based mission has been created that is specifically optimized for retrieving vegetation vertical structure. As such, GEDI promises to advance our understanding of the importance of canopy vertical variations within an ecological paradigm based on structure, composition and function.

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Mapping global forest canopy height through integration of GEDI and Landsat data

2020-11-07

Peter Potapov , Xinyuan Li , Andrés Hernández-Serna +7 more

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DEM Generation from Laser Scanner Data Using Adaptive TIN Models

2000-01-01

Peter Axelsson

Forest Three-Dimensional Reconstruction Method Based on High-Resolution Remote Sensing Image Using Tree Crown Segmentation and Individual Tree Parameter Extraction Model

2025-06-25

Gengyu Ma , Gang Yang , Hao Lu +1 more | Remote Sensing

Efficient and accurate acquisition of tree distribution and three-dimensional geometric information in forest scenes, along with three-dimensional reconstructions of entire forest environments, hold significant application value in precision forestry and … Efficient and accurate acquisition of tree distribution and three-dimensional geometric information in forest scenes, along with three-dimensional reconstructions of entire forest environments, hold significant application value in precision forestry and forestry digital twins. However, due to complex vegetation structures, fine geometric details, and severe occlusions in forest environments, existing methods—whether vision-based or LiDAR-based—still face challenges such as high data acquisition costs, feature extraction difficulties, and limited reconstruction accuracy. This study focuses on reconstructing tree distribution and extracting key individual tree parameters, and it proposes a forest 3D reconstruction framework based on high-resolution remote sensing images. Firstly, an optimized Mask R-CNN model was employed to segment individual tree crowns and extract distribution information. Then, a Tree Parameter and Reconstruction Network (TPRN) was constructed to directly estimate key structural parameters (height, DBH etc.) from crown images and generate tree 3D models. Subsequently, the 3D forest scene could be reconstructed by combining the distribution information and tree 3D models. In addition, to address the data scarcity, a hybrid training strategy integrating virtual and real data was proposed for crown segmentation and individual tree parameter estimation. Experimental results demonstrated that the proposed method could reconstruct an entire forest scene within seconds while accurately preserving tree distribution and individual tree attributes. In two real-world plots, the tree counting accuracy exceeded 90%, with an average tree localization error under 0.2 m. The TPRN achieved parameter extraction accuracies of 92.7% and 96% for tree height, and 95.4% and 94.1% for DBH. Furthermore, the generated individual tree models achieved average Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores of 11.24 and 0.53, respectively, validating the quality of the reconstruction. This approach enables fast and effective large-scale forest scene reconstruction using only a single remote sensing image as input, demonstrating significant potential for applications in both dynamic forest resource monitoring and forestry-oriented digital twin systems.

Comparison of Advanced Terrestrial and Aerial Remote Sensing Methods for Above-Ground Carbon Stock Estimation—A Comparative Case Study for a Hungarian Temperate Forest

2025-06-25

Botond Szász , Bálint Heil , Gábor Kovács +2 more | Remote Sensing

The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of … The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data.

Multi-Resolution UAV Remote Sensing for Anthropogenic Debris Detection in Complex River Environments

2025-06-25

Peaceibisia Jack , Trent Biggs , Daniel Sousa +3 more | Remote Sensing

Anthropogenic debris in urban floodplains poses significant environmental and ecological risks, with an estimated 4 to 12 million metric tons entering oceans annually via riverine transport. While remote sensing and … Anthropogenic debris in urban floodplains poses significant environmental and ecological risks, with an estimated 4 to 12 million metric tons entering oceans annually via riverine transport. While remote sensing and artificial intelligence (AI) offer promising tools for automated debris detection, most existing datasets focus on marine environments with homogeneous backgrounds, leaving a critical gap for complex terrestrial floodplains. This study introduces the San Diego River Debris Dataset, a multi-resolution UAV imagery collection with ground reference designed to support automated detection of anthropogenic debris in urban floodplains. The dataset includes manually annotated debris objects captured under diverse environmental conditions using two UAV platforms (DJI Matrice 300 and DJI Mini 2) across spatial resolutions ranging from 0.4 to 4.4 cm. We benchmarked five deep learning architectures (RetinaNet, SSD, Faster R-CNN, DetReg, Cascade R-CNN) to assess detection accuracy across varying image resolutions and environmental settings. Cascade R-CNN achieved the highest accuracy (0.93) at 0.4 cm resolution, with accuracy declining rapidly at resolutions above 1 cm and 3.3 cm. Spatial analysis revealed that 51% of debris was concentrated within unsheltered encampments, which occupied only 2.6% of the study area. Validation confirmed a strong correlation between predicted debris extent and field measurements, supporting the dataset’s operational reliability. This openly available dataset fills a gap in environmental monitoring resources and provides guides for future research and deployment of UAV-based debris detection systems in urban floodplain areas.

Cyber-Driven Surveillance and Alert System for Illegal Logging Detection in Protected Forest Areas

2025-06-24

| IJIREEICE

Detection of Trees and Objects in Apple Orchard from LiDAR Point Cloud Data Using a YOLOv5 Framework

2025-06-24

Md Rejaul Karim , Md Nasim Reza , Shahriar Ahmed +3 more | Electronics

Object detection is crucial for smart apple orchard management using agricultural machinery to avoid obstacles. The objective of this study was to detect apple trees and other objects in an … Object detection is crucial for smart apple orchard management using agricultural machinery to avoid obstacles. The objective of this study was to detect apple trees and other objects in an apple orchard using LiDAR and the YOLOv5 algorithm. A commercial LiDAR was attached to a tripod to collect apple tree trunk data, which were then pre-processed and converted into PNG images. A pre-processed set of 1500 images was manually annotated with bounding boxes and class labels (trees, water tanks, and others) to train and validate the YOLOv5 object detection algorithm. The model, trained over 100 epochs, resulted in 90% precision, 87% recall, [email protected] of 0.89, and [email protected]:0.95 of 0.48. The accuracy reached 89% with a low classification loss of 0.001. Class-wise accuracy was high for water tanks (96%) and trees (95%), while the “others” category had lower accuracy (82%) due to inter-class similarity. Accurate object detection is challenging since the apple orchard environment is complex and unstructured. Background misclassifications highlight the need for improved dataset balance, better feature discrimination, and refinement in detecting ambiguous objects.

Automated extraction of geometric information from LiDAR point clouds on curved ramps

2025-06-24

Yuchen Wang , Yuhang Liu , Pieter Pauwels +2 more | Automation in Construction

Optimizing integration techniques for UAS and satellite image data in precision agriculture — a review

2025-06-24

Aliasghar Bazrafkan , C. Igathinathane , Nonoy Bandillo +1 more | Frontiers in Remote Sensing

The fusion of unmanned aerial system (UAS) and satellite imagery has emerged as a pivotal strategy in advancing precision agriculture. This review explores the significance of integrating high-resolution UAS and … The fusion of unmanned aerial system (UAS) and satellite imagery has emerged as a pivotal strategy in advancing precision agriculture. This review explores the significance of integrating high-resolution UAS and satellite imagery via pixel-based, feature-based, and decision-based fusion methods. The study investigates optimization techniques, spectral synergy, temporal strategies, and challenges in data fusion, presenting transformative insights such as enhanced biomass estimation through UAS-satellite synergy, improved nitrogen stress detection in maize, and refined crop type mapping using multi-temporal fusion. The combined spectral information from UAS and satellite sources confirms instrumental in crop monitoring and biomass estimation. Temporal optimization strategies consider factors such as crop phenology, spatial resolution, and budget constraints, offering effective and continuous monitoring solutions. The review systematically addresses challenges in spatial and temporal resolutions, radiometric calibration, data synchronization, and processing techniques, providing practical solutions. Integrated UAS and satellite data impact precision agriculture, contributing to improved resolution, monitoring capabilities, resource allocation, and crop performance evaluation. A comparative analysis underscores the superiority of combined data, particularly for specific crops and scenarios. Researchers exhibit a preference for pixel-based fusion methods, aligning fusion goals with specific needs. The findings contribute to the evolving landscape of precision agriculture, suggesting avenues for future research and reinforcing the field’s dynamism and relevance. Future works should delve into advanced fusion methodologies, incorporating machine learning algorithms, and conduct cross-crop application studies to broaden applicability and tailor insights for specific crops.

Automated optimizing method for map labeling of buildings from remote sensing images

2025-06-24

Yiqing Zhang , Yilang Shen | International Journal of Geographical Information Science

Improving aboveground biomass estimation by integrating ICESat-2 LiDAR sampling and tree species stratification strategies using multi-source datasets

2025-06-24

Pan Liu , Chunying Ren , Zongming Wang +1 more | Geo-spatial Information Science

Tree species identification based on convolutional neural network and feature fusion

2025-06-23

J. Wu , Ting Zhong , G. Li +2 more | Computers and Electronics in Agriculture

Quantifying and mapping the ready-to-use veneer volume of European beech trees based on terrestrial laser scanning data

2025-06-23

Elena Larysch , Julian Frey , Zoe Schindler +5 more | European Journal of Forest Research

Abstract Using 3D point clouds obtained with terrestrial laser scanning (TLS), we automatically and non-destructively quantified and mapped the estimated veneer wood volume of standing trees in differently structured beech … Abstract Using 3D point clouds obtained with terrestrial laser scanning (TLS), we automatically and non-destructively quantified and mapped the estimated veneer wood volume of standing trees in differently structured beech stands. To mitigate climate change, we need to utilise wood for long-term carbon storage in products like construction wood and for substituting building materials based on fossil fuels. As the supply of wood from Norway spruce decreases, alternative species like beech must be considered for construction purposes. We present an approach to quantify and map the volume available for veneer production in beech forests. Our method is based on point clouds derived from TLS. We studied three forest plots, each with two different treatments (moderate vs. heavy thinning), resulting in varying stand basal areas ranging from 25 m 2 to 36 m 2 per hectare. We fitted different configurations of veneer rolls into point clouds of tree stems, choosing the configuration that yielded the highest volume of veneer wood. Our automatic optimisation algorithm ensured no misplaced veneer rolls. At the tree level, veneer wood volume was higher in intensely thinned stands. At the stand level, overall veneer volume was higher in moderately thinned stands, whereas the overall veneer share was higher in the heavily thinned stands. The veneer volume of a tree depended on diameter at breast height, crown base height, taper and curvature depth. Our approach detects all trees in a forest potentially ready for veneer production and shows the direct volumetric outcome under bark. This enables the planning of tree selection for harvest based on adaptable requirements for the veneer production.

Correction: Landscape Classification Using an Optimized Ghost Network from Aerial Images

2025-06-23

C. Pushpalatha , B. Sivasankari , A. Ahilan +1 more | Journal of the Indian Society of Remote Sensing

Aerial Imagery as a Tool for Monitoring Urban Tree Retention: Applications, Strengths and Challenges for Backyard Tree Planting Programs

2025-06-23

Aysha Fleming , Tenley M. Conway , Pamela Sleightholm +1 more | Arboriculture & Urban Forestry

Abstract Background Urban tree planting initiatives are a popular way to increase municipal tree presence. Initiatives on private land, including backyard tree planting programs, are essential because most available planting … Abstract Background Urban tree planting initiatives are a popular way to increase municipal tree presence. Initiatives on private land, including backyard tree planting programs, are essential because most available planting space across cities is on private property. Therefore, understanding the success of these programs, including long-term tree retention rates, is crucial for determining future urban forest characteristics and associated ecosystem services. However, few studies evaluate the outcome of backyard planting programs, primarily because of barriers like limited organizational resources and the inaccessibility of trees planted in backyards. To address this issue, our study examined the feasibility of using publicly available aerial imagery to assess long-term retention of trees planted through a backyard tree planting program in Toronto, Ontario. Methods Using 20 years of leaf-off imagery and hand-drawn planting maps, a sample of 2,654 trees was assessed for feasibility of location digitization, presence-absence classification in 2022, and 5-year survivorship. Results We successfully digitized 1,865 (70%) of these trees, but the remaining 30% could not be mapped due to insufficient location information. Of those digitized, we could confidently determine if 1,533 trees (82%) were present or absent in 2022. The status of the remaining 18% of trees was unclear, often due to image obstruction or quality. We were able to determine presence/absence 5 years after planting for 81% of trees in the subset examined. Conclusions Ultimately, using aerial imagery could be a time- and cost-effective approach to long-term, ongoing urban tree monitoring, though challenges associated with image availability and quality should be considered.

Intensity correction of multi-return signal from airborne laser scanning to improve land cover interpretation

2025-06-23

M. Pilarska | Applied Geomatics

Comparative Analysis of Reconstructive Crime Scene

2025-06-22

Miklós Emri , Lau Chong Luh , Mohamad Hezri Razali +4 more | Built Environment Journal

In this new technology era, crime scene reconstruction should be enhanced with the latest technology instead of using conventional two-dimensional (2D) approach. This study expands our knowledge of how to … In this new technology era, crime scene reconstruction should be enhanced with the latest technology instead of using conventional two-dimensional (2D) approach. This study expands our knowledge of how to employ iPhone LiDAR to speed up the process of gathering crime scene data. With the aim of revolutionising forensic efficiency, this study investigates the potential of iPhone LiDAR in crime scene reconstruction with conventional techniques. In this study, distometer, measuring tape, iPhone 14 Pro Max and Total Station were used for data collection. The objectives include creating a crime scene sketch by hand, converting point clouds data into a 2D drawing, and analysing measurements obtained from various techniques. In this study, Total Station was used to conduct the detail survey of the crime scene. In this study, the distometer was used for baseline measurements while the measuring tape was utilised for short distances measurements. IPhone LiDAR 14 Pro Max, which is equipped with a 3D Scanner App, was used as the proposed approach for data collection. The findings demonstrate that the manual measurements using a distometer and measuring tape are less accurate than LiDAR readings when compared with the readings acquired from the Total Station.

High-Precision Reconstruction of Water Areas Based on High-Resolution Stereo Pairs of Satellite Images

2025-06-22

Junyan Ye , Rui Xu , Yixiao Wang +1 more | Remote Sensing

The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite … The use of high-resolution satellite stereo pairs for dense image matching is a core technology for the low-cost generation of large-scale digital surface models (DSMs). However, water areas in satellite imagery often exhibit weak texture characteristics. This leads to serious issues in reconstructing water surface DSMs with traditional dense matching methods, such as significant holes and abnormal undulations. These problems significantly impact the intelligent application of satellite DSM products. To address these issues, this study innovatively proposes a water region DSM reconstruction method, boundary plane-constrained surface water stereo reconstruction (BPC-SWSR). The algorithm constructs a water surface reconstruction model with constraints on the plane’s tilt angle and boundary, combining effective ground matching data from the shoreline and the plane constraints of the water surface. This method achieves the seamless planar reconstruction of the water region, effectively solving the technical challenges of low geometric accuracy in water surface DSMs. This article conducts experiments on 10 high-resolution satellite stereo image pairs, covering three types of water bodies: river, lake, and sea. Ground truth water surface elevations were obtained through a manual tie point selection followed by forward intersection and planar fitting in water surface areas, establishing a rigorous validation framework. The DSMs generated by the proposed algorithm were compared with those generated by state-of-the-art dense matching algorithms and the industry-leading software Reconstruction Master version 6.0. The proposed algorithm achieves a mean RMSE of 2.279 m and a variance of 0.6613 m2 in water surface elevation estimation, significantly outperforming existing methods with average RMSE and a variance of 229.2 m and 522.5 m2, respectively. This demonstrates the algorithm’s ability to generate more accurate and smoother water surface models. Furthermore, the algorithm still achieves excellent reconstruction results when processing different types of water areas, confirming its wide applicability in real-world scenarios.

Identification and Restoration of Forest Degradation Areas in Shaanxi Province Based on the LandTrendr Algorithm

2025-06-21

Qianqian Tian , Bingshu Zhao , Chenyu Xu +3 more | Sustainability

As an important ecological barrier in Northwest China, the health of forest ecosystems in Shaanxi Province is crucial to regional ecological balance and sustainable development. However, forest degradation has become … As an important ecological barrier in Northwest China, the health of forest ecosystems in Shaanxi Province is crucial to regional ecological balance and sustainable development. However, forest degradation has become increasingly prominent in recent years due to both natural and anthropogenic pressures. This study aims to identify the spatio-temporal pattern of forest degradation in Shaanxi Province, construct an ecological network, and propose targeted restoration strategies. To this end, we first built a structural-functional forest degradation (SFD) assessment system and used the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to identify degraded areas and types; subsequently, we used morphological spatial pattern analysis (MSPA) and the minimum cumulative resistance (MCR) model to construct a forest ecological network and identify key restoration nodes. Finally, we proposed a differentiated restoration strategy for near-natural forests based on the Miyawaki method as a conceptual framework to guide future ecological recovery efforts. The results showed that (1) in 1991–2020, the total area of forest degradation in Shaanxi Province was 1010.89 km2, which was dominated by functional degradation (98%) and structural degradation (87.15%), with significant regional differences; (2) the constructed ecological network contained 189 ecological source sites, 189 ecological corridors, 89 key nodes, and 50 urgently restored; and (3) specific restoration measures were proposed for different degradation conditions (e.g., density regulation and forest window construction for functional light degradation and maintenance of the status quo or full reconstruction for structural heavy degradation). This study provides key data and systematic methods for the accurate monitoring of forest degradation, the optimization of ecological networks, and scientific restoration in Shaanxi Province, which holds great practical significance for establishing a robust ecological barrier in Northwest China.

Onboard LiDAR–Camera Deployment Optimization for Pavement Marking Distress Fusion Detection

2025-06-21

Ciyun Lin , Wenjian Sun , G. Sun +2 more | Sensors

Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse … Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse illumination should be above the minimum thresholds and required to undergo inspection periodically. Therefore, an onboard light detection and ranging (LiDAR) and camera deployment optimization method is proposed for pavement marking distress detection to adapt to complex traffic conditions, such as shadows and changing light. First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. Then, the LiDAR–camera deployment problem was mathematically formulated for pavement marking distress fusion detection. Finally, an improved red fox optimization (RFO) algorithm was developed to solve the deployment optimization problem by incorporating a multi-dimensional trap mechanism and an improved prey position update strategy. The experimental results illustrate that the proposed method achieves 5217 LiDAR points, which fall on a 0.58 m pavement marking per data frame for distress fusion detection, with a relative error of less than 7% between the mathematical calculation and the field test measurements. This empirical accuracy underscores the proposed method’s robustness in real-world scenarios, effectively mitigating the challenges posed by environmental interference.

Multi-Scale Context Enhancement Network with Local–Global Synergy Modeling Strategy for Semantic Segmentation on Remote Sensing Images

2025-06-21

Qun Ma , Hongning Liu , Yifan Jin +1 more | Electronics

Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, … Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, and ecological monitoring. In complex geographic scenes, low target-background discriminability in overhead views (e.g., indistinct boundaries, ambiguous textures, and low contrast) significantly complicates local–global information modeling and results in blurred boundaries and classification errors in model predictions. To address this issue, in this paper, we proposed a novel Multi-Scale Local–Global Mamba Feature Pyramid Network (MLMFPN) through designing a local–global information synergy modeling strategy, and guided and enhanced the cross-scale contextual information interaction in the feature fusion process to obtain quality semantic features to be used as cues for precise semantic reasoning. The proposed MLMFPN comprises two core components: Local–Global Align Mamba Fusion (LGAMF) and Context-Aware Cross-attention Interaction Module (CCIM). Specifically, LGAMF designs a local-enhanced global information modeling through asymmetric convolution for synergistic modeling of the receptive fields in vertical and horizontal directions, and further introduces the Vision Mamba structure to facilitate local–global information fusion. CCIM introduces positional encoding and cross-attention mechanisms to enrich the global-spatial semantics representation during multi-scale context information interaction, thereby achieving refined segmentation. The proposed methods are evaluated on the ISPRS Potsdam and Vaihingen datasets and the outperformance in the results verifies the effectiveness of the proposed method.

New Methods for Waterfowl and Habitat Survey Using AI and Drone Imagery

2025-06-20

Zhenduo Zhai , Zhiguang Liu , Yang Zhang +2 more | Drones

Monitoring waterfowl populations is essential for informing habitat management, conservation strategies, and sustainable harvest regulations. Many target species such as mallards and northern pintails are keystone components of wetland ecosystems, … Monitoring waterfowl populations is essential for informing habitat management, conservation strategies, and sustainable harvest regulations. Many target species such as mallards and northern pintails are keystone components of wetland ecosystems, serving as ecological indicators due to their sensitivity to environmental changes. The integration of drone technology and artificial intelligence (AI) is significantly transforming the field of wildlife conservation and habitat monitoring. Existing methods for waterfowl monitoring face critical challenges such as low accuracy in identifying overlapping image regions and limited segmentation accuracy in complex habitats. To address these issues, this paper presents an end-to-end system and several new methods for efficiently and accurately identifying waterfowl populations in their natural habitats using AI and drone imagery. We applied advanced deep learning models to drone imagery for detecting and counting waterfowl. To handle overlapping regions in consecutive images, we developed a bird-location-based method that quickly and accurately identifies overlaps. For habitat segmentation, we proposed an effective approach combining Meta’s Segment Anything Model (SAM) with a ResNet50 classifier. Additionally, we used ChatGPT to generate clear, easy-to-read reports summarizing detection results. Experimental results show that our bird detection model (Faster R-CNN) achieved 86.57% mAP, our habitat segmentation method reached 85.1% accuracy (average F1 score: 81.8%), and our overlap detection method maintained an error rate below 5% with faster performance compared to traditional techniques. These outcomes highlight the practical effectiveness of our integrated pipeline for wildlife conservation and habitat monitoring.

Human Pose Prediction Using Convolution Neural Network 3D based on Binary Voxel Feature Extraction (BIVFE) of Light Detection and Ranging (LiDAR) Point Cloud Data

2025-06-20

| International journal of intelligent engineering and systems

Spatial Localization of Broadleaf Species in Mixed Forests in Northern Japan Using UAV Multi-Spectral Imagery and Mask R-CNN Model

2025-06-20

Nyo Me Htun , Toshiaki Owari , Satoshi Suzuki +6 more | Remote Sensing

Precise spatial localization of broadleaf species is crucial for efficient forest management and ecological studies. This study presents an advanced approach for segmenting and classifying broadleaf tree species, including Japanese … Precise spatial localization of broadleaf species is crucial for efficient forest management and ecological studies. This study presents an advanced approach for segmenting and classifying broadleaf tree species, including Japanese oak (Quercus crispula), in mixed forests using multi-spectral imagery captured by unmanned aerial vehicles (UAVs) and deep learning. High-resolution UAV images, including RGB and NIR bands, were collected from two study sites in Hokkaido, Japan: Sub-compartment 97g in the eastern region and Sub-compartment 68E in the central region. A Mask Region-based Convolutional Neural Network (Mask R-CNN) framework was employed to recognize and classify single tree crowns based on annotated training data. The workflow incorporated UAV-derived imagery and crown annotations, supporting reliable model development and evaluation. Results showed that combining multi-spectral bands (RGB and NIR) with canopy height model (CHM) data significantly improved classification performance at both study sites. In Sub-compartment 97g, the RGB + NIR + CHM achieved a precision of 0.76, recall of 0.74, and F1-score of 0.75, compared to 0.73, 0.74, and 0.73 using RGB alone; 0.68, 0.70, and 0.66 with RGB + NIR; and 0.63, 0.67, and 0.63 with RGB + CHM. Similarly, at Sub-compartment 68E, the RGB + NIR + CHM attained a precision of 0.81, recall of 0.78, and F1-score of 0.80, outperforming RGB alone (0.79, 0.79, 0.78), RGB + NIR (0.75, 0.74, 0.72), and RGB + CHM (0.76, 0.75, 0.74). These consistent improvements across diverse forest conditions highlight the effectiveness of integrating spectral (RGB and NIR) and structural (CHM) data. These findings underscore the value of integrating UAV multi-spectral imagery with deep learning techniques for reliable, large-scale identification of tree species and forest monitoring.

Static evaluation of a virtual fence collar: precision and accuracy relative to a conventional GNSS collar across habitats

2025-06-20

Edward W. Bork , S. Q. Lopes , Alexandra J. Harland +4 more | Animal Biotelemetry

A double-layer ensemble framework for rubber plantation mapping using multi-source data in the google earth engine: a case study of the southwestern border region of China

2025-06-19

Wang Hui , Jie Li , Jinliang Wang +5 more | International Journal of Digital Earth

Multi-Component Remote Sensing for Mapping Buried Water Pipelines

2025-06-19

John S. Lioumbas , Thomas Spahos , Aikaterini Christodoulou +6 more | Remote Sensing

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. … Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications.

A two-step filtering approach for indoor lidar point clouds: efficient removal of jump points and misdetected points

2025-06-19

Yibo Cao , Yonghao Huang , Jingwen Ni | Research Square (Research Square)

<title>Abstract</title> In the simultaneous localization and mapping (SLAM) process of indoor mobile robots, the accuracy and stability of point cloud data are crucial for localization and environment perception. However, in … <title>Abstract</title> In the simultaneous localization and mapping (SLAM) process of indoor mobile robots, the accuracy and stability of point cloud data are crucial for localization and environment perception. However, in practical applications, indoor mobile robots may encounter glass, smooth floors, edge objects, etc. Point cloud data in such environments are often misdetected, especially in the intersection of flat surfaces and the edge of obstacles, which are prone to generating jump points; in the smooth planes due to reflective properties or sensor errors, which may also lead to the emergence of misdetected points.To solve these problems, a two-step filtering method is proposed in this paper. In the first step, a clustering filtering algorithm based on radial distance and tangential span is used for effective filtering against jump points. The algorithm accurately identifies and filters out the jump points by analyzing the spatial relationship between each point in the point cloud and the neighboring points to ensure the accuracy of the data. In the second step, the filtering algorithm based on the grid penetration model is used to further filter out the misdetected points on the smooth plane. The model eliminates unrealistic point cloud data and improves the overall quality of the point cloud by simulating the characteristics of the beam penetrating the object. Experimental results in indoor environments show that this two-step filtering method significantly reduces the jump points and misdetected points in the point cloud and improves the navigation accuracy and stability of indoor mobile robots.

DAENet: A Deep Attention-Enhanced Network for Cropland Extraction in Complex Terrain from High-Resolution Satellite Imagery

2025-06-19

Yushen Wang , Mingchao Yang , Tianxiang Zhang +2 more | Agriculture

Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented … Prompt and precise cropland mapping is indispensable for safeguarding food security, enhancing land resource utilization, and advancing sustainable agricultural practices. Conventional approaches faced difficulties in complex terrain marked by fragmented plots, pronounced elevation differences, and non-uniform field borders. To address these challenges, we propose DAENet, a novel deep learning framework designed for accurate cropland extraction from high-resolution GaoFen-1 (GF-1) satellite imagery. DAENet employs a novel Geometric-Optimized and Boundary-Restrained (GOBR) Block, which combines channel attention, multi-scale spatial attention, and boundary supervision mechanisms to effectively mitigate challenges arising from disjointed cropland parcels, topography-cast shadows, and indistinct edges. We conducted comparative experiments using 8 mainstream semantic segmentation models. The results demonstrate that DAENet achieves superior performance, with an Intersection over Union (IoU) of 0.9636, representing a 4% improvement over the best-performing baseline, and an F1-score of 0.9811, marking a 2% increase. Ablation analysis further validated the indispensable contribution of GOBR modules in improving segmentation precision. Using our approach, we successfully extracted 25,556.98 hectares of cropland within the study area, encompassing a total of 67,850 individual blocks. Additionally, the proposed method exhibits robust generalization across varying spatial resolutions, underscoring its effectiveness as a high-accuracy solution for agricultural monitoring and sustainable land management in complex terrain.

Tree species proportion prediction using airborne laser scanning and Sentinel-2 data within a deep learning based dual-stream data fusion approach

2025-06-19

Brent Murray , Nicholas C. Coops , Joanne C. White +2 more | International Journal of Remote Sensing

Monitoring biweekly dynamics of pan-tropical industrial plantations over 6 years using 100 m PROBA-V data

2025-06-19

A. Boś , Céline Lamarche , Fabrizio Niro +1 more | Frontiers in Remote Sensing

Anthropogenic land conversion profoundly impacts the Earth’s surface, with varying effects across regions. In the tropics, industrial plantations particularly affect natural forests. Monitoring land use and land cover change (LULCC) … Anthropogenic land conversion profoundly impacts the Earth’s surface, with varying effects across regions. In the tropics, industrial plantations particularly affect natural forests. Monitoring land use and land cover change (LULCC) due to agricultural expansion is crucial for achieving sustainable imports into the European Union under the Regulation on Deforestation-free Products (EUDR). Earth observation satellite missions, providing free global imagery with high revisit frequency, are instrumental in monitoring tropical ecosystems and their transformation. However, accurately mapping the correct dates of tree cutting or planting on a global scale remains a challenge. This study addresses this gap by developing a near real-time sensor-agnostic method for monitoring deforestation and plantation rotation. It is developed using 100 m PROBA-V full Collection 2 archive with a 5-day revisit, spanning 2014 to 2020. A novel index enabled distinguishing vegetation from land cleared for plantations. The variability of atmospheric perturbations and both intra- and inter-annual variability of the vegetation spectral signatures were mitigated using spatial standardization. Statistical thresholds identified pixels that deviated from the normal distribution of forest spectral values, capturing LULCC. It results in pan-tropical annual maps series 2015–2020 illustrating the typical dynamics of perennial plantations, from land preparation to mature plantations, including the dates of cutting and planting. Validation using 899 randomly selected samples through confidence-based stratified sampling yielded a global accuracy of 82% <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mo>±</mml:mo></mml:mrow></mml:math> 2% for new plantation detection. 62% of the detections 1 Bos et al. were accurate to the exact year, which represents a significant 19% improvement over previous studies. Our initial estimates of industrial plantation dynamics suggest that new oil palm plantations cover approximately 3,064 km 2 annually, of which 79% is rotation within existing plantations and 21% expansion into new areas. Annual plantations of other perennial plantations cover about 13,875 km 2 , of which 81% is from rotation and 19% from expansion. This work demonstrates the effectiveness of optical 100 m spatial resolution for near real-time pan-tropical mapping of perennial industrial plantations in cloudy regions.

Mobile Mapping Systems

2025-06-19

Magdy Elbahnasawy , Tamer Shamseldin , Ahmed E. Mansour +2 more | IGI Global eBooks

Mobile Mapping Systems (MMS) are transforming agriculture by integrating advanced sensors, geospatial technologies, and real-time data processing to enhance decision-making. These systems improve productivity, optimize resource use, and support sustainable … Mobile Mapping Systems (MMS) are transforming agriculture by integrating advanced sensors, geospatial technologies, and real-time data processing to enhance decision-making. These systems improve productivity, optimize resource use, and support sustainable farming. MMS enables precise mapping of agricultural landscapes, crop health monitoring, and soil analysis, providing valuable insights for smart farming. With increasing demands for sustainable agriculture due to environmental concerns and food security, MMS play a crucial role in addressing these challenges. Their applications range from crop yield prediction to land management and pest detection. This chapter examines MMS architecture, sensor integration, system modelling, and calibration. It also explores the impact of emerging technologies such as AI, machine learning, and cloud computing on MMS functionality. Through case studies and discussions, the chapter highlights current advancements and future trends, offering insights into the evolving role of MMS in modern agriculture.

Tracking the spatial and temporal evolution of salt marsh vegetation based on UAV sampling and seasonal phenology from Landsat data

2025-06-19

Kebing Chen , Jiaxin Xu , Chang Lu +4 more | Journal of Environmental Management

ESCA-enhanced YOLOv5s: A lightweight framework for Chinese fir seedling stage classification and quantity estimation

2025-06-18

Penghui Sun , Tianwei Yun , S.-H. Rong +3 more | Industrial Crops and Products

Evaluation of Simulation Framework for Detecting the Quality of Forest Tree Stems

2025-06-18

Anwar Sagar , Kalle Kärhä , Kalervo Järvelin +1 more | Forests

The advancement of harvester technology increasingly relies on automated forest analysis within machine operational ranges. However, real-world testing remains costly and time-consuming. To address this, we introduced the Tree Classification … The advancement of harvester technology increasingly relies on automated forest analysis within machine operational ranges. However, real-world testing remains costly and time-consuming. To address this, we introduced the Tree Classification Framework (TCF), a simulation platform for the cost-effective testing of harvester technologies. TCF accelerates technology development by simulating forest environments and machine operations, leveraging machine-learning and computer vision models. TCF has four components: Synthetic Forest Creation, which generates diverse virtual forests; Point Cloud Generation, which simulates LiDAR scanning; Stem Identification and Classification, which detects and characterises tree stems; and Experimental Evaluation, which assesses algorithm performance under varying conditions. We tested TCF across ten forest scenarios with different tree densities and morphologies, using two-point cloud generation methods: fixed points per stem and LiDAR scanning at three resolutions. Performance was evaluated against ground-truth data using quantitative metrics and heatmaps. TCF bridges the gap between simulation and real-world forestry, enhancing the harvester technology by improving efficiency, accuracy, and sustainability in automated tree assessment. This paper presents a framework built from affordable, standard components for stem identification and classification. TCF enables the systematic testing of classification algorithms against known ground truth under controlled, repeatable conditions. Through diverse evaluations, the framework demonstrates its utility by providing the necessary components, representations, and procedures for reliable stem classification.

К вопросу о точности распознавания кривизны и высоты деревьев

2025-06-18

И. К. Говядин , Anatoly N. Chubinsky | Известия СПбЛТА

В статье рассматривается инновационный подход к оценке морфометрических параметров деревьев с использованием технологий искусственного интеллекта (ИИ) и компьютерного зрения. Основное внимание уделяется разработке методики точного измерения таких характеристик, как высота … В статье рассматривается инновационный подход к оценке морфометрических параметров деревьев с использованием технологий искусственного интеллекта (ИИ) и компьютерного зрения. Основное внимание уделяется разработке методики точного измерения таких характеристик, как высота дерева, диаметры у основания и вершины, кривизна ствола, объем и площадь поверхности. Авторы подчеркивают, что традиционные методы измерений, основанные на визуальном осмотре и ручных замерах, обладают существенными погрешностями и требуют значительных трудозатрат, в то время как автоматизированные системы на базе ИИ позволяют получать более точные и воспроизводимые результаты. Для сбора данных использовались аэрофотосъемка с дронов и наземная фотосъемка, что обеспечило комплексное покрытие стволов деревьев под разными углами. Полученные изображения обрабатывались с применением алгоритмов компьютерного зрения, включая сверточные нейронные сети (CNN), а также методов трехмерного моделирования на основе облаков точек. Это позволило создать детализированные цифровые модели деревьев, пригодные для точного анализа их геометрических параметров. Результаты исследования продемонстрировали высокую точность предложенного метода: сравнение с ручными замерами показало минимальные расхождения. Кроме того, авторы провели корреляционный анализ, выявивший взаимосвязи между различными параметрами деревьев, что имеет важное значение для оценки качества древесины и планирования лесозаготовок. Разработанная методика открывает новые возможности для устойчивого управления лесными ресурсами, позволяя минимизировать негативное воздействие на экосистемы и оптимизировать процессы лесопользования. Применение ИИ в лесном хозяйстве способствует переходу к более точным и экологически безопасным методам работы, что особенно актуально в условиях растущего спроса на древесину и необходимости сохранения биоразнообразия. The article discusses an innovative approach to assessing tree morphometric parameters using artificial intelligence (AI) and computer vision technologies. The main focus is on the development of a methodology for accurately measuring such characteristics as tree height, base and top diameters, trunk curvature, volume, and surface area. The authors emphasize that traditional measurement methods based on visual inspection and manual measurements have significant errors and require significant labor costs, while automated AI-based systems provide more accurate and reproducible results. Aerial photography from drones and ground photography were used to collect data, which provided comprehensive coverage of tree trunks from different angles. The resulting images were processed using computer vision algorithms, including convolutional neural networks (CNN), as well as 3D modeling methods based on point clouds. This made it possible to create detailed digital models of trees suitable for accurate analysis of their geometric parameters. The results of the study demonstrated the high accuracy of the proposed method: comparison with manual measurements showed minimal discrepancies. In addition, the authors conducted a correlation analysis that revealed the relationships between various tree parameters, which is important for assessing the quality of wood and planning logging. The developed methodology opens up new opportunities for sustainable forest management, allowing to minimize the negative impact on ecosystems and optimize forest management processes. The use of AI in forestry contributes to the transition to more accurate and environmentally friendly working methods, which is especially important in the context of growing demand for wood and the need to preserve biodiversity.

3D visibility analysis of urban parks using LIDAR for enhanced accuracy

2025-06-18

Huishan Cheng , Quanquan Rui , Kunyong Yu +4 more | Journal of Environmental Management

Carbon estimation of old-growth bald cypress knees using mobile LiDAR

2025-06-18

Titilayo T. Tajudeen , Leah C. Rathbun , Marcelo Ardón +1 more | Frontiers in Forests and Global Change

A rapid, reliable, cost-effective tree volume calculation is critical for estimating biomass and carbon sequestration. This estimation is vital for developing better carbon budgets for wetland ecosystems to assess current … A rapid, reliable, cost-effective tree volume calculation is critical for estimating biomass and carbon sequestration. This estimation is vital for developing better carbon budgets for wetland ecosystems to assess current and future climate scenarios. Portable mobile light detection and ranging (LiDAR) systems such as the Apple iPad Pro sensor provide an efficient method for capturing 3D shapes of bald cypress ( Taxodium distichum ) pneumatophores, or “knees.” The knee is a rounded conical structure growing above the water or land from the roots of bald cypress trees, usually a few feet away from the trunk. This study explores remote sensing techniques for mapping individual knees to eventually understand their significance in the carbon balance of forested wetlands. This project was conducted in the Three Sisters Swamp, part of the Black River Reserve in North Carolina, USA. The volume of individual tree knees was estimated using multiple geometric algorithms and compared to allometric estimates from traditional field measurements derived from the shape of a cone. Specifically, we used the convex-hull by slicing (C-hbS) and Canopy-Surface Height (CSH) algorithms to estimate the volume of individual knees after LiDAR data processing. The volume estimates from the CSH and C-hbS methods are higher than the allometric estimates due to the knees’ natural irregular shape and concavities. The CSH method returned the largest volume values on average. The discrepancy in estimated volume between the allometric equation and the two algorithms became more pronounced with increasing knee height. The estimated aboveground mean biomass and carbon of the knees are 61.9 ± 23.4 Mg ha −1 and 32.83 ± 12.38 Mg C ha −1 , respectively. The challenges of algorithmic methods include the time and equipment needed to process dense point clouds. However, they better capture irregularities in knee shape, ultimately leading to better estimates and an understanding of knee structure, which is currently poorly understood.

A Review of Image- and LiDAR-Based Mapping of Shallow Water Scenarios

2025-06-18

Paulina Kujawa , Fabio Remondino | Remote Sensing

There is a growing need for accurate bathymetric mapping in many water-related scientific disciplines. Accurate and up-to-date data are essential for both shallow and deep areas. In this article, methods … There is a growing need for accurate bathymetric mapping in many water-related scientific disciplines. Accurate and up-to-date data are essential for both shallow and deep areas. In this article, methods and techniques for shallow water mapping have been collected and described based on the available scientific literature. The paper focuses on three survey technologies, Unmanned Aerial Systems (UASs), Airborne Bathymetry (AB), and Satellite-Derived Bathymetry (SDB), with multimedia photogrammetry and LiDAR-based approaches as processing methods. The most popular and/or state-of-the-art image and LiDAR data correction techniques are characterized. To develop good practice in shallow water mapping, the authors present examples of data acquired by all the mentioned technologies with selected correction methods.

Accuracy and Time Efficiency of ForestScanner App when Measuring Plot-Level DBH under Diverse Forest Ecosystems

2025-06-18

Gabriel Osei Forkuo , Busquets Vass , A. Forika +3 more | Bulletin of the Transilvania University of Brasov Series II Forestry • Wood Industry • Agricultural Food Engineering

Accurate and efficient measurement of tree diameter at breast height (DBH) is essential for forest inventory and management. While traditional methods are time-consuming, new smartphone-based LiDAR applications like ForestScanner promise … Accurate and efficient measurement of tree diameter at breast height (DBH) is essential for forest inventory and management. While traditional methods are time-consuming, new smartphone-based LiDAR applications like ForestScanner promise rapid, cost-effective solutions. However, their performance across diverse forest ecosystems requires thorough evaluation. This study aimed to assess the accuracy and time efficiency of the ForestScanner app for plot-level DBH measurements compared to manual caliper methods under varied growing conditions in Romania. One hundred circular plots (approx. 300 m² each) were established in forests near Brașov City, encompassing diverse forest tree species, ages, topographies, and understory conditions. DBH of 987 trees was measured manually with calipers and digitally using the ForestScanner app on a LiDAR-equipped iPhone. Time consumption for plot establishment, manual DBH, and app-based DBH measurements was recorded. Accuracy was assessed using bias, mean absolute error (MAE), and root mean squared error (RMSE), with heteroskedasticity checked via Breusch-Pagan and White tests. ForestScanner showed a negligible overall bias (-0.003 cm), but MAE reached 3.66 cm when all measurements were included. Occlusion by vegetation or nearby trees significantly impacted the app’s accuracy; for non-obstructed trees (n = 824), bias was +0.26 cm with an MAE of 2.07 cm. Manual DBH measurement averaged 14 seconds/tree, while ForestScanner averaged 16 seconds/tree. Plot establishment time and measurement time were influenced by tree density. ForestScanner offers a user-friendly, free tool for DBH measurement and tree mapping, but its accuracy may be affected by occlusion. On the other hand, the app comes equipped with several useful features, such as documenting the plots by LiDAR point clouds, real-time DBH measurement, and data storage, while returning comparable time efficiencies. Future work should focus on more diverse forest types to refine its practical application in forestry.

Integrating Spatial Statistics and Digital Processing for Enhanced Surface Quality Classification in the Foundry Industry

2025-06-18

Ronit Shetty , Ahmad Al Majali , Lee J. Wells | International Journal of Metalcasting

Direct Estimation of Forest Aboveground Biomass from UAV LiDAR and RGB Observations in Forest Stands with Various Tree Densities

2025-06-18

Kangyu So , J. L. Chau , Sean Rudd +4 more | Remote Sensing

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and … Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may address scalability limitations associated with traditional forest inventory but require simple forest structures or large sets of manually delineated crowns. Here, we introduce a deep learning approach for crown delineation and AGB estimation reproducible for complex forest structures without relying on hand annotations for training. Firstly, we detect treetops and delineate crowns with a LiDAR point cloud using marker-controlled watershed segmentation (MCWS). Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. Finally, we estimate AGB metrics from tree height- and crown diameter-based allometric equations, all derived from UAV data. We validate our approach using 14 ha mixed forest stands with various experimental tree densities in Southern Ontario, Canada. Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. In unharvested stands, the self-supervised RGB model performs well for height (adjusted R2, Ra2 = 0.79) and AGB (Ra2 = 0.80) estimation. In thinned stands, the performance of both unsupervised and self-supervised methods varied with stand density, crown clumping, canopy height variation, and species diversity. These findings suggest that MCWS can be supplemented with self-supervised deep learning to directly estimate biomass components in complex forest structures as well as atypical forest conditions where stand density and spatial patterns are manipulated.

Accuracy of forest road Digital Terrain Models captured using Airborne and Mobile Light Detection and Ranging Technology and Photogrammetry

2025-06-17

Miroslav Kardoš , Łukasz Borowski , Ivan Sačkov +5 more | Advances in Geodesy and Geoinformation

The evaluation of the accuracy of generated DEMs using three remote sensing techniques on three types of forest road surfaces was performed. As a sample data, we used the forest … The evaluation of the accuracy of generated DEMs using three remote sensing techniques on three types of forest road surfaces was performed. As a sample data, we used the forest road constructed from asphalt, concrete road slabs, and paving stones located in Víglaš, Central Slovakia.We evaluated the vertical accuracy of the DEMs produced by mobile laser scanning (MLS, Leica Pegasus, 840 pts/m2, airborne laser scanning (ALS, Leica ALS 70, 9 pts/m2, and aerial photogrammetry (AP, Leica RCD 30, 5 pts/m2. DEMs were generated in ArcGIS with a final resolution of 0.5m using the IDW method. The accuracy of DEMs was evaluated with the reference dataset on 700 check points. Regarding road surface capture quality, terrain generation, and point density, the MLS method dominates. It provides the RMSE values in range of ± 0.01 m to ± 0.03 m. The ALS method provided balanced RMSE results irrespective of surface type (RMSE ± 0.04 m to ± 0.05 m). The AP has the highest variability on all surface types (RMSE ± 0.12 m to ± 0.22 m). For AP, 0the decimeter-level accuracy is not sufficient for construction and maintenance purposes. This method provided the largest blunders at the road parts closest to the trees. ALS, with its ability to partially penetrate the forest canopy, can provide complex information about forest roads for inventory purposes. MLS provided the best spatial accuracy, enabling both construction and maintenance works. In any case, the advantage is that these data types can be combined.

Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil

2025-06-16

Carlos André de Mattos Teixeira , Thabatta Moreira Alves de Araújo , Evelin Helena Silva Cardoso +3 more | PeerJ Computer Science

Visual inspection is essential to ensure the stability of earth-rock dams. Periodic visual assessment of this type of structure through vegetation cover analysis is an effective monitoring method. Recently, multispectral … Visual inspection is essential to ensure the stability of earth-rock dams. Periodic visual assessment of this type of structure through vegetation cover analysis is an effective monitoring method. Recently, multispectral remote sensing data and machine learning techniques have been applied to develop methodologies that enable automatic vegetation analysis and anomaly detection based on computer vision. As a first step toward this automation, this study introduces a methodology for land cover segmentation of earth-rock embankment dam structures within the Belo Monte Hydroelectric Complex, located in the state of Pará, northern Brazil. Random forest (RF) ensemble models were trained on manually annotated data captured by a multispectral sensor embedded in an uncrewed aerial vehicle (UAV). The main objectives of this study are to assess the classification performance of the algorithm in segmenting earth-rock dams and the contribution of non-visible band reflectance data to the overall model performance. A comprehensive feature engineering and ranking approach is presented to select the most descriptive features that represent the four dataset classes. Model performance was assessed using classical performance metrics derived from the confusion matrix, such as accuracy, Kappa coefficient, precision, recall, F1-score, and intersection over union (IoU). The final RF model achieved 90.9% mean IoU for binary segmentation and 91.1% mean IoU for multiclass segmentation. Post-processing techniques were applied to refine the predicted masks, enhancing the mean IoU to 93.2% and 91.9%, respectively. The flexible methodology presented in this work can be applied to different scenarios when treated as a framework for pixel-wise land cover classification, serving as a crucial step toward automating visual inspection processes. The implementation of automated monitoring solutions improves the visual inspection process and mitigates the catastrophic consequences resulting from dam failures.

Forest attribute maps: a support for small area estimation of forest disturbances

2025-06-16

Ankit Sagar , Cédric Vega , Olivier Bouriaud +2 more | Annals of Forest Science