Environmental Science › Ecology

Remote Sensing in Agriculture

Works Count: 57372

Wikipedia

Description

This cluster of papers focuses on the use of remote sensing technology, particularly MODIS and Landsat data, for monitoring vegetation dynamics, phenology, and biomass estimation in response to global change and climate variability. The papers also explore the application of machine learning techniques for land cover classification and the assessment of ecological responses to environmental change.

Keywords

Remote Sensing; Vegetation Monitoring; Phenology; MODIS; Landsat; NDVI; Global Change; Climate; Biomass Estimation; Machine Learning

Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000

2008-01-16

Navin Ramankutty , Amato T. Evan , Chad Monfreda +1 more

Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and … Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite‐derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite‐derived products (Boston University's MODIS‐derived land cover product and the GLC2000 data set). Our data are presented at 5 min (∼10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2–17.1) million km 2 of cropland (12% of the Earth's ice‐free land surface) and 28.0 (90% confidence range of 23.6–30.0) million km 2 of pasture (22%) in the year 2000.

View PDF Chat

A soil-adjusted vegetation index (SAVI)

1988-08-01

Alfredo Huete

On the relation between NDVI, fractional vegetation cover, and leaf area index

1997-12-01

Toby N. Carlson , David A.J. Ripley

Characteristics of maximum-value composite images from temporal AVHRR data

1986-11-01

B. N. Holben

Abstract Red and near-infrared satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large … Abstract Red and near-infrared satellite data from the Advanced Very High Resolution Radiometer sensor have been processed over several days and combined to produce spatially continuous cloud-free imagery over large areas with sufficient temporal resolution to study green-vegetation dynamics. The technique minimizes cloud contamination, reduces directional reflectance and off-nadir viewing effects, minimizes sun-angle and shadow effects, and minimizes aerosol and water-vapour effects. The improvement is highly dependent on the state of the atmosphere, surface-cover type, and the viewing and illumination geometry of the sun, target and sensor. An example from southern Africa showed an increase of 40 per cent from individual image values to the final composite image. Limitations' associated with the technique are discussed, and recommendations are given to improve this approach

View PDF Chat

An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data

1988-04-01

Pat S. Chavez

Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model

1984-10-01

W. Verhoef

View PDF Chat

NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space

1996-12-01

Bo‐Cai Gao

A modified soil adjusted vegetation index

1994-05-01

Jiaguo Qi , Abdelghani Chehbouni , Alfredo Huete +2 more

Use of a green channel in remote sensing of global vegetation from EOS-MODIS

1996-12-01

Anatoly A. Gitelson , Yoram J. Kaufman , Mark N. Merzlyak

Global land cover mapping at 30m resolution: A POK-based operational approach

2014-10-19

Jun Chen , Jin Chen , Anping Liao +7 more

Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 … Global Land Cover (GLC) information is fundamental for environmental change studies, land resource management, sustainable development, and many other societal benefits. Although GLC data exists at spatial resolutions of 300 m and 1000 m, a 30 m resolution mapping approach is now a feasible option for the next generation of GLC products. Since most significant human impacts on the land system can be captured at this scale, a number of researchers are focusing on such products. This paper reports the operational approach used in such a project, which aims to deliver reliable data products. Over 10,000 Landsat-like satellite images are required to cover the entire Earth at 30 m resolution. To derive a GLC map from such a large volume of data necessitates the development of effective, efficient, economic and operational approaches. Automated approaches usually provide higher efficiency and thus more economic solutions, yet existing automated classification has been deemed ineffective because of the low classification accuracy achievable (typically below 65%) at global scale at 30 m resolution. As a result, an approach based on the integration of pixel- and object-based methods with knowledge (POK-based) has been developed. To handle the classification process of 10 land cover types, a split-and-merge strategy was employed, i.e. firstly each class identified in a prioritized sequence and then results are merged together. For the identification of each class, a robust integration of pixel-and object-based classification was developed. To improve the quality of the classification results, a knowledge-based interactive verification procedure was developed with the support of web service technology. The performance of the POK-based approach was tested using eight selected areas with differing landscapes from five different continents. An overall classification accuracy of over 80% was achieved. This indicates that the developed POK-based approach is effective and feasible for operational GLC mapping at 30 m resolution.

View PDF Chat

A comparison of vegetation indices over a global set of TM images for EOS-MODIS

1997-03-01

Alfredo Huete

Good practices for estimating area and assessing accuracy of land change

2014-04-12

Pontus Olofsson , Giles M. Foody , Martin Herold +3 more

View PDF Chat

Optimization of soil-adjusted vegetation indices

1996-02-01

G. Rondeaux , M. D. Steven , Frédéric Baret

A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter

2004-05-29

Jin Chen , Per Jönsson , Masayuki Tamura +3 more

Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

1998-09-01

Robert O. Green , Michael L. Eastwood , Charles M. Sarture +7 more

A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency

1992-07-01

John A. Gamon , Josep Peñuelas , Christopher B. Field

Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages

2002-07-25

D. A. Sims , John A. Gamon

Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps

2012-09-13

D. J. Mulla

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

2009-10-15

M. A. Friedl , Damien Sulla‐Menashe , Bin Tan +4 more

An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

2005-10-20

Compton J. Tucker , Jorge Enrique Díaz Pinzón , Molly E. Brown +5 more

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 … Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the composite NDVI data. All data are available from the University of Maryland Global Land Cover Facility (http://glcf.umiacs.umd.edu/data/gimms/).

Red and photographic infrared linear combinations for monitoring vegetation

1979-05-01

Compton J. Tucker

View PDF Chat

PROSPECT: A model of leaf optical properties spectra

1990-11-01

Stéphane Jacquemoud , Frédéric Baret

A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status

2002-02-01

Inge Sandholt , Kjeld Rasmussen , Jens Kirk Andersen

Monitoring vegetation phenology using MODIS

2003-02-05

Xiaoyang Zhang , M. A. Friedl , Crystal Schaaf +5 more

The application of small unmanned aerial systems for precision agriculture: a review

2012-07-30

Chunhua Zhang , John M. Kovacs

GLC2000: a new approach to global land cover mapping from Earth observation data

2005-05-01

Étienne Bartholomé , Alan Belward

Abstract A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research … Abstract A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data are available free of charge for all non‐commercial applications from http://www.gvm.jrc.it/glc2000. Acknowledgements The JRC with the endorsement and support of the VEGETATION programme partners coordinated the GLC2000 project. The S1 data were kindly made available under the terms of the VEGA 2000 initiative. The involvement of all GLC2000 partners is gratefully acknowledged. The number in front of their name refers to the geographic window displayed in figure 1. A full list of individuals is provided in Bartholomé et al. (Citation2002). The authors are particularly indebted to the members of the Global Vegetation Unit of the JRC who contributed the GLC2000 project: F. Achard, S. Bartalev, C. Carmona‐Moreno, V. Gond, S. Kolmert, M. Massart, P. Mayaux, M. Merlotti, H. Eva, S. Fritz, B. Glénat, J.‐M. Grégoire, A. Hartley, H.‐J. Stibig, A. Tournier and P. Vogt. 1. (1) US Geological Survey, Sioux Falls, USA: T. Loveland, Z. Zhu, C. Giri.2. (1) Canadian Center for Remote Sensing, Ottawa, Canada: R. Latifovic.3. (10) Institute for Remote Sensing Applications, Beijing, China: Wu B, Xu W.4. (global) CNES, Toulouse, France: H. Jeanjean, G. Saint.5. (3) Lab. de teledeteccion aplicada, Univ. Nacional Agraria, La Molina, Peru: V. Barrena Arroyo.6. (global) VITO, Mol, Belgium: D. Van Speybroeck.7. (7) Centre AGRHYMET, Niamey, Niger: A. Nonguierma.8. (5c) METEO, Toulouse, France: J.‐L. Champeaux.9. (7, global) UNEP/GRID, Geneva, Switzerland: R. Witt, C. Ten Oever.10. (7) Centre de Suivi Ecologique, Dakar, Senegal: O. Diallo.11. (3) INTA, Castelar/Buenos Aires, Argentina: C. di Bella.12. (7) CSIR, Pretoria, South Africa: C. Pretorius.13. (global) Africover, Nairobi, Kenya: A. di Gregorio.14. (5b, 7) Environnemétrie et Géomatique Un. Cath., Louvain‐la‐Neuve, Belgium: P. Defourny, C. Vancutsem, J.‐F. Pekel.15. (3) Ecoforca: Campinas/Sao Paulo, Brazil: A. Dorado, E. de Miranda.16. (3) CIRAD, Forêts, Cayenne/Guyanne, France: V. Gond.17. (12) Institut Pertanian, Bogor, Indonesia: U. R. Wasrin.18. (9) Indian Institute for Remote Sensing, Dehradun UP, India: P. S. Roy, S. Gupta.19. (global, 17) FAO, Roma, Italy: He C. J. Latham, M. Cherlet.20. (8a) Alterra, Wageningen, The Netherland: C. A. Mucher, E. De Badts.21. (6) Metria, Stockholm, Sweden: S. Olovsson, B. Olsson, M. Ledwith.22. (3) Corolab Humboldt, Caracas, Venezuela: O. Huber.23. (5) Instituto de Sciencias de la tierra, Barcelona, Spain: A. Lobo.24. (11) CEReS, Chiba, Japan: R. Tateishi.25. (10) University of New Hampshire, Durham, USA: X. Xiao.26. (7) Tropical Research Institute, Lisbon, Portugal M. J. De Perestrelo, J. Pereira, A. I. Cabral.27. (14) Centre for Ecology and Productivity, Moscow, Russia: D. Ershov, A. Isaev.28. (5d) Dipartimento di Pianificazione, IUAV, Venice Italy, S. Griguolo.29. (3) CREAN, Cordoba, Argentina: A. C. Ravelo.30. (11) Geographical Survey Institute, Tsukuba, Japan: H. Sato.31. (10) Chinese Academy of Forestry, Beijing, China: Zhao X.32. (7) Royal Museum for Central Africa, Tervuren, Belgium: J. Lavreau.33. (7) Regional Centre for Mapping of Resources for Development, Nairobi, Kenya: W. K. Ottichilo.34. (7) Observatoire du Sahara et du Sahel, Tunis, Tunisia: C. Fezzani, W. Essahli.35. (5b) Instituto de Hidràulica, Engenharia Rural e Ambiente, Lisbon, Portugal: A. Perdigão.36. (Global, 2, 3, 4, 5d, 7, 8, 13, 15, 16, 17) Global vegetation Monitoring Unit/JRC, Ispra, Italy: E. Bartholomé, A. S. Belward, F. Achard, S. Bartalev, C. Carmona‐Moreno, H. Eva, S. Fritz, A. Hartley, P. Mayaux, H.‐J. Stibig.

Canopy reflectance, photosynthesis and transpiration

1985-08-01

P. J. Sellers

Abstract A two-stream approximation model of radiative transfer is used to calculate values of hemispheric canopy reflectance in the visible and near-infrared wavelength intervals. Simple leaf models of photosynthesis and … Abstract A two-stream approximation model of radiative transfer is used to calculate values of hemispheric canopy reflectance in the visible and near-infrared wavelength intervals. Simple leaf models of photosynthesis and stomatal resistance are integrated over leaf orientation and canopy depth to obtain estimates of canopy photosynthesis and bulk stomatal or canopy resistance. The ratio of near-infrared and visible reflectances is predicted to be a near linear indicator of minimum canopy resistance and photosynthetic capacity but a poor predictor of leaf area index or biomass.

Potentials and limits of vegetation indices for LAI and APAR assessment

1991-02-01

Frédéric Baret , G. Guyot

Development of a two-band enhanced vegetation index without a blue band

2008-07-28

Zixiao Jiang , Alfredo Huete , K. Didan +1 more

Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance

2000-11-01

Craig S. T. Daughtry

Overview of the radiometric and biophysical performance of the MODIS vegetation indices

2002-11-01

Alfredo Huete , Kamel Didan , Tomoaki Miura +3 more

Global land cover mapping from MODIS: algorithms and early results

2002-11-01

M. A. Friedl , D.K. McIver , J.C.F. Hodges +7 more

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

2000-01-01

Thomas R. Loveland , Bradley C. Reed , J. F. Brown +4 more

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use … Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

An assessment of the effectiveness of a random forest classifier for land-cover classification

2011-12-06

Víctor Rodríguez‐Galiano , Bardan Ghimire , John Rogan +2 more

Global land cover classification at 1 km spatial resolution using a classification tree approach

2000-01-01

Matthew C. Hansen , Ruth DeFries , J. R. Townshend +1 more

This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will … This paper on reports the production of a 1 km spatial resolution land cover classification using data for 1992-1993 from the Advanced Very High Resolution Radiometer (AVHRR). This map will be included as an at-launch product of the Moderate Resolution Imaging Spectroradiometer (MODIS) to serve as an input for several algorithms requiring knowledge of land cover type. The methodology was derived from a similar effort to create a product at 8 km spatial resolution, where high resolution data sets were interpreted in order to derive a coarse-resolution training data set. A set of 37 294 x 1 km pixels was used within a hierarchical tree structure to classify the AVHRR data into 12 classes. The approach taken involved a hierarchy of pair-wise class trees where a logic based on vegetation form was applied until all classes were depicted. Multitemporal AVHRR metrics were used to predict class memberships. Minimum annual red reflectance, peak annual Normalized Difference Vegetation Index (NDVI), and minimum channel three brightness temperature were among the most used metrics. Depictions of forests and woodlands, and areas of mechanized agriculture are in general agreement with other sources of information, while classes such as low biomass agriculture and high-latitude broadleaf forest are not. Comparisons of the final product with regional digital land cover maps derived from high-resolution remotely sensed data reveal general agreement, except for apparently poor depictions of temperate pastures within areas of agriculture. Distinguishing between forest and non-forest was achieved with agreements ranging from 81 to 92% for these regional subsets. The agreements for all classes varied from an average of 65% when viewing all pixels to an average of 82% when viewing only those 1 km pixels consisting of greater than 90% one class within the high-resolution data sets.

First operational BRDF, albedo nadir reflectance products from MODIS

2002-11-01

Crystal Schaaf , Feng Gao , Alan H. Strahler +7 more

View PDF Chat

TIMESAT—a program for analyzing time-series of satellite sensor data

2004-10-01

Per Jönsson , Lars Eklundh

View PDF Chat

A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production

2004-01-01

Steven W. Running , Ramakrishna Nemani , Faith Ann Heinsch +3 more

Until recently, continuous monitoring of global vegetation productivity has not been possible because of technological limitations. This article introduces a new satellite-driven monitor of the global biosphere that regularly computes … Until recently, continuous monitoring of global vegetation productivity has not been possible because of technological limitations. This article introduces a new satellite-driven monitor of the global biosphere that regularly computes daily gross primary production (GPP) and annual net primary production (NPP) at 1-kilometer (km) resolution over 109,782,756 km2 of vegetated land surface. We summarize the history of global NPP science, as well as the derivation of this calculation, and current data production activity. The first data on NPP from the EOS (Earth Observing System) MODIS (Moderate Resolution Imaging Spectroradiometer) sensor are presented with different types of validation. We offer examples of how this new type of data set can serve ecological science, land management, and environmental policy. To enhance the use of these data by nonspecialists, we are now producing monthly anomaly maps for GPP and annual NPP that compare the current value with an 18-year average value for each pixel, clearly identifying regions where vegetation growth is higher or lower than normal.

View PDF Chat

Shifting plant phenology in response to global change

2007-05-03

Elsa E. Cleland , Isabelle Chuine , Annette Menzel +2 more

Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data

2002-11-01

Ranga B. Myneni , Samuel M. Hoffman , Yuri Knyazikhin +7 more

View PDF Chat

Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves

2003-01-01

Anatoly A. Gitelson , Yuri Gritz † , Mark N. Merzlyak

Detecting trend and seasonal changes in satellite image time series

2009-10-02

Jan Verbesselt , Rob Hyndman , Glenn Newnham +1 more

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

2004-03-06

D. Haboudane

Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

2002-07-25

D. Haboudane , John R. Miller , Nicolas Tremblay +2 more

Novel algorithms for remote estimation of vegetation fraction

2002-04-01

Anatoly A. Gitelson , Yoram J. Kaufman , Robert J. Stark +1 more

View PDF Chat

Improvements of the MODIS terrestrial gross and net primary production global data set

2005-02-15

Maosheng Zhao , Faith Ann Heinsch , Ramakrishna Nemani +1 more

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2017-01-01

Jinru Xue , Baofeng Su

Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. … Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.

View PDF Chat

Google Earth Engine: Planetary-scale geospatial analysis for everyone

2017-07-06

Noel Gorelick , M. Hancher , Mike Dixon +3 more

Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, … Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.

View PDF Chat

Completion of the 2006 National Land Cover Database for the conterminous United States.

2011-01-01

J.A. Fry , George Xian , Suming Jin +6 more

The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

2021-08-11

Jie Yang , Xin Huang

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that … Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial–temporal filtering and logical reasoning to further improve the spatial–temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

View PDF Chat

Long-term vegetation quality assessment using multi-spectral vegetation indices: Analyzing trends from 1983 to 2023 for Tiruppur Taluk using machine learning algorithms and remote sensing techniques

2025-06-25

K Kavitha , P. Sivaranjani , G. Kalpana | Journal of Earth System Science

Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods

2025-06-25

Zahra Mohammadi Mobarakeh , Saeid Pourmanafi , Mohsen Ahmadi | International Journal of Applied Earth Observation and Geoinformation

Tracking Vegetation Dynamics in Drylands with MSAVI: Insights from the South Aral Sea

2025-06-24

Shahzoda Alikhanova , Cristina Tarantino , Joseph W. Bull | Earth Systems and Environment

A global daily seamless 9 km vegetation optical depth (VOD) product from 2010 to 2021

2025-06-24

Die Hu , Yuan Wang , Jing Han +6 more | Earth system science data

Abstract. Vegetation optical depth (VOD) products provide information on vegetation water content and correlate with vegetation growth status; these are closely related to the global water and carbon cycles. The … Abstract. Vegetation optical depth (VOD) products provide information on vegetation water content and correlate with vegetation growth status; these are closely related to the global water and carbon cycles. The L-band signal penetrates deeper into the vegetation canopy than the higher-frequency bands used for many previous VOD retrievals. Currently, there are only two operational L-band sensors aboard satellites, i.e., the Soil Moisture and Ocean Salinity (SMOS) satellite launched in 2010 and the Soil Moisture Active Passive (SMAP) satellite launched in 2015. The former has the limitation of a low spatial resolution of only 25 km, while the latter has improved this resolution to 9 km but has a shorter usable time range. Due to the influence of sensor and atmospheric conditions as well as the observation methods of polar-orbiting satellites (such as scan gaps and observation revisit times), the daily data provided by both satellites suffer from varying degrees of missing data. In summary, the existing L-band VOD (L-VOD) products suffer from the defects of missing data and coarse resolution of historical data. There is little research on filling gaps and reconstructing 9 km long-term data for L-VOD products. To solve this problem, our study depends on a penalized least-square regression based on a three-dimensional discrete cosine transform to firstly generate the seamless global daily L-VOD products. Subsequently, the nonlocal filtering idea is applied to spatiotemporal fusion between high-resolution and low-resolution data, resulting in a global daily seamless 9 km L-VOD product from 1 January 2010 to 31 July 2021. In order to validate the quality of the products, time series validation and simulated missing-region validation are used for the reconstructed data. The fusion products are validated both temporally and spatially and are also compared numerically with the original 9 km data during the overlapping period. Results show that the seamless SMOS (SMAP) dataset is evaluated with a coefficient of determination (R2) of 0.855 (0.947) and a root mean squared error (RMSE) of 0.094 (0.073) for the simulated real missing masks. The temporal consistency of the reconstructed daily L-VOD products is ensured with the original time series distribution of valid values. The spatial information of the fusion product and the original 9 km data in the overlapping period is basically consistent (R2: 0.926–0.958, RMSE: 0.072–0.093, and mean absolute error MAE: 0.047–0.064). The temporal variations between the fusion product and the original product are largely synchronized. Our dataset can provide timely vegetation information during natural disasters (e.g., floods, droughts, and forest fires), supporting early disaster warning and real-time responses. This dataset can be downloaded at https://doi.org/10.5281/zenodo.13334757 (Hu et al., 2024).

A in-seasonal phenology monitoring approach for wheat breeding accessions with time-series RGB imagery by using a combination KNN-CNN-RF model

2025-06-23

Meng Zhou , Jie Zhu , Hongxu Ai +7 more | ISPRS Journal of Photogrammetry and Remote Sensing

Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data

2025-06-23

Theodoros Petropoulos , Lefteris Benos , Remigio Berruto +5 more | Applied Sciences

Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, … Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, an interpretable ML framework was developed for predicting lupin yield using Sentinel-2 remote sensing data integrated with georeferenced yield measurements. Data preprocessing involved computing vegetation indices, removing outliers, addressing multicollinearity, normalizing feature scales, and applying data augmentation techniques to correct target imbalance. Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance (R2 = 0.8756) and low error values across MAE, MSE, and RMSE metrics. To enhance model transparency, SHapley Additive exPlanations (SHAP) values were applied to interpret the feature contributions of the XGBoost model. The Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) were found to be key predictors of crop yield, both showing a positive correlation with higher values reflecting greater vegetation vigor and corresponding to increased yield. These were followed by B03 (green) and B12 (short-wave infrared), which captured key reflectance properties associated with chlorophyll activity and water content, respectively. Both of them substantially influence photosynthetic efficiency and plant health, ultimately affecting yield potential.

Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models

2025-06-23

Yi Gao , Changping Huang , Xia Zhang +2 more | Frontiers in Plant Science

Early monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of … Early monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of leaf stratification on VW detection. To address this, vertical spectral traits were examined to improve early diagnosis. A total of 551 in-situ leaf spectra were averaged from thousands of measurements, alongside corresponding RGB images from top, middle, and bottom leaf layers. Five severity levels (SL=0-4) were classified based on lesion coverage. Various vegetation indices and signal features were extracted for VW identification. Three feature selection methods, Relief-F, Lasso, and Random Forest (RF), were integrated with five machine learning models, including LightGBM, ANN, XGBoost, RF, and SVM. Results showed that spectral reflectance varied significantly by severity and layer, with the most pronounced variations in the bottom layer’s visible spectrum. LightGBM with RF-selected features achieved the best performance and fastest training, with accuracies of 0.82, 0.81, and 0.91 for the top, middle, and bottom leaf layers, respectively. Early-stage detection (SL=0-2) was most effective in the lowest layer, showing 38% and 34% higher precision (SL=1) than the upper two. Critical spectral features varied with vertical leaf layers and disease severity, with blue and red-edge bands identified as most important. For assessing five disease severity levels, the most informative features for the top, middle, and bottom layers were Ant Gitelson , Blue Index (B), and PRI 570 . For detecting early symptoms (SL=1), the blue band was particularly effective, followed by water-related bands. At the initial infection stage, the most significant indicators for top, middle, and bottom layers were Blue/red index (BRI), B, and WSCT, respectively. This study deepens understanding of vertical leaf spectral dynamics and enables rapid, non-destructive in vivo detection of cotton Verticillium wilt, enhancing the applicability of portable hyperspectral devices and informing leaf-layer-aware precision disease management strategies.

Estimating rice yield under different wheat residue coverage levels using multispectral Gaofen satellite data and remote sensing indices

2025-06-23

Muhammad Sohail Memon , Shuren Chen , Jun Guo +4 more | Journal of Agricultural Engineering

Satellite remote sensing (RS) offers an efficient, large-scale approach for monitoring crop health, particularly in precise estimation of crop yields. Rice is a staple food for over three billion people … Satellite remote sensing (RS) offers an efficient, large-scale approach for monitoring crop health, particularly in precise estimation of crop yields. Rice is a staple food for over three billion people worldwide, making it crucial to estimate rice yield promptly to ensure food security and support sustainable agriculture. However, traditional field survey methods for yield assessment, are often labor-intensive, and time-consuming. To address this challenge, we propose a novel approach that integrates Gaofen-1 (GF-1) and Gaofen-6 (GF-6) multispectral data for monitoring and evaluating rice crop yield under different wheat residue cover (WRC) percentages. This method employed three Remote Sensing (RS) based vegetation indices (VIs): i) enhanced vegetation index (EVI), ii) normalized difference vegetation Index (NDVI), and iii) green normalized difference vegetation index (GNDVI), with field data collected from 80 sampling points across paddy fields in the Changshu County, China. The results demonstrated that land use and land cover (LULC) mapping effectively classified paddy fields, covering 66% of the study area, with a classification accuracy of 88% (κ = 0.84). Among the relationships tested between VIs and WRC, NDVI showed the highest correlation (R² = 0.66), followed by EVI (R² = 0.60) and GNDVI (R² = 0.51), confirming NDVI as the most effective index for yield modeling. The yield estimation model, based on peak NDVI values correlated with measured rice yield from the calibration dataset (n=52), achieved R² = 0.83 and validation with test data (n=28) showed high accuracy of R² = 0.88 with low error metrics (RMSE = 3.48% and MAPE = 2.35%). Additionally, the findings indicated that the highest rice yields (8.21-8.36 tons/ha) were observed at moderate WRC levels (60-75%) compared to other residue percentages. These outcomes suggest that an appropriate amount of WRC enhances rice yield by supporting moisture retention and nutrient availability, which optimizes overall crop performance.Therefore, we strongly recommend integration of Gaofen satellite data with NDVI could be a scalable, cost-effective solution for accurate yield prediction that supports sustainable residue management practices and precision agriculture.

Inversion of cotton leaf area index under Verticillium wilt stress from UAV multispectral images: Deep learning-based vs. classical-based algorithms

2025-06-23

Yujing Tian , Yefeng Jiang , Mei Zeng +2 more | Industrial Crops and Products

Spatiotemporal Dynamics of Desertification and Driving Factors in the Three-River Headwaters Region from 1988 to 2023

2025-06-23

Pengfei Li , Shiying Wang , Y. H. Wang +1 more | Journal of Sustainable Forestry

Winter wheat yield prediction at a county scale using time series variation features of remote sensing spectra and machine learning

2025-06-23

Guocan Zhu , Chenxuan Zhao , Lili Zhou +2 more | European Journal of Agronomy

Chlorophyll dynamic fusion based on high-throughput remote sensing and machine learning algorithms for cotton yield prediction

2025-06-23

Jiajie Yang , Bo-Wei Xu , Bin Wu +6 more | Field Crops Research

Large-scale mapping and uncertainty assessment of dominant tree species in southern China based on Sentinel-2 time series

2025-06-23

Ben Yang , Yuxin Zhao , Junji Li +4 more | Journal of Environmental Management

Mapping the Cerrado–Amazon Transition Using PlanetScope–Sentinel Data Fusion and a U-Net Deep Learning Framework

2025-06-22

Chuanze Li , Angela Harris , Beatriz Schwantes Marimon +3 more | Remote Sensing

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to … The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region.

Quantifying Crop Damage in Hailstorm-Affected Districts of Haryana: A Satellite-Based Approach

2025-06-21

Amritpal Digra , Akash Goyal , Anirban Guha +2 more | Journal of the Indian Society of Remote Sensing

A long-term paddy rice distribution dataset in Asia at a 30 m spatial resolution

2025-06-21

S. M. Li , Ruoque Shen , Jiale Jiang +5 more | Scientific Data

Rice is a staple food for over half the global population and contributes to more than 10% of global anthropogenic methane emissions. Precise mapping of rice distribution in Asia, the … Rice is a staple food for over half the global population and contributes to more than 10% of global anthropogenic methane emissions. Precise mapping of rice distribution in Asia, the primary region for rice cultivation responsible for over 60% of global production, is crucial for monitoring food security and greenhouse gas emissions. However, due to cloud cover impacts on optical remote sensing imagery, there is still a lack of long-term, high-resolution rice distribution datasets for the entire Asian region. This study develops the Global Crop Dataset-Rice (GCD-Rice) dataset to map rice cultivation across three seasons in 16 Asian countries from 1990 to 2023. Using Landsat and Sentinel-1 datasets, along with a phenological approach and a random forest model, the maps were validated with 258,547 field samples. Results show an average user accuracy of 89.88%, a producer accuracy of 88.52%, and an overall accuracy of 88.85%. Furthermore, comparing with statistical area reveals an overall average R² value of 0.768, a slope of 0.874, and an RMSE of 0.346.

Tracking phycocyanin dynamics in East Lake using Landsat-8 and generalized additive models: impact of COVID-19 and conservation efforts

2025-06-20

Jincan Tang , Wenwen Liu , Ran Li +2 more | Environmental Monitoring and Assessment

The estimation of light transmittance through grassland canopy: Searching for a suitable indirect indicator

2025-06-20

Marek Czerwiński , Barbara Golińska , Andrzej Paszkowski +3 more | Acta Oecologica

Impact of sampling techniques on crop type mapping using multi-temporal composites from Harmonized Landsat-Sentinel images

2025-06-20

Uilson Ricardo Venâncio Aires , Vitor S. Martins , Lucas Borges Ferreira +3 more | Computers and Electronics in Agriculture

Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data

2025-06-20

Łukasz Mikołajczyk , Paweł Hawryło , Paweł Netzel +3 more | Forests

Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification … Tree species classification provides invaluable information across various sectors, from forest management to conservation. This task is most commonly performed using remote sensing; however, this method is prone to classification errors, which modern computational approaches aim to minimize. Convolutional neural networks (CNNs) used to model tabular data have recently gained popularity as a highly efficient classification tool. In the present study, a variation of this method is used to classify satellite multispectral data from the Sentinel-2 mission to distinguish between 18 common Polish tree species. The novel model is trained and tested on data from species-homogeneous forest stands. The data form a multi-seasonal time series and cover five years of observations. The model achieved an overall accuracy of 80% and Cohen Kappa of 0.80 of the raw output and increased to 93% with post-processing procedures. Considering the large number of species classified, this is a promising and encouraging result. The presented results indicate the importance of early vegetation season reflectance data in model training. The spectral bands representing the infrared, red-edge and green wavelengths had the greatest impact on the model.

Comparative Evaluation of the Multispectral Platforms Sentinel-2, CBERS-04A, and UAV for Nitrogen Detection in Maize Crops

2025-06-20

Heloisa Gomes , Gustavo Ferreira da Silva , Juliano Carlos Calonego +3 more | AgriEngineering

Multispectral images provide valuable indicators of crop nutritional status, playing a key role in strategies to reduce fertilizer use and enable supplementary applications in cases of nitrogen deficiency, thereby ensuring … Multispectral images provide valuable indicators of crop nutritional status, playing a key role in strategies to reduce fertilizer use and enable supplementary applications in cases of nitrogen deficiency, thereby ensuring productivity and profitability for farmers. However, the diversity of remote sensing platforms (RSPs) makes the choice challenging, as there are few comparative studies. This study compares the remote sensing platforms Sentinel-2, CBERS-04A, and unmanned aerial vehicle (UAV), assessing their accuracy in detecting different nitrogen doses (NDs) throughout the maize crop cycle in Botucatu-SP, using 10 vegetation indices (VIs). Six NDs were tested (0, 36, 84, 132, 180, and 228 kg ha−1 of nitrogen) in nine assessments during the crop cycle. The results showed that, at the V7 stage, the RSPs were effective in detecting the NDs in eight VIs. However, at the VT stage, only the Sentinel-2 and CBERS-04A satellites demonstrated effectiveness in six VIs. Despite the high correlation among the RSPs, the ability to distinguish the NDs varied depending on the vegetation index (VI) and phenological stage. These findings highlight the importance of selecting the appropriate VI and optimal timing, regardless of the chosen platform.

Large-scale mapping of the spatial distribution and cutting intensity of cultivated alfalfa based on a sample generation algorithm and random forest

2025-06-20

Chunli Miao , Shuai Fu , Sun We +6 more | Computers and Electronics in Agriculture

Classification of Nitrogen-Efficient Wheat Varieties Based on UAV Hyperspectral Remote Sensing

2025-06-20

Yumeng Li , Chunying Wang , Junke Zhu +2 more | Plants

Aiming at tackling the challenges of traditional classification methods, which are labor-intensive, time-consuming, and inefficient, a nitrogen-efficient wheat variety classification method using support vector machine-extreme gradient boosting (SVM-XGBoost) based on … Aiming at tackling the challenges of traditional classification methods, which are labor-intensive, time-consuming, and inefficient, a nitrogen-efficient wheat variety classification method using support vector machine-extreme gradient boosting (SVM-XGBoost) based on unmanned aerial vehicle (UAV) hyperspectral remote sensing was proposed in this study. First, eight agronomic indicators closely related to wheat nitrogen efficiency were analyzed using t-SNE dimensionality reduction and hierarchical clustering, enabling the classification of 12 wheat varieties into nitrogen-efficient and nitrogen-inefficient varieties under different nitrogen stress conditions. Second, a hyperspectral feature band selection method based on least absolute shrinkage and selection operator-competitive adaptive reweighted sampling (Lasso-CARS) was employed using hyperspectral canopy data collected during the wheat heading stage with an UAV to extract feature bands relevant to nitrogen-efficient wheat classification. This approach aimed to mitigate the impact of high collinearity and noise in high-dimensional hyperspectral data on model construction. Furthermore, the SVM-XGBoost method integrated the extracted feature bands with the support vectors and decision function outputs from the preliminary SVM classification. It then leveraged XGBoost to capture nonlinear relationships and construct the final classification model using gradient-boosted trees, achieving intelligent classification of nitrogen-efficient wheat varieties. The model also selected nitrogen fertilization strategies based on the characteristics of different wheat varieties. The results demonstrated robust performance under low, high, and no nitrogen stress, with average overall accuracies of 74%, 83%, and 70% (Kappa coefficients: 0.67, 0.80, and 0.48), respectively. This study provided an efficient and accurate UAV hyperspectral remote sensing-based method for nitrogen-efficient wheat variety classification, offering a technological foundation to accelerate precision breeding.

Estimation of Crude Protein Content in Revegetated Alpine Grassland Using Hyperspectral Data

2025-06-20

Yanfu Bai , Shijie Zhou , Jingjing Wu +7 more | Remote Sensing

Remote sensing plays an important role in understanding the degradation and restoration processes of alpine grasslands. However, the extreme climatic conditions of the region pose difficulties in collecting field spectral … Remote sensing plays an important role in understanding the degradation and restoration processes of alpine grasslands. However, the extreme climatic conditions of the region pose difficulties in collecting field spectral data on which remote sensing is based. Thus, in-depth knowledge of the spectral characteristics of alpine grasslands and an accurate assessment of their restoration status are still lacking. In this study, we collected the canopy hyperspectral data of plant communities in the growing season from severely degraded grasslands and actively restored grasslands of different ages in 13 counties of the “Three-River Headwaters Region” and determined the absorption characteristics in the red-light region as well as the trends of red-light parameters. We generated a model for estimating the crude protein content of plant communities in different grasslands based on the screened spectral characteristic covariates. Our results revealed that (1) the raw reflectance parameters of the near-infrared band spectra can distinguish alpine Kobresia meadow from extremely degraded and actively restored grasslands; (2) the wavelength value red-edge position (REP), corresponding to the highest point of the first derivative (FD) spectral reflectance (680–750 nm), can identify the extremely degraded grassland invaded by Artemisia frigida; and (3) the red valley reflectance (Rrw) parameter of the continuum removal (CR) spectral curve (550–750 nm) can discriminate among actively restored grasslands of different ages. In comparison with the Kobresia meadow, the predictive model for the actively restored grassland was more accurate, reaching an accuracy of over 60%. In conclusion, the predictive modeling of forage crude protein content for actively restored grasslands is beneficial for grassland management and sustainable development policies.

Open-Source Remote Sensing Analysis for E-Governance using Python for NDVI and Vegetation Index Analysis

2025-06-20

Suvarnmala B. Bangar , Ratnadeep R. Deshmukh , Sangramsing Kayte | International Journal of Computer Applications

Seasonal climate change characteristics of the Mu Us Sandy Land based on long time scale

2025-06-19

Liang Lie , Chao Yan , Xiaohan Wang +2 more | Environmental Monitoring and Assessment

Spatial and temporal characteristics of vegetation phenology in the middle and lower reaches of the Yangtze River and its response to climate

2025-06-19

Shikang Zhang , Wei Zhang , Xiaotong Li | Frontiers in Plant Science

This study investigates the response mechanism of vegetation phenology to climate change in the middle and lower reaches of the Yangtze River from 2001 to 2022, aiming to reveal the … This study investigates the response mechanism of vegetation phenology to climate change in the middle and lower reaches of the Yangtze River from 2001 to 2022, aiming to reveal the spatial and temporal evolution patterns of vegetation SOS, EOS, and LOS and their driving factors, and to provide a scientific basis for regional ecological management. Based on the EVI dataset, climate parameters were extracted by S-G filtering and dynamic thresholding method and combined with one-way linear regression, stability analysis, and partial correlation analysis to assess the vegetation climate changes and their responses to air temperature, precipitation, sunshine hours, and surface temperature. The results showed that: (1) SOS advanced overall (0.29 d/a), EOS delayed (0.26 d/a), and LOS prolonged (0.56 d/a). (2) Significant trends of SOS advance and EOS postponement were observed in coniferous forests, agricultural fields, and natural vegetation, and EOS advance was significant in broadleaf forests. (3) In the future, SOS and EOS will continue to advance, and LOS of cropland will continue to extend. (4) Air temperature, precipitation, and sunshine hours have an advancing effect on SOS, surface temperature has a postponing effect on EOS, and precipitation and surface temperature have an extending effect on LOS. Vegetation climate change is affected by the complex interaction of climate factors, and the results of the study reveal its spatial and temporal evolution patterns and response mechanisms to climate change, providing an important reference for regional ecological assessment and management.

Large-Scale Apple Orchard Identification from Multi-Temporal Sentinel-2 Imagery

2025-06-19

Chunxiao Wu , Yundan Liu , Jianyu Yang +7 more | Agronomy

Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar … Accurately extracting large-scale apple orchards from remote sensing imagery is of importance for orchard management. Most studies lack large-scale, high-resolution apple orchard maps due to sparse orchard distribution and similar crops, making mapping difficult. Using phenological information and multi-temporal feature-selected imagery, this paper proposed a large-scale apple orchard mapping method based on the AOCF-SegNet model. First, to distinguish apples from other crops, phenological information was used to divide time periods and select optimal phases for each spectral feature, thereby obtaining spectral features integrating phenological and temporal information. Second, semantic segmentation models (FCN-8s, SegNet, U-Net) were com-pared, and SegNet was chosen as the base model for apple orchard identification. Finally, to address the issue of the low proportion of apple orchards in remote sensing images, a Convolutional Block Attention Module (CBAM) and Focal Loss function were integrated into the SegNet model, followed by hyperparameter optimization, resulting in AOCF-SegNet. The results from mapping the Yantai apple orchards indicate that AOCF-SegNet achieved strong segmentation performance, with an overall accuracy of 89.34%. Compared to the SegNet, U-Net, and FCN-8s models, AOCF-SegNet achieved an improvement in overall accuracy by 3%, 6.1%, and 9.6%, respectively. The predicted orchard area exhibited an approximate area consistency of 71.97% with the official statistics.

<scp>PhenoVision</scp>: A framework for automating and delivering research‐ready plant phenology data from field images

2025-06-19

Russell Dinnage , Erin Grady , Nevyn Neal +6 more | Methods in Ecology and Evolution

Abstract Plant phenology plays a fundamental role in shaping ecosystems, and global change‐induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high‐quality observations … Abstract Plant phenology plays a fundamental role in shaping ecosystems, and global change‐induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high‐quality observations of when plants undergo seasonal transitions such as leaf‐out, flowering and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global‐scale research‐grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist—data spanning all angiosperms and drawn from across the globe—to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy on held‐out test images for flower (98.5%) and fruit presence (95%), as well as a high level of agreement with an expert annotator (98.6% for flowers and 90.4% for fruits). Key to producing research‐ready phenology data is post‐calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, for example leaf phenology, and further integration with other resources to form a global central database integrating all in situ plant phenology resources.

Towards near real-time drought stress assessment in Europe’s temperate forests – comparing remote sensing time series with continuous in-situ tree-level measurements

2025-06-19

Colin K. Bloom , Tiziana L. Koch , Katrin Meusburger +7 more | Ecological Indicators

Determination of surface area change with remote sensing: The case of Salt Lake

2025-06-19

Nezih Furkan ERBAŞ , Abdullah Varlık | Türkiye Coğrafi Bilgi Sistemleri Dergisi

Nowadays, with the advancement of technologies and development of applications, the use of Remote Sensing (RS) techniques is becoming widespread. Thanks to RS, applications such as determination of land surface … Nowadays, with the advancement of technologies and development of applications, the use of Remote Sensing (RS) techniques is becoming widespread. Thanks to RS, applications such as determination of land surface areas, change analysis, protection of water resources, mapping and sustainable management have begun to be realized. RS and Geographic Information Systems (GIS) provide great advantages and convenience especially in monitoring the temporal changes in the surface areas of water/lake resources. The temporal change of the surface area of a selected water source is observed over months and years. Lakes are important water resources located in terrestrial areas. In this study, it is aimed to determine the surface area change of Salt Lake in the last decade (2014-2023) using RS technique. The determined study area was obtained from Landsat 8 OLI_TIRS satellite images, especially in the summer months (7th and 8th months). Iterative Self-Organized Data Analysis Technique (ISODATA) was preferred as the method for unsupervised classification. Due to the low cloudiness, the images were generally obtained in August. From these images, vegetation/water area was analyzed using NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) and drought and wetlands in the region were monitored. According to the NDVI results, the highest value was determined as 0.73 in 2022 and the lowest value was determined as 0.59 in 2017. For the NDWI index in 2022, these values were calculated as 0.66 and -0.98, respectively. The average surface area of the Salt Lake in the last decade was found to be 999,464 km², and the changes between the years were compared with each other.

Harnessing deep learning and CRF for prior-knowledge modeling of crop dynamics

2025-06-19

Laura Elena Cué La Rosa , Dário Augusto Borges Oliveira , Raul Queiroz Feitosa | International Journal of Applied Earth Observation and Geoinformation

Spatiotemporal Dynamics of the Grassland Cover in Xinjiang, China, from 2000 to 2023

2025-06-19

C. Zhang , Yuexin Zhang , Xiuzhi Ma +3 more | Sustainability

A systematic understanding of the spatial and temporal changes of grassland fractional vegetation cover (FVC) in Xinjiang and its drivers provide scientific reference for regional ecological restoration. In this study, … A systematic understanding of the spatial and temporal changes of grassland fractional vegetation cover (FVC) in Xinjiang and its drivers provide scientific reference for regional ecological restoration. In this study, we used MODIS EVI data from 2000 to 2023 and the Pixel binary model to estimate the grassland FVC value of Xinjiang; analyze its spatiotemporal dynamics with combination of trend and persistence detection methods; and explore its driving factors with ridge regression and residual analysis. The results show the following: (1) From 2000 to 2020, the grassland FVC in Xinjiang experienced an upward trend on the whole, yet a significant decrease after 2020. Spatially, the distribution characteristics are high in the northwest and low in the southeast, decreasing from mountains to basins. (2) Precipitation and soil moisture affected FVC positively, with contributions of 18.6% and 38.3%, respectively, while air temperature and solar radiation affected it negatively, with contributions of 22.9% and 20.2%, respectively. (3) The change in the grassland FVC in Xinjiang resulted from a combination of climatic factors and human activity, whose relative contribution rates were 57.2% and 42.8%, respectively; furthermore, the areas with positive effects on the FVC were smaller than those with negative effects. (4) While the FVCs of most grassland types in Xinjiang were dominantly influenced by both climatic factors and human activity, climatic conditions were the dominant drivers of the FVCs of temperate typical grasslands and temperate desert grasslands, whereas human activities had more influence on the FVC of temperate meadow grasslands. This study provides a scientific basis and guidance for optimizing the ecological barrier function and regulating vegetation coverage in arid areas by analyzing the spatiotemporal dynamics of grassland coverage in Xinjiang and quantifying the impact of different environmental factors on it.

Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding

2025-06-19

Shuang Zhao , Yanqun Zhang , Pancen Feng +4 more | Remote Sensing

Estimating leaf water potential (Ψleaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψleaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) … Estimating leaf water potential (Ψleaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψleaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) has been proven to be a simple and efficient technique. However, models constructed by existing methods and VIs still face challenges regarding the generalizability and limited ranges of field experiment datasets. In this study, leaf dehydration experiments of three maize cultivars were applied to provide a dataset covering a wide range of Ψleaf variations, which is often challenging to obtain in field trials. The analysis screened published VIs highly correlated with Ψleaf and constructed a model for Ψleaf estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. Models were constructed using PLSR and MLR for each cultivar and PLSR, MLR, and RF for the samples from all three cultivars. The performance of the models developed for each cultivar was compared with the performance of the cross-cultivar model. Simultaneously, the normalized ratio (ND) and double-difference (DDn) were applied to determine the VIs and models. Finally, the relationship between the optimal VIs and Ψleaf was analyzed using discontinuous linear segmental fitting. The results showed that leaf spectral reflectance variations in the 350~700 nm bands and 1450~2500 nm bands were significantly sensitive to Ψleaf. The RF method achieved the highest prediction accuracy when all three cultivars’ data were used, with a normalized root mean square error (NRMSE) of 9.02%. In contrast, there was little difference in the predictive effectiveness of the models constructed for each cultivar and all three cultivars. Moreover, the simple linear regression model built based on the DDn(2030,45) outperformed the RF method regarding prediction accuracy, with an NRMSE of 7.94%. Ψleaf at the breakpoint obtained by discontinuous linear segment fitting was about −1.20 MPa, consistent with the published range of the turgor loss point (ΨTLP). This study provides an effective methodology for Ψleaf monitoring with significant practical value, particularly in irrigation decision-making and drought prediction.

A Study on Airborne Hyperspectral Tree Species Classification Based on the Synergistic Integration of Machine Learning and Deep Learning

2025-06-19

Dabing Yang , Jinyeop Song , Chaohua Huang +3 more | Forests

Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. … Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote sensing, with its high spectral resolution, shows great promise in tree species classification. However, traditional methods face limitations in extracting joint spatial–spectral features, particularly in complex forest environments, due to the “curse of dimensionality” and the scarcity of labeled samples. To address these challenges, this study proposes a synergistic classification approach that combines the spatial feature extraction capabilities of deep learning with the generalization advantages of machine learning. Specifically, a 2D convolutional neural network (2DCNN) is integrated with a support vector machine (SVM) classifier to enhance classification accuracy and model robustness under limited sample conditions. Using UAV-based hyperspectral imagery collected from a typical plantation area in Fuzhou City, Jiangxi Province, and ground-truth data for labeling, a highly imbalanced sample split strategy (1:99) is adopted. The 2DCNN is further evaluated in conjunction with six classifiers—CatBoost, decision tree (DT), k-nearest neighbors (KNN), LightGBM, random forest (RF), and SVM—for comparison. The 2DCNN-SVM combination is identified as the optimal model. In the classification of Masson pine, Chinese fir, and eucalyptus, this method achieves an overall accuracy (OA) of 97.56%, average accuracy (AA) of 97.47%, and a Kappa coefficient of 0.9665, significantly outperforming traditional approaches. The results demonstrate that the 2DCNN-SVM model offers superior feature representation and generalization capabilities in high-dimensional, small-sample scenarios, markedly improving tree species classification accuracy in complex forest settings. This study validates the model’s potential for application in small-sample forest remote sensing and provides theoretical support and technical guidance for high-precision tree species identification and dynamic forest monitoring.

Genetic algorithm optimization of ensemble learning approach for improved land cover and land use mapping: Application to Talassemtane National Park

2025-06-19

Ali Azedou , Aouatif Amine , Isaya Kisekka +1 more | Ecological Indicators

Wheat leaf area index retrieval from drone-derived hyperspectral and LiDAR imagery using machine learning algorithms

2025-06-19

Gabriel Mulero , David J. Bonfil , David Helman | Agricultural and Forest Meteorology

The Future of Cotton in Brazil: Agroclimatic Suitability and Climate Change Impacts

2025-06-19

João Antônio Lorençone , Pedro Antônio Lorençone , Lucas Eduardo de Oliveira Aparecido +3 more | AgriEngineering

Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for … Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for sustainable expansion is crucial. This study developed agroclimatic zoning maps for cotton (Gossypium hirsutum L.) across Brazil under current and future climate conditions using data from the World-Clim and MapBiomas platforms. Four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were assessed over multiple time periods. Results showed that rising temperatures and reduced rainfall will likely reduce cotton suitability in traditional producing regions such as Bahia. However, areas with potential for cotton cultivation, especially in Mato Grosso, which currently accounts for 90% of national production, remain extensive, with agroclimatic conditions indicating a theoretical expansion potential of up to 40 times the current cultivated area. This projection must be interpreted with caution, as it does not account for economic, logistical, or social constraints. Notably, Brazilian cotton is cultivated with minimal irrigation, low fertilizer input, and high adoption of no-till systems, making it one of the least carbon-intensive globally.

Investigating the Mechanisms of Hyperspectral Remote Sensing for Belowground Yield Traits in Potato Plants

2025-06-19

Wenqian Chen , Yurong Huang , Wei Tan +5 more | Remote Sensing

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment … Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R2 = 0.58–0.84) than indirect approaches (R2 = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction.

Higher Sensitivity of L‐Band Vegetation Optical Depth to Foliage Than Total Aboveground Biomass

2025-06-18

Tianyao Yang , Haibo Gong , Yao Zhang | Geophysical Research Letters

Abstract L‐band vegetation optical depth (VOD) has been widely used to estimate spatiotemporal variations of forest biomass. However, the sensitivity of L‐VOD to different vegetation components, such as foliage and … Abstract L‐band vegetation optical depth (VOD) has been widely used to estimate spatiotemporal variations of forest biomass. However, the sensitivity of L‐VOD to different vegetation components, such as foliage and stems, remains poorly understood despite their distinct interannual variations. Here we investigate the differential sensitivity of L‐VOD to foliage biomass and aboveground biomass (AGB) at various spatiotemporal scales over contiguous US. Our analysis reveals that at interannual scale, L‐VOD's sensitivity to foliage biomass is 4.5–16.8 times greater than to AGB across various forest types. Given that foliage biomass typically exhibits larger interannual fluctuations than woody components, this suggests that L‐VOD may overestimate the interannual variability of total AGB by disproportionately responding to foliage dynamics. Although foliage constitutes only a small fraction of total aboveground biomass (∼2%), it accounts for 21.2% of the interannual variations in L‐VOD. However, when large‐scale disturbances reduce foliage and woody biomass proportionally, VOD effectively captures the AGB declines. These findings highlight the need to account for component‐specific sensitivities in VOD‐based models.

Assessing Intraspecific Variation of Tree Species Based on Sentinel-2 Vegetation Indices Across Space and Time

2025-06-18

Tiziana L. Koch , Martina L. Hobi , Felix Morsdorf +5 more | Remote Sensing

Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—which … Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—which underpins adaptive capacity to environmental change. However, large-scale, continuous monitoring of intraspecific variation remains challenging. Here, we present a remote sensing approach using Sentinel-2 time series of five vegetation indices as proxies for pigment content, canopy structure, and water content to detect intraspecific variation in seven tree species across a broad environmental gradient in Switzerland. Using pure-species plot data from the Swiss National Forest Inventory, we decomposed variation into spatial, temporal, and spatiotemporal components. We found that spatial variation dominated in evergreen species (48–86%), while temporal variation was more pronounced in deciduous species (56–82%), reflecting their stronger seasonality. These findings demonstrate that species-specific Sentinel-2 time series can effectively track intraspecific variation, providing a scalable method for forest monitoring. This approach opens new pathways for studying forest adaptation, informing management strategies, and guiding species selection for conservation under changing climate conditions.

Marginal land identification and grain production capacity prediction of the coverage area of western route of China’s South-to-North Water Diversion Project

2025-06-18

Heng Zhou , Jun Zhou , Kunming Lu +4 more | Frontiers in Environmental Science

The western route of the South-to-North Water Diversion Project (SNWDP) provides opportunities to improve agricultural production by altering regional water availability. This study identifies and evaluates marginal land—defined as undeveloped … The western route of the South-to-North Water Diversion Project (SNWDP) provides opportunities to improve agricultural production by altering regional water availability. This study identifies and evaluates marginal land—defined as undeveloped reserve cultivated land and low-quality and inefficiently-utilized farmland—within provinces along the SNWDP route. Using ecological, topographic, climatic, and soil indicators, we identified 145,062 km 2 of marginal land, including 3,626 km 2 of reserve cultivated land and 141,436 km 2 of low-quality and inefficiently-utilized farmland, mainly concentrated in northwestern Xinjiang, with Qinghai having the least. To assess the grain production potential of these lands, we used maize and wheat as representative crops. Three modeling approaches—random forest regression, gradient boosted regression trees, and two-point machine learning (TPML)—were compared for their predictive accuracy. The TPML model showed the best performance. For maize, the model yielded a root mean square error (RMSE) of 48.94, a mean absolute error (MAE) of 34.01, and a mean absolute percentage error (MAPE) of 7.65%. For wheat, the RMSE was 23.92, MAE 17.67, and MAPE 6.31%. Results reveal that maize has a higher production capacity than wheat, and that grain yields are higher in the west and lower in the east, with Xinjiang showing the highest average yields on marginal land. These findings provide a scientific basis for optimizing land use, improving food self-sufficiency, and supporting regional sustainable development and national food security.

Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model

2025-06-18

Siqi Li , Hu Guyue , Shaoda Li +4 more | International Journal of Digital Earth

Estimation of Canopy Chlorophyll Content of Apple Trees Based on UAV Multispectral Remote Sensing Images

2025-06-18

Wang Ju-xia , Yu Zhang , Fei Han +6 more | Agriculture

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can … The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R2 = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards.

Monitoring Vegetation Dynamics in Desertification Restoration Areas of Wuzhumuqin Grassland Ecosystem

2025-06-18

Fan Yang , Zhiguo Wang , Yongguang Zhai +3 more | Applied Sciences

The desertified ecological restoration vegetation of Wuzhumuqin grassland plays an important role in the ecological restoration and protection of the region. However, there are few studies on the monitoring of … The desertified ecological restoration vegetation of Wuzhumuqin grassland plays an important role in the ecological restoration and protection of the region. However, there are few studies on the monitoring of the changes in ecological restoration vegetation in grassland sandy land in the past. In order to improve the low efficiency of ecological restoration vegetation monitoring, this study used Gaofen-6 (GF-6) remote sensing data to calculate the kernel Normalized Difference Vegetation Index (kNDVI) and vegetation coverage of ecological restoration vegetation and analyze their spatial and temporal trends. At the same time, a transform three-branch network structure based on deep learning is proposed to extract visual features. The kernel Normalized Difference Vegetation Index-position-temporal awareness transformer (kNDVI-PT-Former) model monitoring method based on two-phase remote sensing image features combined with kNDVI for spatio-temporal feature extraction can accurately obtain the vegetation changes in desertification ecological restoration in Wuzhumuqin grassland. The results show that the kNDVI of the study area shows an increasing trend from 2019 to 2024. The kNDVI value is 0.4086 in 2019 and 0.4927 in 2024. From the perspective of the change trend of vegetation coverage, the overall vegetation coverage of the Wuzhumuqin desertification restoration study area showed a gradual increase trend from 2019 to 2024, and the vegetation coverage increased by 19% in 2024 compared with 2019. The transformation of vegetation coverage from low level to high level in the study area is more prominent. Based on the self-built monitoring dataset of more than 5.2 million pairs of grassland vegetation changes, through model comparison and analysis, the kNDVI-PT-Former model obtains that the Class Pixel Accuracy (CPA) is 0.7295, the Intersection over Union (IoU) is 0.7228, and the overall monitoring accuracy of the model is improved by 11%. Furthermore, the stability of the model’s performance was confirmed through evaluation with five-fold cross-validation.

Особенности спектрального отображения лиственницы в осенний период на материалах съемки Sentinel-2

2025-06-18

P.P. Bogdanov , А.S. Ilintsev | Известия СПбЛТА

Исследование посвящено выявлению особенностей спектрального отображения древостоев с участием и преобладанием лиственницы в осенний период на материалах съемки Sentinel-2. Для изучения отображения древостоев с участием и преобладанием лиственницы использован диапазон … Исследование посвящено выявлению особенностей спектрального отображения древостоев с участием и преобладанием лиственницы в осенний период на материалах съемки Sentinel-2. Для изучения отображения древостоев с участием и преобладанием лиственницы использован диапазон съемки от 650,0 нм до 680,0 нм, соответствующий каналу B4 Sentinel-2. В качестве критерия выбора даты съемки использованы фенологические особенности лиственницы и сопутствующих лиственных пород на исследуемой территории. Для проведения полевых работ подобрана территория с участием и преобладанием лиственницы в 76 и 92 кварталах Кепинского участкового лесничества Архангельского лесничества. Для оценки древостоя выбран метод таксации круговыми площадками постоянного радиуса (КППР). Географическая привязка центров КППР осуществлялась с помощью GPS-навигатора Garmin 62. Для целей исследования заложена 71 КППР. Для картографирования крон лиственниц на КППР использовалась съемка в октябре 2022 г. с беспилотного воздушного судна. Для оценки точности аналитического выделения лиственницы по материалам детальной съемки использованы материалы полевого обследования. Фенологические особенности лиственницы позволяют выделить указанную породу на материалах детальной съемки с точностью свыше 90%. Выявлена линейная связь увеличения спектральной яркости КППР на космическом снимке Sentinel-2 с увеличением доли лиственницы. Проведенный комплекс работ, включающий полевые работы, определение на детальной съемке отдельных крон лиственницы и анализ их отображения на космоснимке, может применяться при решении задач по определению ареала распространения лиственницы в Архангельской области и на Европейском Севере. Результаты исследования будут способствовать развитию методов автоматизированного дешифрирования. The study is devoted to revealing the peculiarities of spectral mapping of stands with participation and predominance of larch in the autumn period on the materials of Sentinel-2 imagery. The imaging range from 650.0 nm to 680.0 nm, corresponding to Sentinel-2 channel B4, was used to study the mapping of stands with larch participation and predominance. Phenological peculiarities of larch and associated hardwoods in the study area were used as a criterion for selecting the survey date. For field work, an area with larch participation and predominance was selected in the territory of 76 and 92 quarters of the Kepinsky district forestry of Arkhangelskoye lesnichestvo. For stand assessment, the method of constant radius circular plots (CRCP) was chosen. Geographical reference of the circular plots centers was carried out using a Garmin 62 GPS navigator. For the purposes of the study, 71 CRCPs were established. In October 2022 an unmanned aircraft survey was used to map larch crowns in the circular plots. Field survey materials were used to assess the accuracy of analytical larch identification based on detailed survey data. Phenological peculiarities of larch make it possible to identify this species on the detailed imagery materials with an accuracy of over 90%. A linear relationship between the increase in the spectral brightness of CRCP on the Sentinel-2 satellite image and the increase in the larch share was revealed. The complex of works carried out, including field work, the determination of individual larch crowns on a detailed survey and the analysis of their display on a satellite image, can be used to solve problems of determining the distribution area of larch in the Arkhangelsk region and the European North. The results of the study will contribute to the development of automated decryption methods.