Remote Sensing of Vegetation
Vegetation is a vital component of the Earth’s systems as it is involved in many interactions between the biosphere, atmosphere, hydrosphere, and lithosphere. More particularly, vegetation plays a key role in Earth’s biogeochemical cycles and surface energy balance, converting solar energy to biomas...
Wedi'i Gadw mewn:
| Fformat: | Online |
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| Iaith: | Saesneg |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Mynediad Ar-lein: | ONIX_20260416T142754_9783725859696_7 |
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| _version_ | 1869518682646904832 |
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| collection | Directory of Open Access Books |
| description | Vegetation is a vital component of the Earth’s systems as it is involved in many interactions between the biosphere, atmosphere, hydrosphere, and lithosphere. More particularly, vegetation plays a key role in Earth’s biogeochemical cycles and surface energy balance, converting solar energy to biomass to support the food chain, oxygen production and carbon sequestration, soil development and erosion prevention, heat control, and many other benefits to the humans and environment. Accordingly, mapping vegetation dynamics is significant for many interdisciplinary/multidisciplinary studies and making decisions that directly or indirectly support the United Nations SDGs. Furthermore, time-series monitoring deepens our understanding of vegetation response to anthropogenic activities and natural processes from a climate change perspective. Over recent decades, advances in remote sensing, in conjunction with statistical and machine learning algorithms and powerful cloud computing platforms, have enabled efficient mapping and monitoring of the vegetation. The possibility of acquiring remote sensing data from different sensor sources (e.g., multispectral, SAR, LiDAR, and thermal) and with different spatial, temporal, and radiometric characteristics has created unprecedented opportunities to study vegetation dynamics. This Reprint discusses the application of remote sensing data for vegetation mapping, monitoring, and analysis of change drivers. |
| format | Online |
| id | doab-20.500.12854ir-175402 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1754022026-04-16T20:43:28Z Remote Sensing of Vegetation Jamali, Sadegh Tagesson, Torbern Tian, Feng Amani, Meisam Olsson, Per-Ola Ghorbanian, Arsalan Iran Vegetation cover Normalized difference vegetation index (NDVI) Vegetation trend MODIS Linear trend Non-linear trend Climate variability Seasonality Vegetation dynamics Vegetation growth carryover Yellow River basin Monthly scale Climate extremes Drought NDVI Guangdong Vegetation phenology Mongolian Plateau SIF NIRv Woody vegetation landscape features Change detection Segmentation neural network Cyclic aerial photography Digital orthophoto Fractional vegetation cover Spatio-temporal reconstruction Gap filling LSTM Deep learning Desert steppe Aboveground biomass Remote sensing Machine learning Random forest 3DFVC Spatiotemporal analysis Human activities Geodetector GEE Yan River Basin Alpine vegetation Greenness Consistency Multiple indexes Evergreen vegetation Open Data Cube Spatiotemporal trends Fractional cover Photosynthetic vegetation Djibouti Food security Land cover trends Plant ecological unit’s changes Land change modeler Time-series dataset Markov chain model Deforestation Forest degradation Amazon LandTrendr Google Earth Engine thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Vegetation is a vital component of the Earth’s systems as it is involved in many interactions between the biosphere, atmosphere, hydrosphere, and lithosphere. More particularly, vegetation plays a key role in Earth’s biogeochemical cycles and surface energy balance, converting solar energy to biomass to support the food chain, oxygen production and carbon sequestration, soil development and erosion prevention, heat control, and many other benefits to the humans and environment. Accordingly, mapping vegetation dynamics is significant for many interdisciplinary/multidisciplinary studies and making decisions that directly or indirectly support the United Nations SDGs. Furthermore, time-series monitoring deepens our understanding of vegetation response to anthropogenic activities and natural processes from a climate change perspective. Over recent decades, advances in remote sensing, in conjunction with statistical and machine learning algorithms and powerful cloud computing platforms, have enabled efficient mapping and monitoring of the vegetation. The possibility of acquiring remote sensing data from different sensor sources (e.g., multispectral, SAR, LiDAR, and thermal) and with different spatial, temporal, and radiometric characteristics has created unprecedented opportunities to study vegetation dynamics. This Reprint discusses the application of remote sensing data for vegetation mapping, monitoring, and analysis of change drivers. 2026-04-16T20:43:19Z 2026-04-16T20:43:19Z 2026 book ONIX_20260416T142754_9783725859696_7 9783725859696 9783725859702 https://directory.doabooks.org/handle/20.500.12854/175402 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12320 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5970-2 10.3390/books978-3-7258-5970-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725859696 9783725859702 256 CH open access |
| spellingShingle | Iran Vegetation cover Normalized difference vegetation index (NDVI) Vegetation trend MODIS Linear trend Non-linear trend Climate variability Seasonality Vegetation dynamics Vegetation growth carryover Yellow River basin Monthly scale Climate extremes Drought NDVI Guangdong Vegetation phenology Mongolian Plateau SIF NIRv Woody vegetation landscape features Change detection Segmentation neural network Cyclic aerial photography Digital orthophoto Fractional vegetation cover Spatio-temporal reconstruction Gap filling LSTM Deep learning Desert steppe Aboveground biomass Remote sensing Machine learning Random forest 3DFVC Spatiotemporal analysis Human activities Geodetector GEE Yan River Basin Alpine vegetation Greenness Consistency Multiple indexes Evergreen vegetation Open Data Cube Spatiotemporal trends Fractional cover Photosynthetic vegetation Djibouti Food security Land cover trends Plant ecological unit’s changes Land change modeler Time-series dataset Markov chain model Deforestation Forest degradation Amazon LandTrendr Google Earth Engine thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Remote Sensing of Vegetation |
| title | Remote Sensing of Vegetation |
| title_full | Remote Sensing of Vegetation |
| title_fullStr | Remote Sensing of Vegetation |
| title_full_unstemmed | Remote Sensing of Vegetation |
| title_short | Remote Sensing of Vegetation |
| title_sort | remote sensing of vegetation |
| topic | Iran Vegetation cover Normalized difference vegetation index (NDVI) Vegetation trend MODIS Linear trend Non-linear trend Climate variability Seasonality Vegetation dynamics Vegetation growth carryover Yellow River basin Monthly scale Climate extremes Drought NDVI Guangdong Vegetation phenology Mongolian Plateau SIF NIRv Woody vegetation landscape features Change detection Segmentation neural network Cyclic aerial photography Digital orthophoto Fractional vegetation cover Spatio-temporal reconstruction Gap filling LSTM Deep learning Desert steppe Aboveground biomass Remote sensing Machine learning Random forest 3DFVC Spatiotemporal analysis Human activities Geodetector GEE Yan River Basin Alpine vegetation Greenness Consistency Multiple indexes Evergreen vegetation Open Data Cube Spatiotemporal trends Fractional cover Photosynthetic vegetation Djibouti Food security Land cover trends Plant ecological unit’s changes Land change modeler Time-series dataset Markov chain model Deforestation Forest degradation Amazon LandTrendr Google Earth Engine thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | Iran Vegetation cover Normalized difference vegetation index (NDVI) Vegetation trend MODIS Linear trend Non-linear trend Climate variability Seasonality Vegetation dynamics Vegetation growth carryover Yellow River basin Monthly scale Climate extremes Drought NDVI Guangdong Vegetation phenology Mongolian Plateau SIF NIRv Woody vegetation landscape features Change detection Segmentation neural network Cyclic aerial photography Digital orthophoto Fractional vegetation cover Spatio-temporal reconstruction Gap filling LSTM Deep learning Desert steppe Aboveground biomass Remote sensing Machine learning Random forest 3DFVC Spatiotemporal analysis Human activities Geodetector GEE Yan River Basin Alpine vegetation Greenness Consistency Multiple indexes Evergreen vegetation Open Data Cube Spatiotemporal trends Fractional cover Photosynthetic vegetation Djibouti Food security Land cover trends Plant ecological unit’s changes Land change modeler Time-series dataset Markov chain model Deforestation Forest degradation Amazon LandTrendr Google Earth Engine thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20260416T142754_9783725859696_7 |