Remote Sensing Technology Applications in Forestry and REDD+
Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent dev...
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| Principais autores: | , , , |
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| Formato: | Online |
| Idioma: | inglês |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Acesso em linha: | 44826 |
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| _version_ | 1869527172716167168 |
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| author | Vastaranta, Mikko Calders, Kim Jonckheere, Inge Nightingale, Joanne |
| author_browse | Calders, Kim Jonckheere, Inge Nightingale, Joanne Vastaranta, Mikko |
| author_facet | Vastaranta, Mikko Calders, Kim Jonckheere, Inge Nightingale, Joanne |
| author_sort | Vastaranta, Mikko |
| collection | Directory of Open Access Books |
| description | Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion. |
| format | Online |
| id | doab-20.500.12854ir-58179 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-581792024-04-11T20:34:22Z Remote Sensing Technology Applications in Forestry and REDD+ Vastaranta, Mikko Calders, Kim Jonckheere, Inge Nightingale, Joanne TD1-1066 T1-995 spectral Cameroon quantitative structural model digital hemispherical photograph (DHP) environment effects human activity reference level terrestrial laser scanning topographic effects Guyana predictive mapping aboveground biomass estimation geographic information system Pinus massoniana 3D tree modelling ensemble model destructive sampling model comparison topography remote sensing forest growing stock volume (GSV) local tree allometry tree mapping gray level co-occurrence matrix (GLCM) deforestation REDD+ sentinel imagery geographically weighted regression aboveground biomass random forest random forest (RF) silviculture agriculture crown density hazard mapping model evaluation old-growth forest full polarimetric SAR subtropical forest forest canopy forest classification low-accuracy estimation texture LiDAR Landsat phenology airborne laser scanning tall trees machine learning forest baseline overstory trees support vector machine above-ground biomass multispectral satellite imagery crown delineation specific leaf area forest inventory canopy cover (CC) voxelization forestry leaf area thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion. 2021-02-12T01:48:22Z 2021-02-12T01:48:22Z 2020-04-07 23:07:09 2020 book 44826 9783039284702 9783039284719 https://directory.doabooks.org/handle/20.500.12854/58179 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/2103 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03928-471-9 10.3390/books978-3-03928-471-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039284702 9783039284719 244 open access |
| spellingShingle | TD1-1066 T1-995 spectral Cameroon quantitative structural model digital hemispherical photograph (DHP) environment effects human activity reference level terrestrial laser scanning topographic effects Guyana predictive mapping aboveground biomass estimation geographic information system Pinus massoniana 3D tree modelling ensemble model destructive sampling model comparison topography remote sensing forest growing stock volume (GSV) local tree allometry tree mapping gray level co-occurrence matrix (GLCM) deforestation REDD+ sentinel imagery geographically weighted regression aboveground biomass random forest random forest (RF) silviculture agriculture crown density hazard mapping model evaluation old-growth forest full polarimetric SAR subtropical forest forest canopy forest classification low-accuracy estimation texture LiDAR Landsat phenology airborne laser scanning tall trees machine learning forest baseline overstory trees support vector machine above-ground biomass multispectral satellite imagery crown delineation specific leaf area forest inventory canopy cover (CC) voxelization forestry leaf area thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Vastaranta, Mikko Calders, Kim Jonckheere, Inge Nightingale, Joanne Remote Sensing Technology Applications in Forestry and REDD+ |
| title | Remote Sensing Technology Applications in Forestry and REDD+ |
| title_full | Remote Sensing Technology Applications in Forestry and REDD+ |
| title_fullStr | Remote Sensing Technology Applications in Forestry and REDD+ |
| title_full_unstemmed | Remote Sensing Technology Applications in Forestry and REDD+ |
| title_short | Remote Sensing Technology Applications in Forestry and REDD+ |
| title_sort | remote sensing technology applications in forestry and redd |
| topic | TD1-1066 T1-995 spectral Cameroon quantitative structural model digital hemispherical photograph (DHP) environment effects human activity reference level terrestrial laser scanning topographic effects Guyana predictive mapping aboveground biomass estimation geographic information system Pinus massoniana 3D tree modelling ensemble model destructive sampling model comparison topography remote sensing forest growing stock volume (GSV) local tree allometry tree mapping gray level co-occurrence matrix (GLCM) deforestation REDD+ sentinel imagery geographically weighted regression aboveground biomass random forest random forest (RF) silviculture agriculture crown density hazard mapping model evaluation old-growth forest full polarimetric SAR subtropical forest forest canopy forest classification low-accuracy estimation texture LiDAR Landsat phenology airborne laser scanning tall trees machine learning forest baseline overstory trees support vector machine above-ground biomass multispectral satellite imagery crown delineation specific leaf area forest inventory canopy cover (CC) voxelization forestry leaf area thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | TD1-1066 T1-995 spectral Cameroon quantitative structural model digital hemispherical photograph (DHP) environment effects human activity reference level terrestrial laser scanning topographic effects Guyana predictive mapping aboveground biomass estimation geographic information system Pinus massoniana 3D tree modelling ensemble model destructive sampling model comparison topography remote sensing forest growing stock volume (GSV) local tree allometry tree mapping gray level co-occurrence matrix (GLCM) deforestation REDD+ sentinel imagery geographically weighted regression aboveground biomass random forest random forest (RF) silviculture agriculture crown density hazard mapping model evaluation old-growth forest full polarimetric SAR subtropical forest forest canopy forest classification low-accuracy estimation texture LiDAR Landsat phenology airborne laser scanning tall trees machine learning forest baseline overstory trees support vector machine above-ground biomass multispectral satellite imagery crown delineation specific leaf area forest inventory canopy cover (CC) voxelization forestry leaf area thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | 44826 |
| work_keys_str_mv | AT vastarantamikko remotesensingtechnologyapplicationsinforestryandredd AT calderskim remotesensingtechnologyapplicationsinforestryandredd AT jonckheereinge remotesensingtechnologyapplicationsinforestryandredd AT nightingalejoanne remotesensingtechnologyapplicationsinforestryandredd |