Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass
This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing proce...
সংরক্ষণ করুন:
| বিন্যাস: | Online |
|---|---|
| ভাষা: | ইংরেজি |
| প্রকাশিত: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
|
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20220111_9783036505688_352 |
| ট্যাগগুলো: |
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
|
| _version_ | 1869528599671865344 |
|---|---|
| collection | Directory of Open Access Books |
| description | This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques. |
| format | Online |
| id | doab-20.500.12854ir-76617 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-766172024-03-28T03:31:37Z Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass Aranha, José AGB estimation and mapping mangroves UAV LiDAR WorldView-2 terrestrial laser scanning above-ground biomass nondestructive method DBH bark roughness Landsat dataset forest AGC estimation random forest spatiotemporal evolution aboveground biomass variable selection forest type machine learning subtropical forests Landsat 8 OLI seasonal images stepwise regression map quality subtropical forest urban vegetation biomass estimation Sentinel-2A Xuzhou forest biomass estimation forest inventory data multisource remote sensing biomass density ecosystem services trade-off synergy multiple ES interactions valley basin norway spruce LiDAR allometric equation individual tree detection tree height diameter at breast height GEOMON ALOS-2 L band SAR Sentinel-1 C band SAR Sentinel-2 MSI ALOS DSM stand volume support vector machine for regression ordinary kriging forest succession leaf area index plant area index machine learning algorithms forest growing stock volume SPOT6 imagery Pinus massoniana plantations sentinel 2 landsat remote sensing GIS shrubs biomass bioenergy vegetation indices thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques. 2022-01-11T13:37:08Z 2022-01-11T13:37:08Z 2021 book ONIX_20220111_9783036505688_352 9783036505688 9783036505695 https://directory.doabooks.org/handle/20.500.12854/76617 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4062 https://mdpi.com/books/pdfview/book/4062 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-0569-5 10.3390/books978-3-0365-0569-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036505688 9783036505695 276 Basel, Switzerland open access |
| spellingShingle | AGB estimation and mapping mangroves UAV LiDAR WorldView-2 terrestrial laser scanning above-ground biomass nondestructive method DBH bark roughness Landsat dataset forest AGC estimation random forest spatiotemporal evolution aboveground biomass variable selection forest type machine learning subtropical forests Landsat 8 OLI seasonal images stepwise regression map quality subtropical forest urban vegetation biomass estimation Sentinel-2A Xuzhou forest biomass estimation forest inventory data multisource remote sensing biomass density ecosystem services trade-off synergy multiple ES interactions valley basin norway spruce LiDAR allometric equation individual tree detection tree height diameter at breast height GEOMON ALOS-2 L band SAR Sentinel-1 C band SAR Sentinel-2 MSI ALOS DSM stand volume support vector machine for regression ordinary kriging forest succession leaf area index plant area index machine learning algorithms forest growing stock volume SPOT6 imagery Pinus massoniana plantations sentinel 2 landsat remote sensing GIS shrubs biomass bioenergy vegetation indices thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| title | Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| title_full | Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| title_fullStr | Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| title_full_unstemmed | Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| title_short | Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| title_sort | applications of remote sensing data in mapping of forest growing stock and biomass |
| topic | AGB estimation and mapping mangroves UAV LiDAR WorldView-2 terrestrial laser scanning above-ground biomass nondestructive method DBH bark roughness Landsat dataset forest AGC estimation random forest spatiotemporal evolution aboveground biomass variable selection forest type machine learning subtropical forests Landsat 8 OLI seasonal images stepwise regression map quality subtropical forest urban vegetation biomass estimation Sentinel-2A Xuzhou forest biomass estimation forest inventory data multisource remote sensing biomass density ecosystem services trade-off synergy multiple ES interactions valley basin norway spruce LiDAR allometric equation individual tree detection tree height diameter at breast height GEOMON ALOS-2 L band SAR Sentinel-1 C band SAR Sentinel-2 MSI ALOS DSM stand volume support vector machine for regression ordinary kriging forest succession leaf area index plant area index machine learning algorithms forest growing stock volume SPOT6 imagery Pinus massoniana plantations sentinel 2 landsat remote sensing GIS shrubs biomass bioenergy vegetation indices thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| topic_facet | AGB estimation and mapping mangroves UAV LiDAR WorldView-2 terrestrial laser scanning above-ground biomass nondestructive method DBH bark roughness Landsat dataset forest AGC estimation random forest spatiotemporal evolution aboveground biomass variable selection forest type machine learning subtropical forests Landsat 8 OLI seasonal images stepwise regression map quality subtropical forest urban vegetation biomass estimation Sentinel-2A Xuzhou forest biomass estimation forest inventory data multisource remote sensing biomass density ecosystem services trade-off synergy multiple ES interactions valley basin norway spruce LiDAR allometric equation individual tree detection tree height diameter at breast height GEOMON ALOS-2 L band SAR Sentinel-1 C band SAR Sentinel-2 MSI ALOS DSM stand volume support vector machine for regression ordinary kriging forest succession leaf area index plant area index machine learning algorithms forest growing stock volume SPOT6 imagery Pinus massoniana plantations sentinel 2 landsat remote sensing GIS shrubs biomass bioenergy vegetation indices thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography |
| url | ONIX_20220111_9783036505688_352 |