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The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the world

Technology: general issues --- Landsat --- estuary --- protected area --- land use --- land cover --- change detection --- time series --- Great Barrier Reef --- Sentinel-2 --- ALOS-2 PALSAR-2 --- mangrove --- above-ground biomass --- extreme gradient boosting --- Can Gio biosphere reserve --- Vietnam --- LiDAR --- random forest --- GLAS --- aboveground biomass --- mangrove plantation --- aboveground biomass estimation --- optical images --- SAR --- DSM --- vegetation index --- color --- RGB --- accuracy assessment --- transgression --- mangrove development --- machine learning --- mangrove condition --- classification --- remote sensing --- ecosystem --- upscaling --- Worldview-2 --- Niger Delta Region --- mangroves --- land cover dynamics --- intensity analysis --- fragmentation --- spectral-temporal metrics --- land degradation --- ALOS PALSAR-2 --- JERS-1 --- GLCM --- Markov chain --- cellular automata --- data fusion --- forest monitoring --- Google Earth Engine --- mangrove forests --- multi-temporal analysis --- satellite earth observation --- time series analysis --- GEEMMM --- google earth engine --- Myanmar --- cloud computing --- digital earth --- GAMs --- Generalized Additive Models --- EVI --- phenology --- Landsat --- estuary --- protected area --- land use --- land cover --- change detection --- time series --- Great Barrier Reef --- Sentinel-2 --- ALOS-2 PALSAR-2 --- mangrove --- above-ground biomass --- extreme gradient boosting --- Can Gio biosphere reserve --- Vietnam --- LiDAR --- random forest --- GLAS --- aboveground biomass --- mangrove plantation --- aboveground biomass estimation --- optical images --- SAR --- DSM --- vegetation index --- color --- RGB --- accuracy assessment --- transgression --- mangrove development --- machine learning --- mangrove condition --- classification --- remote sensing --- ecosystem --- upscaling --- Worldview-2 --- Niger Delta Region --- mangroves --- land cover dynamics --- intensity analysis --- fragmentation --- spectral-temporal metrics --- land degradation --- ALOS PALSAR-2 --- JERS-1 --- GLCM --- Markov chain --- cellular automata --- data fusion --- forest monitoring --- Google Earth Engine --- mangrove forests --- multi-temporal analysis --- satellite earth observation --- time series analysis --- GEEMMM --- google earth engine --- Myanmar --- cloud computing --- digital earth --- GAMs --- Generalized Additive Models --- EVI --- phenology

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The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the world

Landsat --- estuary --- protected area --- land use --- land cover --- change detection --- time series --- Great Barrier Reef --- Sentinel-2 --- ALOS-2 PALSAR-2 --- mangrove --- above-ground biomass --- extreme gradient boosting --- Can Gio biosphere reserve --- Vietnam --- LiDAR --- random forest --- GLAS --- aboveground biomass --- mangrove plantation --- aboveground biomass estimation --- optical images --- SAR --- DSM --- vegetation index --- color --- RGB --- accuracy assessment --- transgression --- mangrove development --- machine learning --- mangrove condition --- classification --- remote sensing --- ecosystem --- upscaling --- Worldview-2 --- Niger Delta Region --- mangroves --- land cover dynamics --- intensity analysis --- fragmentation --- spectral-temporal metrics --- land degradation --- ALOS PALSAR-2 --- JERS-1 --- GLCM --- Markov chain --- cellular automata --- data fusion --- forest monitoring --- Google Earth Engine --- mangrove forests --- multi-temporal analysis --- satellite earth observation --- time series analysis --- GEEMMM --- google earth engine --- Myanmar --- cloud computing --- digital earth --- GAMs --- Generalized Additive Models --- EVI --- phenology --- n/a

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Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome.

Savanna --- alternative stable states --- MODIS VCF --- land surface temperature --- albedo --- Cerrado --- Amazon --- vegetation type --- optical --- sar --- synergism --- mapping --- savanna --- post oak --- vegetation index --- ecosystem function --- phenology --- encroachment --- evergreen --- deciduous --- vegetation --- grassland --- fractional cover --- trend --- ecoregion --- bare soil --- livestock --- production systems --- remote sensing --- vegetation dynamics --- vegetation persistence --- conservation --- savannas --- Africa --- vegetation indices --- oak-grass savanna --- hydrology --- Sentinel-2 --- land cover --- grasslands --- forests --- monitoring --- random forest --- spectral indexes --- vegetation seasonality --- aboveground biomass --- Cerrado ecosystem --- SAR --- allometry --- biomass --- carbon --- cost-effective --- LiDAR --- TLS --- plant water availability --- tree phenology --- phenocams --- MODIS --- terrestrial laser scanning (TLS) --- Above Ground Biomass (AGB) --- 3D point cloud --- vegetation structure --- n/a

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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.

Research & information: general --- Geography --- 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

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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.

Research & information: general --- Geography --- 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 --- 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