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Forest succession --- Forests and forestry --- Herbicides --- Weed control --- Environmental aspects

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There is a rising concern among natural resource scientists and managers about decline of the many plant and animal species associated with early successional habitats, especially within the Central Hardwood Region.  Open sites with grass, herbaceous, shrub, or incomplete young forest cover are disappearing as abandoned farmland and pastures return to forest and recently harvested or disturbed forests re-grow.  There are many questions about “why, what, where, and how” to manage for early successional habitats.  Tradeoffs among ecological services such as carbon storage, hydrologic processes, forest products, and biotic diversity between young, early successional habitats and mature forest are not fully understood.  Personal values and attitudes regarding forest management for conservation purposes versus "letting nature take its course," complicate finding common ground on whether and how to create or sustain early successional habitats.  In this book, expert scientists and experienced land managers synthesize knowledge and original scientific work to address critical questions sparked by the decline of early successional habitats.  We focus on habitats created by natural disturbances or management of upland hardwood forests and discuss how they can be sustainably created and managed in a landscape context.  Together, chapters written by ecologists, conservationists, and land managers provide a balanced view of how past, current, and future scenarios affect the extent and quality of early successional habitat and implications for ecosystem services and disturbance-dependant plants and animals in upland hardwood forest of the Central Hardwood Region.  .

Forest ecology -- United States. --- Forest ecology. --- Forestry -- United States. --- Forests. --- Forest succession --- Forest ecology --- Forest management --- Earth & Environmental Sciences --- Forestry --- Ecology --- Life sciences. --- Applied ecology. --- Biodiversity. --- Ecosystems. --- Conservation biology. --- Ecology. --- Wildlife. --- Fish. --- Life Sciences. --- Conservation Biology/Ecology. --- Fish & Wildlife Biology & Management. --- Applied Ecology. --- Wildlife management. --- Endangered ecosystems. --- Environmental protection --- Nature conservation --- Threatened ecosystems --- Biotic communities --- Biological diversification --- Biological diversity --- Biotic diversity --- Diversification, Biological --- Diversity, Biological --- Biology --- Biocomplexity --- Ecological heterogeneity --- Numbers of species --- Animal populations --- Game management --- Management, Game --- Management, Wildlife --- Plant populations --- Wildlife resources --- Natural resources --- Wildlife conservation --- Management --- Ecology . --- Biocenoses --- Biocoenoses --- Biogeoecology --- Biological communities --- Biomes --- Biotic community ecology --- Communities, Biotic --- Community ecology, Biotic --- Ecological communities --- Ecosystems --- Natural communities --- Population biology --- Fish --- Pisces --- Aquatic animals --- Vertebrates --- Fisheries --- Fishing --- Ichthyology --- Balance of nature --- Bionomics --- Ecological processes --- Ecological science --- Ecological sciences --- Environment --- Environmental biology --- Oecology --- Environmental sciences

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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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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