Choose an application

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

Forest type groups --- Forest type groups --- Floodplain forests --- Floodplain forests

Choose an application

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

Forest type groups --- Forests and forestry --- Forest type groups. --- Forests and forestry. --- Maine.

Choose an application

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

This handbook provides an overview of physical, chemical and biological methods used to analyze soils and plant tissue using an ecosystem perspective. The current emphasis on climate change has recognized the importance of including soil carbon as part of our carbon budgets. Methods to assess soils must be ecosystem based if they are to have utility for policy makers and managers wanting to change soil carbon and nutrient pools. Most of the texts on soil analysis treat agriculture and not forest soils and these methods do not transfer readily to forests because of their different chemistry and physical properties. This manual presents methods for soil and plant analysis with the ecosystem level approach that will reduce the risk that poor management decisions will be made in forests. This manual was intended for the instructors that teach students soil and plant analyses; however it can also be used by the research laboratories and by environmental scientists. The laboratory procedures in this manual are outlined in easy-to-follow steps and frequently accompanied with examples of calculations, questions to answer, and also a blank data sheet to use. These methods used in this manual can be used on soil and plant tissues found in agricultural, horticulture, forestry, urban, and natural lands.

Forest ecology. --- Forest type groups. --- Forest plants. --- Forest soils. --- Soils --- Plant communities. --- Organic compound content. --- Environmental Science. --- Laboratory Manual. --- Plant Tissue Analysis. --- Soil Analysis. --- Soil Chemistry. --- Soil Microbiology.

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.

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)

The topics of the book cover forest parameter estimation, methods to assess land cover and change, forest disturbances and degradation, and forest soil drought estimations. Airborne laser scanner data, aerial images, as well as data from passive and active sensors of different spatial, spectral and temporal resolutions have been utilized. Parametric and non-parametric methods including machine and deep learning methods have been employed. Uncertainty estimation is a key topic in each study. In total, 15 articles are included, of which one is a review article dealing with methods employed in remote sensing aided greenhouse gas inventories, and one is the Editorial summary presenting a short review of each article.

Research & information: general --- Environmental economics --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression --- forest structure change --- EBLUP --- small area estimation --- multitemporal LiDAR and stand-level estimates --- forest cover --- Sentinel-1 --- Sentinel-2 --- data fusion --- machine-learning --- Germany --- South Africa --- temperate forest --- savanna --- classification --- Sentinel 2 --- land use land cover --- improved k-NN --- logistic regression --- random forest --- support vector machine --- statistical estimator --- IPCC good practice guidelines --- activity data --- emissions factor --- removals factor --- Picea crassifolia Kom --- compatible equation --- nonlinear seemingly unrelated regression --- error-in-variable modeling --- leave-one-out cross-validation --- digital surface model --- digital terrain model --- canopy height model --- constrained neighbor interpolation --- ordinary neighbor interpolation --- point cloud density --- stereo imagery --- remotely sensed LAI --- field measured LAI --- validation --- magnitude --- uncertainty --- temporal dynamics --- state space models --- forest disturbance mapping --- near real-time monitoring --- CUSUM --- NRT monitoring --- deforestation --- degradation --- tropical forest --- tropical peat --- forest type --- deep learning --- FCN8s --- CRFasRNN --- GF2 --- dual-FCN8s --- random forests --- error propagation --- bootstrapping --- Landsat --- LiDAR --- La Rioja --- forest area change --- data assessment --- uncertainty evaluation --- inconsistency --- forest monitoring --- drought --- time series satellite data --- Bowen ratio --- carbon flux --- boreal forest --- windstorm damage --- synthetic aperture radar --- C-band --- genetic algorithm --- multinomial logistic regression