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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.
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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As sessile organisms, plants have to cope with a multitude of natural and anthropogenic forms of stress in their environment. Due to their longevity, this is of particular significance for trees. As a consequence, trees develop an orchestra of resilience and resistance mechanisms to biotic and abiotic stresses in order to support their growth and development in a constantly changing atmospheric and pedospheric environment. The objective of this Special Issue of Forests is to summarize state-of-art knowledge and report the current progress on the processes that determine the resilience and resistance of trees from different zonobiomes as well as all forms of biotic and abiotic stress from the molecular to the whole tree level.
pure stands --- n/a --- ion relation --- Heterobasidion annosum --- salicylic acid --- antioxidant enzymes --- antioxidant activity --- Luquasorb --- intrinsic water-use efficiency --- Greece --- Pinus koraiensis Sieb. et Zucc. --- ion homeostasis --- photosynthesis --- Pinus massoniana --- Stockosorb --- water relations --- Norway spruce --- rubber tree --- hydrophilic polymers --- drought stress --- ion relationships --- Carpinus betulus --- tree rings --- N nutrition --- disturbance --- Populus simonii Carr. (poplar) --- infection --- subcellular localization --- basal area increment --- mixed stands --- photosynthetic responses --- Aleppo pine --- water potential --- elevation gradient --- living cell --- physiological response --- antioxidant enzyme activity --- ion contents --- signal network --- expression --- soil N --- GA-signaling pathway --- differentially expressed genes --- Ca2+ signal --- climate --- ecophysiology --- Robinia pseudoacacia L. --- Heterobasidion parviporum --- mid-term --- plant tolerance --- canopy conductance --- DELLA --- tapping panel dryness --- osmotic adjustment substances --- abiotic stress --- wood formation --- malondialdehyde --- salinity treatments --- organic osmolytes --- bamboo forest --- non-structural carbohydrate --- Abies alba Mill. --- tree --- salt stress --- Populus euphratica --- proline --- nutrition --- Carpinus turczaninowii --- plasma membrane Ca2+ channels --- gene regulation --- pathogen --- TCP --- forest type --- functional analysis --- Fraxinus mandshurica Rupr. --- long-term drought --- defense response --- cold stress --- silicon fertilization --- gas exchange --- Fagus sylvatica L. --- glutaredoxin --- water availability --- 24-epiBL application --- Konjac glucomannan --- leaf properties --- reactive oxygen species --- sap flow --- ?13C --- salinity --- morphological indices --- chloroplast ultrastructure --- Moso Bamboo (Phyllostachys edulis) --- drought --- soluble sugar --- molecular cloning --- starch --- growth
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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.
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 --- n/a
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