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While international efforts in the development of short rotation woody crops (SRWCs) have historically focused on the production of biomass for bioenergy, biofuels, and bioproducts, research and deployment over the past decade has expanded to include broader objectives of achieving multiple ecosystem services. In particular, silvicultural prescriptions developed for SRWCs have been refined to include woody crop production systems for environmental benefits such as carbon sequestration, water quality and quantity, and soil health. In addition, current systems have been expanded beyond traditional fiber production to other environmental technologies that incorporate SRWCs as vital components for phytotechnologies, urban afforestation, ecological restoration, and mine reclamation. In this Special Issue of the journal Forests, we explore the broad range of current research dedicated to our topic: International Short Rotation Woody Crop Production Systems for Ecosystem Services and Phytotechnologies

rhizospheric soil --- allocation --- acidic soil --- abandoned farmland --- carbon sequestration --- bioenergy --- mycorrhizal fungi --- leaf area index --- foliar nutrient and metal concentration --- aboveground biomass --- inoculation --- stocking level --- site reclamation --- willow --- Salix --- spacing trial --- agricultural field experiment --- Populus --- Populus canadensis --- species variation --- ecosystem services

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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

NDLMA --- n/a --- multi-angle remote sensing --- TerraSAR-X --- above ground biomass --- stem volume --- regression analysis --- ground-based remote sensing --- sensor fusion --- pasture biomass --- grazing management --- livestock --- mixed forest --- SPLSR --- estimation accuracy --- Bidirectional Reflectance Distribution Factor --- forage crops --- Land Surface Phenology --- climate change --- vegetation index --- dry biomass --- mapping --- rangeland productivity --- vegetation indices --- error analysis --- broadleaves --- remote sensing --- applicability evaluation --- ultrasonic sensor --- chlorophyll index --- alpine meadow grassland --- forest biomass --- anthropogenic disturbance --- fractional vegetation cover --- alpine grassland conservation --- carbon mitigation --- conifer --- short grass --- grazing exclusion --- MODIS time series --- random forest --- aboveground biomass --- NDVI --- AquaCrop model --- inversion model --- wetlands --- field spectrometry --- spectral index --- yield --- foliage projective cover --- lidar --- correlation coefficient --- Sahel --- biomass --- dry matter index --- Niger --- Landsat --- grass biomass --- particle swarm optimization --- winter wheat --- carbon inventory --- rice --- forest structure information --- MODIS --- light detection and ranging (LiDAR) --- ALOS2 --- ecological policies --- above-ground biomass --- Wambiana grazing trial --- food security --- forest above ground biomass (AGB) --- Atriplex nummularia --- regional sustainability --- CIRed-edge

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The use of electronics, close-range sensing, and artificial intelligence has changed the management paradigm in many contemporary industries in which Big Data analytics by automated processes has become the backbone of decision making and improvement. Acknowledging the integration of electronics, devices, sensors, and intelligent algorithms in much of the equipment used in forest operations, as well as their use in various forestry-related applications, it is apparent that many disciplines within forestry and forest science still rely on data collected traditionally, which is resource-intensive. In turn, this brings limitations in characterizing the specific behaviors of forest product systems and wood supply chains, and often prevents the development of solutions for improvement or inferring the laws behind the operation and management of such systems. Undoubtedly, many solutions still need to be developed in the future to provide the technology required for the effective management of forests. In this regard, the Special Issue entitled “Electronics, Close-Range Sensors and Artificial Intelligence in Forestry” highlights many examples of how technological improvements can be brought to forestry and to other related fields of science and practice.

Research & information: general --- Biology, life sciences --- Forestry & related industries --- forest fire detection --- deep learning --- ensemble learning --- Yolov5 --- EfficientDet --- EfficientNet --- big data --- automation --- artificial intelligence --- multi-modality --- acceleration --- classification --- events --- performance --- motor-manual felling --- willow --- Romania --- region detection of forest fire --- grading of forest fire --- weakly supervised loss --- fine segmentation --- region-refining segmentation --- lightweight Faster R-CNN --- ultrasound sensors --- road scanner --- terrestrial laser scanning --- TLS --- forest road maintenance --- forest road monitoring --- crowned road surface --- digital twinning --- climate smart --- LiDAR --- digitalization --- forest loss --- land-cover change --- machine learning --- spatial heterogeneity --- random forest model --- geographically weighted regression --- aboveground biomass --- estimation --- remote sensing --- Sentinel-2 --- Iran --- multiple regression --- artificial neural network --- k-nearest neighbor --- random forest --- canopy --- drone --- leaf --- leaves --- foliar --- samples --- sampling --- Aerial robotics --- UAS --- UAV --- IoT --- forest ecology --- accessibility --- wood --- diameter --- length --- close-range sensing --- Augmented Reality --- comparison --- accuracy --- effectiveness --- potential --- forestry 4.0 --- wood technology --- sawmilling --- productivity --- prediction --- long-term --- tree ring --- forestry detection --- resistance sensor --- micro-drilling resistance method --- signal processing --- Signal-to-Noise Ratio (SNR) --- n/a