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This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources.

Research & information: general --- unmanned aerial systems --- canopy height model --- individual tree crown --- segmentation --- forest cover map --- national forest inventory --- aerial images --- Sentinel-2 --- multisensory approach --- forest monitoring --- remote sensing --- forest sampling --- inventory efficiency --- Forest Inventory and Analysis --- forest change --- degradation --- regeneration --- geospatial-temporal analysis --- trend --- Juniperus --- above-ground biomass --- land-use --- allometric equation --- satellite remote sensing --- land cover classification --- NDVI --- vegetation --- Savitzky-Golay filtering --- spatial and temporal analysis --- Ruoergai area

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Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry.

Research & information: general --- Biology, life sciences --- Forestry & related industries --- unmanned aerial vehicles --- seedling detection --- forest regeneration --- reforestation --- establishment survey --- machine learning --- multispectral classification --- UAV photogrammetry --- forest modeling --- ancient trees measurement --- tree age prediction --- Mauritia flexuosa --- semantic segmentation --- end-to-end learning --- convolutional neural network --- forest inventory --- Unmanned Aerial Systems (UAS) --- structure from motion (SfM) --- Unmanned Aerial Vehicles (UAV) --- Photogrammetry --- Thematic Mapping --- Accuracy Assessment --- Reference Data --- Forest Sampling --- Remote Sensing --- Robinia pseudoacacia L. --- reproduction --- spreading --- short rotation coppice --- unmanned aerial system (UAS) --- object-based image analysis (OBIA) --- convolutional neural network (CNN) --- juniper woodlands --- ecohydrology --- remote sensing --- unmanned aerial systems --- central Oregon --- rangelands --- seedling stand inventorying --- photogrammetric point clouds --- hyperspectral imagery --- leaf-off --- leaf-on --- UAV --- multispectral image --- forest fire --- burn severity --- classification --- precision agriculture --- biomass evaluation --- image processing --- Castanea sativa --- unmanned aerial vehicles (UAV) --- precision forestry --- forestry applications --- RGB imagery