Remote Sensing Technology Applications in Forestry and REDD+

Edited by
March 2020
244 pages
  • ISBN978-3-03928-470-2 (Paperback)
  • ISBN978-3-03928-471-9 (PDF)

This book is a reprint of the Special Issue Remote Sensing Technology Applications in Forestry and REDD+ that was published in

Biology & Life Sciences
Environmental & Earth Sciences

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion.

  • Paperback
© 2020 by the authors; CC BY-NC-ND license
sentinel imagery; above-ground biomass; predictive mapping; machine learning; geographically weighted regression; canopy cover (CC); spectral; texture; digital hemispherical photograph (DHP); random forest (RF); gray level co-occurrence matrix (GLCM); forest inventory; LiDAR; tall trees; overstory trees; tree mapping; crown delineation; aboveground biomass; Landsat; random forest; topography; human activity; aboveground biomass estimation; remote sensing; crown density; low-accuracy estimation; model comparison; old-growth forest; multispectral satellite imagery; random forest; forest classification; remote sensing; forestry; phenology; silviculture; forest growing stock volume (GSV); full polarimetric SAR; subtropical forest; topographic effects; environment effects; geographic information system; support vector machine; random forest; ensemble model; hazard mapping; 3D tree modelling; aboveground biomass estimation; destructive sampling; Guyana; LiDAR; local tree allometry; model evaluation; quantitative structural model; Pinus massoniana; specific leaf area; leaf area; terrestrial laser scanning; voxelization; forest canopy; REDD+; Cameroon; reference level; deforestation; agriculture; forest baseline; airborne laser scanning; terrestrial laser scanning; remote sensing; REDD+; forestry