Advances in Remote Sensing for Global Forest Monitoring

Edited by
August 2021
352 pages
  • ISBN978-3-0365-1252-5 (Hardback)
  • ISBN978-3-0365-1253-2 (PDF)

This book is a reprint of the Special Issue Advances in Remote Sensing for Global Forest Monitoring that was published in

Environmental & Earth Sciences

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.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
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; Sentinel-2; CUSUM; Sentinel-1; 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; Sentinel-1; support vector machine; improved k-NN; genetic algorithm; multinomial logistic regression; n/a