Monitoring Forest Carbon Sequestration with Remote Sensing

doi:10.3390/books978-3-0365-7209-3

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Hardback

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Keywords

forest height; synthetic aperture radar (SAR); interferometry; random volume over ground (RVoG) model; three-stage inversion method; bamboo forest; BEPS model; gross primary productivity; net primary productivity; spatiotemporal evolution; climate change; backscatter coefficients; polarization decomposition; collinearity; ridge regression; RF; PCA; aboveground carbon density; LiDAR; stratified estimation; machine learning algorithm; Northeast China; canopy closure; the GOST model; fisheye camera photos; transects; LAI; forest height inversion; three-stage algorithm; coherence optimization; complex coherence amplitude inversion; SRTM; random forest; stochastic gradient boosting; random forest Kriging; wavelet analysis; carbon storage; climate change; land use/cover change; scenario simulation; PLUS model; InVEST model; net primary productivity; remote sensing inversion; dynamic change; driving factors; Shaoguan City; above-ground biomass (AGB); airborne LiDAR; airborne hyperspectral; wavelet transform; feature fusion; Landsat time-series; VCT model; classifying forest types; stochastic gradient boosting; forest aboveground biomass; forest aboveground biomass (AGB); scale effect; random forest (RF); scale correction; phenology; climate change; dynamic threshold method; northeast China; TIMESAT; forest carbon stocks; simulation; LUCC; climate change; spatiotemporal evolution; forest height; multi-source data; feature selection; machine learning algorithm; aboveground biomass; habitat dataset; Landsat 8-OLI images; pine forest; model comparison; 3D green volume; aboveground biomass; UAV-Lidar; urban forest; random forest model; forest aboveground biomass (AGB); remote sensing; MODIS; FY-3C VIRR; Yunnan Province; mangrove forests; Hainan Island; deep learning; spatiotemporal evolution; influential mechanism; Bayesian hierarchical modelling; geostatistics; Eucalyptus grandis; Eucalyptus camaldulensis; Pinus patula; spatial random effects; spatially varying coefficient; rubber plantation; time series; shapelet; carbon storage; InVEST model; Landsat; Pinus densata; terrain niche index; dynamic model; carbon storage; urban forest; UAV-Lidar; canopy volume; diameter at breast height (DBH); aboveground biomass (AGB); stem volume (V); gross primary productivity; near-infrared reflectance of vegetation; urban forest; carbon budget; L-band PolInSAR; RVoG model; forest height; three-stage inversion method; forest density; terrain slope; coherence; extinction coefficient; signal penetration; 3-PG model; eucalyptus; forest age; forest structure; remote sensing; sensitivity; clumping index; estimation; impact analysis; field measurement; Sentinel-2 images; artificial neural network; random forests; quantile regression neural network; Pinus densata forests

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Monitoring Forest Carbon Sequestration with Remote Sensing

Edited by

April 2023

652 pages

ISBN 978-3-0365-7208-6 (Hardback)
ISBN 978-3-0365-7209-3 (PDF)

https://doi.org/10.3390/books978-3-0365-7209-3

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This is a Reprint of the Special Issue Monitoring Forest Carbon Sequestration with Remote Sensing that was published in

Engineering

Environmental & Earth Sciences

Summary

The forest, as the main body of the terrestrial ecosystem, has a huge carbon sink function and plays an important role in coping with global climate change. This reprint on “Monitoring forest carbon sequestration with remote sensing” mainly focuses on new remote sensing theories, methods, and technologies for monitoring carbon sinks in forest ecosystems (including urban forest ecosystems).