Monitoring Forest Carbon Sequestration with Remote Sensing

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

Download Url(s)

https://mdpi.com/books/pdfview/book/7128

Contributor(s)

Du, Huaqiang (editor)

Fan, Wenyi (editor)

Li, Mingshi (editor)

Fan, Weiliang (editor)

Mao, Fangjie (editor)

Language

English

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Abstract

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).

URI

https://directory.doabooks.org/handle/20.500.12854/100035

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; land use/cover change; scenario simulation; PLUS model; InVEST model; 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; forest aboveground biomass; forest aboveground biomass (AGB); scale effect; random forest (RF); scale correction; phenology; dynamic threshold method; northeast China; TIMESAT; forest carbon stocks; simulation; LUCC; multi-source data; feature selection; aboveground biomass; habitat dataset; Landsat 8-OLI images; pine forest; model comparison; 3D green volume; UAV-Lidar; urban forest; random forest model; remote sensing; MODIS; FY-3C VIRR; Yunnan Province; mangrove forests; Hainan Island; deep learning; influential mechanism; Bayesian hierarchical modelling; geostatistics; Eucalyptus grandis; Eucalyptus camaldulensis; Pinus patula; spatial random effects; spatially varying coefficient; rubber plantation; time series; shapelet; Landsat; Pinus densata; terrain niche index; dynamic model; canopy volume; diameter at breast height (DBH); aboveground biomass (AGB); stem volume (V); near-infrared reflectance of vegetation; carbon budget; L-band PolInSAR; RVoG model; forest density; terrain slope; coherence; extinction coefficient; signal penetration; 3-PG model; eucalyptus; forest age; forest structure; sensitivity; clumping index; estimation; impact analysis; field measurement; Sentinel-2 images; artificial neural network; random forests; quantile regression neural network; Pinus densata forests