Reprint

Mapping Tree Species Diversity

Edited by
August 2023
414 pages
  • ISBN978-3-0365-8526-0 (Hardback)
  • ISBN978-3-0365-8527-7 (PDF)

This book is a reprint of the Special Issue Mapping Tree Species Diversity that was published in

Engineering
Environmental & Earth Sciences
Summary

The current UN report on Biodiversity and Ecosystem Services depicts an alarming and shocking picture of the Earth. With accelerating rates of species extinction, our environment is declining globally at an unprecedented rate. Transformative economic and societal change is necessary, and will involve far-reaching alterations in perceptions and actions at both local and global levels. To cope with the pace of global change, a rapid increase in knowledge regarding species numbers, compositions, and conditions is required, as well as species interactions and environments. Remote sensing provides the only feasible way to cost-effectively and repeatedly measure and monitor these changes. Today’s satellite, aircraft, and UAV instruments provide a wide range of observational capabilities in terms of spatial, temporal, and spectral resolutions. Machine learning approaches and computational capacities are improving quickly, offering great potential for enhanced data analysis, including “big data”, and the development of powerful monitoring systems. This reprint focuses on the remote assessment of tree species diversity using various sensor modalities and platforms. It provides an overview of state-of-the art remote sensing solutions and highlights their high potential for distinguishing tree species.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
tree species; forest; biodiversity; time series; spatial autocorrelation; cross-validation; accuracy; tree species classification; Sentinel-2; multi-temporal; Wienerwald biosphere reserve; tree species classification; semideciduous forest; hyperspectral multitemporal information; UAV; classification; segmentation; single trees; forest structure analysis; dead wood; illumination correction; GEE; forest species; Mount Taishan; species diversity; spectral diversity; convex hull volume; AVIRIS-NG; tropical forests; ISRO–NASA campaign; climatic gradient; classification; Sentinel-2; woody vegetation; probability random forest; forest inventory; Serbia; tree species; object-based; classification; mapping; WorldView-3; LiDAR; machine learning; tree species classification; CNN; UAV; RGB; boreal forest; forest cover and species; Siberia; Landsat; spatial divergence; forest stands classification; curve matching; data fusion; multisource remote sensing data; segmentation; tree species mapping; LiDAR; ALS; forestry; tree species; classification; high-resolution remote sensing imagery; individual tree species recognition; individual tree crown delineation; convolutional neural network; tree species; classification; deep learning; convolutional networks; biosecurity; forest pathology; myrtle rust; urban forestry; machine learning; aerial imagery; time series; trees species identification; phenological metrics; scale effect; up-scaling; tropical forests; endangered tree species; selective logging; imbalanced data; pixel-based classification; machine learning algorithm; multi-layer perception; savanna; species distribution model; tree species classification; Sentinel-1; Sentinel-2; multitemporal; random forest; Wienerwald biosphere reserve; BPWW; tree species; classification; biodiversity; machine learning; feature extraction; optical data; SAR; LiDAR; time series; radiative transfer model

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