Reprint

Remote Sensing in Mangroves II

Edited by
October 2023
268 pages
  • ISBN978-3-0365-8886-5 (Hardback)
  • ISBN978-3-0365-8887-2 (PDF)

This book is a reprint of the Special Issue Remote Sensing in Mangroves II that was published in

Engineering
Environmental & Earth Sciences
Summary

Mangrove forests are in constant flux due to both natural and anthropogenic forces. The changing mangroves will have significant consequences to coastal communities. Observation and monitoring of the distribution and dynamics of mangroves is central to a wide range of scientific investigations conducted in both terrestrial and marine ecosystems. Recent advancements in remote sensing data availability, image-processing methodologies, computing and information technology, and human resource development have provided an opportunity to observe and monitor mangroves from local to global scales on a regular basis. The spectral, spatial, and temporal resolution of remote sensing data and their availability have improved, making it possible to observe and monitor mangroves with unprecedented spatial thematic and temporal details. This journal Remote Sensing Special Issue reprint dedicated to the observation and monitoring of mangroves using remote sensing from local to global scales. The Issue broadly covers the application of remote sensing using optical (multi-spectral and hyperspectral), radar, and Lidar data obtained from multiple platforms including ground, air, and space. The research papers published use the latest techniques to acquire, manage, exploit, process, and analyze a wide variety of remote sensing data for mangrove forest applications. Both research papers and innovative review papers are included.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
mangrove; natural recovery; artificial neural network; Sentinel-2; transfer learning; change detection; mangrove; coastal region; remote sensing; fragmentation; productivity; land cover change; mangrove ecosystem; random forest (RF); Google Earth Engine (GEE); Sentinel; synthetic aperture radar (SAR); optical; aerial roots; global sensitivity analysis; PAWN; canopy reflectance model; vegetation index (VI); mangroves; Landsat; mangrove forests; time series; Google Earth Engine; random forests; phenology; TIMESAT; climate; monitoring; Great Barrier Reef; mangrove forests; Hainan Island; CLUE-S; spatio-temporal simulation; future change trends; mangrove species; spectrometer; spectral reflectance; WorldView-2; dendrogram; mangroves; extent; mapping; sentinel-2; global mangrove watch; remote sensing-based monitoring; plantation; restoration; change detection; dieback; Bay of Bengal; mangrove; remote sensing; Landsat; Google Earth Engine; Red River Delta; Vietnam; vegetation index; mangrove index; mangrove forest; mangrove above ground; biomass; carbon sink; bibliometric analysis; Sembilang National Park (Indonesia); mangroves; machine learning; satellites images; geoprocessing; rehabilitation program of mangroves; n/a