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Fine scale mapping of fractional tree canopy cover to support river basin management
Author(s) -
Gao Sicong,
Castellazzi Pascal,
Vervoort R. Willem,
Doody Tanya M.
Publication year - 2021
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.14156
Subject(s) - environmental science , evapotranspiration , lidar , remote sensing , hydrology (agriculture) , canopy , vegetation (pathology) , ecohydrology , arid , multispectral image , ecosystem , geography , ecology , geology , medicine , geotechnical engineering , archaeology , pathology , biology
Management of water, regionally, nationally and globally will continue to be a priority and complex undertaking. In riverine systems, biotic components like flora and fauna play critical roles in filtering water so it is available for human use and consumption. Preservation of ecosystems and associated ecosystem functions is therefore vital. In highly regulated large river basins, natural ecosystems are often supported through provision of environmental flows. Flow delivery, however, should be underpinned by rigorous monitoring to identify and prioritise biotic water requirements. Currently, large‐scale monitoring solutions are scaled from remote sensing data via measurement of field evapotranspiration for woody tree vegetation species. However, as there is generally a mismatch between field data collection area and remote sensing pixel size, new methods are required to proportion tree evapotranspiration based on tree fractional canopy area per pixel. We present a novel method to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi‐arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). We used Sentinel‐1 and Sentinel‐2 (radar and multispectral imagery) in a Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin. A predictor model combining the results of both regions was able to explain between 71%–85% of FTCC variation when compared to LiDAR FTCC when output in 10% increments. Development of this method underpins the advancement of woody vegetation monitoring to inform environmental flow management in the Murray–Darling Basin. The method and fine scale outputs will also be of value to other catchment management concerns such as altered catchment water yields related to bushfires and as such has application to water management worldwide.