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Assessment of ecosystems: A system for rigorous and rapid mapping of floodplain forest condition for Australia's most important river
Author(s) -
Cunningham Shaun C.,
Griffioen Peter,
White Matt D.,
Nally Ralph Mac
Publication year - 2018
Publication title -
land degradation and development
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 81
eISSN - 1099-145X
pISSN - 1085-3278
DOI - 10.1002/ldr.2845
Subject(s) - floodplain , environmental resource management , environmental science , basal area , flooding (psychology) , resource (disambiguation) , ecosystem , geography , hydrology (agriculture) , cartography , ecology , computer science , forestry , psychology , computer network , biology , geotechnical engineering , engineering , psychotherapist
Methods that provide rapid assessments of changing ecosystems at multiple scales are needed to inform management to address undesirable change. We developed a remote‐sensing method in partnership with, and for use by, natural resource managers to predict annually stand condition of floodplain forests along Australia's longest river, the Murray River. A measure of stand condition, which was developed in collaboration with responsible natural resource managers, is a function of plant area index, crown extent, and the percentage live basal area. We surveyed a broad range of spatial and temporal variation in condition, built predictive stand‐condition models using satellite‐derived variables, and validated predictions with surveys of new sites. A multiyear model using data from 2 drought years and a year following extensive floods provided better predictions of stand condition than did models on the basis of the data for individual years. The model provided good predictions for data collected after the build for 50 sites and for resurveys of build sites in later years ( R 2 ≥ 0.86). There was limited, temporary improvement in stand condition after the extensive flooding (2010 to late 2010) that followed a 13‐year (1997 to early 2010) drought. Forest condition can be mapped accurately and annually at medium resolution (25 × 25 m) for large areas (100,000s ha) if quantitative ground surveys, satellite imagery, machine learning, and future validation are combined. Regular assessments of forest condition can be related to likely causes of change by using regular, rapid assessments and hence can provide important management information.