English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Mapping surface water extent is important for managing water supply for agriculture and the environment. Remote sensing technologies, such as Landsat, provide an affordable means of capturing surface water extent with reasonable spatial and temporal coverage suited to this purpose. Many methods are available for mapping surface water including the modified Normalised Difference Water Index (mNDWI), Fisher’s water index (FWI), Water Observations from Space (WOfS), and the Tasseled Cap Wetness index (TCW). While these methods can discriminate water, they have their strengths and weaknesses, and perform at their best in different environments, and with different threshold values. This study combines the strengths of these indices by developing rules that applies an index to the environment where they perform best. It compares these indices across the Murray-Darling Basin (MDB) in southeast Australia, to assess performance and compile a heuristic rule set for accurate application across the MDB. The results found that all single indices perform well with the Kappa statistic showing strong agreement, ranging from 0.78 for WOfS to 0.84 for TCW (with threshold −0.035), with improvement in the overall output when the index best suited for an environment was selected. mNDWI (using a threshold of −0.3) works well within river channels, while TCW (with threshold −0.035) is best for wetlands and flooded vegetation. FWI and mNDWI (with threshold 0.63 and 0, respectively) work well for remaining areas. Selecting the appropriate index for an environment increases the overall Kappa statistic to 0.88 with a water pixel accuracy of 90.5% and a dry pixel accuracy of 94.8%. An independent assessment illustrates the benefit of using the multi-index approach, making it suitable for regional-scale multi-temporal analysis.

Development of a Multi-Index Method Based on Landsat Reflectance Data to Map Open Water in a Complex Environment