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Historical change alerts relating to hydroperiod, Gulf of Carpentaria

As a contribution to an Australian Mangrove Monitoring System, a system for generating historical and near real time alerts is being developed, with this based on time-series comparison of environmental variables retrieved from radar, optical and lidar space borne datasets (Lucas and Mitchell, 2017). The intention is to provide such information as and when new satellite sensor data become available and examples and outputs will be made available on the TERN Mangrove Portal.

Alerts are generated on the basis of evidence. As an example, time-series comparison of Landsat sensor data (Digital Earth Australia) highlighted a rapid decline in the Normalised Difference Vegetation Index (NDVI) suggesting defoliation but not necessarily dieback. This was attributed to 'natural' causes as the spatial pattern of change was not characteristics of deforestation and mangroves are protected by Australian law (knowledge). Furthermore, no changes in the height of mangroves were observed (e.g., using Tandem-X, Worldview-3 and/or airborne LIDAR). Dieback of woody vegetation was indicated by the decline in ALOS PALSAR L-band HH backscatter between 2014 and 2016.

Capacity is also being developed to provide online and near real-time alerts to identify new events but also to track progress in loss or recovery of mangroves.

References Lucas, R.M. and Mitchell, A.M. (2017). Integrated Land Cover and Change Classifications. In: The Roles of Remote Sensing in Nature Conservation: A Practical Guide and Case Studies. Díaz-Delgado, R., Lucas, R. & Hurford, C. 2017 Springer Nature. 332 p.
Deforestation.png Deforestation, indicated by lack of characteristic patterning and knowledge that mangroves are protected in Australia Defoliation.png Defoliation, indicated by a reduction in Landsat NDVI HeightChange.png The canopy height prior to and after the event has not changed, as indicated using interferometric SAR, stereo imagery or LIDAR SARMoistureChanges.png Woody vegetation moisture changes can be detected through time-series comparison of ALOS-2 PALSAR-2