Remote Sensing Metrics

Manual detection of secondary signs of fauna species can be time consuming, repetitively cumbersome and costly. Habitat utilisation uses high-resolution imagery and deep learning algorithms to identify secondary fauna features, such as habitat structures, nests, tracks and burrows. Quantification is automated, occurrences mapped and changes in habitat utilisation monitored over time.

Habitat suitability uses species distribution models, remote sensing data and deep learning algorithms to identify the likelihood of occurrence for individual species. Using this information a number of structural and textural components of potential habitat can also be identified such as rock cover, the presence of large woody debris and the occurrence of caves.

Fringing vegetation not only provides important habitat for wetland fauna, it also has a significant influence on water quality by filtering nutrients and sediment. Monitoring the condition of fringing vegetation is therefore important for wetland management. Vegetation condition can be monitored with indices such as the normalised difference vegetation index as shown. Colours from orange to dark green correspond to an increase in condition.

The extent of surface water can be mapped using water detection indices. In the example shown, the normalised difference water index was used to identify the area of inundation (light blue). Changes in inundation across a time period can also be mapped with this index. In this example, red areas indicate contraction of the water body and dark blue areas expansion.

In comparison to clear water, turbid water reflects light more strongly in red and green wavelengths than in blue. An index based on the ratio of red to blue reflectance enables turbidity to be mapped across water bodies. A turbidity map was derived from Sentinel satellite data in the image shown - turbidity increases from blue to orange.

Surface water temperature can be a predictor of potential water quality problems such as algal blooms. Surface water temperature can be estimated from the Landsat thermal band. In the example shown, cooler temperatures are shown in dark blue and warmer temperatures in yellow.

Environment heterogeneity is a strong indicator of biological diversity and can be used to describe the relative species richness of a habitat. Environment heterogeneity uses Rao-Qs index to map heterogeneity in vegetation via standard indices such as mSAVI and NDVI. This information can be used in biodiverse planting and restoration programs to compare relative species richness between sites as well as relative changes in species composition over time.

Potential algal blooms can be detected at an early stage by monitoring chlorophyll in water bodies. The normalised difference chlorophyll index is one tool that can be used for this purpose as shown in the image - colours from blue to orange indicates higher concentrations of algae. The data were derived from Sentinel satellite imagery. If algal blooms form, data can be updated weekly to map the extent and severity of the problem