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Mangrove and saltmarsh vegetation are two important communities in the wetland ecosystem that require continuous monitoring considering their ecological threats and status. Remote Sensing observation is one of the tools to monitor this community. However algorithms are also important for the best sensor to map it accurately. Although there is some research about eCognition-based mapping, a unique rule set is important to monitor this community. Considering this research gap, a unique threshold-based ruleset has been generated in the Random Forest Model environment. A range of vegetation indices, individual bands of Sentinel 2 and Digital Elevation Model (DEM) data were tested in the feature selection method to find the best features for a Random Forest (RF) model. Top three variables were selected and those are (a) reNDVI_A, (b) VIRE_A, and (c) SWIR1 which gave 98.37% accuracy for the test data. A similar trend was found for the other three sites when they were compared with observed data. For site 1, Mangrove was 84.73%, Saltmarsh was 60.19%, and Mixed was 70.12% accurate. A similar trend was found for other three sites with overall accuracy 87.74 % for site-2, 66.23% for site 3, and 72.98% for site 4. A unique ruleset for wetland extent estimation will help to map a wide range of areas with a very minimum level of fieldwork. It will also reduce the cost of mapping done by visual observation, measurement and extensive fieldwork. The results of this work will provide the necessary insight and motivation for ecologists, and environmentalists.

Sustainable water resources management relies on advanced hydrological models calibrated from long-term satellite observations of water cycles. Monitoring water resources from Earth-observing satellites is enabled by cloud computing environments like Google Earth Engine (GEE) that support easy processing and analysis of archived data. While there are different satellite-based indicators of water resources availability, this paper focuses on mapping irrigated areas for each summer harvest season. The paper presents an automated workflow to extract yearly irrigated areas from imagery made public by Landsat and Sentinel-2. This method uses a normalised difference vegetation index (NDVI) to identify fields that exhibit higher vegetation greenness than their surroundings and are potentially being irrigated. As a first step, Google Earth Engine was used to create a seasonal maximum NDVI composite on which a grid-based thresholding algorithm was applied to identify irrigated areas in different parts of a catchment. The irrigated areas were then refined using morphological image processing and a quality-control step was conducted with ancillary datasets such as the most recent landuse classification, evapotranspiration data and crop phenology information. Finally, real-world use cases are presented to demonstrate the ability of satellite remote sensing to provide critical information on irrigation practices over the last 4 decades at the catchment scale.

Saltmarsh is one of the important communities of wetlands. Due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of distinct spectral characteristics is essential to monitor this EEC. This research was conducted to classify saltmarsh species based on spectral characteristics in the VNIR wavelength of Hyperion Hyperspectral and Worldview 2 multispectral remote sensing data. Signal Noise Ratio (SNR) and Principal Component Analysis (PCA) were applied in Hyperion data to test data quality and to reduce data dimensionality respectively. FLAASH atmospheric correction was done to get surface reflectance data. Based on spectral and spatial information a supervised classification followed by Mapping Accuracy (%) was used to assess the classification result. SNR of Hyperion data was varied according to season and wavelength and it was higher for all land cover in VNIR wavelength. There was a significant difference between radiance and reflectance spectra. It was found that atmospheric correction improves the spectral information. Based on the PCA of 56 VNIR band of Hyperion, it was possible to segregate 16 bands that contain 99.83 % variability. Based on reference 16 bands were compared with 8 bands of Worldview 2 for classification accuracy. Overall Accuracy (OA) % for Worldview 2 was increased from 72 to 79 while for Hyperion, it was increased from 70.47 to 71.66 when bands were added orderly. Considering the significance test with z values and kappa statistics at 95% confidence level, Worldview 2 classification accuracy was higher than Hyperion data.