Automated delineation of salt-affected lands and their progress in coastal India using Google Earth Engine and machine learning techniques.

Assessment of salt-affected land (SAL) is still a major challenging task worldwide, especially in developing nations. The advancement of remotely sensed digital satellite images of different spectral bands has enabled the assessment of soil salinity. Sentinel-2 and Landsat 8 and 5 images of 2020, 2015 and 2009 and Shuttle Radar Topographical Mission data of 2014 were obtained from the Google Earth Engine data catalogue. Twenty spectral indices have been used which include four vegetation indices, twelve soil salinity indices, four topographical characteristics and their spectral bands. The Random Forest model was used to detect SAL. A total of 593 soil samples were used in the model. Of the electrical conductivity values of samples collected in the field, 70% of the soil samples were used for the model training, and the remaining 30% were used for validation. Also, fivefold cross-validation was carried out to validate the model prediction. The predicted SAL extent identified during 2020 was 134.4 sq. km with an overall accuracy of 93% using fivefold cross-validation. In 2015 and 2009, the total SAL was 128.42 and 120.41 sq. km, respectively. The total SAL has increased by 11.6% during the study period. The present study demonstrated the strength of remote sensing techniques to assess the SAL, which will help quantify the unproductive lands at the state or national level for reclamation or other productive use.

Assessment of land and water ecosystems capability to support aquaculture expansion in climate-vulnerable regions using analytical hierarchy process based geospatial analysis.

The fast-unregulated expansion of shrimp aquaculture led to increasing ecological concerns and conflicts among the coastal resource users worldwide, thus calling for techniques to identify the suitable aquaculture zones to ensure sustainability. The multi-criteria decision-support approach was used to assess the ecosystem's ability to support the shrimp farming expansion using a hierarchical analytical process based on multiple criteria decision analysis, and the assessment of the carrying capacity of the source water bodies. Eighteen thematic layers were grouped into four major groups, namely land type, source water quality, soil characteristics, and infrastructure availability. The pairwise comparison matrix was used to assign the weights to each criterion based on its relative importance. Spatial restriction rules were framed based on the guidelines of the Coastal Aquaculture Authority of India. The favorable conditions exist in 85-100% area for expanding aquaculture in terms of water, soil, and resource availability, but the land was a major controlling factor. The extent of the appropriate area for shrimp aquaculture was found to be 7426 ha. The carrying capacity of twelve source water bodies in the study region indicates that 83.3% of water bodies can accommodate the total identified area associated with it, but the rest 17.7% water bodies can withstand the development up to70 to 72% of the available space. This approach illustrates the suitability of geospatial planning in combination with carrying capacity assessment of source water bodies for sustainable resource use in the shrimp growing nations of the world.

Shoreline change and potential sea level rise impacts in a climate hazardous location in southeast coast of India.

Climate change impact on the environment makes the coastal areas vulnerable and demands the evaluation of such susceptibility. Historical changes in the shoreline positions and inundation based on projected sea-level scenarios of 0.5 and 1 m were assessed for Nagapattinam District, a low-lying coastal area in the southeast coast of India, using high-resolution Shuttle Radar Topography Mission data; multi-dated Landsat satellite images of 1978, 1991, 2003, and 2015; and census data of 2011. Image processing, geographical information system, and digital shoreline analysis system methods were used in the study. The shoreline variation indicated that erosion rate varied at different time scales. The end point rate indicated the highest mean erosion of - 3.12 m/year, occurred in 73% of coast between 1978 and 1991. Weighted linear regression analysis revealed that the coast length of 83% was under erosion at a mean rate of - 2.11 m/year from 1978 to 2015. Sea level rise (SLR) impact indicated that the coastal area of about 14,122 ha from 225 villages and 31,318 ha from 272 villages would be permanently inundated for the SLR of 0.5 and 1 m, respectively, which includes agriculture, mangroves, wetlands, aquaculture, and forest lands. The loss of coastal wetlands and its associated productivity will severely threaten more than half the coastal population. Adaptation measures in people participatory mode, integrated into coastal zone management with a focus on sub-regional coastal activities, are needed to respond to the consequences of climate change.