Faculty Publications

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Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data
(Springer, 2022) Keerthana, N.; Salma, S.; Dodamani, B.M.
In India, the majority of the population relies heavily on rice as it is their primary source of nutrition. Rice crop yield productivity depends on seasonal variations and mainly depends on hydrological conditions. Long-term water clogging in rice fields for an extended period causes crop flooding and reduces production in terms of quality and quantity. This study deals with the identification of rice crop fields and their flooding due to surface irrigation using Sentinel-1 SAR data. The identification of rice fields was attempted by classifying the image data using a random forest algorithm. For crop flooding analysis, the temporal backscatter of the corresponding fields has been extracted from SAR data and local thresholding is used. The temporal analysis of the SAR backscattering showed a similar tendency in terms of crop growth. The overall accuracy of rice crop classification for VH and VV is 97.30% and 92.24% with RMSE errors of 0.0143 and 0.0145, respectively, obtained at the peak stage of the crop. From the crop flooding analysis, it is observed that crop fields have been flooded at the growth stage due to surface irrigation and rainfall. We identified crop flooding even at the crop mature stage. In the analysis, it has been observed that the flooding is not due to irrigation water but is due to the precipitation water. © 2022, Indian Society of Remote Sensing.
Identifying Municipal Solid Waste Dumping Site Location Using AHP and GIS Techniques: A Case Study of Coimbatore District, India
(Springer, 2022) Aishwarya, V.; Salma, S.; Dodamani, B.M.
Increased municipal solid waste generation in urban areas is a result of fast population growth and urbanization. Dumping or landfilling in unsuitable areas becomes the biggest concern for solid waste management authorities. The present dump yard at Vellalore, Coimbatore district, affect nearby settlements with a foul stench and flying ashes due to strong winds. The study’s main goal was to provide alternative landfilling sites in the Coimbatore district using GIS and analytic hierarchy process (AHP) techniques. Nine criteria were considered. These were population density, slope, geology, geomorphology, land use/land cover, and proximity to road, river, railway, and airport. Weighted overlay, a spatial analyst tool that reclassifies raster maps and a final suitability map, is generated. According to the findings, the possible landfill zones were found in the northeastern region of Coimbatore. Hence, the environmentally suitable sites can be selected by using remote sensing and GIS techniques. © 2022, Indian Society of Remote Sensing.
An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data
(Taylor and Francis Ltd., 2023) Salma, S.; Keerthana, N.; Dodamani, B.M.
To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

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