Faculty Publications
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Item Spatio-temporal Dynamics of Land Use Land Cover Changes and Future Prediction Using Geospatial Techniques(Springer, 2022) Abraham, A.; Kundapura, S.Land use land cover (LULC) plays a key role in earth surface processes, and it is important to understand the spatio-temporal dynamics of LULC in an area. The study is carried out in the Meenachil and Manimala basins in Kerala, India, using land change modeller (LCM) to predict future LULC. The Random Forest (RF) classifier is used to classify the LULC in Google Earth Engine (GEE) for the years 1990, 2000, 2008, 2018 and 2021. The overall accuracy obtained for the years 1990, 2000, 2008, 2018 and 2021 is 92.53%, 91.42%, 96.92%, 87.79% and 95.54%, respectively, followed by a Kappa coefficient of 90.67%, 89.27%, 96.12%, 84.55% and 94.39%. LCM is utilised for LULC change detection, the model is validated successfully in predicting the LULC distribution in 2021, and the results were compared with the actual 2021 LULC. The results revealed the expansion of the built-up area and the decline of the agriculture class in these basins. The study then utilised LCM to predict future LULC up to the year 2050 at decadal intervals. The predicted future LULC maps revealed the drastic expansion of built-up; these basins might witness in the coming decades. The built area from 1990 to 2050 is expected to increase to 100.88 km2 and 60.75 km2 in Meenachil and Manimala basins, respectively. The agriculture area showed a decrease from 861.7 to 728.29 km2 in Meenachil and 743.5–676.89 km2 in Manimala basin. The outcome of the study showed the transformation of the considered land cover classes due to developmental activities in the region. The outcomes of the study can be considered as suitable inputs to land use planners for effective land use planning and management. © 2022, Indian Society of Remote Sensing.Item Spatiotemporal variation in the water quality of Vembanad Lake, Kerala, India: a remote sensing approach(Springer Science and Business Media Deutschland GmbH, 2023) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.Water quality is one of the essential parameters of environmental monitoring; even a slight variation in its characteristics may significantly influence the ecosystem. The water quality of Vembanad Lake is affected by anthropogenic effects such as industrial effluents and tourism. The optical parameters representing water quality, such as diffuse attenuation (Kd), turbidity, suspended particulate matter (SPM), and chlorophyll-a (Chl-a), are considered in this study to evaluate the water quality of Vembanad Lake, Kerala, India. As this lake is regarded as of ecological importance by the Ramsar Convention and has faced severe concerns over recent years, there was a substantial change in the water quality during the lockdowns of the COVID-19 pandemic. This research is aimed at examining the change in water quality using optical data from Sentinel-2 satellites in the ACOLITE processing software from 2016 to 2021. The analyses showed a 2.5% decrease in the values of Kd, whereas SPM and turbidity show a reduction of about 4.3% from the year 2016 to 2021. The flood and the COVID lockdown had an impact on the improvement in the quality of water from 2018 to 2021. The findings indicated that the reduction in industrial activities and tourism had a more significant effect on the improvement in the water quality of the lake. There was no substantial change in the Chl-a until 2020, whereas an average decrease of 12% in Chl-a values was observed throughout 2021. This decrease can be attributed to the reduction in the lake’s hydrological residence time (HRT). Thus, these findings will be a valuable reference to help the government and non-government organizations (NGO) during strategic planning. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Spatial Mapping of Flood Susceptibility Using Decision Tree-Based Machine Learning Models for the Vembanad Lake System in Kerala, India(American Society of Civil Engineers (ASCE), 2023) Kulithalai Shiyam Sundar, P.; Kundapura, S.Floods have claimed the lives of countless people and caused significant property damage in many countries, putting their livelihoods in the jeopardy. The Vembanad lake system (VLS) in Kerala, India, has faced adverse mishappening during 2018, 2019, and 2021 floods in the state due to torrential rainfall. The goal of this research is to construct effective decision tree-based machine learning models such as adaptive boosting (AdaBoost), random forest (RF), gradient boosting machines (GBMs), and extreme gradient boosting (XGBoost) for integrating data, processing, and generating flood susceptibility maps. There are 18 conditioning parameters considered, which include seven categories and 11 numerical data. These seven categorical data were converted to numerical data, bringing the total amount of input data to 61. The recursive feature elimination (RFE) was utilized as the feature selection technique, and a total of 22 layers were chosen to feed into the machine learning models to generate the flood susceptibility maps. The efficiencies of the models were evaluated using receiver operating characteristic (ROC)-area under the ROC curve (AUC), F1 score, accuracy, and kappa. According to the results, the performance of all four models demonstrated their practical application; however, XGBoost fared well in terms of the model's metrics. For the testing data set, the ROC-AUC values of XGBoost, GBM, and AdaBoost are 0.90, whereas it was 0.89 for RF. The accuracy varied significantly among the four models, with XGBoost scoring 0.92, followed by GBM (0.88), RF (0.87), and AdaBoost (0.87). As a result, this map may be utilized for early mitigation actions during future floods, as well as for land-use planners and emergency managers, assisting in the reduction of flood risk in regions prone to this hazard. © 2023 American Society of Civil Engineers.Item Temporal Assessment of Meteorological Drought Events Using Stationary and Nonstationary Drought Indices for Two Climate Regions in India(American Society of Civil Engineers (ASCE), 2023) Sajeev, A.; Kundapura, S.This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary models using the generalized additive model in location, scale, and shape (GAMLSS) for the period, 1951-2004. A comparative study has been conducted to assess the statistical performance of stationary and nonstationary models on various time scales (3, 6, 12, and 24 months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. © 2023 American Society of Civil Engineers.Item Comparative evaluation of meteorological and hydrological drought using stationary and non-stationary indices in a semi-arid river basin in India(Springer Science and Business Media B.V., 2024) Sajeev, A.; Kundapura, S.Few researchers have incorporated climate change in drought indices calculations and conducted comparative analyses of meteorological and hydrological droughts using non-stationary indices. The primary objective of this research is to develop non-stationary indices for assessing meteorological and hydrological droughts in the Shetrunji River basin in India. The climate oscillations are used as covariates to create non-stationary models by applying the generalized additive model in location, scale, and shape from 1971 to 2015. The statistical performance of stationary and non-stationary models has been compared across various time scales (3-, 6-, 12- and 24-months), and the results indicate that non-stationary models more effectively capture meteorological and hydrological drought events than stationary models. The drought and flood events detected by non-stationary indices are compared with historical episodes to assess the robustness of the indices. The results are also compared with drought events obtained from rainfall and streamflow departures. The annual and seasonal departures in rainfall and streamflow show the highest deficiency of rainfall and streamflow in 1987. The probability of different drought classes is calculated, and a higher likelihood of severe to extreme dry conditions is observed compared to very wet and extreme wet conditions in the basin. Investigation has been conducted on the impact of meteorological drought on hydrological drought and a correlation analysis between both types of drought. A significant correlation is observed between meteorological and hydrological drought at all analyzed time scales. Meteorological drought impacts surface water resources with a one-month lag at all time scales, with the highest response rate obtained at 6-month scale (91.13%). The study also examines the impact of drought on yield loss in kharif (bajra) and rabi (wheat) crops. Bajra and wheat yield loss rates strongly correlate with non-stationary drought indices, with a more significant effect of drought on bajra yield than wheat during major drought events. This novel dimension of drought studies provides practical insights into semi-arid regions in a changing environment. The findings can be utilized by various sectors, including drought management, agricultural planners, and policymakers, to reduce crop loss due to drought. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.