Faculty Publications
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Item Examining the effects of vented dams on land use and land cover in the Shambhavi Catchment: a multitemporal sentinel imagery analysis(Elsevier Ltd, 2024) Chandana, S.; Aishwarya Hegde, A.; Umesh, P.; Chandan, M.C.The rapid expansion of the global economy has given rise to concerning ecological consequences, notably a dramatic increase in land cover change (LCC). This section presents how to use the Google Earth Engine (GEE) cloud platform to explore the administrative divisions of the Southern Indian Dakshina Kannada (DK) district, which were chosen for their LCC susceptibility. Leveraging GEE, we generated a time series dataset tracking LCC over a 4-year period (2019–22). Our findings demonstrate an impressive overall accuracy (OA) of 96.30% for 2019 and 95.47% for 2022. A significant revelation in our study is the 13.64% reduction in forested areas, accompanied by a 0.68% increase in urban development within the district. This research attempt offers vital insights into the impact of dam construction on LCC, aiding informed decisions on water resource management. This research promotes a sustainable and ecologically conscious approach to holistic development in the study region and beyond. © 2024 Elsevier B.V.Item Assessment of surface soil moisture from ALOS PALSAR-2 in small-scale maize fields using polarimetric decomposition technique(Springer Science and Business Media Deutschland GmbH, 2021) Gururaj, P.; Umesh, P.; Shetty, A.Surface soil moisture knowledge is important, especially in agriculture and irrigation management. Properties of microwave remote sensing like penetration power and longer wavelength facilitate retrieval of surface soil moisture. ALOS PALSAR-2, quad polarized data are used to retrieve surface soil moisture using polarization decomposition techniques in a marginal farmer small-scale maize field. The focus of the study is to explore the utility of ALOS PALSAR-2 in retrieving surface soil moisture using the polarization decomposition technique. The demonstration of the study is carried out in Malavalli village, southern India, an agricultural predominant area. The study involves field soil moisture sampling in synchronous with satellite pass, measuring soil properties, preprocessing of SAR data, polarization decomposition, proportional analysis, regression analysis, model calibration and validation. Van Zyl decomposition gave the highest surface scattering component (43%) and reduced volumetric scattering component compared to Yamaguchi and Freeman–Durden decomposition. Surface scattering component of Yamaguchi decomposition gave a good coefficient of determination (R2 = 0.8029) with field-measured surface soil moisture. The semi-empirical model (SEM) was developed using surface scattering component and depolarization ratio with adjusted R2 = 0.75 at 95% confidence interval. On its comparison with existing soil moisture models, it is observed that the developed model is performing well with RMSE and AEmax of 1.81 and 2.88, respectively. Implying the applicability of ALOS PALSAR-2 in soil moisture retrieval in marginal farmer small-scale maize fields gave satisfactory results of accuracy. © 2021, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.Item Modeling of surface soil moisture using C-band SAR data over bare fields in the tropical semi-arid region of India(Springer Science and Business Media Deutschland GmbH, 2021) Gururaj, P.; Umesh, P.; Shetty, A.Spatial variability of surface soil moisture is a prime factor in modeling many environmental and meteorological processes. This study aims to model surface soil moisture in bare fields using Sentinel-1A SAR data at a regional scale. The site/plot selected for the study falls in the tropical semi-arid region of Malavalli, Karnataka, India. The study site is divided into 43 grids to collect soil moisture samples from bare field plots synchronized with Sentinel-1A pass. Sentinel-1A, dual-polarized (VV and VH) data with 5.405-GHz frequency and central incidence angle of 33° are used. Six SAR imageries were procured from ESA, out of which five were used to model field soil moisture and one for validation. Processing of the SAR imageries is carried out using SNAP 7.0 software’s standard tools, and the backscattered energy of each sample grid is extracted using R software. The relation between SAR backscatter energy with soil parameters like moisture, dielectric constant, and roughness was used to model soil moisture. Results revealed that Sentinel-1A has a high potential to record the soil moisture spatial variation at the plot scale. Volumetric soil moisture and backscattered energy showed a positive correlation with R2 of 0.59 and 0.51 for VV and VH polarization. Dielectric constant also showed a positive correlation with backscattered energy having R2 of 0.54 and 0.48 for VV and VH polarization. With this knowledge, surface soil moisture is modeled over bare fields and mapped. Soil moisture modeled is validated using field data, which has R2 of 0.88 and RMSE of 1.93. The developed model and surface soil moisture map are helpful in regional hydrological studies and crop water requirement assessment. © 2021, Società Italiana di Fotogrammetria e Topografia (SIFET).Item Dependability of rainfall to topography and micro-climate: an observation using geographically weighted regression(Springer, 2022) Shetty, S.; Umesh, P.; Shetty, A.The dependability of rainfall to topography and micro-climate of the region in an eco-sensitive Western Ghats of India is evaluated using the geographically weighted regression method. The correlation between rainfall and topographical features, namely, elevation, slope, Terrain Ruggedness Index, topography, and distance from the coast/ridge, varies seasonally with consistent variation across the years. The Normalized Differential Vegetation Index and rainfall have an inverse relationship due to the adverse effect of high spell rainfall on vegetation growth in the monsoon season. The rainfall negatively correlates with maximum land surface temperature and conversely with a minimum land surface temperature in the windward side of the Ghats other than monsoon season. The connection between rainfall and other variables differs significantly throughout space, with vast differences on the mountain’s windward and leeward sides, as well as in the Ghats’ southern and northern regions. The effect of the terrain is amplified in the broad, gradually sloping intermediate rough mountain that is close to the coast. The maximum rainfall depends on the mountain’s steepness on the windward side; at isolated mountains, maximum rainfall occurs at an elevation range of 500–800 m and in cascaded mountain ranges at 800–1200 m along with the influence of other driving factors. Also, the control exerted by the ridge of the mountain on the rain-bearing wind is prominent until 120 km from the mountain ridge. These results are useful in understanding the reliance of rainfall on topographic and micro-climatic parameters and can be used in hydro-geological applications. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Item Vertical accuracy assessment of open source digital elevation models under varying elevation and land cover in Western Ghats of India(Springer Science and Business Media Deutschland GmbH, 2022) Shetty, S.; Vaishnavi, P.C.; Umesh, P.; Shetty, A.The selection of suitable DEM from available open-source DEMs like SRTM, ALOS World 3D, CARTOSAT-1, ASTER-GDEM, TanDEM-X which are acquired through different techniques is difficult without prior guidelines, especially on the rugged mountainous terrain. Therefore, this article aimed to evaluate the role of land cover and altitude on the vertical accuracy of open-source DEMs with near to ground measurements taken by Ice Cloud and Land Elevation (ICESat) Geoscience Laser Altimetry System (GLAS) in and around Western Ghats (WG) of India. The SRTM (30 m) DEM outperformed other DEMs at the scale of WG and in the dense vegetation cover with least performance by ASTER DEM (30 m). The vertical accuracy of DEM is varying with different elevation ranges and land cover conditions and is found to be better than the vertical accuracy specified by the mission. The overestimation of elevation in low terrain relief area, and underestimation on higher elevation with steep terrain is substantive in all the DEMs. The role of land cover and altitude is significant on the elevation and slope more than the aspect and roughness. Good performance by 90-m resolution DEM over 30-m resolution DEMs proves the potential of InSAR in elevation measurement in vegetated areas with low cost and high accuracy. These results help in the selection of pertinent DEM for any geo-climatical applications and in development of merged DEM based on the terrain relief and land cover of the region. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.Item An exploratory analysis of urbanization effects on climatic variables: a study using Google Earth Engine(Springer Science and Business Media Deutschland GmbH, 2022) Shetty, A.; Umesh, P.; Shetty, A.Rapid global economic expansion has resulted in a drastic increase of urbanization while impacting the Earth’s entire ecology. This study evaluates the impact of historical land-use/land-cover (LU/LC) change signatures on seasonal variation of climatic variables using a cloud platform-Google Earth Engine. Due to rapid urbanization and the noticeable spatio-temporal difference in the climate, administrative units of Dakshina Kannada district are taken for demonstration. The LU/LC of the district extracted from high-resolution images of Landsat using random forest classification, land surface temperature (LST) extracted from the thermal band of Landsat images using the mono window algorithm, evapotranspiration (ET) data extracted from MOD16A2.006 and precipitation data from CHIPRS was used. The data was extracted for the pre-monsoon and post-monsoon period 2001–2019. The district has seen a 13.67% reduction in the forest area with 18.81% increase in the built-up areas. The LST and ET has seen a progressive drift in the past two decades, with an increase of 4.07 °C in median temperature in forest areas and a decline of 2.19 mm in median ET value, which necessitates monitoring forest encroachment. The higher variation in maximum LST in built-up land (0.36∘C/year/sq.km) (near the industrial area) indicates that LU/LC change signature is the predominant driving factor and is associated with the physical characteristics of the built-up area. The ET exhibited a decreasing rate of 0.62 mm/year/sq.km of the built-up land. This study highlights the power of Google Earth Engine and free availability of satellite data in environmental protection, land-use management and sustainable development in the region. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Future transition in climate extremes over Western Ghats of India based on CMIP6 models(Springer Science and Business Media Deutschland GmbH, 2023) Shetty, S.; Umesh, P.; Shetty, A.The effect of climate change on the tropical river catchments in the Western Ghats of India is studied using the Coupled Model Intercomparison Project-6 data (CMIP-6). Multi-model ensembles of rainfall and temperature are constructed using the Random Forest ensemble technique for bias-corrected GCMs in the near future (2014–2050) and far future (2051–2100) horizons. For the two catchments each in the southern, central, and northern Ghats, the trend in minimum and maximum temperatures, precipitation, and other indices are calculated. By 2100, dry sub-humid and humid catchments will see a higher increase in mean annual temperature than per-humid central catchments. In future decades, the warm days and nights increase by 45–50% and 40–70%, respectively, with twofold warming in the winter season. Under a climate change scenario, annual rainfall increases in Vamanapuram, Ulhas, and Purna, while Chaliyar, Netravati, and Aghanashini catchments experience a decrease in rainfall in the far future with an increase in pre-monsoon rainfall. The southern catchments are anticipated to have contrasting variations in the rainfall extremes; northern catchments face a substantial increase in very wet to extremely wet days and medium to heavy rainfall. In all catchments (excluding Vamanapuram), cumulative wet days increase with a decrease in cumulative dry days. After the mid-twenty-first century, humid to per-humid catchments encompass an increase in cool nights, whereas it disappears in dry sub-humid catchments of the Ghat. Interestingly, warming tendencies begin to slow down after 2050. This investigation can assist in comprehending the regional climate extremes in the Western Ghats to formulate better climate risk planning and adaptation strategies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item The effectiveness of machine learning-based multi-model ensemble predictions of CMIP6 in Western Ghats of India(John Wiley and Sons Ltd, 2023) Shetty, S.; Umesh, P.; Shetty, A.The popularity of cutting-edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top-performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi-Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo-climatic basins. Precipitation is best simulated by EC-Earth3 and BCC-CSM2-MR. Maximum temperature by MPI-ESM1-2-HR, EC-Earth3-Veg, INM-CM5-0 and MPI-ESM1-2-LR. Minimum temperature by INM-CM5-0 and MPI-ESM1-2-LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per-humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms. © 2023 Royal Meteorological Society.Item Climate indices and drought characteristics in the river catchments of Western Ghats of India(Springer Science and Business Media Deutschland GmbH, 2024) Shetty, S.; Umesh, P.; Shetty, A.The study addresses the long-term trend in rainfall, minimum and maximum temperature, and the climate indices for the river catchments located in the diverse climate of the Western Ghats of India. The dry sub-humid Chaliyar catchment and humid Kajvi catchment have shown a dramatic change in the decadal rainfall, with the decade 1950–1960 being the point of change. The monsoon rainfall has decreased in the Chaliyar and Netravati catchments and increased insignificantly in the Kajvi catchment. With the increase in mean temperature, the number of rainy days is decreasing, and intense rainfall is increasing in the pre-monsoon. The increase in minimum temperature is more severe in all three catchments, irrespective of the region’s climate. The decline in rainy days is more figurative in the humid and per-humid catchments and has seen a 16–20% decrease in R×1 day, R×3 day, and R×5 day in the past six decades with an insignificant increase in the dry sub-humid catchment. The frightful increase in warm days/nights with a decrease in cool days/nights has been alarming for the extremity of temperature in future years. The significant changes in the forest area in Chaliyar and Kajvi catchment and the increase in a built-up area in Netravati may have a decisive role in the nonseasonal variability in rainfall and temperature along with increasing greenhouse gases. In the case of meteorological drought studied using the Standardized Precipitation Index (SPI), moderate droughts have occurred over the Chaliyar and Kajvi, and extreme droughts over the Netravati catchments with no reduction in the frequency or severity of short-duration extreme rainfall events. The geographical location of the catchment has a greater impact on the characteristics of the rainfall and meteorological drought, and these changes in the hydrological regimes of the catchment have a significant bearing on the water availability in the catchments in the future years. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2023.Item Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery(Elsevier B.V., 2024) Mruthyunjaya, P.; Shetty, A.; Umesh, P.This article introduces an improved method for estimating Soil Organic Carbon (SOC) using Sentinel 2 images, with a specific emphasis on the Dakshina Kannada area in India. By examining 364 soil samples, SOC estimation models were constructed using Random forests (RF) and Partial Least Squares Regression (PLSR), focusing on the impact of nearby vegetation pixels. The approach consisted of classifying soil samples by the presence of plant pixels at distances of 0, 10, and 20 m, and evaluating the influence of dry vegetation by the use of the Normalised Burn Ratio 2 (NBR2). The findings demonstrated a significant improvement in the precision of the model (by up to 20 %) when vegetation pixels within a 20-meter radius of the sample locations were omitted. The research also included a temporal analysis utilizing Sentinel-2 images from April 2017 to May 2023. This analysis showed strong relationships between the amount of exposed soil and the accuracy of predicting soil organic carbon (SOC) levels. These results emphasize the need to take into account both the spatial dynamics of vegetation and the temporal variations in bare soil covering to get an accurate estimate of soil organic carbon (SOC). This study improves the accuracy and dependability of SOC evaluations by including geographical and temporal aspects, providing useful insights for agricultural and ecological applications. © 2024 The Author(s)