Faculty Publications
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Item Land use scenario analysis and prediction of runoff using SCS-CN method: A case study from the Gudgudi tank, Haveri district, Karnataka, India(2011) Bhagwat, N.B.; Shetty, A.; Hegde, V.S.Runoff from the Gudgudi tank catchment (209 ha) near Hangal in the Northern Karnataka is estimated employing Soil Conservation Services(SCS) model based on the hydrological data and land use/ land cover data. Rainfall measured for 2006 using a tipping bucket indicated annual rainfall of 887.7mm in the tank catchment. Textural characteristics of the soil indicate sandy-clayey type which corresponds to hydrological soil group "C and D". Average Soil infiltration rate of 0.18 cm/hour for the forest-land and 0.21 cm/hour for agriculture land has been observed. Weighted curve number is arrived based on the antecedent moisture conditions, and runoff is estimated for the existing land-use. Areastorage curve is constructed using the tank bed contours. Considering the hypothetical changes in the agriculture and forest area coverage, optimum conditions for maximizing the runoff and storage in the tank is arrived. The analysis suggests land use pattern of 15% of forest cover and 85% of agriculture land coverage in this region provide maximum runoff and storage in the tank for sustainable development. © 2011 CAFET-INNOVA TECHNICAL SOCIETY.Item The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru(Springer International Publishing, 2019) Govind, N.R.; Ramesh, H.In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis–Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989–2001 than 2001–2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28 years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6 °C and the mean LST of urban area increased by about 8 °C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future. © 2019, Springer Nature Switzerland AG.Item Characterization of spatial variability of vertisol micronutrients by geostatistical techniques in Deccan Plateau of India(Springer Science and Business Media Deutschland GmbH, 2020) Vinod, V.; Shetty, A.; Shrihari, S.In vertisols, accounting for the spatial variability of micronutrients is important for sustainable agriculture. In this study, the assessment of spatial variability maps is carried out by the geostatistical technique in SpaceStat 4.0®. A total of 68 random soil samples were collected from small-scale agricultural lands from Kalaburagi, Karnataka, India. The chemical analysis for iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) was carried out in microwave plasma-atomic emission spectroscopy. The coefficient of variation (CV) showed different micronutrients variability (CV > 35%). The significant correlation is among Cu with Fe and Mn (r = 0.753 and 0.258, respectively). The Box–Cox transformation converted the raw data to normal distribution efficiently. Spherical semivariogram model defined the spatial structure for all micronutrients. The nugget/sill ratio specifies that the Zn showed strong spatial dependence and rest micronutrients moderate. Ordinary kriging is applied for generating maps. The spatial variability maps exhibited different distribution pattern; maps generated are utilized as initial guidance for site-specific management practices and the amount of fertilizer application rate planned in the vertisols. The obtained range and spatial distribution maps act as the baseline in this region for administration planners. © 2019, Springer Nature Switzerland AG.Item Land cover change and its implications to hydrological variables and soil erodibility in Lower Baro watershed, Ethiopia: a systematic review(Springer Science and Business Media Deutschland GmbH, 2023) Deneke, F.; Shetty, A.; Fufa, F.Water-induced soil erodibility is the most severe kind of land degradation, with substantial environmental and social consequences. Few studies have been conducted on land cover change and soil erodibility in Ethiopia. During the data search, 83 articles were looked at, with studies published from 2007 to 2022. Only 2% of the abstracts that were considered for assessment were eventually accepted. The review was conducted using the preferred reporting items for systematic reviews and a meta-analysis approach. According to this study, when compared to the values predicted in the river basin’s master plan, Baro Akobo’s estimated surface water potential has been reduced by about 3.6 Bm3. As a result, changes in land cover affected a variety of fundamental processes in watersheds, at several spatial and temporal scales. As a result, of the reviewed, in lower Baro, built-up/settlement, agricultural land, water body, bare/outcrop, and commercial farm all rose by roughly + 195, + 48, + 35, + 35, and + 1%, respectively. Shrubland, rangeland, forest land, and wetland, on the other hand, all decreased by − 1, − 0.5, − 5, and − 10%, respectively. The K-factors are 0.31, 0.23, 0.14, and 0.07 for chromatic vertisols, humic cambisols, eutric cambisols, and eutric nitosols, respectively. From the results of the review studies, the RUSLE looks to be a good alternative for assessing soil erodibility in lower Baro, and soil water conservation measures are crucial for minimizing soil erodibility. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Soil Moisture Retrieval Over Crop Fields from Multi-polarization SAR Data(Springer, 2023) Shilpa, K.; Suresh Raju, C.; Mandal, D.; Rao, Y.S.; Shetty, A.Soil moisture estimation from agriculture fields using SAR measurements is a challenging process owing to the presence of vegetation canopy. In this study, the soil moisture (SM) is retrieved from multi-polarization airborne L- and C-band E-SAR data of different agriculture fields by using the radar parameter, Radar Vegetation Index (RVI). The retrieval methodology employs the semi-empirical Water Cloud Model (WCM) for vegetation-soil system modeling, followed by an inversion algorithm based on a Look Up Table approach. The impact of using different vegetation descriptors, both from in situ measured (Leaf Area Index, Wet Biomass and Vegetation Water Content) and radar derived (L-band RVI and C-band RVI), on the WCM inversion for SM retrieval is examined. The use of the RVI as the vegetation descriptor, which is obtained from C-band data, improves soil moisture retrieval with an RMSE of 7–8% volumetric soil moisture at L-band. © 2023, Indian Society of Remote Sensing.Item INFLUENCE OF LAND USE LAND COVER CHANGE ON RUNOFF CHARACTERISTICS OF NETRAVATHI RIVER CATCHMENT, KARNATAKA, INDIA(Zibeline International Publishing Sdn. Bhd., 2024) Dwarakish, G.S.; Pai, J.B.; Jubina, C.K.The effect of LU/LC on the streamflow characteristics of the Netravathi river basin, Karnataka, India, is studied using Soil and Water Assessment Tool (SWAT) model. Landsat images, soil map from FAO, ASTER DEM (30m grid) and streamflow data, forms the database for the present work. The most significant changes from 1981 to 2015, in the LU/LC includes agricultural land (31.86%), built-up area (67.9%), forest cover (-20.01%), coconut plantation (55.12%), other vegetation (-18.55%) and others (-11.82%). The verification of performance of model was carried out by the coefficient of determination values (R2 > 0.8) and N S E (NSE > 0.78) were obtained and hence proved that SWAT model performance in estimating streamflow.. The average streamflow is increased by 13.74% from 1981 to 2015, which is mainly due to dynamic changes in LU/LC. Hence, it can be concluded that changes in LU/LC have a direct impact on streamflow in the study area. © 2024, Zibeline International Publishing Sdn. Bhd.. All rights reserved.Item Integrating Soil Spectral Library and PRISMA Data to Estimate Soil Organic Carbon in Crop Lands(Institute of Electrical and Electronics Engineers Inc., 2024) Reddy, B.S.; Shwetha, H.R.The increasing demand for precise soil organic carbon (SOC) monitoring in croplands is crucial for food security (SDG 2), and has led to the exploration of fusing soil spectral libraries (SSLs) with hyperspectral sensing data for SOC estimation. However, the widespread adoption of SSL for SOC estimation faces challenges, particularly in developing nations, due to inconsistent calibration libraries and reliable estimation models. Furthermore, SSL rely on regular soil sample collection and spectral data recording using spectroradiometers, which is impractical in agricultural-predominant countries, such as India, with limited time for sample collection between crop rotations. To address this challenge, we developed synthesized SSL in laboratory conditions and integrated it with hyperspectral data using machine learning (ML) algorithms to bridge the gap between synthesized SSL and hyperspectral data for local-scale SOC mapping. This approach was tested by mapping SOC in Mysore, India, using spectroradiometer hyperspectral measurements and PRISMA sensor data. The proposed approach and synthesized SSL exhibited better performance prediction accuracies, R2 of 0.92 and 0.79, and the RMSE values of 2.31 and 9.91 g/kg, respectively, for PRISMA and laboratory spectroscopy data. These results highlight the potential of synthesized SSL for SOC prediction in alluvial soils, leveraging local datasets, and hyperspectral data. Our future work will expand the synthesis approach to other study areas, particularly those with alluvial soil origins, further enhancing the applicability of this methodology for SOC estimation and aiding food security efforts. © 2004-2012 IEEE.Item Effect of irrigation on farm efficiency in tribal villages of Eastern India(Elsevier B.V., 2024) Kalli, R.; Jena, P.R.; Timilsina, R.R.; Rahut, D.B.; Sonobe, T.Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. © 2023 The AuthorsItem Analysis of RVI for rice crops in small-scale agricultural fields using Sentinel-1 SAR data: case study on LAI retrieval using regression algorithms(Springer Science and Business Media Deutschland GmbH, 2025) Salma, S.; Ket, S.K.; Dodamani, B.M.Leaf Area Index (LAI) is a crucial indicator for assessing plant growth, canopy structure, photosynthetic capacity, and overall productivity. The Radar Vegetation Index (RVI), a well-established microwave metric, serves as an effective tool for retrieving the LAI due to its sensitivity to vegetation characteristics. The primary objective of utilizing RVI in LAI studies is to improve the accuracy and reliability of LAI estimation, where optical methods may be hindered by atmospheric conditions. Over the past decade, numerous studies have explored the relationship between RVI and LAI, highlighting the potential of RVI for accurate LAI estimation in crops. In particular, for rice crop analysis in this study, the RVI is derived by incorporating the Degree of Polarization (DOP) from a 2 × 2 covariance matrix as the coefficient, along with the polarization backscatter of Sentinel-1 C-band Synthetic Aperture Radar (SAR) data. The study also explores RVI derivation from M-chi (m-?) and M-delta (m-?) decomposition (assuming circularity in dual-polarized data) and linear backscattering intensities. Using the RVI’s, machine learning regression models are applied to retrieve LAI. The DOP over crop period, the temporal analysis of RVI, and in-situ LAI has been employed to examine trends during crop growth. Notably, among all derived RVIs, the one obtained using the DOP technique, particularly when combined with random forest regression, consistently exhibits superior performance for rice crop LAI estimation (R = 0.91; RMSE = 0.25 m2/m2), whereas, the R value for other models ranges a lower value of 0.63 to a higher value of 0.83 with RMSE of higher value 0.64 m2/m2 to a lower value of 0.32 m2/m2. The findings in the study highlights the sensitivity of SAR data to the DOP and the vegetation structure of rice crops in small-scale agricultural fields. © The Author(s), under exclusive licence to the International Society of Paddy and Water Environment Engineering 2024.