Faculty Publications
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Publications by NITK Faculty
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Item Modelling the land use system process for a pre-industrial landscape in India(Springer Science and Business Media Deutschland GmbH, 2017) Ghosh, S.; Shetty, A.Land in India is changing in a rapid pace since the green revolution during 1960 and industrial policy reforms during 1990. Certainly land cover land use (LCLU) changes have huge impacts on countries overall ecological balance and climate change. The most intriguing fact is LCLU change is an interconnected phenomenon like a system. The understanding of local level LCLU dynamics are yet to get a momentum in India. The present study is an attempt: (1) to examine the land use change drivers active at the studied landscape of coastal Karnataka in India and (2) to model the LCLU changes in pre-industrialized period using Dyna-CLUE model. Binary logistic regression was used to categorize land change drivers and to estimate the probability of changes. Odd ratio from logistic regression indicates that the biophysical drivers are most prominent in determining location of LCLU. They being slope, relative relief, drainage density and availability of ground water are the most influential drivers for most of the land classes. The Dyna-CLUE model is successful to simulate the LCLU change at aggregate level but the spatial allocation needs improvement. © 2017, Springer International Publishing Switzerland.Item Prediction accuracy of soil organic carbon from ground based visible near-infrared reflectance spectroscopy(Springer, 2018) Minu, S.; Shetty, A.The present study was conducted to predict soil organic carbon (SOC) from ground visible near-infrared (Vis-NIR, 400- 2500 nm) spectroradiometer reflectance spectra. The objective was to study the effect of various pre-processing methods and prediction models on the accuracy of SOC estimated. Measured SOC content and reflectance spectra from pasture and cotton fields of Narrabri, Australia were used in the analysis. Reflectance spectra were pretreated with different smoothing methods such as: moving average, median filtering, gaussian smoothing and Savitzky Golay smoothing. A comparison between principal component regression, partial least square regression (PLSR) and artificial neural network models was carried out to get an optimum model for organic carbon prediction. The results indicate that PLSR model performs better with Savitzky Golay as the best pre-processing method for the study area, yielding R2cal = 0:84, RPDcal = 2.55 and RPIQcal = 4.02 in the calibration set and R2val = 0:77, RPDval = 2.17 and RPIQval = 3.19 in the validation set. The study recommends a suitable method in case of limited number of soil data. Based on the study, it can be said that properly pretreated reflectance spectra show tremendous potential in soil organic carbon prediction. © Indian Society of Remote Sensing 2017.Item A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge(Springer, 2019) Yathish, H.; Athira, K.V.; Konkathi, K.; Umesh, U.; Shetty, A.Despite repeated occurrences of forest fire, very less scientific studies have been reported in the Indian context especially in Kudremukh region to mitigate and suppress the fire. The objective of this article was to pool the expert knowledge on forest fire triggering factors from officials working in wildlife division in the Western Ghats of India through a questionnaire and to validate the risk zones obtained from three popular fire risk zone mapping methods namely logistic regression, multi-criteria decision analysis, and weighted overlay. Based on the earlier studies and expert knowledge, fire ignition parameters considered are elevation, slope, and aspect, proximity to roads, water bodies and area of human activities, normalized difference vegetation index (NDVI), land surface temperature (LST), and vegetation type. The regression model was based on previous fire occurrences and the other two based on expert’s opinion. The three models were validated and compared using past fire occurrence events. The logistic regression model gave 88.89% of accuracy and that of multi-criteria decision analysis with 74.6% accuracy, and that of weighted overlay method with an accuracy of 68.24% for the specific study area. The logistic regression model is useful in the presence of historical data, whereas expert knowledge is helpful for mapping risk zones using multi-criteria decision analysis and weighted overlay analysis when historical data are scarce or not available for mapping risk zones. The obtained risk maps can be used for deciding watchtower locations, installation of sensors, cameras, etc. In every forest division, it is recommended to prepare a standard questionnaire form and document their experiences on forest fire in the region under their supervision before they are getting transferred to another location. © 2019, Indian Society of Remote Sensing.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.