Faculty Publications
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Item A QGIS Plug-in for Processing MODIS Data(Institute of Electrical and Electronics Engineers Inc., 2019) Aishwarya Hegde, A.; Umesh, U.; Shetty, A.In the past few decades number of Earth-observing satellites are continuously gathering information and only about 10 percent of the information is utilized by the users. With so much accessible information the researchers have not explored the datasets completely as there is absence of effective tool to process the information. MODIS data sensors have accessible data at various temporal and spatial resolutions. To productively use these datasets in open-source GIS programming like QGIS, there is a need to pre-process the dataset using a plug-in. The plug-in is built using python and PyQt interface for QGIS.The plug-in operates on MODIS Data (Terra/Aqua/Combined) computerizes and process the functionalities for MODIS products like MOD11, MOD09, MOD21. The processed datasets can be largely used in investigation of time series analysis for some earth resource application. © 2019 IEEE.Item Static Fire Risk Index for the Forest Resources of Karnataka(Institute of Electrical and Electronics Engineers Inc., 2019) Konkathi, P.; Shetty, A.; Venkatesh, V.; Yathish, P.H.; Umesh, U.Forest fires are the major cause of degradation of forest. Forest fires have caused substantial damage in the state of Karnataka in terms of economic, social, environmental impacts on humans and also loss of biodiversity. Fire risk indices are important tools for the management of forest fires. They are developed based on static and/or dynamic factors influencing the occurrence of fire and propagation of fire. The objective of the present study was to develop a new static fire risk index based on parameters influencing forest fire such as fuel type, elevation, slope, aspect, terrain ruggedness, proximity to a road, proximity to water bodies and proximity to settlements. MODIS Land cover type yearly L3 global 500m SIN grid(MCD12Q1) was used to compute fuel type index based on historical fire data, SRTM DEM was used to compute slope index, aspect index, elevation index, and terrain ruggedness index. Road index, settlement index, and water body index were developed from the proximity maps generated. A geographic information system (GIS) was utilized adequately to join diverse forest fire causing factors for demarcating static fire risk index. The evaluated exactness was around 87%, i.e., the developed GIS-based static fire risk index of the examination zone was observed to be in solid concurrence with actual fire affected regions. The study area exhibited 32.38% prone to fire risk. © 2019 IEEE.Item Assessment of consumption and availability of water in the upper Omo-Gibe basin, Ethiopia(Springer, 2020) Nesru, M.; Nagaraj, M.K.; Shetty, A.Understanding water balance components is imperative for proper policy and decision making, specifically in the upper part of the Omo-Gibe basin (UOGB) Ethiopia. The objective of this study is to explore the possibility of assessing consumption and availability of water using freely available satellite data and secondary data. Using twenty-three rain gauge stations data, a spatial average of rainfall was computed using the Thiessen polygon approach. Actual evapotranspiration (ETa) was estimated through the Surface Energy Balance System (SEBS). Input data used are, 16 clouds free Moderate Resolution Imaging Spectroradiometer (MODIS) images covering the study area for estimation of the spatial distribution of actual evapotranspiration covering the whole cropping year from the months of November 2003 to October 2004. Additionally, Priestly and Taylor’s approach was used to estimate reference evapotranspiration (ET0). For the study period, the result of estimated precipitation and ETa showed that the UOGB received 41,080 mm3 of precipitation, while 24,135 mm3 become evapotranspired. The assessed outflow from the basin is 17.6% of the precipitation and demonstrated that water is a scares resource in the UOGB. © 2019, Saudi Society for Geosciences.Item Inter comparison of post-fire burn severity indices of Landsat-8 and Sentinel-2 imagery using Google Earth Engine(Springer Science and Business Media Deutschland GmbH, 2021) Konkathi, P.; Shetty, A.Forest fires are significant catastrophic events that affect the landscape and vegetation in forested lands. They cause loss of biodiversity, land degradation & ecological imbalance. As the forest fires cause extreme damage to the habitat, it is of utmost necessity to assess the impact of fire on canopy/vegetation. Post-fire assessment is an essential element for finding the effects of fire on vegetation and implementing mitigation strategies. In this article, a Post-fire burn severity assessment was carried out with high-resolution multi-spectral images such as Sentinel-2 and Landsat-8 employing Google Earth Engine (GEE) to locate the burnt areas and fire severity. Three commonly used fire severity indices based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR, namely differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR) are computed and compared based on their accuracy with the active fire points provided by MODIS & VIIRS. Both Sentinel-2 and Landsat-8 exhibited a similar trend in mapping burn severity. The RdNBR resulted in high accuracy over heterogeneous landscapes with 61.52% for Sentinel-2 and 64.1% for Landsat-8 followed by dNBR (41.67% for Sentinel-2 and 47.44% for Landsat-8) and weak performance by RBR with 32.69% for Sentinel-2 and 26.92% for Landsat-8. Hence RdNBR burn severity maps are considered highly appropriate for mapping burnt areas. Even though severity analysis from both Sentinel-2 and Landsat-8 is at an acceptable level, the Landsat based burn severity maps provided an adequate assessment of the degree of damage. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.