Conference Papers

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Author: search.filters.author.Venkatesh, V.

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    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.
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    A statistical approach for comparison of secondary precipitation products
    (Springer Science and Business Media Deutschland GmbH, 2021) Kommu, R.; Kundapura, S.; Venkatesh, V.
    Meteorological data retrieval is the fundamental process for any hydrological research. Precipitation data collection from some constrained territories like high slant geography and inaccessible areas is exceptionally troublesome. Setting the rain gauges is a matter of expense and timely maintenance. To overcome these issues, satellite sensors producing high spatial and temporal resolution datasets can be utilized in the studies involving precipitation component. These satellite products are affected by biases, and hence, there is a need for calibration and verification by using ground observation data based on the statistical coefficients. In this study, the most accessible satellite data products, i.e., CHIRPS, PERSIANN-CDR and TRMM, are employed to check the accuracies against IMD gridded data for the years 2000–2012 using a statistical approach. Selecting the data product having a high coefficient of correlation and low PBIAS is utmost necessary. The current study was performed based on catchment-to-catchment (C-C) method by comparing IMD gridded data with satellite datasets obtained from Google Earth Engine. The results can highlight the data product which can conquer the issue of data inaccessibility in the investigation territory and can be utilized as reference precipitation dataset for different hydrological applications. © Springer Nature Singapore Pte Ltd 2021.
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    Monitoring land use and land cover changes in coastal karnataka
    (Springer Science and Business Media Deutschland GmbH, 2021) Kumar, M.S.; Venkatesh, V.; Gowthami, S.B.; Anjita, N.A.; Nayana, N.; Regi, L.; Dwarakish, G.S.
    The dynamics of land use/land cover can be studied by using digital change detection techniques which are highly significant for the evaluation and development of management strategies in a region. The environmental and hydrological processes prevailing in the area can be interpreted only by analyzing the alterations in the past and present land use and land cover classes. In view of this, the present study is executed to analyze the typical land use change in the coastal region over the three decades by analyzing historical and current land use/land cover (LU/LC) datasets. Landsat 5 and Landsat 8 satellite datasets were considered for change detection analysis using unsupervised classification method. K-means algorithm, a widely used unsupervised classification technique, was adopted in this study to classify coastal region of Karnataka for the years 1990 and 2019. The level-II classification was performed on LU/LC raster datasets (Landsat 5 and 8) which segregated the entire study area into ten classes, namely agricultural land, barren land, built-up area, water, forest, fallow or cultivated land, scrub forest, sandy area, swampy forest and wetlands. This study encapsulated that about 40% of the study area was occupied by water body followed by forestry with a percentage of around 30%. Major changes were observed in the barren land and scrub forest between 1990 and 2019, where the barren land was replaced by scrub forest in 2019. The accuracy assessment is performed to analyze the efficiency of the algorithm and the precision of the classified image which showed an accuracy of 81% in 1990 and 84% in 2019 demonstrating the ability of an algorithm to classify reliably. © Springer Nature Singapore Pte Ltd 2021.