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Author: search.filters.author.Umesh, P.Subject: search.filters.subject.accuracy assessment

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    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.
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    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)