Browsing by Author "Prashant K Srivastava"

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An improved volume power approach to estimate LAI from optimized dual-polarized SAR decomposition
(Taylor and Francis Ltd., 2023) Shubham Kumar Singh; Rajendra Prasad; Vivek Tiwari; Prashant K Srivastava
Synthetic Aperture Radar (SAR) imagery has proven to be a valuable tool for monitoring the Earth’s surface, particularly vegetation. However, many studies using SAR have focused solely on backscattering sensitivity without considering the structural and physical properties of the vegetation being analysed. This study proposes a new approach for vegetation monitoring using the Volume Power (VP) analysis technique. The proposed method aims to improve the accuracy of VP derived from the Freeman-Durden (FD) decomposition technique for dual polarimetric SAR data. To increase the sensitivity of vegetation scattering in SAR analysis, this study modified the VP using Depolarized Volume Power (DVP) and Anisotropic Volume Power (AVP). The first modification, DVP, was achieved by incorporating the degree of polarization ((Formula presented.)) in the analysis. The second modification, AVP, is achieved by considering the anisotropic scattering properties of vegetation. The modified VP is then used to estimate Leaf Area Index (LAI) using an empirical relationship between LAI and the modified VPs. The accuracy of the LAI estimation is evaluated using ground truth measurements. The results demonstrate that the proposed method provides more accurate LAI estimates than traditional methods. The approach also shows improved sensitivity to vegetation scattering compared to the original VP from the FD, indicating the effectiveness of the degree of polarization and anisotropic scattering in reducing the impact of unwanted scattering mechanisms from the surface. The accuracy (R 2) between in-situ LAI and the LAI retrieved from AVP and DVP was 0.85 and 0.82, whereas for FDVP, it was 0.74. These findings highlight the potential of the proposed approach for improving the accuracy of VP estimation in dual-polarimetric SAR data and enabling more accurate and efficient estimation of LAI. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Drought hazard zonation using GIS based multi-critaria evaluation approach with remotely sensed datasets
(Asian Association on Remote Sensing, 2017) Varsha Pandey; Prashant K Srivastava
Drought is an intricate phenomenon governed by several atmospheric factors such as precipitation, temperature, evapotranspiration, soil moisture, vegetation cover, stream flow etc. To monitor drought hazard, several criteria and factors will need to be evaluated. The main objective of this study was to evolve a drought hazard map with the selected five main parameters viz., standardized precipitation index, land surface temperature, soil moisture, evapotranspiration and normalized difference vegetation index during the monsoon period from 2002 to 2014 employing GIS aided multi-criteria evaluation (MCE) technique. To standardized the input data layers and deciding the factors weight for the MCE, analytical hierarchy process (AHP) approach was applied. Bundelkhand region of Uttar Pradesh was selected for this study, as drought is very frequent and dominant here. The results depicted that about 55.9% and 7.5% of the total area is classified under high and extreme drought hazard zone respectively however, about 36.59% of the total area found to be the least vulnerable (moderate to low) to drought hazard. Central parts of the region namely Jhansi, Mahoba, Jalaun and Hamirpur districts are highly affected by drought condition. Based on finding of this study we recommend the use of MCE techniques for effective and precise drought hazard zonation. © 2017 ACRS. All rights reserved.
Evaluation of Satellite Precipitation Data for Drought Monitoring in Bundelkhand Region, India
(Institute of Electrical and Electronics Engineers Inc., 2019) Varsha Pandey; Prashant K Srivastava
Drought is a recurrent phenomenon in the semiarid regions of India that significantly affects the regional social, economic, and environmental conditions. Drought monitoring and assessment are challenging especially for regions that have sparse or very limited rain gauge observation networks. In this study, a comparative analysis was performed between satellite precipitation products including Tropical Rainfall Measuring Mission (TRMM-3B42), Climate Hazards Group InfraRed Precipitation (CHIRP) and ground-measured Indian Meteorological Department (IMD) precipitation data over the Bundelkhand region of Uttar Pradesh, India to assess the meteorological drought in this region. The district wise study was done to determine the regional differences among these satellite precipitation products and to statistically verify their performance in estimating the degree and spatial pattern over the study area. The Standardized Precipitation Index (SPI) was computed using the open-source DRINC software. The IMD derived SPI was found more correlated with TRMM compared to CHIRP for SPI-1, 3 and 12 with r values 0.74, 0.81 and 0.65 respectively. However, SPI-6 shows low positive and negative and correlation for both TRMM and CHIRP data. The current case study highlights the outperformance of TRMM data that enables real-time drought assessment owing to better accuracy and higher spatiotemporal resolution. © 2019 IEEE.
Retrieval of soil moisture deficit through climate change initiative (CCI) soil moisture data and probability distributed model
(Asian Association on Remote Sensing, 2017) Prashant K Srivastava; Swati Maurya; Varsha Pandey; Dharmendra Pandey
Soil moisture deficit (or SMD) is very important variable for many applications such as for flood and drought modelling, which is now possible to estimate using the remote sensing data. This study is an attempt to evaluate the climate change initiative (CCI) soil moisture data for SMD estimation at a catchment scale. The SMD (Soil Moisture Deficit) estimated from the Probability Distribution Model, by using the in situ station data was used as a benchmark for all the comparisons. Approaches based on generalized linear model and relevance vector machine are provided for the estimation of SMD. The overall analysis reveals that CCI soil moisture is of reasonable quality in estimating the soil moisture deficit at a catchment level. Therefore, this study provides first time comprehensive evaluation of CCI soil moisture in Indian condition and the result provides supportive evidence of the potential value of this product for meso-scale studies and hydrological applications. © 2017 Institute of Mechanics and Mechatronics, Faculty of Mechanical, TU Wien. All rights reserved.
Synergy of dual–polarimetric radar vegetation descriptor and Gaussian processes regression algorithm for estimation of leaf area index
(Taylor and Francis Ltd., 2022) Shubham Kumar Singh; Rajendra Prasad; Vijay Pratap Yadav; Suraj A. Yadav; Jyoti Sharma; Prashant K Srivastava
Gaussian Process Regression (GPR) emerged as a powerful algorithm since last decade in many applications. However, it is not fully explored in remote sensing applications, especially for predicting plant biophysical variables in the heterogeneous environment of India. This kernel-based machine learning technique has effectively replaced conventional approaches for estimating vegetation characteristics from remotely sensed data. In this work, an attempt has been made to test the ability of GPR to estimate the leaf area index (LAI) of wheat crops using the Sentinel-1 (S1) derived Dual-Polarized Radar Vegetation Index (DpRVI) and Sentinel-2 (S2) Top of Atmosphere (TOA) products. Further, the ability of the atmospheric correction procedure was tested by S2 Bottom of Atmosphere (BOA) images. To accomplish this, the field measurements of LAI were carried out from January to March 2020. Further comparisons of the GPR’s performance were made with the Artificial Neural Network (ANN) coupled with the PROSAIL (PROSPECT+SAIL) radiative transfer model, available through the Sentinel Application Platform (SNAP) Biophysical processor. The accuracy of the estimated LAI was evaluated using the statistical indicators, for example, coefficient of determination (R 2), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The results showed that the synergy of the GPR and DpRVI provided the most accurate result (R 2 = 0.822, RMSE = 0.503 m2 m−2, NSE = 0.831) as compared to the GPR and TOA (R 2 = 0.816, RMSE = 0.596 m2 m−2, NSE = 0.803) and SNAP biophysical processor (based on ANN) (R 2 = 0.696, RMSE = 0.760 m2 m−2, NSE = 0.578). Therefore, the study demonstrated the importance of S1 SAR images and GPR as an alternative tool among the other well-established machine learning algorithms to estimate the crop biophysical parameters. © 2022 Informa UK Limited, trading as Taylor & Francis Group.