Browsing by Author "Bhagyashree Verma"

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Drones in high resolution land use assessment using artificial intelligence
(Elsevier, 2024) Bhagyashree Verma; Prachi Singh; Sumana Khamrai; Bharat Prajapati; Rajendra Prasad; Prashant K. Srivastava
Unmanned aerial vehicle (UAV) remote sensing has enormous potential for land use land cover mapping in complicated landscapes and locations. This is given to the ultra-high-resolution images that can be collected at low altitudes using these vehicles. Due to cargo capacity limits, off-the-shelf digital cameras are widely employed on medium- and small-sized UAVs. When it comes to land use and land cover mapping, the low spectral resolution that digital cameras have can be a disadvantage, but it can be solved with the help of texture features and sophisticated machine learning classifiers. Although numerous machine learning techniques are routinely employed in satellite remote sensing applications, there is little information available on how it is utilized for UAV image classification, and it has not yet been investigated whether or not these algorithms are beneficial for classifying images with low spectral resolution. The applicability of a variety of machine learning algorithms, such as Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), was evaluated in order to determine how accurately they could differentiate between the various types of land use and land covers found in the agricultural farms located at Banaras Hindu University, Varanasi. © 2025 Elsevier Ltd. All rights reserved.
Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content
(MDPI, 2022) Bhagyashree Verma; Rajendra Prasad; Prashant K. Srivastava; Prachi Singh; Anushree Badola; Jyoti Sharma
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval. © 2022 by the authors.
Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X- and C-band
(Elsevier Inc., 2022) Suraj A. Yadav; Rajendra Prasad; Vijay P. Yadav; Bhagyashree Verma; Shubham K. Singh; Jyoti Sharma; Prashant K. Srivastava
Dual-polarimetric (i.e., HH and VV) scattering responses at X- and C-bands from indigenously designed far-field bistatic specular (bi-spec) scatterometer acquired over the entire rice crop phenology have been analyzed using a modified parametric radiative transfer model (MRTM). The scattering responses are examined over a wide-ranging bi-spec incidence angle varying from 20° to 60° at 10° intervals. Furthermore, optimization of the bi-spec scatterometer system showed high sensitivity at 40° specular angle of incidence based on the correlation analysis between the measured value of bi-spec scattering coefficient (σMeasured0) and vegetation biophysical parameters such as leaf area index (LAI) and plant water content (PWC). The MRTM implied to investigate the dominance of surface (σSurface0) and vegetation(σVegetation0) specular scattering components within the total value of simulated bi-spec scattering coefficient (σSimulated0) in forward scattering alignment (FSA) convention. The vegetation phase function (VPF) and a bi-directional reflectance distribution function (BRDF) are parameterized to approximate scattering responses from the vegetation volume layer and the surface beneath vegetation. In addition, empirical frequency-specific parameters (i.e., b1and b2) are used to simulate temporal dynamics of σSimulated0 using a linear relationship between vegetation optical depth (VOD) with LAI and PWC. The model and empirical frequency-specific parameters are calibrated using a constrained non-linear least square optimization algorithm, and the results are validated against the value of σMeasured0. According to the simulation findings, the total specular scattering decomposition offers a robust model for interpreting time-series microwave scattering scenarios through vegetation in the FSA convention. Moreover, as compared to C-band, the inverse modeling of MRTM showed high retrieval accuracies of LAI at VV polarization and PWC at HH polarization for the X-band. © 2022 Elsevier Inc.
Identification of Optimal Absorbance Spectral Bands from Aviris-Ng Using Standard Derivative Analysis
(IEEE Computer Society, 2022) Prachi Singh; Prashant K. Srivastava; R.K. Mall; Bhagyashree Verma; Rajendra Prasad
The high dimensional hyperspectral data due to its narrow bands ranging between 250-3500 nm, becomes a serious issue for data processing and analysis. Selection of optimal absorbance spectral bands from original spectra brings great possibility in removing the redundancy, quantifying pigments such as chlorophyll, carotenoids, anthocyanin and the retrieval of biophysical variables such as LAI, Biomass corresponding to crops using advance derivative techniques. As part of this study atmospheric corrected reflectance AVIRIS-NG (Airborne Visible Infrared Imaging Spectroradiometer-Next Generation) hyperspectral sensor data over Anand study site on wheat crop were used. Two well proven techniques continuum removal and second derivative were used for the identification of absorption bands and also useful to capture the subtle difference in the spectra required in order to locate any specific feature present in the spectra. In the continuum removed graph, chlorophyll reflection is observed in the green region of electromagnetic spectrum, in red region a peak of reflection is observed due to the presence of carotenoid pigment, and in the NIR (Near - Infrared) region dip is observed due to the presence of anthocyanins pigment and Leaf area index. Therefore, findings of the study might be useful to separate and better identification of absorption bands of biophysical and biochemical parameters presence in crop spectra. which indicated that they can be used for species identification of any crop. © 2022 IEEE.
Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms
(Elsevier B.V., 2022) Bhagyashree Verma; Rajendra Prasad; Prashant K. Srivastava; Suraj A. Yadav; Prachi Singh; R.K. Singh
With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data. © 2021
Leaf chlorophyll content retrieval for AVIRIS-NG imagery using different feature selection and wavelet analysis
(Elsevier Ltd, 2024) Bhagyashree Verma; Prachi Singh; Rajendra Prasad; Prashant K. Srivastava; Rucha Dave
The Leaf Chlorophyll Content (LCC) is a crucial indicator of plant vitality. It plays a crucial role in photosynthetic processes and regulates metabolic activities in plants. Thus, it is an important task for the scientific community to estimate its precise quantity. In this study, we used AVIRIS-NG imagery, a wavelet analysis, and a number of feature selection approaches to estimate the chlorophyll content of several agricultural species. Eight different Discrete Wavelet Transform (DWT) methods, including Daubechies (db), Biorthogonal (bior), Reverse biorthogonal (rbio), etc., were employed to generate denoised vegetation spectra, in which bior produced the best approximation. Recursive Feature Elimination (RFE), Regularized Random Forest (RRF), Least Absolute Shrinkage and Selection Operator (LASSO), and Partial Least Square (PLS) were used to select features from the approximated signals, and the top three bands were chosen to be used in the creation of new indices for LCC retrieval. In order to estimate LCC, linear regression models were developed using these indicators. The best results were obtained by the PLS-based LCC retrieval model, with a correlation of r = 0.948, a Root Mean Square Error (RMSE) of 5.464, and a bias of 3.305. The LASSO model yielded the worst results. Hence, for any hyperspectral images such as of AVIRIS-NG, the chlorophyll content may be reliably estimated using a PLS model combined with wavelet analysis. © 2023 COSPAR
Optimization of dual-polarized bistatic specular scatterometer for studying microwave scattering response and vegetation growth parameters retrieval of paddy crop using a machine learning algorithm
(Elsevier B.V., 2020) Suraj A. Yadav; Rajendra Prasad; A.K. Vishwakarma; Jyoti Sharma; Bhagyashree Verma; Prashant K. Srivastava
Bistatic specular (Bi-spec) scatterometer measurement system was indigenously designed at X- and C-bands in the incidence angular range from 20° to 60° at the interval of 10° to study the scattering mechanism of vegetation growth parameters of paddy crop and their retrieval at HH and VV polarizations using machine learning algorithms. The contributions of coherent and incoherent scattering to the total reflected or scattered power was measured by the bi-spec scatterometer system to derive bi-spec scattering coefficient(σ0). The effect of vegetation growth parameters such as leaf area index (LAI), plant height (PH), vegetation water content (VWC) and fresh biomass (FBm) on the σ0was investigated. The values of σHH0 at C-band were observed to be higher at HH-polarization as compared to σHH0at X-band for all specular incidence angles due to higher penetrating ability than the X-band. An approach was made to find out the optimum parameters of the bi-spec scatterometer system by correlation analysis between the computed σ0and vegetation growth parameters of paddy crop and their retrieval by the SVR model using linear, polynomial and radial kernels. The optimum parameters of the bi-spec scatterometer system for the retrieval of LAI, FBm and VWC of paddy crop were found to be HH polarization, 40° angle of incidence at C-band. While, for PH retrieval, the optimum parameters were found to be VV polarization, 40° angle of incidence at X band. The potential of the developed SVR model was evaluated by computing centered root mean square error (CRMSE), standard deviation (SD) and correlation coefficient (R) between estimated and observed vegetation growth parameters. The retrieval of the vegetation growth parameters of paddy crop by the developed SVR model using radial kernel provided better results in comparison to linear and polynomial kernels for LAI, FBm and VWC at C-band and PH at X-band using bi-spec scatterometer data. © 2020 Elsevier B.V.
Retrieval of Leaf Area Index Using Inversion Algorithm
(IEEE Computer Society, 2022) Bhagyashree Verma; Rajendra Prasad; Prashant K. Srivastava; Prachi Singh
With the development in sensor technology, there is a spectroradiometer with resolution as high as 1nm and data capture extending from 350nm-2500nm; it helps in viewing spectral variability of the subject of interest. The advantage of such instruments opens up many opportunities for the development of hyperspectral data analysis in precision agriculture. In the presented work, estimation of Leaf Area Index (LAI) is done with inversion technique using Transformed Vegetation Index (TVI), SR (Simple Ratio), NDVI (Normalized difference ratio index) vegetation indices as input parameters, and modeled LAI separately for these three indices. The estimation was done for different growth stages of Maize (Zea mays), Mustard (Brassica), pink Lentils (Lens esculenta), and Wheat (Triticum). A comprehensive comparative analysis was done based on the value of R2. For the variation in LAI, the SR index gave the highest correlation for lentils (R2=0.9329), Mustard (R2=0.893), and wheat (R2=0.9712) whereas, for Maize, NDVI was found to be the best estimator with a correlation of (R2=0.7781). © 2022 IEEE.