Browsing by Author "Bimal K. Bhattacharya"

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A hyperspectral R based leaf area index estimator: model development and implementation using AVIRIS-NG
(Taylor and Francis Ltd., 2022) Prachi Singh; Prashant K. Srivastava; R.K. Mall; Bimal K. Bhattacharya; Rajendra Prasad
Hyperspectral Remote Sensing (HRS) data is vital for crop growth monitoring due to availability of contiguous bands. This research work provides a new novel crop estimator model given the name “Crop Stage estimator” developed using the HRS data on an open-source R platform. The generic model structure provides an easy way to test and modify the importance of crop parameter namely Leaf Area Index to deduce crop growth stages of winter wheat (Triticum aestivum L.) particularly during –heading, tillering and booting. Further, to know the LAI variations at different agriculture sites, the best model was implemented using the AVIRIS-NG (Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation) hyperspectral datasets. The analysis indicates that during tillering stage the performance was found best during calibration (r = 0.66, RMSE =0.40, and Bias =-0.80) and validation (r = 0.98, RMSE =0.20, and Bias =0.12) in comparison to the ground measurements. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Crop type discrimination using Geo-Stat Endmember extraction and machine learning algorithms
(Elsevier Ltd, 2024) Prachi Singh; Prashant K. Srivastava; Dharambhai Shah; Manish K. Pandey; Akash Anand; Rajendra Prasad; Rucha Dave; Jochem Verrelst; Bimal K. Bhattacharya; A.S. Raghubanshi
The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging Spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871, 87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time with high accuracy. © 2022 COSPAR
Denoising AVIRIS-NG Data for Generation of New Chlorophyll Indices
(Institute of Electrical and Electronics Engineers Inc., 2021) Prachi Singh; Prashant K. Srivastava; Ramandeep Kaur M. Malhi; Sumit K. Chaudhary; Jochem Verrelst; Bimal K. Bhattacharya; Akhilesh S. Raghubanshi
The availability of Airborne Visible and Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) data has enormous possibilities for quantification of Leaf Chlorophyll Content (LCC). The present study used the AVIRIS-NG campaign site of Western India for generation and validation of new chlorophyll indices by denoising the AVIRIS-NG data. For validation, concurrent to AVIRIS-NG flight overpass, field samplings were performed. The acquired AVIRIS-NG was subjected to Spectral Angle Mapper (SAM) classifier for discriminating the crop types. Three smoothing techniques i.e., Fast-Fourier Transform (FFT), Mean and Savitzky-Golay filters were evaluated for their denoising capability. Raw and filtered data was used for developing new chlorophyll indices by optimizing AVIRIS-NG bands using VIs based on parametric regression algorithms. In total, 20 chlorophyll indices and corresponding 20 models were developed for mapping LCC in the area. SAM identified 17 crop types in the area, while FFT found to be the best for filtering. Performance of these models when checked based on Pearson correlation coefficient ( {r} ) and Centered Root Mean Square Difference (CRMSD), indicated that LCC-CCI10 based on normalized difference type index formed through Near Infrared band and blue band is the best estimator of LCC ({r}_{textit {cal}}=0.73,{r}_{textit {val}}=0.66,CRMSD=4.97). The approach was also tested using AVIRIS-NG image of the year 2018, which also showed a promising correlation ( {r} =0.704 , CRSMD = 8.98, Bias = -0.5) between modeled and field LCC. © 2001-2012 IEEE.
Spectral mixture analysis of AVIRIS-NG data for grouping plant functional types
(Elsevier Ltd, 2024) Ramandeep Kaur M. Malhi; G. Sandhya Kiran; Prashant K. Srivastava; Bimal K. Bhattacharya; Agradeep Mohanta
In a recent scenario, the prediction of environmental changes through plant functional types can aid in simplification of ecosystem processes. The present study attempts to identify and map plant functional types (PFTs) in the AVIRIS-NG campaign site, namely Shoolpaneshwar Wildlife Sanctuary (site id 67), using AVIRIS-NG data combined with spectral mixture analysis that accounts for endmember variability. Due to the occurrence of heterogeneous vegetation in the selected AVIRIS-NG site, the measured spectral signal for every pixel of surface reflectance data will be the outcome of fractions in which various plant functional types and the soil background exist. The interest of the present research lies in these fractions; hence spectral mixture analysis was applied. Ground truthing was carried out simultaneous to AVIRIS-NG flight pass. Six plant functional traits, namely Diameter at breast height (DBH), Height, biomass, leaf chlorophyll content (CC), Fraction of Photosynthetic Active Radiation (FPAR), and Leaf Area Index (LAI), were measured for twenty-three tree species found in the study site which were further used for plant functional grouping by applying k-means clustering algorithm. Trait-based cluster analysis classified the tree species into four plant functional types. Endmember selection from AVIRIS-NG image for these plant functional types was done using a manual field observation-based approach, which was used as input in spectral mixture analysis. The final product of the analysis is a set of fractional abundance images for each plant functional type. Considerable accuracy was obtained on validating the fractional abundance image by in situ data. The study highlighted the potential of a spectral mixture analysis classifier in identifying and mapping different plant functional types using AVIRIS-NG data when performed using an appropriate number of end members. © 2022