BHU Institutional Repository :: Browsing by Author "Malhi, Ramandeep Kaur M."

Browsing by Author "Malhi, Ramandeep Kaur M."

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An integrated spatiotemporal pattern analysis model to assess and predict the degradation of protected forest areas
(MDPI, 2020) Malhi, Ramandeep Kaur M.; Anand, Akash; Srivastava, Prashant K.; Sandhya Kiran, G.; Petropoulos, George P.; Chalkias, Christos
Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model's predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis-with the use of FRAGSTATS 4.2 software-was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction. © 2020 by the authors.
Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators
(Taylor and Francis Ltd., 2022) Malhi, Ramandeep Kaur M.; Pandey, Manish Kumar; Anand, Akash; Srivastava, Prashant K.; Petropoulos, George P.; Singh, Prachi; Sandhya Kiran, G.; Bhattarcharya, B.K.
Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about foliar traits. The current study investigated the performance of three novel Band Selection (BS) algorithms (i.e. the Chi-squared-statistics based attribute evaluator (CSS), the Recursive elimination of features-based attribute evaluator (REF) and the Correlation-based attribute subset evaluator (CBS)) in identifying the spectral bands of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) from visible and Near Infrared (NIR) regions that are sensitive to variation in Chlorophyll Content (CC). Identified bands were employed to formulate Hyperspectral Indices (HIs) by incorporating combinations of Blue, Green, Red, and NIR regions. CC models were built by establishing a linear fit between ground CC and HIs. For all the three BS algorithms, optimum bands varied for visible and NIR regions. REF-HI (NIR,R), REF-HI(NIR,R + G), CSS-HI(NIR,R) and CSS-HI(NIR,R + G) had the best correlation with CC. HI(NIR,R) is identified as the best HI and REF the best BS algorithm for retrieving CC. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Denoising AVIRIS-NG Data for Generation of New Chlorophyll Indices
(Institute of Electrical and Electronics Engineers Inc., 2021) Singh, Prachi; Srivastava, Prashant K.; Malhi, Ramandeep Kaur M.; Chaudhary, Sumit K.; Verrelst, Jochem; Bhattacharya, Bimal K.; Raghubanshi, Akhilesh S.
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.
Harnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach
(John Wiley and Sons Inc, 2025) Mohanta, Agradeep; Sandhya Kiran, Garge; Malhi, Ramandeep Kaur M.; Prajapati, Pankajkumar C.; Oza, Kavi K.; Rajput, Shrishti; Shitole, Sanjay; Srivastava, Prashant Kumar
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques. A comprehensive spectral library was developed, encompassing data from 130 plant species, with a focus on their spectral features to support precise PFT classification. The spectral data were collected using AVIRIS-NG hyperspectral imaging and ASD Handheld Spectroradiometer, capturing a wide range of wavelengths (400–1600 nm) to encompass the key physiological and biochemical traits of the plants. Plant species were grouped into five distinct PFTs using Fuzzy C-means clustering. Key spectral features, including band reflectance, vegetation indices, and derivative/continuum properties, were identified through a combination of ISODATA clustering and Jeffries-Matusita (JM) distance analysis, enabling effective feature selection for classification. To assess the utility of the spectral library, three advanced machine learning classifiers—Parzen Window (PW), Gradient Boosted Machine (GBM), and Stochastic Gradient Descent (SGD)—were rigorously evaluated. The GBM classifier achieved the highest accuracy, with an overall accuracy (OAA) of 0.94 and a Kappa coefficient of 0.93 across five PFTs. © 2025 John Wiley & Sons Ltd.
Identification of functionally distinct plants using linear spectral mixture analysis
(Elsevier, 2020) Malhi, Ramandeep Kaur M.; Srivastava, Prashant K.; Kiran, G. Sandhya
The quantification of functional variations in vegetation aids in understanding the response of ecosystems to the changing environment. The determination of variations in the plant functional traits of different plants can help in identifying functionally distinct plants or plant functional types. This chapter discusses the importance of plant functional traits (FTs) in ecosystem functioning. Further, it discusses the classification of species based on these FTs into plant functional types and the potential of hyperspectral data in determining these plant functional types or functionally distinct plants. Additionally, a case study carried out to map functionally distinct plants in the Shoolpaneshwar Wildlife Sanctuary using Earth observation data is discussed in this chapter. Hyperion data combined with spectral mixture analysis are used for mapping functionally distinct plants. Four different FTs of dominant plant species of the study area, namely Tectona grandis, Butea monosperma, and Bambusa bambos, were estimated, which showed the significant functional variability amongst these plants. Additionally, the canopy spectra of these plants were collected from Hyperion images with high-dimensional spectral information. Pixel purity index was used to derive the endmembers from the Hyperion data based on field data of pure patches of these species. Linear spectral mixture analysis was carried out using determined endmembers and a set of fractional abundance images for each functionally distinct plant was obtained. Higher fractions of T. grandis and B. bambos endmembers were observed in the study area compared to B. monosperma. The case study highlights the potential of hyperspectral data and spectral mixture analysis in adequately characterizing functionally distinct plants. © 2020 Elsevier Ltd All rights reserved.
Mapping plant functional types through phenological insights: a novel approach
(Taylor and Francis Ltd., 2024) Mohanta, Agradeep; Garge, Sandhya Kiran; Prajapati, PankajKumar C.; Rajput, Shrishti; Oza, Kavi; Malhi, Ramandeep Kaur M.; Srivastava, Prashant Kumar; Shitole, Sanjay
This study integrates phenological data with Plant Functional Types (PFTs) to map biodiversity and ecosystem dynamics. It introduces Phenological Functional Types (PhFTs) by combining remote sensing with ground-based observations, enhancing vegetation modeling’s temporal resolution. Analysing 130 tree species in Shoolpaneshwar Wildlife Sanctuary reveals unique PhFTs with distinct phenological signatures linked to ecological functions. Using advanced remote sensing, especially the Enhanced Vegetation Index (EVI), enables detailed temporal and spatial analyses across large geographic scales. Cluster analysis and Fuzzy C-Means (FCM) clustering categorize phenological stages, highlighting phenological diversity’s role in ecosystem services and conservation. The study identifies significant phenological shifts likely due to climate change, impacting ecosystem structure and function. It underscores the importance of multidisciplinary approaches in addressing ecological issues, contributing insights to global change biology and landscape ecology for biodiversity conservation and sustainable ecosystem management. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Optimal band characterization in reformation of hyperspectral indices for species diversity estimation
(Elsevier Ltd, 2022) Anand, Akash; Malhi, Ramandeep Kaur M.; Srivastava, Prashant K.; Singh, Prachi; Mudaliar, Ashwini N.; Petropoulos, George P.; Kiran, G. Sandhya
Species diversity quantification is a crucial step towards the biodiversity conservation and ecosystem health. The technological advancements and existing limitations of multispectral remote sensing has increased the popularity of hyperspectral remote sensing which found its use in the estimation of species diversity. The contiguous narrow bands available in hyperspectral data enables the improvised assessment of diversity index but the overlapping of the information could result in the redundancy that needs to be handled. Due to this, the idenfication of optimal bands is very important; hence, the current study provides modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS), India. Narrow hyperspectral bands of EO-1 Hyperion image were screened and the best optimum wavelength from visible and Near Infrared (NIR) regions were identified based on coefficient of determination (r2) between band reflectance and in situ measured species diversity. For in situ species diversity measurements, quadrat sampling was carried out in SWS and different Diversity Indices (DIs) namely the Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI were calculated. The identified optimum wavelengths were then employed for modifying 38 existing spectral indices which were then investigated for testing their relation with the in situ DIs. The obtained optimum bands in visible and NIR regions were found to be in correspondence with four DIs. Among several indices used in this study, during validation, modified Non-linear index, modified Red Edge Position Index, modified Structure Insensitive Pigment Index and modified Red Green Ratio Index were identified as the best hyperspectral indices for determining Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI, respectively. © 2021 Elsevier Ltd
Performance assessment of potential evapotranspiration derived from INSAT-3D satellite using in situ measurements
(Asian Association on Remote Sensing, 2018) Singh, Prachi; Srivastava, Prashant K.; Mall, R.K.; Malhi, Ramandeep Kaur M.
Quantification of changes in potential evapotranspiration can provide significant information for understanding of hydrological processes and climate change. However, accurate measurements and predictions of evapotranspiration are difficult especially at large spatial scales. Remote sensing provides a cost-effective approach to determine potential evapotranspiration at both regional and global scales. In the present study, effectiveness of Hamon's method for measuring potential evapotranspiration based on INSAT-3D satellite data sets was evaluated for agricultural areas of Varanasi region, India. Satellite derived potential evapotranspiration data was compared with in situ data which indicate that the satellite derived product can be used for estimation of potential evapotranspiration with satisfactory performance. © 2018 Proceedings - 39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018
Revisiting hyperspectral remote sensing: Origin, processing, applications and way forward
(Elsevier, 2020) Srivastava, Prashant K.; Malhi, Ramandeep Kaur M.; Pandey, Prem Chandra; Anand, Akash; Singh, Prachi; Pandey, Manish Kumar; Gupta, Ayushi
After several years of research and development in hyperspectral imaging systems that enriched our knowledge and enhanced our capacity to explore the Earth, these systems have been widely accepted by the remote sensing community. They have evolved as major techniques and have now entered the mainstream of the earth observation data users. This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectral datasets, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification. The chapter concludes by looking at the way forward for hyperspectral remote sensing. © 2020 Elsevier Ltd All rights reserved.
Spectral mixture analysis of AVIRIS-NG data for grouping plant functional types
(Elsevier Ltd, 2024) Malhi, Ramandeep Kaur M.; Kiran, G. Sandhya; Srivastava, Prashant K.; Bhattacharya, Bimal K.; Mohanta, Agradeep
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
Synergetic use of in situ and hyperspectral data for mapping species diversity and above ground biomass in Shoolpaneshwar Wildlife Sanctuary, Gujarat
(Springer, 2020) Malhi, Ramandeep Kaur M.; Anand, Akash; Mudaliar, Ashwini N.; Pandey, Prem C.; Srivastava, Prashant K.; Sandhya Kiran, G.
Biodiversity loss in tropical forests is rapidly increasing, which directly influence the biomass and productivity of an ecosystem. In situ methods for species diversity assessment and biomass in synergy with hyperspectral data can adeptly serve this purpose and hence adopted in this study. Quadrat sampling was carried out in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon–Weiner Diversity Index (H′). Above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots. Four spectral indices, namely Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Photochemical Reflectance Index (PRI), and Structure Insensitive Pigment Index (SIPI) were derived from the EO-1 Hyperion Data. Spearman and Pearson’s correlation analysis was performed to examine the relationship between H′, AGB and spectral indices. The best fit model was developed by establishing a relationship between H′ and AGB. Fifteen models were developed by performing multiple linear regression analysis using all possible combinations of spectral indices and H′ and their validation was performed by relating observed H′ with model predicted H′. Pearson’s correlation relation showed that SIPI has the best relationship with the H′. Model 15 with a combination of NDVI, PRI and SIPI was determined as the best model for retrieving H′ based on its statistics performance and hence was used for generating species diversity map of the study area. Power model showed the best relationship between AGB and H′, which was used for the development of AGB map. © 2020, International Society for Tropical Ecology.
Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India
(Elsevier Ltd, 2022) Malhi, Ramandeep Kaur M.; Anand, Akash; Srivastava, Prashant K.; Chaudhary, Sumit K.; Pandey, Manish K.; Behera, Mukund Dev; Kumar, Amit; Singh, Prachi; Sandhya Kiran, G.
Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast's Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests. © 2021
Use of hyperion for mangrove forest carbon stock assessment in bhitarkanika forest reserve: A contribution towards blue carbon initiative
(MDPI AG, 2020) Anand, Akash; Pandey, Prem Chandra; Petropoulos, George P.; Pavlides, Andrew; Srivastava, Prashant K.; Sharma, Jyoti K.; Malhi, Ramandeep Kaur M.
Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy. © 2020 by the authors.

Browsing by Author "Malhi, Ramandeep Kaur M."

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