Browsing by Author "Akash Anand"

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An integrated spatiotemporal pattern analysis model to assess and predict the degradation of protected forest areas
(MDPI, 2020) Ramandeep Kaur M. Malhi; Akash Anand; Prashant K. Srivastava; G. Sandhya Kiran; George P. Petropoulos; Christos Chalkias
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.
Appraisal of Visible/IR and microwave datasets for land surface fluxes estimation using machine learning techniques
(Elsevier Ltd, 2024) Ajay Shankar; Vishal Prasad; Prashant K. Srivastava; Akash Anand; Vikas Dugesar
Land surface fluxes such as Soil Moisture (SM) and Soil Temperature (ST) are very important variables for many applications that includes agriculture water management, weather and climate prediction, natural disasters etc. Further, they are important for understanding soil processes, hydrological balances as well as changes in microbial population. Mapping of the soil moisture content at various depth is crucial for the sustenance of water resources and also to understand about the development of crops in forms of quality and yield. With changing environmental conditions, there is a need of approaches for estimating SM and ST in various climatic and geographic situations. Towards this, Earth Observation datasets at higher resolutions from satellites such as Sentinel 1 and 2, could play an important role in the monitoring of SM and ST over the larger areas. For estimation of SM and ST, machine learning approaches could be effective. This research looked into the possibilities of using Earth Observation (EO) data of Sentinel-1 (S1) and Sentinel-2 (S2) simultaneously to estimate SM and ST by using the machine learning methods such as random forest (RF) and Support Vector Machines (SVM). The coefficient of correlation (r), root mean square error (RMSE), and Bias are utilized in model enactment for accuracy and comparative analysis of the models used. The overall analysis indicates that the SVM model (r = 0.85, RMSE = 2.54, Bias = −0.05) is the second most appropriate after the RF model (r = 0.89, RMSE = 2.34, Bias = 0) for estimating land surface fluxes (SM and ST). © 2024 Elsevier Ltd
Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables
(SPIE, 2022) Akash Anand; Prashant K. Srivastava; Prem C. Pandey; Mohammed L. Khan; Mukund D. Behera
Species distribution models (SDMs) have been used extensively in the field of landscape ecology and conservation biology since its origin in the late 1980s. But there is still a void for a universal modeling approach for SDMs. With recent advancements in satellite data and machine learning algorithms, the prediction of species occurrence is more accurate and realistic. Presently, four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas. To establish complex relation between the occurrence data and regional climatic and land use parameters, several satellite products, namely, MODIS, Sentinel-5p, GPM, ECOSTRESS, and shuttle radar topography mission (SRTM), are acquired and used as predictor variables to the different SDM algorithms. The performance evaluation has been conducted using the area under curve (AUC), which showed the best result for Maxent (AUC = 0.871) and poor result was observed for RF (AUC = 0.755) among all. The overall prediction confirmed the distribution of Rhododendron arboreum in the mid to high altitudes of central and southern parts of the Garhwal Division. We provide crucial evidence that combining multisatellite products using machine learning algorithms can provide a much better understanding of species distribution that can eventually help the researchers and policymakers to take the necessary step toward its conservation. © 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).
Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators
(Taylor and Francis Ltd., 2022) Ramandeep Kaur M. Malhi; Manish Kumar Pandey; Akash Anand; Prashant K. Srivastava; George P. Petropoulos; Prachi Singh; G. Sandhya Kiran; B.K. Bhattarcharya
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.
Comparative changes in seasonal marine heatwaves and cold spells over the Tropical Indian Ocean during recent decades and disentangling the drivers of highly intense events
(John Wiley and Sons Ltd, 2023) Anand Singh Dinesh; Alok Kumar Mishra; Aditya Kumar Dubey; Suruchi Kumari; Akash Anand
The unprecedented increase in the Sea Surface Temperature (SST) in the warming climate yield stress to the system and pose severe threats to the marine ecosystem. Marine Heatwaves (MHWs) and Marine Cold Spells (MCSs) are two extreme events related to SST variability. For better management of ocean productivity, marine ecosystem, marine services, and fisheries, the understanding of seasonal discrepancies rather than annual documentation of MHWs and MCSs metrics is more utilitarian. This study documents the decadal changes in the MHWs and MCSs over the Tropical Indian Ocean (TIO) for all seasons. Additionally, highly intense events (based on intensity and duration) are identified and demonstrate the associated drivers. During the past two decades (1982–1990, 1991–2000), the MCSs were more frequent than MHWs in every season. However, in the recent two decades (2001–2010, 2011–2020), TIO become more prone to MHWs with considerably more frequent and prolonged events in JJAS months. Moreover, MCSs are disappearing from the TIO. It was noted that the choice of baseline period has an impact on the magnitude of MHWs and MCSs changes, but the spatial pattern (regions with high/low magnitude MHWs and MCSs) stays fairly constant in all baseline period sensitivity checks. The investigation of highly intense events reveals that MHWs and MCSs are produced and sustained by the same drivers when they are at their opposing edges. In general, the coherence effort from winds, net heat fluxes (shortwave radiation, longwave radiation, latent heat flux, and sensible heat flux), mixed level depth, and mean sea level pressure contribute to the genesis of seasonal MHWs or MCSs events. Additionally, in some cases, a single driver (e.g., wind) may also play a crucial role in these extreme events. The remote climate modes of variability, such as El Niño–Southern Oscillation, also contribute significantly to the MHWs and MCSs. El Niño (La Niña) events not only increase the spatial coverage of MHWs (MCSs) but also increases the intensity. © 2023 Royal Meteorological Society.
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
Delineation of groundwater potential zone and site suitability of rainwater harvesting structures using remote sensing and in situ geophysical measurements
(wiley, 2021) Prachi Singh; Akash Anand; Prashant K. Srivastava; Arjun Singh; Prem Chandra Pandey
The present situation of groundwater tables is falling at a rapid rate, because regular withdrawal of groundwater is high compared to the recharge rate. This study focuses on the delineation of a groundwater potential zone in Lalganj Ajhara block, District Pratapgarh, Uttar Pradesh, using in situ vertical electric survey data, remote sensing (RS), and geographic information system (GIS). Present research covers the spatial analysis of different thematic layers generated through satellite images and field data for the identification of suitable sites for rainwater harvesting structures within the study area. A Schlumberger array is generated through the geotechnical field survey and soil depth analysis is done for three cross-section profiles which are generated through the sample locations. Sentinel-2A data along with the digital elevation model (DEM) are used to prepare different thematic layers, and density analysis is done using GIS tools. The groundwater potential is estimated using multi-criteria overlay analysis and using in situ measurements. The site suitability analysis is done by estimating probable location of check dams, desilting points, dug well, and recharge pit within the study area. © 2021 John Wiley & Sons Ltd. All rights reserved.
Development of hyperspectral indices for anti-cancerous Taxol content estimation in the Himalayan region
(Taylor and Francis Ltd., 2022) Ayushi Gupta; Prachi Singh; Prashant K. Srivastava; Manish K. Pandey; Akash Anand; K. Chandra Sekar; Karuna Shanker
Monitoring and management of rare and economically important species in the highly complex terrain are challenging and thus need advanced technological development. In this study, the hyperspectral radiometer data of Taxus wallichiana were acquired at highly complex terrain of the Pindari region of the Himalaya and processed by using several sophisticated algorithms to deduce Taxol content in the plants. The spectroradiometer data were denoised through three different types of smoothing filters such as Average Mean, Savitzky Golay, and Fast Fourier Transform (FFT) followed by feature selection for allocation of best bands for Taxol content estimation. The results showed that the Average Mean filter in combination with feature selection performed best for Taxol spectral indices generation, model development, and Taxol content prediction. The best model showed a correlation of 0.719 with a relative root mean square error (RMSEr) value of 0.678 for Taxol content prediction. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
GIS-based analysis for soil moisture estimation via kriging with external drift
(Elsevier, 2020) Akash Anand; Prachi Singh; Prashant K. Srivastava; Manika Gupta
Spatial distribution analysis of in-situ measurements within a study area using geostatistical approach is always a complex thing to perform. Present study deals with a geostatistical method to map the distribution of soil moisture and soil temperature throughout the study area using Hydra probe in-situ data. As soil moisture plays an important role in short- and long-term meteorological modelling and also is a vital component for sustaining life supporting systems at micro- and mega-scale, it is required to monitor its spatial and temporal variation with high precision. Presently, a multivariate geostatistical approach, i.e., Kriging with External Drift (KED), is used to improve the accuracy of spatial distribution mapping of soil moisture within the study area. Semi-variogram analysis is done to estimate the semi-variance in the model and the stability of the interpolated results. The correlation is established between the observed and predicted soil moisture that has shown R2 of 0.989 and Root Mean Square Error of 0.32, which shows that the model performed very well. © 2021 Elsevier Inc. All rights reserved.
Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology
(Nature Research, 2021) Ram Kumar Singh; Martin Drews; Manuel De la Sen; Prashant Kumar Srivastava; Bambang H. Trisasongko; Manoj Kumar; Manish Kumar Pandey; Akash Anand; S.S. Singh; A.K. Pandey; Manmohan Dobriyal; Meenu Rani; Pavan Kumar
The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections. © 2021, The Author(s).
Impact of Environmental Gradients on Phenometrics of Major Forest Types of Kumaon Region of the Western Himalaya
(MDPI, 2022) Vikas Dugesar; Koppineedi V. Satish; Manish K. Pandey; Prashant K. Srivastava; George P. Petropoulos; Akash Anand; Mukunda Dev Behera
Understanding ecosystem functional behaviour and its response to climate change necessitates a detailed understanding of vegetation phenology. The present study investigates the effect of an elevational gradient, temperature, and precipitation on the start of the season (SOS) and end of the season (EOS), in major forest types of the Kumaon region of the western Himalaya. The analysis made use of the Normalised Difference Vegetation Index (NDVI) time series that was observed by the optical datasets between the years 2001 and 2019. The relationship between vegetation growth stages (phenophases) and climatic variables was investigated as an interannual variation, variation along the elevation, and variation with latitude. The SOS indicates a delayed trend along the elevational gradient (EG) till mid-latitude and shows an advancing pattern thereafter. The highest rate of change for the SOS and EOS is 3.3 and 2.9 days per year in grassland (GL). The lowest rate of temporal change for SOS is 0.9 days per year in mixed forests and for EOS it is 1.2 days per year in evergreen needle-leaf forests (ENF). Similarly, the highest rate of change in SOS along the elevation gradient is 2.4 days/100 m in evergreen broadleaf forest (EBF) and the lowest is −0.7 days/100 m in savanna, and for EOS, the highest rate of change is 2.2 days/100 m in EBF and lowest is −0.9 days/100 m in GL. Winter warming and low winter precipitation push EOS days further. In the present study area, due to winter warming and summer dryness, despite a warming trend in springseason or springtime, onset of the vegetation growth cycle shows a delayed trend across the vegetation types. As vegetation phenology responds differently over heterogeneous mountain landscapes to climate change, a detailed local-level observational insight could improve our understanding of climate change mitigation and adaptation policies. © 2022 by the authors.
Integrating multi-sensors data for species distribution mapping using deep learning and envelope models
(MDPI AG, 2021) Akash Anand; Manish K. Pandey; Prashant K. Srivastava; Ayushi Gupta; Mohammed Latif Khan
The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor
(Springer Science and Business Media B.V., 2024) Pavan Kumar; Aishwarya; Prashant Kumar Srivastava; Manish Kumar Pandey; Akash Anand; Jayanta Kumar Biswas; Martin Drews; Manmohan Dobriyal; Ram Kumar Singh; Manuel De la Sen; Sati Shankar Singh; Ajai Kumar Pandey; Manoj Kumar; Meenu Rani
There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.
Optimal band characterization in reformation of hyperspectral indices for species diversity estimation
(Elsevier Ltd, 2022) Akash Anand; Ramandeep Kaur M. Malhi; Prashant K. Srivastava; Prachi Singh; Ashwini N. Mudaliar; George P. Petropoulos; G. Sandhya Kiran
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
Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system
(Taylor and Francis Ltd., 2022) Akash Anand; Anand Singh Dinesh; Prashant K. Srivastava; Sumit Kumar Chaudhary; Atul Kumar Varma; Pavan Kumar
Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Revisiting hyperspectral remote sensing: Origin, processing, applications and way forward
(Elsevier, 2020) Prashant K. Srivastava; Ramandeep Kaur M. Malhi; Prem Chandra Pandey; Akash Anand; Prachi Singh; Manish Kumar Pandey; Ayushi Gupta
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.
Soil erosion in future scenario using CMIP5 models and earth observation datasets
(Elsevier B.V., 2021) Swati Maurya; Prashant K. Srivastava; Aradhana Yaduvanshi; Akash Anand; George P. Petropoulos; Lu Zhuo; R.K. Mall
Rainfall and land use/land cover changes are significant factors that impact the soil erosion processes. Therefore, the present study aims to investigate the impact of rainfall and land use/land cover changes in the current and future scenarios to deduce the soil erosion losses using the state-of-the-art Revised Universal Soil Loss Equation (RUSLE). In this study, we evaluated the long-term changes (period 1981–2040) in the land use/land cover and rainfall through the statistical measures and used subsequently in the soil erosion loss prediction. The future land use/land cover changes are produced using the Cellular Automata Markov Chain model (CA-Markov) simulation using multi-temporal Landsat datasets, while long term rainfall data was obtained from the Coupled Model Intercomparison Project v5 (CMIP5) and Indian Meteorological Department. In total seven CMIP5 model projections viz Ensemble mean, MRI-CGCM3, INMCM4, canESM2, MPI-ESM-LR, GFDL-ESM2M and GFDL-CM3 of rainfall were used. The future projections (2011–2040) of soil erosion losses were then made after calibrating the soil erosion model on the historic datasets. The applicability of the proposed method has been tested over the Mahi River Basin (MRB), a region of key environmental significance in India. The finding showed that the rainfall-runoff erosivity gradually decreases from 475.18 MJ mm/h/y (1981–1990) to 425.72 MJ mm/h/y (1991–2000). A value of 428.53 MJ mm/h/y was obtained in 2001–2010, while a significantly high values 661.47 MJ mm/h/y has been reported for the 2011–2040 in the ensemble model mean output of CMIP5. The combined results of rainfall and land use/land cover changes reveal that the soil erosion loss occurred during 1981–1990 was 55.23 t/ha/y (1981–1990), which is gradually increased to 56.78 t/ha/y in 1991–2000 and 57.35 t/ha/y in 2000–2010. The projected results showed that it would increase to 71.46 t/h/y in 2011–2040. The outcome of this study can be used to provide reasonable assistance in identifying suitable conservation practices in the MRB. © 2020
Spatial distribution of mangrove forest species and biomass assessment using field inventory and earth observation hyperspectral data
(Springer Netherlands, 2019) Prem Chandra Pandey; Akash Anand; Prashant K. Srivastava
The objective of this research is to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove Forest, Bhitarkanika India. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove species identification and biomass estimation. However, estimation at a regional level with species-wise biomass distribution has been insufficiently investigated in the past because either research focuses on the species distribution or biomass assessment. Study shows that good relationship has been achieved between stem volume (field measured data) and Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from satellite image and further these two indices are employed to estimate the biomass in the study site. Three models- linear, logarithmic and polynomial (second degree) are used to estimate biomass derived from EVI and NDVI. The hyperspectral data (spatial resolution ~ 30 m) is utilised to identify ten mangrove plant species. We have prepared the spatial distribution map of these species using spectral angle mapper. We have also generated mangrove species-wise biomass distribution map of the study site along with areal coverage of each species. The results indicate that the Sonneratia apetala Buch.-Ham. and Cynometra iripa Kostel has the highest biomass among all ten identified species, 643.12 Mg ha−1 and 652.14 Mg ha−1. Our study provided a positive relationship between NDVI, EVI, and the estimated biomass of Bhitarkanika Forest Reserve Odisha India. The study finds a similar results for both NDVI and EVI derived biomass, while linear regression has more significant results than the polynomial (second degree) and logarithmic regression derived biomass. The polynomial is found slightly better than the logarithmic when using the EVI as compared to NDVI derived biomass. The spatial distribution of species-wise biomass map obtained in this study using both, EVI and NDVI could be used to provide useful information for biodiversity assessment along with the sustainable solutions to different problems associated with the mangrove forest biodiversity. Thus, biomass assessment of larger regions can be achieved by utilization of remote sensing based indices as concluded in the present study. © 2019, Springer Nature B.V.
Synergetic use of in situ and hyperspectral data for mapping species diversity and above ground biomass in Shoolpaneshwar Wildlife Sanctuary, Gujarat
(Springer, 2020) Ramandeep Kaur M. Malhi; Akash Anand; Ashwini N. Mudaliar; Prem C. Pandey; Prashant K. Srivastava; G. Sandhya Kiran
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) Ramandeep Kaur M. Malhi; Akash Anand; Prashant K. Srivastava; Sumit K. Chaudhary; Manish K. Pandey; Mukund Dev Behera; Amit Kumar; Prachi Singh; G. Sandhya Kiran
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