Browsing by Author "Vikas Dugesar"

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Appraisal of Sentinel-2 Derived Vegetation Indices Using Uav Mounted with Visible-Ir Sensors
(IEEE Computer Society, 2022) Vikas Dugesar; Prashant K. Srivastav
The aim of the present work is to compare sentinel-2 and UAV derived vegetation indices from a part of the Jageshwar forest range of Uttarakhand Himalaya in India. UAV survey takes place on 3rd march 2021, using the acquired images, vegetation indices were calculated without resampling the UAV data. Sensitivity of the two techniques is depicted by their correlation coefficient between the vegetation indices using the random point data of all the indices. The difference in the point value and range is due to the difference in spatial resolution. Five, chosen indices, shown good correlation between two techniques. NDVI shows the maximum similarity with a correlation coefficient of 0.77. © 2022 IEEE.
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
Climate change effect on water resources in Varanasi district, India
(John Wiley and Sons Ltd, 2020) Mărgărit-Mircea Nistor; Praveen K. Rai; Vikas Dugesar; Varun N. Mishra; Prafull Singh; Aman Arora; Virendra Kumar Kumra; Iulius-Andrei Carebia
Evapotranspiration and water availability are driven by changing climate and land cover parameters. In the present study, climatological records and land cover data were analysed simultaneously to accomplish the spatial distributions of climate change effects on water resources in Varanasi district, north India. Humidity–aridity was assessed by Lang's rain factor and De Martonne's aridity index, based on mean monthly rainfall and air temperature from seven meteorological stations. The climate change effect on water resources was evaluated using a 5 × 5 matrix that includes water availability and the aridity index by considering two time periods: 1941–1970 (1950s) and 1971–2000 (1980s). The methodology is based on seasonal crop evapotranspiration (ETc) (initial, mid-season, end season and cold season) and annual water availability calculations. The high values (≤ 1,045 mm) of ETc were identified during the mid-season stage. Water availability indicates decreases in the maximums from 718 to 636 mm during the two analysed periods, with a negative impact at the spatial scale. Lang's rain factor (< 40) indicates an arid climate in the northwest, west, east and central parts of the district and a humid climate (Lang's rain factor > 40) in the south. De Martonne's aridity index indicates rapid aridization from south to north (28.3 in the 1950s and 25.6 in the 1980s). The high and very high climate effects on water resources in Varanasi district were found mainly in the crop lands, while in the urban areas the climate effect is low. The much affected area by climate change and land cover was depicted during the recent period (1980s). This statement was proved also by the Mann and Kendall test, which indicates a negative trend for annual precipitation at all stations (for the period 1941–2000), while the mean annual temperature had a positive trend for four stations. These findings suggest that climate change had a negative effect on water resources during the last 60 years in the study area. © 2019 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
Estimation of evapotranspiration using surface energy balance system and satellite datasets
(Elsevier, 2020) Garima Shukla; Prasoon Tiwari; Vikas Dugesar; Prashant K. Srivastava
Study of actual evapotranspiration (ET) at regional scale is essential in order to facilitate proper irrigation practices. This chapter depicts the usage of satellite-based remote sensing data and Geographical Information System (GIS) for evaluating changes in actual evapotranspiration over the time-period of 20 years. This work implemented the use of Surface Energy Balance System (SEBS), a physically based model for estimation of turbulent heat transitions and territorial evapotranspiration at regional scale using RS techniques during winter season. This study was carried out using Landsat 8-OLI and Landsat-TM to achieve the desired objectives at study site of Chhatarpur and Panna zones of Madhya Pradesh, India. Actual ET estimated from the SEBS model was found to be significant and influenced by the land-use change that occurred during the 20 years’ time-period. Land-cover changes that occurred in 20 years duration affected the ET rate and may cause changes in irrigation practices in the study area. © 2021 Elsevier Inc. All rights reserved.
Estimation of instantaneous evapotranspiration using remote sensing based energy balance technique over parts of North India
(International Society for Photogrammetry and Remote Sensing, 2018) Triparna Sett; Bhaskar Ramchandra Nikam; Subrata Nandy; Abhishek Danodia; Rajarshi Bhattacharjee; Vikas Dugesar
Evapotranspiration (ET) is an essential element of the hydrological cycle and plays a significant role in regional and global climate through the hydrological circulation. Estimation and monitoring of actual crop evapotranspiration (ET) or consumptive water use over large-area holds the key for better water management and regional drought preparedness. In the present study, the remote sensing based energy balance (RS-EB) approach has been used to estimate the spatial variation of instantaneous evapotranspiration (ETinst). The (ETinst) is evaluated as the residual value after computing net radiation, soil heat flux and sensible heat flux using multispectral remote sensing data from Landsat-8 for the post-monsoon and summer season of 2016–2017 over the parts of North India. Cloud free temporal remote sensing data of October 12, 2016; November, 13, 2016; March 05, 2017 and May 24, 2017 were used as primary data for this study. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of (ETinst). © Authors 2018.CC BY 4.0 License.
High resolution retrieval of leaf chlorophyll content over Himalayan pine forest using Visible/IR sensors mounted on UAV and radiative transfer model
(Elsevier B.V., 2023) Prachi Singh; Prashant K. Srivastava; Jochem Verrelst; R.K. Mall; Juan Pablo Rivera; Vikas Dugesar; Rajendra Prasad
Forests play an essential role towards net primary productivity, biological cycles and provide habitat to flora & fauna. To monitor key physiological activities in forest canopies such as photosynthesis, respiration, transpiration, spatially-explicit and precise information of the biochemical (biological) variables such as Leaf Chlorophyll Content (LCC) is required. While lookup-table (LUT)-based Radiative Transfer Model (RTM) inversion against optical remote sensing imagery is regarded as a physically sound and robust approach for retrieving biochemical and biophysical variables, regularization procedures are required to offset the problem of ill-posedness. To optimize the RTM inversion of LCC over a sub-tropical pine forest plantation, in the Western Himalaya, we investigated the role of: (1) cost functions (CFs), (2) added noise, and (3) multiple finest solutions in LUT inversion. Principal CFs were evaluated belonging to three categories: information measures, M-estimates, and minimal contrast approaches. The inversion approaches were applied to a LUT produced by the coupled leaf-canopy model known as PROSAIL RTM and tested in contrast field spectral data obtained from reflectance data derived from UAV (Unmanned Aerial Vehicle) images taken over the canopies of covered pine forests. The Bhattacharyya divergence, an information measure, outperformed all other CFs in LCC inversion, with R2 of 0.94, RMSE of 6.20 μg/cm2 and NRMSE of 12.27% during the validation. The optimized inversion strategy was subsequently applied to a UAV-acquired multispectral image at an 8.2 cm pixel resolution for detailed landscape forest LCC mapping. The associated residuals as provided by the LUT-based inversion provided insights in the spatial consistency of the LCC map. © 2023 Elsevier B.V.
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.
Performance assessment of the Sentinel-2 LAI products and data fusion techniques for developing new LAI datasets over the high-altitude Himalayan forests
(Taylor and Francis Ltd., 2023) Vikas Dugesar; Manish K. Pandey; Prashant K. Srivastava; George P. Petropoulos; Sanjeev Kumar Srivastava; Virendra Kumar Kumra
The present study evaluates the accuracy of SNAP-Sentinel-2 Prototype Processor (SL2P) derived Leaf Area Index (LAI) and proposes a new simple method to generate new datasets of LAI through data fusion. Rigorous optimization of the data fusion approaches (Kalman filter and Linear weighted) were performed for the generation of new LAI products over the complex hilly terrain of the Himalayan region. The results showed a good correlation (r = 0.79) and low error (RMSE = 1.63) between SNAP-derived (at 20 m) and ground-observed LAI. A lower correlation was obtained between the ground observed LAI data and the corresponding global LAI products for the Moderate Resolution Imaging Spectroradiometer (MODIS) (r = 0.1, RMSE = 1.19), Copernicus Global Land Service (CGLS) (r = 0.1, RMSE = 0.61) and the Visible Infrared Imaging Radiometer Suite (VIIRS) (r = 0.04, RMSE = 1.25). Notably, after implementing the data fusion, both SNAP-derived LAI and Global LAI products exhibited much-improved performance statistics with ground observed data sets. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Precision mapping of boundaries of flood plain river basins using high-resolution satellite imagery: A case study of the Varuna river basin in Uttar Pradesh, India
(Springer, 2019) Mallikarjun Mishra; Vikas Dugesar; K.N. Prudhviraju; Shyam Babu Patel; Kshitij Mohan
Accurate demarcation of river basin boundaries is an important input for any programme connected with watershed management. In the present study, the boundary of the Varuna river basin is automatically derived using coarse- and medium-resolution digital elevation models (DEMs) of SRTM-30 m, ASTER-30 m, Cartosat-30 m, ALOS Palsar-12.5 m and Cartosat-10 m as well as manually through on-screen digitisation from a very high-resolution 1 m × 1 m remote sensing data available as Google Earth image. The study demonstrated the efficacy of on-screen digitisation from high-resolution Google Earth image supported by detailed field observations in the precision mapping of the place of origin of the Varuna River, its stream network and basin boundary when compared to the maps generated through automatic methods using DEMs of various resolutions. The Varuna river system takes its headwaters from the areas surrounding Umran and Dain ‘tals’ (shallow, large depressions/basins) but not from the west of Mau Aima town as has been previously reported. © 2019, Indian Academy of Sciences.
Retrieval and Validation of Sentinel 2 LAI Product: A Comparison with Global Products Over High-Altitude Himalayan Forests
(Institute of Electrical and Electronics Engineers Inc., 2022) Vikas Dugesar; Prashant K. Srivastava; V.K. Kumra
The main objective of the study was to validate the Sentinel-2 Level 2 Prototype Processor (SNAP-SL2P) derived LAI with ground observations and to verify consistency of global LAI products with ground observed LAI and upscaled validated LAI products. In present study, decametric Sentinel-2 LAI product was retrieved by the SL2P and validated using field measured LAI. Validated LAI product was upscaled at Copernicus (Sentinel-3/OLCI, PROBA-V), MODIS (combined terra and aqua) and VIIRS spatial scales for intercomparison with ground observed LAI and global LAI products, was presented as a way forward to validate hectometric LAI products. © 2022 IEEE.
Seeing from space makes sense: Novel earth observation variables accurately map species distributions over Himalaya
(Academic Press, 2023) K.V. Satish; Vikas Dugesar; Manish K. Pandey; Prashant K. Srivastava; Dalbeer S. Pharswan; Zishan Ahmad Wani
Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors’ biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions. © 2022 Elsevier Ltd
Soil chemical properties estimation using hyperspectral remote sensing: A review
(Elsevier, 2024) Prashant K. Srivastava; Swati Srivastava; Prachi Singh; Ayushi Gupta; Vikas Dugesar
Understanding the geographical variability of soil requires proper evaluation of its physical and chemical properties. Traditionally, soil samples are taken and examined in a laboratory to determine the soil fertility characteristics and to measure spatial variability. The moisture content, organic carbon content, particle size, and kind of clay minerals are some of the significant soil properties that are reflected in the spectral signatures of soil. Rapid and precise mapping of soil properties is crucial for agricultural, forestry, and environmental management. With a reasonable degree of accuracy, hyperspectral spectroscopy has been introducing to be helpful in estimating soil chemical properties. The prediction of soil qualities can also be done by using the various popular spectral indices. Pure soil spectra can be used to estimate several soil characteristics, such as pH and organic carbon, as well as some physical and chemical properties, such as soil texture, soil organic carbon, fertility (NPK), CEC, pH, salinity etc. The chapter provided a review of the studies, techniques and methodologies for the evaluation of several soil chemical properties. This chapter could be more beneficial for researchers that are working on alternative techniques for the estimation soil properties especially for the larger areas. © 2025 Elsevier Ltd. All rights reserved.
Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles
(John Wiley and Sons Ltd, 2024) Smrutisikha Mohanty; Prem C. Pandey; Prachi Singh; Vikas Dugesar; Prashant K. Srivastava
Wetlands are an integral part of our global ecosystems and play crucial roles in ecological functions such as carbon sequestration, flood mitigation, water purification, and recreational activities. The Ramsar Convention is the most significant wetland protection pact and is doing tremendous work in conserving wetlands worldwide. However, the wetlands area is still under threat due to anthropogenic activity. The current study utilized drone images, chlorophyll measurements and machine leaning to discriminate and map vegetation at marsh wetland area—the Ramsar site. The high-resolution, multispectral imagery is acquired using a drone-mounted MICAsense sensor. Eight spectral indices such as Normalized Difference Water Index (NDWI), Two-Band Algorithm (2BDA), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Vegetation Index (NDVI) Enhanced Normalized Difference Vegetation Index (ENDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalised Difference RedEdge (NDRE) were calculated on the acquired imagery in order to discriminate the different vegetation covers such as floating aquatic vegetation (FAV), open water, and other vegetations types. These include the following: Eichhornia, Nymphea, Oleracea, Paspalam, and Oryza from agriculture land at the study site. Two models (viz., the Taylor plot and the Lek Profile methods) were employed to assess the sensitivity of the spectral indices for prediction of chlorophyll and vegetation discrimination. It is inferred from both methods that NDCI was most sensitive for chlorophyll prediction of vegetation followed by NGRDI/ ENDVI/ 2BDA and NDVI for chlorophyll prediction in wetland ecosystems. Further, three machine learning algorithms, support vector machine (SVM), random forest (RF), and gradient tree boost (GTB), were utilized for classification, and the performance accuracy of GTB was found to be the highest (0.893), followed by RF (0.851) and SVM (0.723). The GTB algorithm was applied over NDCI for vegetation discrimination. The study revealed that Eichhronia sp. is abundantly present at the study site; hence, strategic management plans should be carried out for the eradication of invasive species and proper management of wetland vegetation. © 2024 John Wiley & Sons Ltd.