Browsing by Author "Pulakesh Das"

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Development of decadal (1985-1995-2005) land use and land cover database for India
(MDPI AG, 2015) Parth S. Roy; Arijit Roy; Pawan K. Joshi; Manish P. Kale; Vijay K. Srivastava; Sushil K. Srivastava; Ravi S. Dwevidi; Chitiz Joshi; Mukunda D. Behera; Prasanth Meiyappan; Yeshu Sharma; Atul K. Jain; Jamuna S. Singh; Yajnaseni Palchowdhuri; Reshma. M. Ramachandran; Bhavani Pinjarla; V. Chakravarthi; Nani Babu; Mahalakshmi S. Gowsalya; Praveen Thiruvengadam; Mrinalni Kotteeswaran; Vishnu Priya; Krishna Murthy V.N. Yelishetty; Sandeep Maithani; Gautam Talukdar; Indranil Mondal; Krishnan S. Rajan; Prasad S. Narendra; Sushmita Biswal; Anusheema Chakraborty; Hitendra Padalia; Manoj Chavan; Satish N. Pardeshi; Swapnil A. Chaudhari; Arur Anand; Anjana Vyas; Mruthyunjaya K. Reddy; M. Ramalingam; R. Manonmani; Pritiranjan Behera; Pulakesh Das; Poonam Tripathi; Shafique Matin; Mohammed L. Khan; Om P. Tripathi; Jyotihman Deka; Prasanna Kumar; Deepak Kushwaha
India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study. © 2015 by the authors.
Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy
(Taylor and Francis Ltd., 2024) Varsha Pandey; Prashant Kumar Srivastava; Pulakesh Das; Mukunda Dev Behera; Eswar Rajasekaran
Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. This study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season, 1998–2016. Crop yield was linked with SMDI and other covariates using a random forest machine learning-based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in the years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security. © 2024 IAHS.
Impact of extreme weather events on cropland inundation over Indian subcontinent
(Springer Science and Business Media Deutschland GmbH, 2023) A Jaya Prakash; Shubham Kumar; Mukunda Dev Behera; Pulakesh Das; Amit Kumar; Prashant Kumar Srivastava
Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The “Gulab” cyclonic storm formed on the 24th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the “Gulab” cyclonic event. The Otsu’s thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km2), followed by Telangana (~2040 km2) and Odisha (~1132 km2), and the least in Chhattisgarh (~93.75 km2). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Irrigation water demand estimation in Bundelkhand region using the variable infiltration capacity model
(Elsevier, 2020) Varsha Pandey; Prashant K. Srivastava; Pulakesh Das; Mukunda Dev Behera
The soil moisture level effectively estimates the extremity of irrigation demand, and thus acts as a significant indicator of irrigation water management. In recent time due to increase in population, demand for freshwater in all competing sectors is being a constraint for irrigation that raises the need to optimise utilisation of irrigation water with its high efficiency. Therefore, an accurate and precise assessment of soil moisture content (SMC) is required for optimal allocation and management of water in agriculture. Owing to this, the current study aims to simulate SMC from water balance-based macro-scale Variable Infiltration Capacity (VIC) hydrological model for various applications in irrigation water management. The current study was carried out in the Bundelkhand region of Uttar Pradesh, India, for a period of 52 value ranging from 0.73 to 0.90 and low RMSE value ranging from 0.03 to 0.05 m3m-3. The dataset of simulated and CCI-SM content values was close to the 1:1 scale line for approximately all the periods. Additionally, the Soil Moisture Deficit Index (SMDI), a dryness index, was computed for estimating irrigation demand from VIC-derived SMC for Kharif season (July-October) from 2010 to 2014; this shows more irrigation water demand in 2014 and sowing period of 2010 and 2012. © 2021 Elsevier Inc. All rights reserved.
Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India
(MDPI, 2022) Sujit M. Ghosh; Mukunda D. Behera; Subham Kumar; Pulakesh Das; Ambadipudi J. Prakash; Prasad K. Bhaskaran; Parth S. Roy; Saroj K. Barik; Chockalingam Jeganathan; Prashant K. Srivastava; Soumit K. Behera
Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height <10 m, while 44.51% accounted for 10–20 m and 22.34% of forests demonstrated a higher canopy height (>20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists. © 2022 by the authors.
Shifting cultivation induced burn area dynamics using ensemble approach in Northeast India
(Elsevier B.V., 2022) Pulakesh Das; Mukunda Dev Behera; Saroj Kanta Barik; Sujoy Mudi; Buddolla Jagadish; Swarup Sarkar; Santa Ram Joshi; Dibyendu Adhikari; Soumit Kumar Behera; Kiranmay Sarma; Prashant Kumar Srivastava; Puneet Singh Chauhan
Identifying shifting cultivation areas and assessing their spatio-temporal dynamics are essential in framing climate-adaptive policies for efficient forest management and agriculture practices for the benefit of people. The current study attempts to develop an alternative approach to classify the shifting cultivation areas using an ensemble technique, integrating multiple spectral indices in three states of northeast India (NEI), such as Assam, Manipur, and Meghalaya. The adopted approach integrates green cover and leaf water content changes during shifting cultivation land preparation in Landsat imagery. The deforested burned area patches were identified based on threshold values using Landsat data-derived indices on vegetation, burned area and leaf water, and digital elevation model (DEM). The ensemble approach provided shifting cultivation maps with good overall accuracy (> 83%). The maximum shifting cultivation area was observed in Assam (126.87 km2), followed by Meghalaya (51.53 km2) and Manipur (46.04 km2) in 2016. The normalized difference vegetation index (NDVI) and NDVI difference performed better than other vegetation indices. The ensemble approach can be applied in other regions with minor modifications in threshold values, thus having the potential for accounting to shifting cultivation dynamics on an operational basis. Future research may include blending local traditional knowledge and modern scientific solutions for improved forest and land resources planning for the benefit of inhabitants and the mountain environment under the climate change scenarios. © 2021