Pulakesh Das | Indian Institute of Technology Kharagpur (original) (raw)
Papers by Pulakesh Das
Hydrological sciences journal, Apr 26, 2024
Research Square (Research Square), Aug 30, 2023
Although the impact of climate change is slow, the transformation in climate regime can lead to a... more Although the impact of climate change is slow, the transformation in climate regime can lead to an ecosystem structure change from one stable to another stable state through intermediate bistable or metastable conditions. Therefore, the state transition or resilience in nature can never be sharp or be quanti ed with a single tipping point across the scales; rather, it should be understood through a tipping point range (tipping zone) across hysteresis loop(s). This study uses a satellite data-derived actual forest cover state map of India and high-resolution long-term average precipitation data to predict various tipping point range hysteresis for different forest cover states such as forest, scrubland, grassland and vegetation-less. The forest and vegetation-less states could have one-way, while scrubland and grassland have two-way transition probabilities with a probable shift in precipitation regime. In the dry conditions, the precipitation tipping zone predicted between 154 mm and 452 mm for the forest to scrubland transitions, while the reverse transition (from scrubland to forest) could occur in wet conditions between 1080 mm and 1400 mm. Similarly, the transition between scrubland and grassland, between grassland and vegetation-less state, may occur in contrasting dry and wet conditions, creating a hysteresis loop. The study indicates that the reversal of state change requires differential energy spent during the onward transition. The study proposes a novel characteristic curve demonstrating the varied precipitation tipping points/ zones, precipitation overlaps and distribution of the various life forms, and coexistence zones. The characteristic curve offers valuable inputs to explain life form transition and demarcate regions where forest enrichment and degradation may occur due to climate regime shifts. Such a spatially explicit database could provide vital inputs for planning forest cover restoration and management activities and mitigate the climate change impact.
Geoenvironmental disasters, Mar 26, 2024
Background Operational large-scale flood monitoring using publicly available satellite data is po... more Background Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu's algorithm). Objectives The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Summary The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness ofemergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.
Tropical Ecology, May 17, 2023
For the last several years, the air quality of India's capital Delhi and surrounding region (NCR)... more For the last several years, the air quality of India's capital Delhi and surrounding region (NCR) has been degrading to a very poor and severe category during the autumn season. In addition to the various sources of air pollutants within the NCR region, the stubble burning in Punjab and Haryana states contributes to the poor air quality in this region. The current study employs the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products and TROPOspheric Monitoring Instrument (TROPOMI) products on carbon monoxide (CO) and nitrogen dioxide (NO 2) concentrations for spatio-temporal assessment of stubble burning and associated emissions. The analysis performed in the Google Earth Engine (GEE) platform indicated a nearly threefold rise in crop residue burning in November than in October, with 92.58% and 7.42% reported from Punjab and the Haryana states in November, respectively. The study highlights the availability of near-real-time remote sensing observations and the utility of the GEE platform for rapid assessment of stubble burning and emissions thereof, having the potential for developing mitigation strategies.
CRC Press eBooks, Oct 17, 2022
Research Square (Research Square), Jun 26, 2023
Operational large-scale ood monitoring using publicly available satellite data is possible with t... more Operational large-scale ood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) ood mapping day and night, even in cloudy monsoon seasons. Automated ood inundation area identi cation in near-real-time involves advanced geospatial data processing platforms such as Google Earth Engine (GEE) and robust methodology (Otsu's algorithm). The current study employs the Sentinel-1 microwave data for ood extent mapping using machine learning (ML) algorithms in Assam state, India. We generated a ood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classi cation and Regression Tool (CART) and Support Vector Machine (SVM) ML algorithms were applied to generate the ood hazard map. The highest prediction accuracy was observed for the RF model (overall accuracy [OA]: > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Further, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Over 26% of the study area indicated high ood hazard-prone areas, and about 60% showed a high and severe potential for soil erosion due to ooding. The automated ood mapping platform is an essential resource for emergency responders and decision-makers, as they help to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. The periodic ood inundation maps will help in long-term planning and policymaking, ood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, climate resilience studies, etc.
Environmental Monitoring and Assessment, Dec 1, 2019
Harvesting surface runoff during monsoon season for further utilization in crop production during... more Harvesting surface runoff during monsoon season for further utilization in crop production during the post-monsoon season is now becoming an effective solution to mitigate water scarcity problems. In this study, multi-criteria analysis-analytic hierarchy process (MCA-AHP)-based approach was envisaged for rainwater harvesting (RWH) zoning for a case study area, i.e., two districts of Odisha state situated in Eastern India. In spite of having a large irrigation network in the study area, major portion of these two densely populated and agriculture dominated districts remains fallow during dry seasons. Suitable locations for RWH structures such as farm pond, check dam, and percolation tanks were identified through Boolean conditions. RWH potential map was generated using different thematic layers namely land use/land cover (LU/LC), geomorphology, slope, stream density, soil type, and surface runoff. AHP-based MCA technique was used to integrate these thematic layers by assigning weights to the thematic layers and ranks to the individual theme features on 1-9 AHP Saaty's scale, considering their relative importance on RWH potential of the study area. The Natural Resources Conservation Service-Curve Number method was used to derive surface runoff using Climate Hazards Group Infra-Red Precipitation with Station rainfall data, satellite-derived LU/LC and FAO soil maps. In comparison to single cropped areas in 48% of the total study area, only 4% area was under double and triple cropped areas during 2016-2017. Moderate runoff was observed in > 50% of the study area dominated by agricultural landscape. Nearly 40%, 25.11%, and 32.45% of the study area indicated very high, high, and moderate RWH potentials, respectively. Particularly, very high RWH potential is observed in the eastern and central portion of the study area. The use of appropriate RWH structures in less irrigated areas will facilitate multiple cropping and will substitute the use of subsurface water harvesting practices. In these two districts, 73 check dams and 153 percolation tanks are prescribed along the 2nd-and 3rd-order streams. In coarser textured soil, nearly 306 km 2 and 608 km 2 areas are identified as moderate and highly suitable zones for percolation tank construction on ground, while in fine soil, around 786 km 2 area is identified as suitable for farm pond construction. Majority of the suitable zones for percolation tanks is found in Jajpur district, while suitability for adoption of farm pond and check dam is more in Bhadrak district. It is expected that implementation of the prescribed RWH structures can mitigate the threats of flood, drought, soil erosion, and enhance the soil moisture and cropping intensity significantly. The use
Biodiversity and Conservation, Apr 4, 2019
With the threats of climate change, the forest cover in India necessitates the study of its survi... more With the threats of climate change, the forest cover in India necessitates the study of its survival probability and the precipitation thresholds value trigger life form regime shift. With a mega-biodiversity ecosystem, the assessment of forest cover resilience will enhance the effectiveness of climate adaptive conservation strategies. In the current study, we have used an open source tree canopy cover percentage (TCC %) data to map the spatial distribution of forest, scrub, grassland and treeless, and to relate with long term annual precipitation. The natural occurrences forest, scrub, grassland and treeless were identified in the precipitation ranges as 340-8650 mm, 196-1018 mm, 167-995 mm, and 34-965 mm precipitation, respectively; whereas their mean values were observed as 1952 mm, 779 mm, 760 mm, and 322 mm respectively. We applied binary logistic regression with the binary presence and absence of life forms, and used the probability value to define the resilience state and precipitation thresholds. Only 0.02% of the total forest covers in India are estimated least resilient observed in the dry regions in the trans-Himalaya. Whereas, the forest covers in the wet climate regimes as the Western Ghats, Western Himalaya, Eastern Ghats and NorthEast (NE) India are predicted highly resilient. The forest cover resilience curve saturates about 1400 mm precipitation, indicating majority forest covers in India are extremely resilient that can withstand large precipitation alterations in addition to the shorter drought periods. However, the TCC % loss and gain during 2000-2017 were observed dominantly in highly resilient forest covers areas may be indicating its anthropogenic origin. The precipitation thresholds of each life forms and forest cover resilience are critically important in ecological research. Moreover, the spatially explicit forest cover resilience map offers to integrate with other spatial and non-spatial data to frame uniform and improved conservation and management policies in India under the threats to climate alteration.
Environmental Monitoring and Assessment, Oct 17, 2022
Environmental Monitoring and Assessment, Nov 1, 2022
Flood Inundation mapping and satellite imagery monitoring are critical and effective responses du... more Flood Inundation mapping and satellite imagery monitoring are critical and effective responses during flood events. Mapping of a flood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV + VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The Normalized Difference Water Index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identified as 440.3 km2 in 201...
CRC Press eBooks, Oct 17, 2022
Environmental Monitoring and Assessment
CRC Press eBooks, Oct 17, 2022
Remote Sensing, Nov 25, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
World Academy of Science, Engineering and Technology, International Journal of Geological and Environmental Engineering, 2018
Environmental Monitoring and Assessment, 2019
more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landsca... more more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.
Hydrological sciences journal, Apr 26, 2024
Research Square (Research Square), Aug 30, 2023
Although the impact of climate change is slow, the transformation in climate regime can lead to a... more Although the impact of climate change is slow, the transformation in climate regime can lead to an ecosystem structure change from one stable to another stable state through intermediate bistable or metastable conditions. Therefore, the state transition or resilience in nature can never be sharp or be quanti ed with a single tipping point across the scales; rather, it should be understood through a tipping point range (tipping zone) across hysteresis loop(s). This study uses a satellite data-derived actual forest cover state map of India and high-resolution long-term average precipitation data to predict various tipping point range hysteresis for different forest cover states such as forest, scrubland, grassland and vegetation-less. The forest and vegetation-less states could have one-way, while scrubland and grassland have two-way transition probabilities with a probable shift in precipitation regime. In the dry conditions, the precipitation tipping zone predicted between 154 mm and 452 mm for the forest to scrubland transitions, while the reverse transition (from scrubland to forest) could occur in wet conditions between 1080 mm and 1400 mm. Similarly, the transition between scrubland and grassland, between grassland and vegetation-less state, may occur in contrasting dry and wet conditions, creating a hysteresis loop. The study indicates that the reversal of state change requires differential energy spent during the onward transition. The study proposes a novel characteristic curve demonstrating the varied precipitation tipping points/ zones, precipitation overlaps and distribution of the various life forms, and coexistence zones. The characteristic curve offers valuable inputs to explain life form transition and demarcate regions where forest enrichment and degradation may occur due to climate regime shifts. Such a spatially explicit database could provide vital inputs for planning forest cover restoration and management activities and mitigate the climate change impact.
Geoenvironmental disasters, Mar 26, 2024
Background Operational large-scale flood monitoring using publicly available satellite data is po... more Background Operational large-scale flood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) flood mapping day and night, even in cloudy monsoon seasons. Automated flood inundation area identification in near-real-time involves advanced geospatial data processing platforms, such as Google Earth Engine and robust methodology (Otsu's algorithm). Objectives The current study employs Sentinel-1 microwave data for flood extent mapping using machine learning (ML) algorithms in Assam State, India. We generated a flood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classification and Regression Tool (CART), and Support Vector Machine (SVM) ML algorithms were applied to generate the flood hazard map. Furthermore, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Summary The highest prediction accuracy was observed for the RF model (overall accuracy [OA] > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Over 26% of the study area indicated high flood hazard-prone areas, and approximately 60% showed high and severe potential for soil erosion due to flooding. The automated flood mapping platform is an essential resource for emergency responders and decision-makers, as it helps to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness ofemergency response efforts. Periodic flood inundation maps will help in long-term planning and policymaking, flood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, and climate resilience studies.
Tropical Ecology, May 17, 2023
For the last several years, the air quality of India's capital Delhi and surrounding region (NCR)... more For the last several years, the air quality of India's capital Delhi and surrounding region (NCR) has been degrading to a very poor and severe category during the autumn season. In addition to the various sources of air pollutants within the NCR region, the stubble burning in Punjab and Haryana states contributes to the poor air quality in this region. The current study employs the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products and TROPOspheric Monitoring Instrument (TROPOMI) products on carbon monoxide (CO) and nitrogen dioxide (NO 2) concentrations for spatio-temporal assessment of stubble burning and associated emissions. The analysis performed in the Google Earth Engine (GEE) platform indicated a nearly threefold rise in crop residue burning in November than in October, with 92.58% and 7.42% reported from Punjab and the Haryana states in November, respectively. The study highlights the availability of near-real-time remote sensing observations and the utility of the GEE platform for rapid assessment of stubble burning and emissions thereof, having the potential for developing mitigation strategies.
CRC Press eBooks, Oct 17, 2022
Research Square (Research Square), Jun 26, 2023
Operational large-scale ood monitoring using publicly available satellite data is possible with t... more Operational large-scale ood monitoring using publicly available satellite data is possible with the advent of Sentinel-1 microwave data, which enables near-real-time (at 6-day intervals) ood mapping day and night, even in cloudy monsoon seasons. Automated ood inundation area identi cation in near-real-time involves advanced geospatial data processing platforms such as Google Earth Engine (GEE) and robust methodology (Otsu's algorithm). The current study employs the Sentinel-1 microwave data for ood extent mapping using machine learning (ML) algorithms in Assam state, India. We generated a ood hazard and soil erosion susceptibility map by combining multi-source data on weather conditions and soil and terrain characteristics. Random Forest (RF), Classi cation and Regression Tool (CART) and Support Vector Machine (SVM) ML algorithms were applied to generate the ood hazard map. The highest prediction accuracy was observed for the RF model (overall accuracy [OA]: > 82%), followed by the SVM (OA > 82%) and CART (OA > 81%). Further, we employed the multicriteria evaluation (MCE) analytical hierarchical process (AHP) for soil erosion susceptibility mapping. Over 26% of the study area indicated high ood hazard-prone areas, and about 60% showed a high and severe potential for soil erosion due to ooding. The automated ood mapping platform is an essential resource for emergency responders and decision-makers, as they help to guide relief activities by identifying suitable regions and appropriate logistic route planning and improving the accuracy and timeliness of emergency response efforts. The periodic ood inundation maps will help in long-term planning and policymaking, ood management, soil and biodiversity conservation, land degradation, planning sustainable agriculture interventions, crop insurance, climate resilience studies, etc.
Environmental Monitoring and Assessment, Dec 1, 2019
Harvesting surface runoff during monsoon season for further utilization in crop production during... more Harvesting surface runoff during monsoon season for further utilization in crop production during the post-monsoon season is now becoming an effective solution to mitigate water scarcity problems. In this study, multi-criteria analysis-analytic hierarchy process (MCA-AHP)-based approach was envisaged for rainwater harvesting (RWH) zoning for a case study area, i.e., two districts of Odisha state situated in Eastern India. In spite of having a large irrigation network in the study area, major portion of these two densely populated and agriculture dominated districts remains fallow during dry seasons. Suitable locations for RWH structures such as farm pond, check dam, and percolation tanks were identified through Boolean conditions. RWH potential map was generated using different thematic layers namely land use/land cover (LU/LC), geomorphology, slope, stream density, soil type, and surface runoff. AHP-based MCA technique was used to integrate these thematic layers by assigning weights to the thematic layers and ranks to the individual theme features on 1-9 AHP Saaty's scale, considering their relative importance on RWH potential of the study area. The Natural Resources Conservation Service-Curve Number method was used to derive surface runoff using Climate Hazards Group Infra-Red Precipitation with Station rainfall data, satellite-derived LU/LC and FAO soil maps. In comparison to single cropped areas in 48% of the total study area, only 4% area was under double and triple cropped areas during 2016-2017. Moderate runoff was observed in > 50% of the study area dominated by agricultural landscape. Nearly 40%, 25.11%, and 32.45% of the study area indicated very high, high, and moderate RWH potentials, respectively. Particularly, very high RWH potential is observed in the eastern and central portion of the study area. The use of appropriate RWH structures in less irrigated areas will facilitate multiple cropping and will substitute the use of subsurface water harvesting practices. In these two districts, 73 check dams and 153 percolation tanks are prescribed along the 2nd-and 3rd-order streams. In coarser textured soil, nearly 306 km 2 and 608 km 2 areas are identified as moderate and highly suitable zones for percolation tank construction on ground, while in fine soil, around 786 km 2 area is identified as suitable for farm pond construction. Majority of the suitable zones for percolation tanks is found in Jajpur district, while suitability for adoption of farm pond and check dam is more in Bhadrak district. It is expected that implementation of the prescribed RWH structures can mitigate the threats of flood, drought, soil erosion, and enhance the soil moisture and cropping intensity significantly. The use
Biodiversity and Conservation, Apr 4, 2019
With the threats of climate change, the forest cover in India necessitates the study of its survi... more With the threats of climate change, the forest cover in India necessitates the study of its survival probability and the precipitation thresholds value trigger life form regime shift. With a mega-biodiversity ecosystem, the assessment of forest cover resilience will enhance the effectiveness of climate adaptive conservation strategies. In the current study, we have used an open source tree canopy cover percentage (TCC %) data to map the spatial distribution of forest, scrub, grassland and treeless, and to relate with long term annual precipitation. The natural occurrences forest, scrub, grassland and treeless were identified in the precipitation ranges as 340-8650 mm, 196-1018 mm, 167-995 mm, and 34-965 mm precipitation, respectively; whereas their mean values were observed as 1952 mm, 779 mm, 760 mm, and 322 mm respectively. We applied binary logistic regression with the binary presence and absence of life forms, and used the probability value to define the resilience state and precipitation thresholds. Only 0.02% of the total forest covers in India are estimated least resilient observed in the dry regions in the trans-Himalaya. Whereas, the forest covers in the wet climate regimes as the Western Ghats, Western Himalaya, Eastern Ghats and NorthEast (NE) India are predicted highly resilient. The forest cover resilience curve saturates about 1400 mm precipitation, indicating majority forest covers in India are extremely resilient that can withstand large precipitation alterations in addition to the shorter drought periods. However, the TCC % loss and gain during 2000-2017 were observed dominantly in highly resilient forest covers areas may be indicating its anthropogenic origin. The precipitation thresholds of each life forms and forest cover resilience are critically important in ecological research. Moreover, the spatially explicit forest cover resilience map offers to integrate with other spatial and non-spatial data to frame uniform and improved conservation and management policies in India under the threats to climate alteration.
Environmental Monitoring and Assessment, Oct 17, 2022
Environmental Monitoring and Assessment, Nov 1, 2022
Flood Inundation mapping and satellite imagery monitoring are critical and effective responses du... more Flood Inundation mapping and satellite imagery monitoring are critical and effective responses during flood events. Mapping of a flood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping. Flood mapping in Eastern India's Baitarani River Basin for 2018, 2019, 2020, 2021, and 2022 was performed in this study using Sentinel-1 imagery and Google Earth Engine with Otsu's algorithm. Different machine-learning algorithms were used to map the LULC of the study region. Dual polarizations VH and VV and their combinations VV×VH, VV + VH, VH-VV, VV-VH, VV/VH, and VH/VV were examined to identify non-water and water bodies. The Normalized Difference Water Index (NDWI) map derived from Sentinel-2 data validated the surface water inundation with 80% accuracy. The total inundated areas were identified as 440.3 km2 in 201...
CRC Press eBooks, Oct 17, 2022
Environmental Monitoring and Assessment
CRC Press eBooks, Oct 17, 2022
Remote Sensing, Nov 25, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
World Academy of Science, Engineering and Technology, International Journal of Geological and Environmental Engineering, 2018
Environmental Monitoring and Assessment, 2019
more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landsca... more more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.