Resonance Excitations in 7Be(d,p)8Be*to Address the Cosmological Lithium Problem

The anomaly in lithium abundance is a well-known unresolved problem in nuclear astrophysics. A recent revisit to the problem tried the avenue of resonance enhancement to account for the primordial 7 Li abundance in standard big-bang nucleosynthesis. Prior measurements of the 7 Be ( d , p ) 8 Be * reaction could not account for the individual contributions of the different excited states involved, particularly at higher energies close to the Q value of the reaction. We carried out an experiment at HIE-ISOLDE, CERN to study this reaction at E c . m . = 7.8 MeV , populating excitations up to 22 MeV in 8 Be for the first time. The angular distributions of the several excited states have been measured and the contributions of the higher excited states in the total cross section at the relevant big-bang energies were obtained by extrapolation to the Gamow window using the talys code. The results show that by including the contribution of the 16.63 MeV state, the maximum value of the total S factor inside the Gamow window comes out to be 167 MeV b as compared to earlier estimate of 100 MeV b. However, this still does not account for the lithium discrepancy.The authors thank the ISOLDE engineers in charge, RILIS team and Target Group at CERN for their support. D. G. acknowledges research funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 654002 (ENSAR2) and ISRO, Government of India under Grant No. ISRO/RES/2/378/15-16. O. T. would like to acknowledge the support by the Spanish Funding Agency (AEI/FEDER, EU) under the project PID2019-104390GB-I00. I. M. would like to acknowledge the support by the Ministry of Science, Innovation and Universities of Spain (Grant No. PGC2018-095640-B-I00). J. C. acknowledges grants from the Swedish Research Council (VR) under Contracts No. VR-2017-00637 and No. VR-2017-03986 as well as grants from the Royal Physiographical Society. J. P. would like to acknowledge the support by Institute for Basic Science (IBS-R031-D1). S. S. acknowledges support by the Academy of Finland (Grant No. 307685)

Assessing the transferability of machine learning algorithms using cloud computing and earth observation datasets for agricultural land use/cover mapping

Mapping of agricultural land use/cover was initiated since the past several decades for land use planning, change detection analysis, crop yield monitoring etc. using earth observation datasets and traditional parametric classifiers. Recently, machine learning, cloud computing, Google Earth Engine (GEE) and open source earth observation datasets widely used for fast, cost-efficient and precise agricultural land use/cover mapping and change detection analysis. Main objective of this study was to assess the transferability of the machine learning algorithms for land use/cover mapping using cloud computing and open source earth observation datasets. In this study, the Landsat TM (L5, L8) of 2018, 2009 and 1998 were selected and median reflectance of spectral bands in Kharif and Rabi season were used for the classification. In addition, three important machine learning algorithms such as Support Vector Machine with Radial Basis Function (SVM-RBF), Random forest (RF) and Classification and Regression Tree (CART) were selected to evaluate the performance in transferability for agricultural land use classification using GEE. Seven land use/cover classes such as built-up, cropland, fallow land, vegetation etc. were selected based on literature review and local land use classification scheme. In this classification, several strategies were employed such as feature extraction, feature selection, parameter tuning, sensitivity analysis on size of training samples, transferability analysis to assess the performance of the selected machine learning algorithms for land use/cover classification. The result shows that SVM-RBF outperforms the RF and CART for both spatial and temporal transferability analysis. This result is very helpful for agriculture and remote sensing scientist to suggest promising guideline to land use planner and policy-makers for efficient land use mapping, change detection analysis, land use planning and natural resource management

Evaluation of the performance of SAR and SAR-optical fused dataset for crop discrimination

Crop discrimination and acreage play a vital role in interpreting the cropping pattern, statistics of the produce and market value of each product. Sultan Battery is an area where a large amount of irrigated and rainfed paddy crops are grown along with Rubber, Arecanut and Coconut. In addition, the northern region of Sultan Battery is covered with evergreen and deciduous forest. In this study, the main objective is to evaluate the performance of optical and Synthetic Aperture Radar (SAR)-optical hybrid fusion imageries for crop discrimination in Sultan Bathery Taluk of Wayanad district in Kerala. Seven land use classes such as paddy, rubber, coconut, deciduous forest, evergreen forest, water bodies and others land use (e.g., built-up, barren etc.) were selected based on literature review and local land use classification policy. Both Sentinel-2A (optical) and sentinel-1A (SAR) satellite imageries of 2017 for Kharif season were used for classification using three machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Classification and Regression Trees (CART). Further, the performance of these techniques was also compared in order to select the best classifier. In addition, spectral indices and textural matrices (NDVI, GLCM) were extracted from the image and best features were selected using the sequential feature selection approach. Thus, 10-fold cross-validation was employed for parameter tuning of such classifiers to select best hyperparameters to improve the classification accuracy. Finally, best features, best hyperparameters were used for final classification and accuracy assessment. The results show that SVM outperforms the RF and CART and similarly, Optical+SAR datasets outperforms the optical and SAR satellite imageries. This study is very supportive for the earth observation scientists to support promising guideline to the agricultural scientist, policy-makers and local government for sustainable agriculture practice

Vulnerability and risk assessment mapping of Bhitarkanika national park, Odisha, India using machine-based embedded decision support system

The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15)

Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement

Urbanization is a growing challenge for city planners and policymakers who are continuously focusing on computer-based statistical models, and machine learning for a sustainable and livable city. The main objectives of this article were to develop a robust artificial intelligence-based hybrid geo-simulation model to support multi-scenario urban growth modeling for urban policy improvement. In this study, earth observation datasets, Artificial Neural Network-Multilayer Perceptron coupled with Markov Chain (MLP-Markov) and Cellular Automata and Markov Chain (CA-Markov) were applied and the best performance was measured for urban growth modeling. The result shows that the urban land use was 25.79, 31.40, 45.19, 89.22 and 147.96 square km in 1971, 1981, 1991, 2001 and 2011 which has been predicted for 2021, 2031, 2041 and 2051 based on the planned and unplanned development scenarios. The predicted urban land use of the planned development scenario is 242.10, 312.69, 363.80 and 400.72 square km while 242.91, 314.31, 366.23 and 403.98 square km of the unplanned development scenario during 2021, 2031, 2041 and 2051. The uncertainty result shows that overall agreement (84.99%) and other indices are higher, and disagreement is lower (15.01%) for MLP-Markov than the CA-Markov for the urban land use prediction. The hybrid geo-simulation models were tested over multiple urban planning indicators to understand urban growth patterns and related scenarios. The result shows that the geo-simulation model is extremely sensitive to the complex pattern of urban growth and disperse indicators over space and time. This study provides a promising guideline for urban planners and conservation scientists to implement a robust artificial intelligence-based hybrid geo-simulation model for compact, organized, and integrated land use-transportation development.HIGHLIGHTS Raipur city passing through sprawling and unplanned development due to uncontrol population growth and unmanaged development practices by local government and planning authority. Master plan of the development authority of the city failed to restrict unplanned development. Proposed urban growth model promote a compact, organized, integrated land use-transportation development and sustainable urban planning. Study, highlighted complexity in the modeling and suggested simplified Machine-based hybrid geo-simulation model for future urban growth for urban policy improvement

Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

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Delineation and classification of rural–urban fringe using geospatial technique and onboard DMSP–Operational Linescan System

This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow <39.50% of urbanity index. Shape index indicates that the city is going through unplanned development following cross to star shape growth

Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

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Prediction of soil erosion risk using earth observation data under recent emission scenarios of CMIP6

The earth observation data and CMIP6 models were used to predict plausible soil loss from the Ghaghara river basin. The decadal prediction of soil loss (28.64 ton/ha/year) was found high for SSP585 of CanESM5 during 2015–2025. However, the lower value was reported as 21.71 ton/ha/year for SSP245 of MRI-ESM2-0 during 2035–2045. The century level future rainfall erosivity factor was found lowest for SSP245, however highest for SSP585 of Access-ESM1-5, CanESM5, and IPSL-CM6A-LR. The SSP585 (Access-ESM1-5, CanESM5, and IPSL-CM6A-LR) have maximum soil erosion rate as 29.07, 28.03, and 28.0 ton/ha/year, respectively. For the SSP585, increments were observed as 35.93%, 31.04%, and 30%, respectively, compared to the baseline year (2014). Whereas, lowest was reported as 21.7 and 24.9 ton/ha/year and consequently the low increment as 1.31% and 16.55% for both scenarios of MRI-ESM2-0 compared to baseline. We observed that the soil erosion rate is aligned with the predicted rainfall erosivity factor