Remote Sensing and GIS Tool to Detect Hydrocarbon Prospect in Nagapattinam Sub Basin, India

Prabaharan, S. Beski; Ramalingam, M.; Subramani, T.; Lakshumanan, C.

doi:10.1007/s10706-012-9589-z

Cited by 7 publications

(3 citation statements)

References 5 publications

(2 reference statements)

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“…This is followed Includes open water sources such as rivers, ponds. indicates no agreement, 0-0.2 indicates slight agreement, 0.2-0.41 indicates fair agreement, 0.41-0.60 indicates moderate agreement, 0.60-0.80 indicates significant agreement, and 0.80-1.0 indicates practically perfect agreement (Maingi et al, 2002;Prabaharan et al, 2010;Wang et al, 2020). We utilized the Classify function within Google Earth Engine to generate a confusion matrix based on a 20% sample plot data that was split for accuracy assessment purposes.…”

Section: Methodological Framework For Classificationmentioning

confidence: 99%

Machine learning approach to detect Land Use Land Cover (LULC) change in Chure region of Sarlahi district, Nepal

Kafle,

K.C.,

Poudyal

et al. 2023

Arch. Agric. Environ. Sci.

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Land Use and Land Cover (LULC) changes are major global environmental issues, affecting ecological systems, climate change, and biodiversity. The Chure region of the Sarlahi district in Nepal is a critical ecological zone that has experienced significant LULC changes in recent years. In this study, our aim was to apply a machine learning approach to detect LULC changes in the Chure region using Google Earth Engine (GEE) and the Random Forest classifier. We utilized Landsat imagery of 2007 and 2022 to generate land cover maps for each year, which were then compared to identify changes over time. The major findings of this study indicate that the forest cover in the region has increased by approximately 16% over the past 15 years, while the agriculture and built-up areas have also shown a significant increase. Conversely, the barren land and water areas have decreased. The classifier obtained an overall accuracy of 85.7% and a kappa coefficient of 81.2% for the year 2022, and an overall accuracy of 82.2% and a kappa coefficient of 76.8% for the year 2007, which demonstrates the high accuracy of the proposed approach. The use of GEE and random forest classifiers provided a cost-effective and efficient method for analysing large datasets and producing accurate LULC maps. Our findings can inform policymakers and conservationists about the need for sustainable land management practices to preserve the ecological integrity of the Chure region. The approach can be applied to other regions to monitor and manage LULC changes and support effective conservation efforts.

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Section: Methodological Framework For Classificationmentioning

confidence: 99%

Machine learning approach to detect Land Use Land Cover (LULC) change in Chure region of Sarlahi district, Nepal

Kafle,

K.C.,

Poudyal

et al. 2023

Arch. Agric. Environ. Sci.

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“…As lineament is the surface and subsurface expression of faults which reflect the migration path for hydrocarbon leading to micro seepage on the surface therefore it is necessary to detect the prospective zones of hydrocarbon migration to the surface (Figure 11). Lineament extraction is considered as one of the most important parameters as far as petroliferous basin are concerned (Prabaharan et al, 2013). In the present study area surface Lineament extraction was performed (Figure 12) on the multispectral Landsat8 image using PCI Geomatica (Catalyst Professional) with optimum Threshold values mentioned below (Table 7).…”

Section: Lineamentsmentioning

confidence: 99%

Mapping potential hydrocarbon sites in Upper Assam shelf using geospatial and geo-structural techniques

Ashish Aggarwal, Devesh Rajendra Umredkar, Nirlipta Nayak

2023

tjjpt

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Remote Sensing and Spatial analysis tools are used to identify the hydrocarbon prospective zones as subsurface hydrocarbon traps are not perfectly sealed and moves in the upward direction from the reservoir to the surface in the form of traces and alter the vegetation which can be recognized through several techniques. Landsat-8 OLI and Sentinel 2A satellite images were used and spectral signatures such as Mineral Indices, Vegetation Index, Hydrothermal Alteration were analyzed to detect possible hydrocarbon sites in the study area. Mineral Indices namely clay carbonate, ferrous, and ferric indices were used to confirm and locate the hydrocarbon zones which is directly related to the detection of hydrocarbon micro seepage on the surface. Vegetation indices such as NDVI, GNDVI, SAVI, and Chlorophyll Index Green were investigated to narrow down the prospective areas, indicating chemical alteration in the soil and vegetation, giving an idea about the hydrocarbon prospective zones. Magnetic and gravity data were also collected to correlate these findings which shown anomalies in the study area and gave evidence for the prospect of hydrocarbon. Lineament extraction was performed to delineate the micro seepage zones in the study area. After combining all the data, the prospective zones of hydrocarbon and micro seepage in Upper Assam Shelf were located. Furthermore, a comparison study was conducted to identify prospective areas from analyzing both Sentinel 2A and Landsat 8 images. The results of the study demonstrated the effectiveness of remote sensing and spatial analysis tools in identifying hydrocarbon prospective areas in the region.

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“…Hydrocarbons constitute 80% of global energy and are vital non-renewable sources found in fossil fuels and biofuels 1,2 . Exploration and development of hydrocarbons require multidisciplinary data due to their presence in fractures and faults within sedimentary terrains 3,4 . Satellite remote sensing effectively assesses hydrocarbon-rich areas by analyzing geological inputs derived from remote sensing data 5 .…”

Section: Introductionmentioning

confidence: 99%

Mapping potential areas for hydrocarbon microseepage in Lower Agusan River Basin, Caraga, Philippines using Sentinel 2 imagery

Evangelio,

Miraballes,

Varela

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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Satellite remote sensing assesses hydrocarbon-rich areas in sedimentary basins by analyzing multi-spectral remote sensing-derived geological data. This study used geospatial datasets and techniques to map potential hydrocarbon microseepage areas in the Lower Agusan River Basin, Caraga, Philippines. The study employed parameters such as clay-carbonate alteration symptoms, ferric iron, ferrous iron minerals, Normalized Difference Vegetation Index (NDVI), and geological characteristics. Principal component analysis (PCA) was conducted on Sentinel 2 multispectral satellite imagery to detect mineral alteration phenomena. The PCA processing resulted in having PCA 3, with a value of -1.31862, to be used to extract the ferric iron mineralization. Moreover, PCA 1 has the highest eigenvector value for the ferrous iron band ratio, with a value of 1.34926. Lastly, PCA 2 extraction reveals clay carbonate mineralization with an eigenvector value of -1.19985. The fuzzy logic method was then applied to each parameter and integrated to determine the distribution of hydrocarbon microseepage areas. The results revealed that 83.3% of the study area exhibits a high potential for hydrocarbon microseepage, while 15.4% and 1.3% indicate moderate and low potential, respectively.

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Remote Sensing and GIS Tool to Detect Hydrocarbon Prospect in Nagapattinam Sub Basin, India

Cited by 7 publications

References 5 publications

Machine learning approach to detect Land Use Land Cover (LULC) change in Chure region of Sarlahi district, Nepal

Machine learning approach to detect Land Use Land Cover (LULC) change in Chure region of Sarlahi district, Nepal

Mapping potential hydrocarbon sites in Upper Assam shelf using geospatial and geo-structural techniques

Mapping potential areas for hydrocarbon microseepage in Lower Agusan River Basin, Caraga, Philippines using Sentinel 2 imagery

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