Assessment and Predicting of LULC by Kappa Analysis and CA Markov model using RS and GIS Techniques in Udham Singh Nagar District, India

Arumugam, Thangavelu U; Yadav, Ram Lakhan; Kinattinkara, Sapna

doi:10.21203/rs.3.rs-141832/v1

Cited by 15 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accuracy makes the value of information of the resulted data to the user. This procedure employs three geospatial tools in ArcMap: Construct Accuracy Assessment Points, Modify Accuracy Assessment Points, and Calculate Confusion Matrix (Arumugam et al, 2021).…”

Section: -The Accuracy Assessment Estimator and The Kappa Coefficient...mentioning

confidence: 99%

Preparation of Curve Number Grid Map of Rawanduz River Basin in KRI Using GIS Techniques

Ahmed

Suleimany

2023

Polytechnic j.

View full text Add to dashboard Cite

The study is being conducted with the goal of applying various models and techniques for catchment delineation and providing the opportunity to determine the Curve Numbers (CN) for the Rawanduz river basin (2956 Km2), which is located in the northeastern section of Iraq, Kurdistan region, by utilizing Geographic Information System (GIS). Satellite data (Landsat 8 OLI) for 2020 was utilized to build land cover classes, with a classification accuracy of 90% and a Kappa index of 86.18%. The hydrologic soil map for the region is digitized from the Iraqi soil map. In specifically, Soil Conservation Service-Curve Number (SCS-CN) is technique that will be utilized to predict direct surface runoff. This technique incorporates a range of significant watershed features, such as Hydrological Soil Groups (HSG) and land use/land cover (LU/LC), to be utilized as input variables for numerous hydrologic processes. In terms of land use and hydrologic soil group combination, the lowest CN value was found to be 58 in forest areas, which corresponds to an increased ability of the soil to retain rainfall and will produce much less runoff, and the highest CN value was found to be 92 in medium-residential areas, which causes most of the rainfall to appear as runoff, with minimal losses.

show abstract

Section: -The Accuracy Assessment Estimator and The Kappa Coefficient...mentioning

confidence: 99%

Preparation of Curve Number Grid Map of Rawanduz River Basin in KRI Using GIS Techniques

Ahmed

Suleimany

2023

Polytechnic j.

View full text Add to dashboard Cite

show abstract

“…Contrarily, the overall accuracy quantifies the percentage of correctly identified pixels throughout the complete image (Arumugam et al 2021). The following equation ( 2) is used to compute it:…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Land Use/Cover Dynamics and Associated Impacts on Eutrophication, Land Surface Temperature, and Ecosystem Service Values: An Eco-Climatological Investigation of Chilika Lake, India.

Saini,

Gupta

et al. 2023

Preprint

View full text Add to dashboard Cite

Asia’s largest water lagoon, Chilika Lake, located in Odisha, India, is a vital economic and environmental source for the region. For the last few decades, many studies provided fragmented reports on the rapid deterioration of environmental conditions due to climate changes from intensive urban and industrial activities. The present study, for the first time provides comprehensive information about the transition of the landscape, which had occurred in the region with their associated impact on surface temperature and ecological services. Remote sensing techniques were utilised to classify land use and land cover dynamics over a period of 30 years (1991–2021). Landsat data were used to create maps for the Normalised Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Land Surface Temperature (LST) to apprehend the spatial and temporal variability and its ecological functions. The findings revealed that the lake area shrank by around 11.7% in the last three decades, owing to the conversion of mangrove and wetland areas into agricultural and urban land. The NDVI maps revealed a decline in plant cover in the catchment region, indicating a decrease in the lake's ecological services, whereas the NDWI maps indicated a reduction in the catchment area of the lake, and these maps highlighted eutrophication spots on lake surface images due to increasing nutrient concentrations and decreased water quality in the lake. LST maps indicated a rise in surface temperature, which might be attributed to decreasing vegetative cover and water bodies, which tend to alter the lake's thermal regime. The CA-Markov model forecast map for 2026 suggested that the lake area would further decrease while urban and agricultural land would rise. The prediction map for 2021 compared to the existing map showed a slight improvement in lake levels caused by the pandemic, limited urban activity, and other possibilities.

show abstract

“…As the present year data on area covered by crops and area of built-up land is not available, previous year (2019-20) data were taken from the Directorate of Economics and Statistics official website (https://eands.dacnet.nic.in) whereas actual forest area was retrieved from the Forest Survey of India, 2019 report. Arumugam et al [21] reported that the waterbodies area in Udham Singh Nagar is 6,272 ha. As the data regarding the 'Other' class was not available, this class is not taken in comparison.…”

Section: Data Sources and Collectionmentioning

confidence: 99%

Crop Classification and Crop Acreage Estimation Using Geospatial Technology in the Upper Gangetic Plains of Uttarakhand, India

Hegde,

Ranjan,

Jha

et al. 2023

IJECC

View full text Add to dashboard Cite

Timely and accurate crop mapping plays an important role in food security, economic and environmental policies. Crop maps are also utilized for agro-environmental assessments and crop water usage monitoring. As a result, accurate and timely crop classification is essential for agricultural management and monitoring. Because it provides periodic large-scale observations of ground objects, satellite remote sensing has been regarded as an advanced tool to characterize crop types and their distributions on a regional scale. High-resolution, multispectral images of October 13, 2021, December 7, 2021 and March 6, 2022 of sentinel-2 satellite released by the European Space Agency (ESA) have been used for classification. Ground truth points have been collected manually with the android app ‘Mapmarker’ and Google Earth. Further, pre-processing of satellite imageries such as resampling, mosaicking and sub-setting have been done with the Sentinel Application Platform (SNAP) software. Crop classification and acreage estimation was conducted using Maximum Likelihood approach. It is the first time an attempt was made to estimate cropping intensity using geospatial technology in the upper Gangetic plains of Uttarakhand state. Rice and sugarcane areas of 108,884 ha and 11,479 ha, respectively, were estimated from the October 13, 2021 image. Pea crop area was estimated as 6,227 ha from December 7, 2021 image. Using March 6, 2022 image, wheat and mustard crop areas were estimated as 105,334 ha and 2,018 ha, respectively.

show abstract

Assessment and Predicting of LULC by Kappa Analysis and CA Markov model using RS and GIS Techniques in Udham Singh Nagar District, India

Cited by 15 publications

References 24 publications

Preparation of Curve Number Grid Map of Rawanduz River Basin in KRI Using GIS Techniques

Preparation of Curve Number Grid Map of Rawanduz River Basin in KRI Using GIS Techniques

Land Use/Cover Dynamics and Associated Impacts on Eutrophication, Land Surface Temperature, and Ecosystem Service Values: An Eco-Climatological Investigation of Chilika Lake, India.

Crop Classification and Crop Acreage Estimation Using Geospatial Technology in the Upper Gangetic Plains of Uttarakhand, India

Contact Info

Product

Resources

About