An Empirical Review on Image Dehazing Techniques for Change Detection of Land Cover

Kumar, Ch. Mohan Sai; Valarmathi, R.; Aswath, S.

doi:10.1109/asiancon51346.2021.9544917

Cited by 2 publications

(1 citation statement)

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“…For the calculation of the area covered by other land use classes, the "attribute table" was obtained after the land use classification was analyzed. The pixels covered by each type were divided by the total pixels of the study area to get a percentage change in cover for each class (Kumar et al, 2021).…”

Section: Change Detectionmentioning

confidence: 99%

Monitoring Land Use and Land Cover Changes Prospects Using Remote Sensing and GIS for Mahanadi River Delta, Orissa, India

Vaggela

Sanapala

Mokka

2022

Geoplanning J. Geomatics Plann.

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Natural landscapes have altered dramatically via anthropogenic activity, particularly in places that are heavily influenced by climate change and population increase, such as nation like India. It is crucial for sustainable development, particularly effective water management methods, to know about the influence of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing (RS) were employed for monitoring land use changes utilising quantum ArcGIS and ERDAS Imagine were done for prediction of LULC changes. This research studied the variations in LULC in the Mahanadi river basin delta, Orissa for the years 2010, 2015, and 2020. Landsat satellite pictures were employed to track the land use changes. For the categorization of Landsat images, maximum- likelihood supervised classification was applied. The broad categorization identifies four basic groups in the research region, including (i) waterbodies, (ii) agriculture fields (iii) forests (iv) barren lands (v) built-up areas, and (vi) aquaculture. The findings indicated a big growth in forests from the year 2010 to 2020, but a substantial increase in barren lands had happened by the year 2020, while built-up lands use has witnessed a quick climb. The kappa coefficient was used to measure the validity of identified photos, with an overall kappa coefficient of 0.82, 0.84, and 0.90 for the years 2010, 2015, and 2020, respectively. However, a large drop will occur in agriculture fields in the predicted years. The study effectively demonstrates LULC alterations showing substantial pattern of land use change in the Mahanadi delta. This information might be valuable for land use management and future planning in the region

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Section: Change Detectionmentioning

confidence: 99%