Assessment of land use/land cover of Ludhiana district

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dc.contributor.advisorBiwalkar, Nilesh
dc.contributor.authorDhiman, Prince
dc.date.accessioned2023-06-30T06:32:35Z
dc.date.available2023-06-30T06:32:35Z
dc.date.issued2022
dc.description.abstractLand use/land cover (LULC) variations of the Ludhiana district were found using Landsat imageries of 2001, 2009, 2015, 2020, and 2022, studied in the cloud-based Google Earth Engine (GEE) platform. The Landsat imagery was filtered based on selected time period, study area and cloud cover less than 10 percent. NDVI, NDBI, MNDWI, and BSI indices along with slope and elevation layers from DEM were also generated and stacked into a composite imagery. Training and validation datasets were generated for each class (built-up, vegetation, bare soil, and water) for various periods by adding the ground control points (GCP) to the satellite imagery. LULC classification was done using a random forest classifier in GEE to get temporal LULC maps for the study area. By generating a confusion matrix for each of the classified imagery, validation was done. Field verification was done for the classified 2022 scenario. Land cover area change analysis was calculated for 2001, 2009, 2015, 2020, and 2022 in QGIS software. Prepared classified images were used to predict the future land use/land cover in the MOLUSCE plugin. Spatial variables like distance from the road, distance from waterways, slope, and elevation maps were also used for the prediction. Artificial Neural Network (ANN) was used for modeling and training purposes. The simulated map was prepared based on iteration/s and the model was validated, which showed excellent results. The predicted map of 2033 revealed that the built-up area in the Ludhiana sdistrict is increasing while the other classes are decreasing.en_US
dc.identifier.citationDhiman, Prince (2022). Assessment of land use/land cover of Ludhiana district (Unpublished M.Tech. thesis). Punjab Agricultural University, Ludhiana, Punjab, India.en_US
dc.identifier.urihttps://krishikosh.egranth.ac.in/handle/1/5810197869
dc.keywordsLandsat, Google Earth Engine, MOLUSCE, Artificial Neural Network, Predictionen_US
dc.language.isoEnglishen_US
dc.pages61en_US
dc.publisherPunjab Agricultural University, Ludhianaen_US
dc.research.problemAssessment of land use/land cover of Ludhiana districten_US
dc.subSoil and Water Engineeringen_US
dc.themeAssessment of land use/land cover of Ludhiana districten_US
dc.these.typeM.Tech.en_US
dc.titleAssessment of land use/land cover of Ludhiana districten_US
dc.typeThesisen_US
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