Lulc For Uttarakhand For Year 2000 Using Gee And Machine Learning Algorithm
Shail Ratna Bhatt, Y. P. Raiwani, Saeer Saran · Journal of Mountain Research · 2025
Temporal remote sensing satellite data is useful for natural resources and environment monitoring. Environmental landscape modelling, urban planning, and historical land cover change studies benefit from an accurate representation of landscape attributes and precise assessment of spatio-temporal changes. Manual methods analysing temporal satellite data, for identifying changes is a time-consuming process. Automated procedures analysing remote sensing satellite data using online platforms can be efficient solution in land use land cover (LULC) studies. Goal of this study is to assess LULC changes using online geospatial processing tools and multi-temporal remote sensing satellite data. Open-source Landsat data for 2000 is used for identifying the changes. Different regions including urban, semi-urban & rural are selected for assessing the performance of the proposed procedures. The region Uttrakhand have been studied to quantify long-term LULC changes and identify the driving reasons behind them. The LULC classification has been implemented on the Google Earth Engine (GEE) platform and results are obtained for Random Forest Algorithm. The RF classifier produced a better result . The quantitative study of LULC maps reveals divergent trends for different classes .