Identifying Land Use Change Trends Using Multi-temporal Remote Sensing Data for the New Damietta City, Egypt

Abstract

The current study aims to utilize the use of multi-remote sensing data for land use land cover changes and trend analysis for the New Damietta city in Damietta governorate. Three different sensors were used in this study in different dates (SPOT-4 2007, SPOT-5 2011, and Kanopus-V1 2016). The FAO classification system (FAO-LCCS) was used to identify the different land use/cover classes in the study area. Results showed 13 main land use/land cover classes exist in the study area. The land use/land cover maps are produced for 2007, 2011 and 2016 with overall accuracies of 0.91, 0.92, 0.91 and kappa statistics of 0.88, 0.86 and 0.89 respectively. Results revealed that four different classes had a significant change over the study period. These classes are urban areas, cultivated lands, fish farms and bare areas. Trend analysis revealed that urban areas had the highest increase rate (+2.76 km2/year short term & +2.73 km2/year long-term) while cultivated land and bare areas suffer from the highest decrease rates (-1 km2/year short and long-term, -1.54 km2/year short-term and -1.59 km2/year long term respectively).