Assessment, prediction and future simulation of land cover dynamics using remote sensing and gis techniques

Abstract EN

Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of environmental hazards and risk, including global warming, earthquakes, landslide, erosion, flooding, etc.

These rapid changes adversely affect the environment and have potential economic and social impacts.

Thus, detailed accurate information about changes is urgently needed for updating LULC maps, to provide information for policymakers to support sustainable development, and the management of natural resources.

The purpose of this paper was to extract reliable land cover information from two Landsat imageries with moderate resolution (Landsat 5 TM and Landsat 8 OLI) over a 15 years period (1999 to 2014) using post-classification change detection analysis.

Traditional post-classification change detection approach based on pixel-based classification.

However, in this paper, both of pixel based and segment-based classification approaches are deployed and the appropriateness of the classifications to derive accurate land cover maps.

Then, Markov model is used to predict and simulate trends of LULC changes during the period of 1999 to 2014 and a future land cover map of the year 2050 are produced.

The results showed that image segmentations led to better classification accuracy (86.67% in 1999 and 94.09% in 2014).

Vice versa, traditional classification led to poorer accuracy (83.33% in 1999 and 93.33% in 2014).