The study of land cover change using change vector approach integrated with unsupervised classification method: A case in duy tien (Vietnam)

Investigating information on land cover changes is an indispensable task in studies related to the variation of the environment. Land cover changes can be monitored using multi-temporal satellite images at different scales. The commonly used method is the post-classification change detection which can figure out the replacement of a land cover by the others. However, the magnitude and dimension of the changes are not been always exploited. This study employs the mixture of categorical and radiometric change methods to investigate the relations between land cover classes and the change magnitude, the change direction of land covers. Applying the Change Vector Analysis (CVA) method and unsupervised classification for two Landsat images acquired at the same day of years in 2000 and in 2017 in Duy Tien district, the experimental results show that a low magnitude of change occurs in the largest area of direction I and direction IV regarding the increase of Normalized Difference Vegetation Index (NDVI), but the opposite trend of (Bare soil Index) BI in the rice field. Alternately, the high magnitude of change is seen in the build-up class which occupies the smallest area with 1700 ha. The characterized changes produced by the CVA method provide a picture of change dynamics of land cover over the period of 2000-2017 in the study area. © 2020, Russian Geographical Society. All rights reserved.