Presenter/Author Information

Nguyen Ba Duy
Tran Thi Huong Giang
Tong Si Son

Keywords

land cover change, change detection, change vector analysis

Start Date

1-7-2012 12:00 AM

Description

In recent years, the rapidly urbanization and industrialization has caused dramatic changes in land cover. The study on change detection of land cover plays a signification role in making strategy of the managers. Approaches in change detection research using remote sensing and GIS can be categorized into two main groups: post-classification change detection analysis and pre-classification changing spectral determination. The pre-classification approach is further divided into different methods such as: Image Differencing, Multi-date Principal Component Analysis (MPCA); Change Vector Analysis (CVA). Among these, the CVA method is based on two important indices in order to reveal the primary feature of land cover which can be named as vegetation index (NDVI) and barren land index (BI). The ability of applying method of CVA has been indicated by many specialists (Corey Baker et al., 2007; Ding Yuan et al., 1998; Eric F. Lambin et al., 1994). However, the NDVI indices selection and variability threshold haven’t been applied in change detection assessment. This paper proposes application to solve these two problems.

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Jul 1st, 12:00 AM

Study on vegetation indices selection and changing detection thresholds selection in Land cover change detection assessment using change vector analysis

In recent years, the rapidly urbanization and industrialization has caused dramatic changes in land cover. The study on change detection of land cover plays a signification role in making strategy of the managers. Approaches in change detection research using remote sensing and GIS can be categorized into two main groups: post-classification change detection analysis and pre-classification changing spectral determination. The pre-classification approach is further divided into different methods such as: Image Differencing, Multi-date Principal Component Analysis (MPCA); Change Vector Analysis (CVA). Among these, the CVA method is based on two important indices in order to reveal the primary feature of land cover which can be named as vegetation index (NDVI) and barren land index (BI). The ability of applying method of CVA has been indicated by many specialists (Corey Baker et al., 2007; Ding Yuan et al., 1998; Eric F. Lambin et al., 1994). However, the NDVI indices selection and variability threshold haven’t been applied in change detection assessment. This paper proposes application to solve these two problems.