Normalized difference vegetation change index: a technique for detecting vegetation changes using Landsat imagery

• Main Authors: Rokni, Komeil, Musa, Tajul Ariffin
• Format: Article
• Published: Elsevier B.V. 2019
• Subjects: NA Architecture; SB469-476 Landcsape architecture
• Online Access: http://eprints.utm.my/id/eprint/87944/; http://dx.doi.org/10.1016/j.catena.2019.03.007

Vegetation indices have been developed to characterize and extract the Earth's vegetation cover from space using satellite images. For detection of vegetation changes, temporal images are usually independently analyzed or vegetation index differencing is implemented. In this study, a vegetation change index, named normalized difference vegetation change index (NDVCI), was developed to directly detect vegetation changes between two different time images with improved accuracy. The effectiveness of the proposed method to detect vegetation changes was evaluated in comparison with that of enhanced vegetation index (EVI) differencing and normalized difference vegetation index (NDVI) differencing methods at seven test sites under different environmental conditions in Iran, Malaysia, and Italy. Landsat imagery as one of the most widely used sources of data in remote sensing was used for this purpose. Overall accuracy, kappa coefficient, and omission and commission errors were calculated to assess the accuracy of the resulting change maps. The results demonstrated superiority and higher performance of NDVCI compared to EVI and NDVI differencing for detection of vegetation changes. In five out of the seven test sites, the classification accuracy of NDVCI was higher compared to that of the other methods. In contrast, the results revealed lower accuracy of EVI differencing for vegetation change detection at all the test sites, while NDVI differencing was superior at two of the test sites. In conclusion, the study demonstrated great performance of NDVCI for monitoring vegetation changes at different environmental conditions. Accordingly, this technique may improve the vegetation change detection in future studies.