A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

Abstract

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

Apportioning Human-Induced and Climate-Induced Land Degradation: A Case of the Greater Sekhukhune District Municipality

“…NDVI data obtainable from NOAA-AVHRR sensors is used in various studies to analyse the dynamics and trends of vegetation production in different regions [10,39,40]. NDVI is computed using Near-InfraRed (NIR) and the visible RED spectral bands because healthy vegetation highly reflects in the NIR and absorbs in the RED bands, respectively [41].…”

Tracing and Determining the Duration of Illegal Sand Mining in Specific River Channels in the Limpopo Province

“…On the other hand, a TCC image is a combination of three bands that represent the three basic colours (red, green and blue) in order to generate a natural colour form of targeted features. The advantage of a TCC image is that the targeted features appear in their true natural form, which simplifies visual interpretability and the selection of training samples [32].…”

“…The difference between these bands can be used to indicate the presence or absence of vegetation [32,34,42]. Thus, this makes the NDVI a good indicator in reflecting seasonally or periodically dynamic changes in vegetation conditions that may have been triggered by mining activities.…”

“…However, later on, 22 new mining sites appeared, and they extended geographically to cover an area of 8.1 ha in the year 2022. ping is that the vegetation is highly reflective and absorptive in NIR and visible red, respectively. The difference between these bands can be used to indicate the presence or absence of vegetation [32,34,42]. Thus, this makes the NDVI a good indicator in reflecting seasonally or periodically dynamic changes in vegetation conditions that may have been triggered by mining activities.…”