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.Significance:
 
 Vegetation change detection is essential in environmental monitoring and management of an area. This study presents a simple approach for assessing vegetation change over time. The approach involveschange detection through the difference of spectral values of vegetation pixels of time series remotelysensed images.
Highlights
Time series remote sensing is an invaluable resource for dynamic monitoring of the environment over short and long time spans.[1]
This study presents a simple approach for assessing vegetation change over time
This is because of the ability of remote sensors to cover a large area in a short period of time as well as their capability to revisit and acquire data for the exact area, which optimises environmental monitoring of large areas based on time series image analysis.[2,3,4]
Summary
Time series remote sensing is an invaluable resource for dynamic monitoring of the environment over short and long time spans.[1]. Rugged and hilly terrains can be expensive and cumbersome to access for point measurements.[6] In addition to this, remotely sensed data of satellite platforms, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat, can be accessed at no cost as their data are readily available and accessible. Remote sensing has become an integral part of environmental monitoring because of its flexibility, efficiency, accessibility, and cost-effectiveness. Time series remote sensing analysis has been widely utilised in environmental monitoring and measurements of scale of land degradation[3,7,8,9,10,11,12], including monitoring of environmental improvement during and after mine rehabilitation processes[13,14,15]. The accuracy of this classification method depends heavily on the quality of training sites and the spectral distinctness of the classes
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