Unsupervised Monitoring Vegetation after the Closure of an Ore Processing Site with Multi-Temporal Optical Remote Sensing

Abstract

Ore processing is a source of soil heavy metal pollution. Vegetation traits (structural characteristics such as spatial cover and repartition; biochemical parameters—pigment and water contents, growth rate, phenological cycle…) and plant species identity are indirect and powerful indicators of residual contamination detection in soil. Multi-temporal multispectral satellite imagery, such as the Sentinel-2 time series, is an operational environment monitoring system widely used to access vegetation traits and ensure vegetation surveillance across large areas. For this purpose, methodology based on a multi-temporal fusion method at the feature level is applied to vegetation monitoring for several years from the closure and revegetation of an ore processing site. Features are defined by 26 spectral indices from the literature and seasonal and annual change detection maps are inferred. Three indices—CIred-edge (CIREDEDGE), IRECI (Inverted Red-Edge Chlorophyll Index) and PSRI (Plant Senescence Reflectance Index)—are particularly suitable for detecting changes spatially and temporally across the study area. The analysis is conducted separately for phyto-stabilized vegetation zones and natural vegetation zones. Global and specific changes are emphasized and explained by information provided by the site operator or meteorological conditions.

The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland

“…The advantage of its use consists in a linear dependence of the index value on the content of chlorophyll. Low values of the CI red-edge index may indicate a low content of chlorophyll and high plant stress [41]:…”

“…The last spectral index tested was the Heavy Metal Stress Sensitive Index (HMSSI), proposed by [41], and calculated according to formula (6). It combines the advantages of the two previous indices.…”

Unsupervised monitoring of vegetation in a surface coal mining region based on NDVI time series

“…For centroid-based clustering, the cluster number must be adapted to the application case (Fabre et al, 2020). The cluster number selected must represent the change traits of vegetation.…”

Exploitation of Spectral and Temporal Information for Mapping Plant Species in a Former Industrial Site

“…Here, different indices were tested on the time series of MS images. Vegetation indexes suited for Sentinel-2 temporal series and efficient for vegetation characterization in an impacted area, namely the Normalized Difference Vegetation Index (NDVI), the Plant Senescence Reflectance Index (PSRI), and the Inverted Red-Edge Chlorophyll Index (IRECI) were computed alone and combined (Fabre et al 2020). Since only vegetation was classified in this study, two NDVI variations, the Green Normalized Difference Vegetation Index (GNDVI) (Gitelson, Merzlyak, 1998) and the Wide Dynamic Range Vegetation Index (WDRVI) (Gitelson, 2004), and an index linked with chlorophyll content in leaves (Lichtenthaler et al, 1996) were also tested.…”

“…Also, IRECI is defined to estimate the slope of the red edge, a spectral characteristic directly linked to chlorophyll content (Frampton et al, 2013). On the contrary, PSRI is an index specifically designed for senescent vegetation (e.g., grass mixtures) sensitive to the ratio between carotenes and chlorophylls (Fabre et al, 2020). Using these 4 indices, 7 Sentinel-2 bands were exploited to highlight vegetation differences.…”