Evaluating Metal Effects on the Reflectance Spectra of Plant Leaves during Different Seasons in Post-Mining Areas, China

Abstract

This study examined the relationship between the leaf reflectance of different seasons and the concentration of heavy metal elements in leaves, such as Co, Cu, Mo, and Ni in a post-mining area. The reflectance spectra and leaf samples of three typical plants were measured and collected in a whole growth cycle (June, July, August, and September). The Red Edge Position (REP), Readjustment Normalized Difference Vegetation Index (RE-NDVI), and Photochemical Reflectance Index (PRI) were extracted and used to explore its relation with the heavy metals concentrations in leaves between different seasons. The results show that all three Vegetation Indices (VIs) were insensitive indicators for monitoring the metal effects of vegetation in different seasons, which showed similar trends. Based on this, the Continuum Removal Indices (CRIs) were proposed from the continuum removed approach and extended for detecting the effects of heavy metal pollution over a full growth cycle. The relationship between the metal concentrations and CRIs of different plants was respectively analyzed by Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR). It is found that a significant correlation exists between the band depth and the concentration of Cu and Ni based on the White birch data sets using the PLSR, resulting in a small deviation from the established relationships. Compared with VIs, the approach of coupling CRIs and multiple regressions was effective for improving the estimation accuracy. The presented study provides a detection model of leaf heavy metals that can be adapted to different growing cycles, even an arbitrary growing cycle.

Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties

“…However, no method has been yet proposed in literature. Previous studies focused on quantifying heavy metal contamination deriving from mining or agricultural activities, a very different case of soil contamination [27][28].…”

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

“…Previous studies have proven the interest of hyperspectral data (typically airborne or in-field measurements) for monitoring HM stress [ 9 , 26 , 94 ]. On the one hand, the approach, proposed to assess the potential of leaf optical properties for predicting Total Petroleum Hydrocarbon concentrations and based on the retrieval of leaf pigment using the PROSPECT model, could be applied in this context on a range of plant species over a full seasonal cycle in order to identify the most suitable conditions for detecting and predicting HM concentrations [ 10 , 19 ].…”

“…Many other indices have been proposed to compensate for the disadvantages of NDVI related to its sensitivity to soil brightness and its saturation when used for dense canopy cover [ 25 ]. Some of these indices, such as Photochemical Reflectance Index (PRI) or Ratio Vegetation Index (RVI), have been applied on hyperspectral or multispectral reflectance data to detect vegetation stress related to heavy metals [ 9 , 26 ]. Recent indices have also been proposed for HM-stress detection in rice crops such as Heavy Metal Stress Sensitive Index (HMSSI), which is the ratio between two existing red-edge indices CI red-edge (named CIREDEDGE) and PSRI ((Plant Senescence Reflectance Index) or Heavy metal Cd stress-sensitive Spectral Index (HCSI) [ 27 , 28 ].…”

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

“…The second method of quantification consists of linking the spectral signatures to TPH concentrations directly using multiple regression. Several approaches have been proposed in previous studies aiming to quantify heavy metals from vegetation reflectance in the field, especially partial least square regression (PLSR) [66,68]. PLSR is not suitable for TPH quantification [43], therefore, in this study, we proposed another regression approach, the elastic net (ENET [69]), which shows multiple advantages but remains underexploited in remote sensing of vegetation [70,71].…”