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Environmental problems caused by extraction of minerals have long been a focus on environmental earth sciences. Vegetation growing conditions are an indirect indicator of the environmental problem in mining areas. A growing number of studies in recent years made substantial efforts to better utilize remote sensing for dynamic monitoring of vegetation growth conditions and the environment in mining areas. In this article, airborne and satellite hypersectral remote sensing data-HyMap and Hyperion images are used in the Mount Lyell mining area in Australia and Dexing copper mining area in China, respectively. Based on the analyses of biogeochemical effect of dominant minerals, the vegetation spectrum and vegetation indices, two hyperspectral indices: vegetation inferiority index (VII) and water absorption disrelated index (WDI) are employed to monitor the environment in the mining area. Experimental results indicate that VII can effectively distinguish the stressed and unstressed vegetation growth situation in mining areas. The sensitivity of VII to the vegetation growth condition is shown to be superior to the traditional vegetation index-NDVI. The other index, WDI, is capable of informing whether the target vegetation is affected by a certain mineral. It is an important index that can effectively distinguish the hematite areas that are covered with sparse vegetation. The successful applications of VII and WDI show that hyperspectral remote sensing provides a good method to effectively monitor and evaluate the vegetation and its ecological environment in mining areas.

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Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R 2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.LAI remote sensing retrieval methods have been widely investigated for several decades. During the past years, researchers have conducted studies on different vegetation types like broadleaf forest, coniferous forest and crop including soybean, maize and winter wheat [4][5][6]. LAI values vary with different vegetation types for different phenology. According to previous studies, LAI retrieval methods can be classified into three groups: (1) Physical methods like radiative transfer model (RTM), PROSPECT, SAIL models which study the physical mechanisms between light and vegetation to describe the light transmission on inner leaf [7,8] or canopy level [9,10]; (2) vegetation indices-based empirical methods, which engage on the relationships between spectral reflectance data and biophysical or biochemical parameters using statistical models [11][12][13][14][15]; and (3) the new research frontiers like machine learning methods, including artificial neural network and support vector machine to map LAI on large scales [16][17][18][19][20][21][22]. Among these approaches, vegetation indices-based empirical model has been widely used because of its simplicity and computational efficiency.Crop canopy reflectance is dependent both on biophysical parameters like LAI and biochemical parameters like chlorophyll content [23]. To avoid influence from interfering factors including external factors like atmospheric effect, soil background and intrinsic factors like leaf pigment content, leaf inclination angle, saturation effect, and other structural parameters, substantial efforts were conducted in improving classical VIs and developing new indices. Therefore, indices for different purposes were created. A...