Assessment of mining activities on tree species and diversity in hilltop mining areas using Hyperion and Landsat data

Effects of gold and copper mining on the structure and diversity of the surrounding plant communities in Northeast Tiger and Leopard National Park

Introduction

Northeast China Tiger and Leopard National Park is home to the largest and only breeding family of wild tigers and leopards in China. The mining of open-pit gold and copper mines in the core zone might affect the surrounding forest ecosystem and the survival activities of wild tigers and leopards.

Methods

In order to understand the impacts of gold and copper mining on the structure and diversities of the surrounding plant communities, the vegetation of the forest layer, shrub layer and herb layer of the forest community in the original forest area, mining area, tailings area and restoration area of the Northeast China Tiger and Leopard National Park were investigated, and the influence of plant community structure on species diversity was also evaluated.

Results

This study concluded that there are 25 species belonging to 11 families, 16 genera of trees, 43 species belonging to 22 families, 35 genera of shrubs, and 57 species belonging to 23 families, 46 genera of herb in the sampling sites. There were no significant differences in the community structure characteristics and species diversities of the tree layer and the shrub layer in different operational areas. However, in herb layer, the heights, the coverage and the species diversity index were higher in the restoration area. Additionally, the community structure was one of the major factors that influence the diversity indices, which might be an important way for mining to impact plant diversity.

Discussion

Therefore, mining had some impacts on the structure and diversity of the surrounding plant communities, but the impacts did not reach a significant level. These results could provide scientific support for the management of the forest ecosystems around the mining area of Northeast Tiger and Leopard Park.

Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites

This paper focuses on vegetation health conditions (VHC) assessment and mapping using high resolution airborne hyperspectral AVIRIS-NG imagery and validated with field spectroscopy-based vegetation spectral data. It also quantified the effect of mining on vegetation health for geo-environmental impact assessment at a fine level scale. In this study, we have developed and modified vegetation indices (VIs) based model for VHC assessment and mapping in coal mining sites. We have used thirty narrow banded VIs based on the statistical measurement for suitable VIs identification. The highest Pearson's r, R², lowest RMSE, and P values indices have been used for VIs combined pixels analysis. The highest different (Healthy vs. unhealthy) vegetation combination index (VCI) has been selected for VHC assessment and mapping. We have also compared VIs model-based VHC results to ENVI (software) forest health tool and Spectral-based SAM classification results. The 1st VCI result showed the highest difference (72.07%) from other VCI. The AUC values of the ROC curve have shown a better fit for the VIs model (0.79) than Spectral classification (0.74), and ENVI FHT (0.68) based on VHC results. The VHC results showed that unhealthy vegetation classes are located at low distances from mine sites, and healthy vegetation classes are situated at high distances. It is also seen that there is a highly significant positive relationship (R² =0.70) between VHC classes and distance from mines. These results will provide a guideline for geo-environmental impact assessment in coal mining sites.

Predicting plant diversity in beach wetland downstream of Xiaolangdi reservoir with UAV and satellite multispectral images

Accurate and timely acquisition of plant diversity information downstream of the reservoir is helpful to understand the impact mechanism of reservoir operation on wetland plant diversity and formulate reasonable water and sediment regulation strategies. In this study, we conducted field surveys in two communities (Phragmites australis and Tamarix chinensis) at a typical wetland in the lower reaches of Xiaolangdi Reservoir on the Yellow River, and employed UAV and Gaofen 1B multispectral images to estimate the wetland plant diversity. Results showed that most diversity indexes had a higher correlation with the mean of spectral vegetation indexes (DVI, RVI, NDVI, SAVI, and MSAVI). The diversity indexes (C_SP and C_SW) constructed by relative coverage had a better overall correlation with spectral indexes. Interestingly, opposite correlations were found between Tamarix chinensis and Phragmites australis plots. We further gave a deep insight into the interspecific associations in Phragmites australis and Tamarix chinensis plots with the variance ratio (VR) method. It was found that plant species in Tamarix chinensis plot showed positive association (VR > 1), with a VR value of 1.095. Plant species in Phragmites australis plot had a negative association (VR < 1), with a VR value of 0.983. In Phragmites australis plot, C_SP and C_SW showed a significant decreasing trend (r² of 0.36 and 0.33 respectively, and P values less than 0.001) with the increase of Phragmites australis coverage. Moreover, the effect of spatial resolution was not significant on plant diversity estimation. Correlations between remote sensing indexes and diversity indexes were improved with the quadrat size changing from 2 m × 2 m to 4 m × 4 m. These findings demonstrate promising approaches for remote sensing of wetland plant diversity and indicate that the type of wetland plant community determines the relationship between diversity index and spectral index.

Ecological Restoration of Wetland Polluted by Heavy Metals in Xiangtan Manganese Mine Area

Due to manganese mining and slag accumulation, the geological structure of the wetland polluted by heavy metals in Xiangtan Manganese Mine area was seriously damaged, hence biodiversity loss, severe soil, and water pollution, as well as serious heavy metal pollution to food, vegetables, and other natural sources. In order to restore the ecological environment of the mining area, in 2015, the ecological restoration test of heavy metal polluted wetlands in the mining area was carried out. The results showed that the Mn content of different parts of Koelreuteria paniculata root from high to low order: fine root > small root > medium root > large root. The Mn content of different parts of Elaeocarpus decipiens root from high to low order: large root > medium root > small root > fine root. The order of Mn content in plants of the wetland restoration from high to low is as follows: Canna warscewiezii > Thalia dealbata > Boehmeria > Pontederia cordata > Typha orientalis > Nerium oleander > Softstem bulrush > Iris germanica > Acorus calamus > Arundo donax > Phragmites australis; The order of Internal Cu content from high to low is as follows: Acorus calamus > Thalia dealbata > Softstem bulrush > Canna warscewiezii > Typha orientalis > Arundo donax > Boehmeria > Iris germanica > Pontederia cordata > Nerium oleander > Phragmites australis; Zn content from high to low order is as follows: Canna warscewiezii > Acorus calamus > Thalia dealbata > Typha orientalis > Pontederia cordata > Arundo donax > Softstem bulrush > Iris germanica > Boehmeria > Phragmites australis > Nerium oleander; Cd content from high to low order is as follows: Phragmites australis > Softstem bulrush > Thalia dealbata > Nerium oleander > Boehmeria > Canna warscewiezii > Acorus calamus > Iris germanica > Typha orientalis > Pontederia cordata > Arundo donax. The results of this study have provided a theoretical basis and decision-making reference for the evaluation of heavy metals polluted wetland restoration, protection, and reconstruction effects and the selection of ecological restoration modes.

Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region

This work mainly focused on deforestation susceptibility (DS) assessment and its prediction based on statistical models (FR, LR & AHP) in the Saranda forest, India. Also, efforts had been made to quantify the effect of mining on deforestation. We had considered twenty-five (twenty present and five predicted) causative variables of deforestation, including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The predicted variables have been generated from different simulation models. Also, very high-resolution, Google Earth imagery have been used in time series analysis for deforestation from 1987 to 2020 data and generated dependent variable. On deforestation analysis, it was observed that a total of 4197.84 ha forest areas were lost in the study region due to illegal mining, agricultural and tribal people allied activities. The DS results have shown that of total existing forest area, 11.22% area were under very high, 16.08% under high, 16.18% under moderate, 24.25% under low, and 32.27% falls very low categories. According to the DS assessment and predicted results, the very high susceptibility classes were found at and close to mines, agricultural, roads and settlement's surrounding sites. The sensitivity analysis results also shown that some causative variables (maximum temperature (2.95%), minimum temperature (0.51%), rainfall (2.69%), LST (4.56%), hot spot (7.36%), aspect (1.14%), NDVI (2.64%), forest density (3.78%), lithology (3.26%), geomorphology (3.00%), distance from agricultural (19.40%), soil type (2.05%), solar radiation (5.97%), LULC (3.26%), drought (3.16%), altitude (2.85%), slope (5.97%), distance from mines (18.05%), roads (2.17%), and settlements (5.18%)) were more sensitive to deforestation. Most of the sensitive parameters showed a positive correlation with DS. The AUC values of the ROC curve had shown a better fit for AHP (0.72) than (0.69) FR and LR (0.68) models for present DS results. The correlation results had shown a good inverse relationship between DS and distance from mines and foliar dust concentration. This work will espouse the future work in the effective planning and management of the mining-affected forest region and predicted deforestation susceptibility would be helpful for forest ecosystem study and policymaking.