Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas

Spatio-temporal evolutionary analysis of surface ecological quality in Pingshuo open-cast mine area, China

The open-pit mining area is highly affected by human activities, which aggravate soil erosion and disturb surface ecology, bringing many problems and challenges to its environmental management and restoration, which has received widespread attention. The establishment of an objective, timely and quantitative remote sensing monitoring, and evaluation system for the spatio-temporal evolution of the surface ecological environment in the open-pit mining area is of great significance for its environmental protection, management decisions, and sustainable social development. Based on the Google Earth Engine (GEE) platform, this paper uses Landsat images to construct and calculate the remote sensing ecological index (RSEI) of the Pingshuo open-cast mine area (POMA) from 1990 to 2020 and monitor and evaluate its surface ecological environment. Combined with the Theil-Sen median, Mann-Kendall test, and Hurst index, the spatio-temporal process was analyzed. The results showed that the ecological environmental quality of the mining area first decreased and then increased from 1990 to 2020. 1990-2000 was a period of serious ecological degradation, followed by improvement. The overall improvement area reached 87.03%, and the degradation was concentrated in the coal mining area. Between 1990 and 2020, the Hurst index of the mining area was 0.452, indicating that the region has a fragile ecological environment and has difficult maintaining its stability. The global Moran's I mean value of the RSEI of the study area is 0.92, which combined with Moran's scatter plot to indicate that there is a strong positive spatial correlation rather than a random distribution of its ecological environment. During the study period, the impact on the climate of the ecological environmental change of POMA was weak, and human factors such as coal mining, land reclamation, and social construction were the main driving forces for the change in ecological quality. The results of this study reveal the changing trend of surface ecology in the mining area over the past 30 years, which is helpful for understanding its impact mechanism on ecological quality and provides support for the management of the region.

The Evolution of Landscape Patterns and Its Ecological Effects of Open-Pit Mining: A Case Study in the Heidaigou Mining Area, China

This paper investigates the impact of land use/cover type changes in the Haideigou open-pit coal mine on the evolution of the landscape patterns and ecological and environmental quality in the mine area, based on medium- and high-resolution remote sensing images in 2006, 2011, 2016, and 2021 using ArcGIS 10.5, Fragstats 4.2, and the Google Earth Engine platform. The results show that: (1) From 2006 to 2021, the area of cropland and waste dumps in the Heidaigou mining area changed significantly, the land use shifted in a single direction, and the overall land use change was unbalanced. (2) Through the analysis of landscape indicators, it was shown that the diversity of the landscape patches in the study area increased, connectivity decreased, and the patches became more fragmented. (3) Based on the changes in the mean value of the RSEI over the past 15 years, the ecological environment quality of the mining area deteriorated first and then improved. The quality of the ecological environment in the mining area was significantly affected by human activities. This study provides an important basis for achieving the sustainability and stability of ecological environmental development in mining areas.

Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains' Northern Slopes, Xinjiang, China

Accurately capturing the changing patterns of ecological quality in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) and researching its significant impacts responds to the requirements of high-quality sustainable urban development. In this study, the spatial and temporal distribution patterns of remote sensing ecological index (RSEI) were obtained by normalization and PCA transformation of four basic indicators based on Landsat images. It then employed geographic detectors to analyze the factors that influence ecological change. The result demonstrates that: (1) In the distribution of land use conversions and degrees of human disturbance, built-up land, principally urban land, and agricultural land, represented by dry land, are rising, while the shrinkage of grassland is the most substantial. The degree of human disturbance is increasing overall for glaciers. (2) The overall ecological environment of the northern slopes of Tianshan is relatively poor. Temporally, the ecological quality changes and fluctuates, with an overall rising trend. Spatially, ecological quality is low in the north and south and high in the center, with high values concentrated in the mountains and agriculture and low values in the Gobi and desert. However, on a large scale, the ecological quality of the Urumqi-Changji-Shihezi metropolitan area has worsened dramatically compared to other regions. (3) Driving factor detection showed that LST and NDVI were the most critical influencing factors, with an upward trend in the influence of WET. Typically, LST has the biggest influence on RSEI when interacting with NDVI. In terms of the broader region, the influence of social factors is smaller, but the role of human interference in the built-up area of the oasis city can be found to be more significant at large scales. The study shows that it is necessary to strengthen ecological conservation efforts in the UANSTM region, focusing on the impact of urban and agricultural land expansion on surface temperature and vegetation.

Active and Low-Cost Hyperspectral Imaging for the Spectral Analysis of a Low-Light Environment

Hyperspectral imaging is capable of capturing information beyond conventional RGB cameras; therefore, several applications of this have been found, such as material identification and spectral analysis. However, similar to many camera systems, most of the existing hyperspectral cameras are still passive imaging systems. Such systems require an external light source to illuminate the objects, to capture the spectral intensity. As a result, the collected images highly depend on the environment lighting and the imaging system cannot function in a dark or low-light environment. This work develops a prototype system for active hyperspectral imaging, which actively emits diverse single-wavelength light rays at a specific frequency when imaging. This concept has several advantages: first, using the controlled lighting, the magnitude of the individual bands is more standardized to extract reflectance information; second, the system is capable of focusing on the desired spectral range by adjusting the number and type of LEDs; third, an active system could be mechanically easier to manufacture, since it does not require complex band filters as used in passive systems. Three lab experiments show that such a design is feasible and could yield informative hyperspectral images in low light or dark environments: (1) spectral analysis: this system's hyperspectral images improve food ripening and stone type discernibility over RGB images; (2) interpretability: this system's hyperspectral images improve machine learning accuracy. Therefore, it can potentially benefit the academic and industry segments, such as geochemistry, earth science, subsurface energy, and mining.

The role of energy consumption and economic growth on the ecological environment in ASEAN countries

Currently, environmental degradation has become a global issue that needs to address frequently and also needs the attention of recent studies and regulators. Therefore, the present research examines the impact of energy consumption such as fossil fuel energy use, electric power use, and energy import along with economic growth on the ecological environment of ASEAN countries. The current study has gathered secondary data from world development indicators (WDI) from 1989 to 2019. The authors also used the pooled mean group (PMG) panel autoregressive distributed lag (ARDL) model and (FEM) to explore the association among the constructs. The findings revealed that fossil fuel energy use, electric power use, energy import, and economic growth have a positive association with carbon (CO2) emission and affect the ecological environment of ASEAN countries. This article guides the policymakers while formulating regulations regarding energy use and the ecological environment.

Assessing the Landscape Ecological Risks of Land-Use Change

In recent years, a changing global climate and the continuous expansion of the intensity and scope of human activities have led to regional differentiation in the surface landscape. This has caused numerous ecological risks under multiple pressure sources, gradually becoming an important factor restricting the sustainable development of economic and social health. With the continuous development of the social economy, land use and associated ecological risks will inevitably change. According to the forest transformation theory and the environmental Kuznets curve, we put forward the theoretical framework of ecological risk transformation of land-use change and took Zhangjiachuan County (China) as an example to verify it. Therefore, on the basis of Landsat satellite data, this paper used landscape structures to calculate an ecological risk index, and evaluated the ecological risk of land-use changes through pattern index analyses. The results show that, from 2000 to 2020, the ecological risk index of land-use change in Zhangjiachuan County exhibited an increasing and then decreasing trend, showing an overall "inverted U-shaped" trend of change consistent with the transformation theoretical framework of ecological risks of land use change. Secondly, in terms of patterns, the ecological risk of land-use change in Zhangjiachuan County showed a distribution feature of high in the west and low in the east. In 2000, high-risk areas were mainly concentrated in the central and northern areas, while low-risk areas were mainly concentrated in the eastern areas. From 2000 to 2015, the medium-risk areas expanded to the west and midwest, and the geographic centers of the risk areas were slightly offset. From 2015 to 2020, the overall pattern of ecological risk areas was basically the same as that of the previous stage, but the medium-risk areas were slightly reduced. In terms of quantity, from 2000 to 2015, the areas of the lowest risk level and low risk level decreased, while the areas of medium risk level, high risk level, and the highest risk level increased; from 2015 to 2020, the areas of the lowest risk level and low risk level increased, and the areas of medium risk level, high risk level, and highest risk level decreased. Lastly, the spatial aggregation of ecological risks in Zhangjiachuan County weakened slightly from 2000 to 2005, gradually increased from 2005 to 2015, and then slightly weakened from 2015 to 2020.

Investigating the environmental impacts of coal mining using remote sensing and in situ measurements in Ruqigou Coalfield, China

China is the largest producer and consumer of coal in the world. The extraction of coal is increasing intensively to meet the needs of the ever-increasing population and industries. However, coal mining has resulted in environmental changes, including deforestation, air, water, soil, and landform deterioration. This study investigates the impact of mining on the environment in Ruqigou coalfield by utilising in situ and remote sensing data. Field data collected include temperature, gas compositions, and water samples. Multi-temporal Landsat data of 1991, 2003, and 2019 were used in monitoring the impact of mining on different land covers, especially vegetation. A supervised classification was performed to assess the changes in land cover. In order to track the changes in vegetation, normalised difference vegetation index (NDVI) was employed. To study the changes in coal fire areas, thermal anomalies were extracted from the thermal infrared data using a dynamic thresholding technique. The results of in situ analyses show that water quality is unfit for domestic, industrial, and agricultural use. All the gas sampling sites emit noxious gases such as CO₂, CO, NO₂ and degrade the local air quality. The classified maps and vegetation indices show a significant decrease in vegetation. The thermal anomalies show an increase in fire areas over the years. Thus, it could be concluded that the conjunctive use of field-based measurements and remote sensing data can be a powerful tool for gaining a comprehensive understanding of the environmental impacts associated with large-scale mining.

Research on the Evaluation of Geological Environment Carrying Capacity Based on the AHP-CRITIC Empowerment Method

In this study, two districts, Erdaojiang District and Dongchang District, in Tonghua City, Jilin Province, were evaluated for their geological environment carrying capacity. A total of 14 evaluation indicators were selected from the three aspects of the geological environment, ecological environment, and social environment to make it more comprehensive to evaluate the carrying capacity of the geological environment. Using the AHP and CRITIC methods, the subjective weight and objective weight of each evaluation index are obtained, and the combined weight is calculated by game theory. When combined with the GIS and combined weights, the distribution map of the geological environment carrying capacity is obtained, and it is classified into four grades: excellent, good, medium, and poor. A comprehensive evaluation of the carrying capacity of the geological environment is carried out. The following conclusions are drawn: the overall carrying capacity of the geological environment in the study area is good, and the carrying capacity of individual areas is poor. The comparative analysis of the good and poor areas provides a scientific basis for future environmental governance and urban planning and provides a scientific basis for geological disasters and mines.

Remote Sensing Monitoring of Ecological-Economic Impacts in the Belt and Road Initiatives Mining Project: A Case Study in Sino Iron and Taldybulak Levoberezhny

Under the Belt and Road Initiatives, China’s overseas cooperation in constructing mining projects has developed rapidly. The development and utilization of mining resources are essential requirements for socio-economic development. At the same time, the ecological impacts of the exploitation and utilization of mining resources have increasingly aroused the widespread concern of the international community. This paper uses Landsat images, high-resolution images, and nighttime light (NTL) data to remotely monitor Sino Iron in Australia and Taldybulak Levoberezhny in Kyrgyzstan in different development periods to provide a reference for the rational development of mineral resources and environmental management. The results show that the Chinese enterprises have achieved good results in the ecological protection of the mining area during the construction period. The development of the mine has caused minor damage to the surrounding environment and has not destroyed the local natural ecological pattern. The different NTL indices show an overall rising trend, indicating that the construction of mines has dramatically promoted the socio-economic development of countries along the Belt and Road in both time and space. Therefore, relevant departments should practice green development in overseas projects, establish a scientific mine governance system, and promote a win-win economic growth and environmental governance situation.

Are Iron Tailings Suitable for Constructing the Soil Profile Configuration of Reclaimed Farmland? A Soil Quality Evaluation Based on Chronosequences

Iron tailings used as soil substitute materials to construct reclaimed farmland soil can effectively realize the large-scale resource utilization of iron tailings and reduce environmental risks. It is vital to understand the mechanisms affecting reclaimed soil quality and determine the appropriate pattern for reclamation with iron tailings. Thus, a soil quality index (SQI) was developed to evaluate the soil quality of reclaimed farmland with iron tailings in a semi-arid region. Soil samples were collected from two reclamation measures (20 cm subsoil + 20 cm iron tailings + 30 cm topsoil and 20 cm subsoil + 20 cm iron tailings + 50 cm topsoil) with reclamation years of 3 (R3), 5 (R5), and 10 (R10) at three soil depths (0–10, 10–20, and 20–30 cm) to measure 13 soil physicochemical properties in western Liaoning, China. Adjacent normal farmland (NF) acted as a reference. Results indicated that iron tailings were suitable for constructing the soil profile configuration of reclaimed farmland. SQI of reclaimed soil increased with the reclamation year, but it has not reached the NF level after 3 years, while it was better than NF after 5 years. The nutrient content of reclaimed soil increased with the reclamation year, but it still did not reach the NF level after 10 years. SQI of R10 (with 50 cm topsoil) was also better than NF but slightly lower than R5 (with 30 cm topsoil). For the semi-arid region with sticky soil texture, the topsoil thickness of reclamation was not the thicker the better, and 30 cm topsoil covered on iron tailings in western Liaoning could achieve a better reclamation effect than 50 cm.

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.

Comprehensive Evaluation of the Eco-Geological Environment in the Concentrated Mining Area of Mineral Resources

The urbanization rate in China has been increasing in recent years, and along with the increasing intensity of human engineering activities, ecological and geological (eco-geological) degradation have become key factors impeding sustainable urban development. Taking the concentrated mineral exploitation area of Tonghua City as an example, the distribution of mines in the area is concentrated and the spatial heterogeneity is significant. This paper includes 14 evaluation indicators in three aspects: eco-geological environment background, anthropogenic and mining engineering activities, and environmental pollution. Then, based on game theory combined with ANP-CV (Analytic Network Process and Coefficient of Variation), two empowerment methods, GIS spatial calculation is used to evaluate the eco-geological environment quality (EEQ). The results showed that the EEQ was divided into grades I–V from high to low, with areas of 21.13%, 30.35%, 27.00%, 14.30%, and 7.22%, respectively; the EEQ of the Hun River basin has a high spatial autocorrelation and low EEQ, and the EEQ grade of mines was divided on this basis; the hot spot analysis is useful for determining the EEQ, as well as for allocating mine restoration resources in a sensible manner. Finally, we propose countermeasures to improve EEQ, and this study can provide a scientific basis for ecological construction and geological environmental protection in Tonghua City.

Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.

Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data

Following vegetation reclamation in mining areas, secondary damage may occur at any time, especially in locations that have been mined for decades or even hundreds of years. Effective monitoring strategies are required to accurately assess plant growth and to detect the ecological effects of reclamation. Single satellite monitoring is often difficult to ensure vegetation monitoring needs, therefore multi-source remote sensing is preferred. Different sensor parameters and variation in spectral bands can lead to differences in the type of data obtained, and subsequently, methods for evaluating these differences are required for simultaneous sensor/band use. In this study, NDVI was selected to characterize the vegetation growth of the Antaibao Open-pit Coal Mine Dump by analyzing the correlation between different types of sensors (Landsat 8, HJ, Sentinel-2) and vegetation greenness in order to facilitate satellites’ replacement and supplement. Results show that: (1) Landsat 8 and Sentinel-2 satellite have a high relevance for monitoring the vegetation, but the correlation between these two sensors and HJ is relatively low, (2) the correlation between NDVI values varied by vegetation type, tree (R = 0.8698) > combined grass, shrub and tree (R = 0.7788) > grass (R = 0.7619) > shrub (R = 0.7282), and (3) the phenomenon of “Low value is high, high value is low” in the NDVI value with HJ satellite monitoring may have been caused by a weak signal strength and low sensitivity of the HJ sensor. Comparing the correlation of multi-source sensors to monitor the vegetation in the mining areas can be helpful to determine the alternative supplement of sensors through conversion formulas, which are helpful in realizing the long-term monitoring of dumps and detecting reclamation response in mining areas.

Analysis of Spatial Heterogeneity and Influencing Factors of Ecological Environment Quality in China's North-South Transitional Zone

The ecological environment is important for the natural disaster prevention of human society. The monitoring of ecological environment quality has far-reaching practical significance for the functional construction of ecosystem services and policy coordination. Based on Landsat 8 operational land image (OLI)/thermal infrared sensor (TIRS) remote sensing image data, this study selected the normalized vegetation (NDVI), tasseled cap transformation humidity (WI), bare soil (SI), construction index (NDSI), and land surface temperature (LST) indexes from the aspects of greenness, humidity, dryness, and heat. Using spatial principal component analysis (SPCA) and the remote sensing ecological index (RSEI) analyzed the spatial differentiation characteristics and influencing factors of the original remote sensing ecological index (RSEI₀). The results showed that: (1) the overall RSEI average value of the Qinling-Daba Mountains in 2017 was 0.61, and the ecological environment quality was at a “Good” level. Greenness contributed the most to the comprehensive index of the area, and vegetation distribution had a significant impact on the ecological environment quality of the study area. Heat is a secondary impact, and it has an inhibitory effect on habitat quality; (2) the overall distribution of regional ecological environment quality was quite different, with the ecological environment quality level showing a decreasing trend from low to high altitude; RSEI₀ spatial heterogeneity at the optimal scale of 2 km was the largest, and the nugget effect was 88% which indicated a high degree of spatial variability, mainly affected by structural factors; (3) Slope, relief amplitude, elevation, the proportion of high-vegetation area, proportion of construction land area, and average population density significantly impact the spatial differentiation of RSEI₀. The explanatory powers of slope and relief amplitude were 56.1% and 65.3%, respectively, which were the main factors affecting the spatial differentiation of the ecological environment quality in high undulation. The results can provide important scientific support for ecological environment construction and ecological restoration in the study area.

Research on Temporal and Spatial Resolution and the Driving Forces of Ecological Environment Quality in Coal Mining Areas Considering Topographic Correction

Over the last few years, under the combined effects of climate change and human factors, the ecological environment of coal mining areas has undergone tremendous changes. Therefore, the rapid and accurate quantitative assessments of the temporal and spatial evolution of the ecological environment quality is of great significance for the ecological restoration and development planning of coal mining areas. This study applied the ecological environment index after topographic correction to improve the remote sensing ecological index (RSEI). Based on a series of Landsat images, the ecological environment quality of Yangquan Coal Mine in Shanxi Province from 1987 to 2020 was monitored and evaluated by an improved remote sensing ecological index. The results show that after topographic correction, the topographic effect of the remote sensing ecological index was greatly reduced, and its practicability was improved. From 1987 to 2020, the ecological environment quality of Yangquan Coal Mine was improved, and the mean of the RSEI increased from 0.4294 to 0.6379. The ecological environment quality of the six coal mines in the study area was improved. Among the six coal gangue dumps, the ecological environmental quality of D1, D2, D3, and D4 has improved, and the ecological environment quality of D5 and D6 worsened. The percentages of improved, unchanged, and degraded ecological environment quality in the entire coal mining area were 77.08%, 0.99%, and 21.93%, respectively. The global Moran’s index was between 0.7929 and 0.9057, and it was shown that there was a strong positive correlation between the ecological environmental qualities of the study area, and that its spatial distribution was clustered rather than random. The LISA cluster map showed that the aggregation and dispersion degree of ecological environment quality was mainly high–high clustering and low–low clustering over the whole stage. During the study period, temperature and precipitation had limited impacts on the ecological environment quality of Yangquan Coal Mine, while the coal mining activities and urbanization construction seriously affected the local ecological environment quality and the implementation of ecological restoration policies, regulations, and measures was the main reason for the improvement of the ecological environment quality.

A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.

Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

Assessment of Agricultural Drought Risk in the Lancang-Mekong Region, South East Asia

Natural disasters worldwide regularly impact on human activities. As a frequently occurring natural disaster, drought has adverse impacts on agricultural production. The Lancang-Mekong River is a transnational river running through China and five Southeast Asian countries and it is a vital water resource for irrigation in the region. Drought in the Lancang-Mekong Region (LMR) has occurred frequently in recent years. Assessing the risk of drought in the region is essential for rational planning of agricultural production and formulation of drought relief measures. In this study, an assessment of drought risk has been achieved by combining the hazard and vulnerability assessments for drought. The assessment of the drought hazard depends mainly on the standardized precipitation index (SPI). The assessment of drought vulnerability takes into account various indicators such as climatic factors (e.g., crop water stress index), soil factors (e.g., available water capacity), and irrigation factors (e.g., irrigation support). The results reveal that: (1) Drought distribution in the LMR is characterized by a spreading of the drought to countries along the middle and lower reaches of the Mekong River. Countries located in the middle and lower reaches of the Mekong River are more prone to drought. Laos, Thailand, and Cambodia are the regions with higher and high-drought risk levels. (2) The spatial distributions for the drought hazard and the drought vulnerability in the LMR exhibit significant differences as evidenced in the mapping results. High-hazard and high-vulnerability areas are mainly distributed in the middle LMR, and the middle to higher hazard areas and the middle to higher vulnerability areas are mainly distributed in the south-central LMR, while the low-hazard areas and the low-vulnerability areas are mainly in the north. (3) The majority of planting areas for sugarcane, rice, and cassava are located in the high-hazard areas. The distributions of drought-prone and high-hazard areas also correspond to the main agricultural areas in the LMR.

Analysis on Decoupling between Urbanization Level and Urbanization Quality in China

After the first industrial revolution, urbanization level worldwide has increased rapidly. As the largest developing country in the world, China has witnessed a rapid improvement in its urbanization level in recent years. Nevertheless, the quality of urbanization has not been improved simultaneously. The relationship between the level and the quality of urbanization has thus become a hot topic for researchers. By introducing the concept and model of decoupling in the field of resources and environment into the analysis of urbanization level and quality, this study evaluated the relationship between urbanization level and urbanization quality of 285 prefecture-level cities in China from 2005 to 2014. It was found that: (1) The urbanization level and urbanization quality in China are unbalanced because the former is growing in a faster rate than the latter. The average urbanization level of China has increased by 27.40% from 42.99% in 2005 to 54.77% in 2014, while the increase of urbanization quality, however, is much slower with only 11.21% for the same period. It can be concluded that China has paid more attention to urbanization level than urbanization quality. (2) From 2005 to 2014, the relationship between China’s urbanization level and quality showed a total of eight decoupling states, of which the main ones were strong negative decoupling (non-ideal state) and growth negative decoupling (close to ideal state), accounting for 38.32% and 33.49% of the total number of samples in China, respectively. (3) The change of urbanization level and urbanization quality in China can be divided into two stages: for the first stage from 2005 to 2010, with rapid improvement in urbanization level, and the other from 2011 to 2014, with rapid improvement in urbanization quality. (4) Spatially, the areas with significant decoupling between urbanization level and urbanization quality are mainly distributed in underdeveloped areas such as the west; and the decoupling presents the spatial pattern of the highest in the west, the second in the middle, and the lowest in the east.