Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

Quantitative assessment of the relative contributions of climate change and human activities to NPP changes in the Southwest Karst area of China

Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000 m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m^-2·year^-1 and 2.29 gC·m^-2·year^-1, respectively. The contributions rates of climatic change and human activities separately accounted for 35% and 65%. The contribution of human activities on NPP was much higher than that of climatic change in the Southwest Karst area, and the results suggested that we should focus on the role of human activities on NPP change.

Evaluating the Accuracy and Spatial Agreement of Five Global Land Cover Datasets in the Ecologically Vulnerable South China Karst

Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

Influence of management on vegetation restoration in coal waste dump after reclamation in semi-arid mining areas: examining ShengLi coalfield in Inner Mongolia, China

Conservation management usually carried out for a period of time to maintain the vegetation restoration of coal waste dumps after reclamation. However, the natural restoration of vegetation is faced with great challenges in semi-arid mining areas without management, due to the fragile ecological environment. Therefore, it is necessary to determine a reasonable management plan so that vegetation restoration can reach a stable state although the abandonment of the management. The objective was to explore the difference of vegetation restoration under different management modes in a typical semi-arid mining area. Two reclaimed coal waste dumps under different management measures, the north waste dump (ND) and the south waste dump (SD), were examined in the ShengLi coalfield in Inner Mongolia, China. The normalized difference vegetation index (NDVI) dataset based on Landsat series imagery was obtained using the Google Earth Engine (GEE) platform, and the landscape metrics were also calculated based on different vegetation coverage. The results proved that 3 years of management was not enough to stabilize vegetation restoration. A serious vegetation degradation occurred at the ND after the management stopped, with 40.1% of the pixels recorded a significant decrease (ρ = 0.05). The vegetation coverage became fragmented, and there was a tendency of succession to lower coverage. On the contrary, the vegetation restoration of SD was better under continuous management, and no significant degradation trend was observed. Furthermore, the results indicated that rainfall is the main influencing factor on vegetation restoration in semi-arid mining areas. The coal waste dump was more susceptible to weather change in natural restoration. By contrast, continuous management measures will resist such climate disturbances, even in dry years. This research will provide support for the formulation of the reclamation management plan of coal waste dumps in semi-arid mining areas.

Spatiotemporal analysis and potential impact factors of vegetation variation in the karst region of Southwest China

The karst region of Southwest China is one of the largest in the world. Due to the effects of human activities and climate change, rocky desertification has become the primary ecological disaster which has significantly hindered the economic growth in Southwest China. In recent decades, the Chinese government has carried out a number of ecological restoration projects in Southwest China. This study aims to analyze the changes in vegetation coverage and its main driving factors in the Southwest China and the karst region of Southwest China from 2001 to 2015 through trend analysis, Hurst index correlation analysis, correlation analysis, and residual analysis. The results showed that (1) both Southwest China and the karst region of Southwest China experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0028 year^-1 and 0.0029 year^-1, respectively; (2) the NDVI of the Southwest China and the karst region of Southwest China was stable, and the vegetation coverage areas showed low to medium fluctuations, accounting for 97.17% and 98.32% respectively; (3) the NDVI of the Southwest China and the karst region of Southwest China had strong sustainability, and the sustainable and improved regions account for 74.79% and 75.77% respectively; and (4) climate change had little influence on vegetation restoration, and human activities had a great influence on vegetation restoration. The relative contribution rates of human activities and climate change to vegetation NDVI changes in the Southwest China were 86% and 14%, respectively, and 90% and 10% in karst regions of Southwest China. Our findings contribute to a better understanding of the mechanisms of vegetation change in karst region and may provide scientific support for local vegetation restoration and conservation policies.

Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R2 = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran’s I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide.

46-Year (1973–2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification

Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km2) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km2 in 1973), declined by 88 km2 (11.8%) from 1973 to 1986 but had a recovery of 78 km2 (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km2 in 1973), increased by 86 km2 (46.5%) between 1973 and 1986 and decreased by 90 km2 (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km2 (1973) to 37 km2 (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China.

Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.

How Large-Scale Anthropogenic Activities Influence Vegetation Cover Change in China? A Review

Vegetation cover plays a key role in terrestrial ecosystem; therefore, it is important for researchers to investigate the variation and influencing factors of vegetation cover. China has experienced a large-scale vegetation cover change in recent years. We summarized the literature of vegetation cover change and revealed how large-scale anthropogenic activities influence vegetation cover change in China. Afforestation and intensification of cropland played a key role in large-scale greening. Urbanization showed a “U” shape to influence vegetation cover change. Mining and reclamation, land abandonment and land consolidation, and regional natural protection all had a unique influence on the change of vegetation cover. Indeed, the large-scale vegetation cover change was caused by interaction of anthropogenic factors and part human-driven climate change. Anthropogenic factors influenced climate change to indirectly alter the condition of plant growth. Interaction between climate change and human activities influence on vegetation cover still needs to be further investigated in the future.

Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

High Spatiotemporal Resolution Mapping of Surface Water in the Southwest Poyang Lake and Its Responses to Climate Oscillations

Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km² on 2 March 2017 to 1042.57 km² on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km² (i.e., increasing from 474.70 km² up to 708.12 km²). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales.