Spatiotemporal variation of vegetation coverage and its associated influence factor analysis in the Yangtze River Delta, eastern China

Identification of suitable vegetation restoration areas and carrying capacity thresholds on the Loess Plateau

The Loess Plateau is one of the most ecologically fragile areas in the world. It has long faced the twin dilemmas of ecological degradation and water resource shortage. In recent decades, large-scale vegetation restoration projects have been carried out on the Loess Plateau with the aim of improving the ecological environment. However, as the vegetation cover increases, the water consumption of vegetation also increases, which further exacerbates the problem of water resource shortages. In order to effectively utilize water resources and balance the relationship between forests and water use, suitable vegetation restoration areas were identified on the Loess Plateau by constructing a vegetation suitability evaluation model based on multiple index factors (precipitation, temperature, altitude, slope, aspect, soil texture, soil depth, soil organic matter and ecological water consumption). The suitable restoration area results are given as follows: trees comprised 18.58% of the total vegetation coverage area and were mainly distributed across the central and southern Loess Plateau; shrublands comprised 32.58% of the total vegetation coverage area and were mainly distributed across the northern part of the Loess Plateau; and grasslands comprised 48.84% of the total vegetation coverage area and were mainly distributed across the western and northeastern regions of the Loess Plateau. On this basis, the Eagleson model was used to identify the bearing capacity of vegetation in the suitable restoration area. The optimal simulated vegetation coverage values of the suitable restoration areas are given as follows: grassland, 0.246-1.000; shrubland, 0.186-0.783; and trees, 0.137-0.868. These results can help guide the local ecological environment construction, offer theoretical support for the ecological restoration of similar areas and provide a scientific reference for the effective use of water resources and vegetation restoration on the Loess Plateau.

An improved framework for quantifying the contribution of climatic and anthropogenic factors to vegetation dynamics - A case study of China

Accurately quantifying the impact of climatic and anthropogenic factors on vegetation change is critical for developing and evaluating ecological management strategies. However, most presented studies typically ignore the climate temporal effects and assume that all pixels are affected by both climate change (CC) and human activities (HA), which often introduce uncertainties. In this study, Leaf area index (LAI), temperature, precipitation, solar radiation, and land cover data from 1982 to 2020 were used to detect and attribute vegetation dynamics in China. We used partial correlation analysis, generalize linear model, trend analysis, and improved RESTREND to analyze the spatiotemporal variation characteristics of vegetation LAI from 1982 to 2020 and to quantify the effects of CC and HA on vegetation dynamics since the implementation of the Grain for Green Project (GGP). The results indicate that significant vegetation greening appeared in most areas (66.2%) between 2000 and 2020. Both CC and HA have positive effects on vegetation greening. In arid and semi-arid regions, precipitation was the primary driver of vegetation change, while in high-latitude areas of southern and southwestern China, temperature was the primary determinant. After considering the temporal effects, the explanatory power of climate variables for vegetation dynamics increased by 4.0% compared to ignoring the temporal effects, accounting for 72.81%. After dividing the pixels into those affected by CC and those affected by both CC and HA, the contribution of HA was decreased from 31.19% to 25.96%. Although the contribution of HA is lower than that of CC, ecological engineering has effectively promoted vegetation greening. These research findings provide scientific data and theoretical basis for ecological environment protection and natural resource management.

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth

Introduction

As an exceptional geographical entity, the vegetation of the Qinghai-Tibetan Plateau (QTP) exhibits high sensitivity to climate change. The Baima Snow Mountain National Nature Reserve (BNNR) is located in the south-eastern sector of the QTP, serving as a transition area from sub-tropical evergreen broadleaf forest to high-mountain vegetation. However, there has been limited exploration into predicting the temporal and spatial variability of vegetation cover using anti-interference methods to address outliers in long-term historical data. Additionally, the correlation between these variables and environmental factors in natural forests with complex terrain has rarely been analyzed.

Methods

This study has developed an advanced approach based on TS (Theil-Sen slope estimator) MK (Mann-Kendall test)-FVC (fractional vegetation cover) to accurately evaluate and predict the time and spatial shifts in FVC within the BNNR, utilizing the GEE (Google Earth Engine). The satellite data utilized in this paper consisted of Landsat images spanning from 1986 to2020. By integrating TS and MK methodologies to monitor and assess the FVC trend, the Hurst index was employed to forecast FVC. Furthermore, the association between FVC and topographic factors was evaluated, the partial correlation between FVC and climatic influences was analyzed at the pixel level (30×30m).

Results and discussion

Here are the results of this research: (1) Overall, the FVC of the BNNR exhibits a growth trend, with the mean FVC value increasing from 59.40% in 1986 to 68.67% in 2020. (2) The results based on the TS-MK algorithm showed that the percentage of the area of the study area with an increasing and decreasing trend was 59.03% (significant increase of 28.04%) and 22.13% (significant decrease of 6.42%), respectively. The coupling of the Hurst exponent with the Theil-Sen slope estimator suggests that the majority of regions within the BNNR are projected to sustain an upward trend in FVC in the future. (3) Overlaying the outcomes of TS-MK with the terrain factors revealed that the FVC changes were notably influenced by elevation. The partial correlation analysis between climate factors and vegetation changes indicated that temperature exerts a significant influence on vegetation cover, demonstrating a high spatial correlation.

Examining the complex relationship between Urbanization and ecological environment in ecologically fragile areas: a case study in Southwest China

The sustainable development of ecologically fragile areas and the implementation of regional coordinated development strategies cannot be separated from the coordinated development and common progress of urbanization and the ecological environment, and this is particularly the case in Southwest China. This study examines the interplay between urbanization and the ecological environment across 26 cities in Southwest China from 2009 to 2019, utilizing 30 statistical indicators to analyze their coupling coordination relationship and its spatiotemporal evolution. The Entropy TOPSIS method, the coupling coordination degree model, and the obstacle factors model were used to calculate the subsystem score, coupling coordination degree, and obstacle factors, respectively. Our findings reveal an upward trajectory in urbanization scores across the 26 cities, juxtaposed with a fluctuating downward trend in ecological environment scores. The coupling coordination degree of urbanization and ecological environment in most cities maintained a rapid upward trend and showed spatial distribution characteristics of "strong core, weak middle, and edge." Moreover, our analysis identified public transport facilities, aggregate purchasing power, and cultural supply service services as primary obstacle factors impeding the development of coupling coordination degrees. These research results offer valuable insights for informing future endeavors in achieving high-quality development and fostering ecological civilization.

Evaluation of vegetation restoration effectiveness along the Yangtze River shoreline and its response to land use changes

Assessing the effectiveness of vegetation restoration along the Yangtze River shoreline and exploring its relationship with land use changes are imperative for providing recommendations for sustainable management and environmental protection. However, the impact of vegetation restoration post-implementation of the Yangtze River Conservation Project remains uncertain. In this study, utilizing Sentinel-2 satellite imagery and Dynamic World land use data from pre- (2016) and post- (2022) Yangtze River Conservation Project periods, pixel-based binary models, transition matrices, and geographically weighted regression models were employed to analyze the status and evolution of vegetation coverage along the Yangtze River shoreline. The results indicated that there had been an increase in the area covered by high and high-medium vegetation levels. The proportion of vegetation cover shifting to better was 4201.87 km2 (35.68%). Hotspots of vegetation coverage improvement were predominantly located along the Yangtze River. Moreover, areas witnessing enhanced vegetation coverage experienced notable land use changes, notably the conversion of water to crops (126.93 km2, 22.79%), trees to crops (59.93 km2, 10.76%), and crops to built area (59.93 km2, 10.76%). Notably, the conversion between crops and built area emerged as a significant factor influencing vegetation coverage improvement, with average regression coefficients of 0.68 and 0.50, respectively. These outcomes underscore the significance of this study in guiding ecological environmental protection and sustainable management along the Yangtze River shoreline.

Impacts of water surge from mountain railroad tunnels on ecological environments based on the RSEI model

Tunnels play a significant role in mountain railroad routes and increase the efficiency of railroad traffic. However, water surge from tunnels can seriously impact the ecological environment during the construction period. This study selected a typical mountain railroad tunnel in southwest China and used the remote sensing ecological index (RSEI) to evaluate the changes in the ecological environment along the tunnel surge water path and relate the impacts to the main influencing factors throughout the whole tunnel construction cycle. The following conclusions were obtained: (1) The RSEI from 2005 to 2020 mostly ranged within 0.25-0.75. The most severe ecological disturbances occurred in areas directly affected by tunnel construction and along the water surge path. (2) In addition to affecting the surrounding ecological environment during the construction period, tunnel surge water continued to adversely affect the environment during the post-construction period. (3) In the post-construction period, the areas 300-450 m and 750-850 m from the tunnel exit had the largest changes in RSEI. This study provides scientific evidence to support environmental planning for mountain railroad tunnel construction, which is necessary to achieve both efficient tunnel construction and environmental protection.

Analysis of Net Primary Productivity Variation and Quantitative Assessment of Driving Forces-A Case Study of the Yangtze River Basin

Net primary productivity (NPP) can indirectly reflect vegetation's capacity for CO2 fixation, but its spatiotemporal dynamics are subject to alterations to some extent due to the influences of climate change and human activities. In this study, NPP is used as an indicator to investigate vegetarian carbon ability changes in the vital ecosystems of the Yangtze River Basin (YRB) in China. We also explored the NPP responses to climate change and human activities. We conducted a comprehensive analysis of the temporal dynamics and spatial variations in NPP within the YRB ecosystems from 2003 to 2020. Furthermore, we employed residual analysis to quantitatively assess the contributions of climate factors and human activities to NPP changes. The research findings are as follows: (1) Over the 18-year period, the average NPP within the basin amounted to 543.95 gC/m2, displaying a noticeable fluctuating upward trend with a growth rate of approximately 3.1 gC/m2; (2) The areas exhibiting an increasing trend in NPP account for 82.55% of the total study area. Regions with relatively high stability in the basin covered 62.36% of the total area, while areas with low stability accounted for 2.22%, mainly situated in the Hengduan Mountains of the western Sichuan Plateau; (3) NPP improvement was jointly driven by human activities and climate change, with human activities contributing more significantly to NPP growth. Specifically, the contributions were 65.39% in total, with human activities contributing 59.28% and climate change contributing 40.01%. This study provides an objective assessment of the contributions of human activities and climate change to vegetation productivity, offering crucial insights for future ecosystem development and environmental planning.

Prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and quantitative analysis of driving factors

The prediction of the spatiotemporal dynamic evolution of vegetation cover in the Huainan mining area and the quantitative evaluation of its driving factors are of great significance for protecting and restoring the environment in this area. This study uses the Landsat 5 TM and Landsat 8 OLI time-series data to estimate the vegetation cover and uses the transition matrix to analyze the spatiotemporal transfer of vegetation cover from 1989 to 2004, 2004 to 2021, and 2021 to 2030. In addition, a structural equation model (SEM) was established in this study to assess the driving factors of vegetation cover. The quantitative analysis and the cellular automata (CA)-Markov model were performed to predict the future vegetation cover in the Huainan mining area. The results are as follows: (1) In different periods, the vegetation cover types were mainly high cover types transferred to other vegetation cover types; (2) human activities are the key factors affecting the vegetation growth, while topographical factor is the most influential factor promoting the vegetation growth; (3) highly consistent CA-Markov and multi-criteria evaluation (MCE) predicted results of vegetation cover in 2030 compared to that in 2021. The proportion of bare soil and low cover types had increased significantly, mainly concentrated in the internal area of the mines. The prediction of the spatiotemporal evolution of vegetation cover in the Huainan mining area and the quantitative change in driving factors are of significant importance for the restoration of the environment in mining areas.

Response of vegetation variation to climate change and human activities in semi-arid swamps

Vegetation is a sensitive factor in marsh ecosystems, which can provide nesting sites, foraging areas, and hiding places for waterfowl and can affect their survival environment. The Jilin Momoge National Nature Reserve, which consists of large areas of marshes, is located in the semi-arid region of northeast China and is an important stopover site for the critically endangered species of the Siberian Crane (Grus leucogeranus). Global climate change, extreme droughts and floods, and large differences in evaporation and precipitation in this region can cause rapid vegetation succession. In recent years, increased grain production and river-lake connectivity projects carried out in this area to increase grain outputs and restore wetlands have caused significant changes in the hydrological and landscape patterns. Therefore, research on the response of variation trends in vegetation patterns to the main driving factors (climate change and human activities) is critical for the conservation of the Siberian Crane. Based on the Google Earth Engine (GEE) platform, we obtained and processed the Normalized difference vegetation index (NDVI) data of the study area during the peak summer vegetation period for each year from 1984 to 2020, estimated the annual vegetation cover using Maximum value composites (MVC) method and the image dichotomy method, calculated and analyzed the spatial and temporal trends of vegetation cover, explored the response of vegetation cover change in terms of climate change and human activities, and quantified the relative contribution of both. The results revealed that first, from the spatial and temporal changes, the average annual growth rate of regional vegetation was 0.002/a, and 71.14% of the study area was improved. The vegetation cover showed a trend of degradation and then recovery, in which the percentage of high vegetation cover area decreased from 51.22% (1984-2000) to 28.33% (2001-2005), and then recovered to 55.69% (2006-2020). Second, among climate change factors, precipitation was more correlated with the growth of vegetation in the study area than temperature, and the increase in precipitation during the growing season could promote the growth of marsh vegetation in the Momoge Reserve. Third, overall, human activities have contributed to the improvement of vegetation cover in the study area with the implementation of important ecological projects, such as the return of farmland to wetlands, the return of grazing to grass, and the connection of rivers and lakes. Fourth, climate change and human activities jointly drive vegetation change, but the contribution of human activities in both vegetation improvement and degradation areas (85.68% and 78.29%, respectively) is higher than that of climate change (14.32% and 21.71%, respectively), which is the main reason for vegetation improvement or degradation in the study area. The analysis of vegetation pattern change within an intensive time series in semi-arid regions can provide a reference and basis for studying the driving factors in regions with rapid changes in vegetation and hydrological conditions.

Dynamic Changes of Plantations and Natural Forests in the Middle Reaches of the Yangtze River and Their Relationship with Climatic Factors

Based on Landsat TM/ETM/OLI images and MODIS NDVI time series remote sensing data from 1999 to 2015, the changes of land use/cover types (including natural forests and plantations) through NDVI trends and their relationship with meteorological factors in the middle reaches of the Yangtze River (MRYR) were analyzed by supervised classification, coefficient of variation, trend analysis, rescaled range analysis, and partial correlation analysis. The results showed that, in the past 17 years, the main landscape type in the MRYR is forestland (accounting for more than 50%), and the built-up land and plantations area increased by four fifths and one fifth, respectively. The area of natural forests had been reduced by one fifth. Additionally, NDVI showed an upward trend (0.37%), especially in natural forests (0.57%). Two thirds of the natural forests had NDVI values greater than 0.80, and 89.21% of them were significantly improved. The area with an uncertain future development trend of all vegetation was more than half of the area. At the same time, partial correlation analysis with climate factors showed that relative humidity had an inhibitory effect on vegetation growth (p < 0.05). Climate factors had a certain lag effect on the growth of natural forests and plantations. Generally speaking, sunshine duration had a positive effect on forests growth, while relative humidity had a negative effect. The results showed that if the forest land was studied as a whole, many of the problems of natural forests and plantations would be ignored. The continuous decrease of natural forests and possible further degradation in the future are worthy of attention. The results could provide a reference for forest ecological protection in other areas.

Attribution of vegetation coverage change to climate change and human activities based on the geographic detectors in the Yellow River Basin, China

Quantitatively, analyzing the driving mechanism of vegetation coverage change is of important significance for regional ecological environment evaluation and protection. Based on time series NDVI data and meteorological data of the Yellow River Basin (Inner Mongolia Section), the trend and significance of climate factors and vegetation coverage in the YRB (IMS) and four sub-regions (the Hetao Irrigation district, the Ten Tributaries region, the Hunhe river basin, and the Dahei river basin) from 2000 to 2018 were ascertained. We used geographic detectors to quantitatively analyze the effects of detection factors on vegetation coverage change. The results indicated that the spatial pattern of vegetation variation and climate change had obvious spatial heterogeneity. During 2000-2018, the regions with vegetation improvement (72.87%) were much greater than that with degradation (26.55%) in the YRB (IMS). Annual precipitation change (4.55%) was a key driving factor to the vegetation coverage change in the YRB (IMS). Among the four sub-regions, the land use conversion type demonstrated the largest explanatory power, but the q values of the four sub-regions were different from each other. The results of the interaction showed that land use change and annual precipitation change were the major driving factors that influenced regional vegetation coverage change. This study has an important reference value for improving the basin's ecological environment.

Is There Spatial Dependence or Spatial Heterogeneity in the Distribution of Vegetation Greening and Browning in Southeastern China?

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes.

Monitoring and Evaluation of Eco-Environment Quality Based on Remote Sensing-Based Ecological Index (RSEI) in Taihu Lake Basin, China

Rapid and effective access to the spatiotemporal patterns and evolutionary trends of the regional eco-environment is key to regional environment protection and planning. Based on the Google Earth Engine platform, we use 165 Landsat images from the summer and autumn seasons (May–November) of 2000, 2010, and 2018 as data sources to calculate the RSEI, which represents the quality of the eco-environment, and then analyze the factors influencing the spatial heterogeneity of the eco-environment and the relationship between eco-environment and land-use changes based on RSEI. The results showed the following: (1) From 2000 to 2018, the overall ecological environment quality of the Taihu Lake Basin showed a stage of rapid decline (2000–2010) and a stage of slow decline (2010–2018). (2) The factors were ranked in order of their explanatory power for the spatial heterogeneity of the RSEI: land-use (0.594) > population density (0.418) > slope (0.309) > elevation (0.308) > GDP (0.304) > temperature (0.233) > precipitation (0.208). An interactive effect was found for each factor of the RSEI, which is mainly represented by a mutual enhancement. (3) From 2000 to 2010, the rapid urban expansion was the main reason for the deterioration of ecological quality. From 2010 to 2018, urban expansion slowed down, and the trend of ecological quality deterioration was effectively curbed. Therefore, promoting the intensive use of land and reducing construction land expansion are key to ensuring sustainable regional socio-economic development.

Data-Driven Artificial Intelligence Model of Meteorological Elements Influence on Vegetation Coverage in North China

Based on remote sensing data of vegetation coverage, observation data of basic meteorological elements, and support vector machine (SVM) method, this study develops an analysis model of meteorological elements influence on vegetation coverage (MEVC). The variations for the vegetation coverage changes are identified utilizing five meteorological elements (temperature, precipitation, relative humidity, sunshine hour, and ground temperature) in the SVM model. The performance of the SVM model is also evaluated on simulating vegetation coverage anomaly change by comparing with statistical model multiple linear regression (MLR) and partial least squares (PLS)-based models. The symbol agreement rates (SAR) of simulations produced by MLR, PLS, and SVM models are 55%, 57%, and 66%, respectively. The SVM model shows obviously better performance than PLS and MLR models in simulating meteorological elements-related interannual variation of vegetation coverage in North China. Therefore, the introduction of the intelligent analysis method in term of SVM in model development has certain advantages in studying the internal impact of meteorological elements on regional vegetation coverage. It can also be further applied to predict the future vegetation anomaly change.

Quantitatively Assessing the Impact of Driving Factors on Vegetation Cover Change in China’s 32 Major Cities

After 2000, China’s vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO2 to vegetation in China’s 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China’s 32 major cities and quantified the effects of CO2, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO2 were the major factors that affected FVC change. The average contributions of climate factors, CO2, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development.

Vegetation Coverage Prediction for the Qinling Mountains Using the CA–Markov Model

The Qinling Mountains represent the dividing line of the natural landscape of north-south in China. The prediction on vegetation coverage is important for protecting the ecological environment of the Qinling Mountains. In this paper, the data accuracy and reliability of three vegetation index data (GIMMS NDVI, SPOT NDVI, and MODIS NDVI) were compared at first. SPOT, NDVI, and MODIS NDVI were used for calculating the vegetation coverage in the Qinling Mountains. Based on the CA–Markov model, the vegetation coverage grades in 2008, 2010, and 2013 were used to simulate the vegetation coverage grade in 2025. The results show that the grades of vegetation coverage of the Qinling Mountains calculated by SPOT, NDVI, and MODIS NDVI are highly similar. According to the prediction results, the grade of vegetation coverage in the Qinling Mountains has a rising trend under the guidance of the policy, particularly in urban areas. Most of the vegetation coverage transit from low vegetation coverage to middle and low vegetation coverage. The grades of the vegetation coverage, which were predicted by the CA–Markov model using SPOT, NDVI, and MODI NDVI, are consistent in spatial distribution and temporal variation.

Vegetation dynamics and its linkage with climatic and anthropogenic factors in the Dawen River Watershed of China from 1999 through 2018

The Dawen River Watershed (DRW), an important sub-basin of the Yellow River, has been experiencing substantial climatic and anthropogenic stresses. Identifying how stressors relate to shifts in vegetation growth is critical for maintaining the health and stability of its regional ecosystems. To address this, we constructed a 20-year dataset (1999-2018) reflecting changes in satellite-based normalized difference vegetation index (NDVI), climate variables, and land use in the DRW. We then used time series, principal component, and partial correlation analyses to detect spatial and temporal patterns in vegetation dynamics over time, as well as linkages with temperature, precipitation, and anthropogenic activities. Over 20 years, the DRW exhibited a warming-greening trend and experienced four regime shifts in its climate-vegetation system, roughly centered on 2001, 2006, 2013, and 2016. Both the average and maximum NDVI increased in all seasons, likely due to favorable changes in seasonal climatic conditions. Temperature was the dominant factor promoting vegetative growth in spring, autumn, and throughout the growing season. Precipitation had a considerable positive effect on the average NDVI during the summer. Spatial analyses indicated that 67.94% of the study area exhibited significant increase in NDVI values over time, mainly locating in the mountains and in Dongping County; Significant NDVI decrease was generally located in the urban expansion areas around cities and counties. Land cover types and annual growth cycles appeared to govern spatial patterns and the extent of variation in vegetation growth, followed by land use-related drivers and climate anomalies. These findings offer an insight on appropriate ecological management and climatic adaptation within the Dawen River Watershed.

Macro analysis of spatiotemporal variations in ecosystems from 1996 to 2016 in Xishuangbanna in Southwest China

This study used remote sensing images from 1996 to 2016 as the main data source, and selected the average annual ecosystem type net change rate, ecosystem type transfer matrix, and comprehensive index of land development degree, to analyze the macro change of the ecosystem pattern in Xishuangbanna Dai Autonomous Prefecture in the past 20 years. Quantitative analysis was performed on amplitude, rate, type of transition, and degree of disturbance of human activities. The results reveal the spatial and temporal changes of the Xishuangbanna ecosystem and their regional differentiation. The results showed that (1) from 1996 to 2016, Xishuangbanna as a whole was dominated by forest ecosystems and rubber ecosystems, followed by tea, farmland, built-up area, and water ecosystems. (2) During 1996-2016, the ecosystem in Xishuangbanna accounted for more than 99% of the total area has not changed. From 1996 to 2003, the transfer of ecosystem types in Xishuangbanna was mainly between forest and rubber ecosystem. (3) The extent of land development and utilization in Xishuangbanna in the past 20 years is relatively low, slightly lower than the national average, and the overall level of land use is at a medium level of utilization, and over time, the degree of disturbance of human activities has shown an increasing trend.

Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience

Vegetation restoration is an important way to improve the sustainability of the ecosystem in the hilly Loess Plateau. The variation of vegetation coverage, caused by the combined effects of meteorological factors and human activities, reflects the succession trend of regional ecosystems. Given the complexity and the diversity of landform in the hilly Loess Plateau, vegetation restoration is more affected by topographic factors. Nevertheless, few studies have considered the characteristics and trends of vegetation restoration under different micro-topographic types in the long-time series. From the perspective of ecological resilience based on the fractional vegetation cover (FVC), the trend, the hurst exponent, and the geographical spatial research were used to analyze the variation and future sustainability of vegetation restoration on different micro-topographic types for 20 years. Besides, the spatial autocorrelation, principal component analysis (PCA) and geographically weighted regression (GWR) were applied to identify the driving factors of vegetation restoration. The results showed: (1) the average of the overall regional vegetation coverage was 61.32%, and only 0.95% of the regional vegetation was degraded in the past 20 years. However, in the future, 69.87% of the area would be degraded from improvement, and 0.52% would be significantly decreased; (2) the vegetation coverage in descending order was as follows: ridge area with shady and steep slope, gully area with shady and steep slope, ridge area with sunny and steep slope, gully area with sunny and steep slope, gully area with shady and gentle slope, ridge area with shady and gentle slope, ridge area with sunny and gentle slope, gully area with sunny and gentle slope, valley area; (3) the difference of vegetation degradation among micro-topography was remarkable, and the valley area and gully area with sunny and steep slope have the greatest decrease; (4) the primary factors affecting vegetation restoration in the hilly Loess Plateau were temperature, moisture, soil quality, and social economical condition, and the dominant factors were various under different micro-topographic types and villages. Therefore, it is necessary to adjust ecological engineering measures by comprehensively considering the regional differences among dominant factors of vegetation restoration.

Study on the spatial-temporal differences and evolution of ecological security in the typical area of the Loess Plateau

The development and utilization of energy in the Loess Plateau has caused a wide range of ecological security issues, and Yan'an has become a typical area for ecological security research on the Loess Plateau. Ecological security evaluation research can provide data support and scientific reference value for the sustainable development, which is of great significance to the overall social and economic development of the region. In this study, the pressure-state-response (PSR) model was used to establish the evaluation index system in the evaluation of ecological security in Yan'an region (YAR), then the fuzzy analytic hierarchy process (FAHP) was used to determine the internal index weight of each element, and finally the ecological security index value (ESI) was calculated. The GIS technology was used to simulate the distribution map of ecological security in YAR and then analyzed the temporal and spatial changes and evolution of ecological security in YAR. The results showed that from 1997 to 2016, the ecological security in the western part of Luochuan County and the eastern part of Yanchuan County was still very high, while the ecological security index was relatively low in the southern part of Huanglong County. The ecological security index of Baota District had increased significantly, from 1.85 in 1997 to 2.76 in 2016. The proportion of III and IV ecological security regions had increased significantly, and the ecological security of the entire YAR was generally in a good situation. This study could clarify the temporal and spatial characteristics of ecological security and provided some reference for the study of ecological security evolution in typical regions of the Loess Plateau.

Vegetation Cover Change and Its Attribution in China from 2001 to 2018

It is confirmed that China has been greening over the last two decades. Such greening and its driving factors are therefore significant for understanding the relationship between vegetation and environments. However, studies on vegetation changes and attribution analyses at the national scale are limited in China after 2000. In this study, fractional vegetation cover (FVC) data from Global Land Surface Satellite (GLASS) was used to detect vegetation change trends from 2001 to 2018, and the effects of CO2, temperature, shortwave radiation, precipitation, and land cover change (LCC) on FVC changes were quantified using generalized linear models (GLM). The results showed that (1) FVC in China increased by 14% from 2001 to 2018 with a greening rate of approximately 0.0019/year (p < 0.01), which showed an apparent greening trend. (2) On the whole, CO2, climate-related factors, and LCC accounted for 88% of FVC changes in China, and the drivers explained 82%, 89%, 90%, and 89% of the FVC changes in the Qinghai–Tibet region, northwest region, northern region, and southern region, respectively. CO2 was the major driving factor for FVC changes, accounting for 31% of FVC changes in China, indicating that CO2 was an essential factor in vegetation growth research. (3) The statistical results of pixels with land cover changes showed that LCC explained 12% of FVC changes, LCC has played a relatively important role and this phenomenon may be related to the ecological restoration projects. This study enriches the study of vegetation changes and its driving factors, and quantitatively describes the response relationship between vegetation and its driving factors. The results have an important significance for adjusting terrestrial ecosystem services.

Monitoring vegetation change and their potential drivers in Yangtze River Basin of China from 1982 to 2015

Monitoring vegetation change and their potential drivers are important to environmental management. Previous studies on vegetation change detection and driver discrimination were two independent fields. Specifically, change detection methods focus on nonlinear and linear change behaviors, i.e., abrupt change (AC) and gradual change (GC). But driver discrimination studies mainly used linear coupling models which rarely concerned the nonlinear behaviors of vegetation. The two diagnoses need be treated as sequential flow because they have inner causality mechanisms. Furthermore, ACs concealed in time series may induce over/under-estimate contributions from human. We chose the Yangtze River Basin of China (YRB) as a study area, first separated ACs from GCs using breaks for additive and seasonal trend method, then discriminated drivers of GCs using optimized Restrend method. Results showed that (1) 2.83% of YRB were ACs with hotspots in 1998 (30.2%), 2003 (10.4%), and 2002 (7.6%); 66.7% of YRB experienced GC with 94.8% of which were positive; and (2) climate induced more area but less dramatic GCs than human activities. Further analysis showed that temperature was the main climate driver to GCs, while human-induced GCs were related to local eco-policies. The widely occurring ACs in 1998 were related to the flooding catastrophe, while the dramatic ACs in sub-basin 12 in 2003 may result from urbanization. This paper provides clear insights on the vegetation changes and their drivers at a relatively long perspective (i.e., 34 years). Sequential combination of specifying different vegetation behaviors with driver analysis could improve driver characterizations, which is key to environmental assessment and management in YRB.

An integrated system for rapid assessment of ecological quality based on remote sensing data

Ecological quality assessment (EQA) is important for regional socio-economic development and its sustainability. To assess the status of land ecological quality more precisely, an ecological quality assessment system with 11 indicators of ecological stability, ecosystem service function, and habitat stress was established using the analytic hierarchy process for Guangdong Province, a highly urbanized region of China. Remotely sensed data were mainly used to quantify the 11 indicators and acquire regional EQA graphs at high spatial resolution. In addition, we used the spatial autocorrelation measure Moran's I to capture dynamic signatures of spatial organization of ecological quality in the study area. The results show that the ecological quality of the study area is heterogeneous spatially but relatively consistent in different regions. Significant positive spatial autocorrelation for EQI in Guangdong was revealed by global Moran's I. Potential ecological hot spot or cold spot were detected based on the spatial clustering patterns that were obtained by local Moran's I. Lands with low ecological quality is mainly distributed in economically developed areas such as the Pearl River Delta and coastal cities in eastern and western Guangdong, while those with high ecological quality are mostly situated in northern mountainous areas that have lush vegetation. The low assessment scores for Guangdong, especially for the Pearl River Delta, are highly correlated with large populations and degrees of industrial agglomeration; this is mainly because urbanization and economic development jeopardize the environment. The presented case study can facilitate information provision and targeted strategy making for environmental protection. This study provides a helpful approach to assess and to analyze the ecological status in the future research. In contrast with methods that employ a single metric and limited data, the assessment system proposed in this study expands the potential application of the remotely sensed data and enriches the methodological system for EQAs.

Quantitative Assessment of the Impact of Human Activities on Terrestrial Net Primary Productivity in the Yangtze River Delta

The continuous growth of the economy and population have promoted increasing consumption of natural resources, and raised concerns regarding the upper limits of the terrestrial ecosystems with biomass accessible for humanity. Here, human appropriation of net primary production (HANPP) was employed to assess the influence of human activities on terrestrial net primary production (NPP), and a detailed method was introduced to simulate the magnitude and trends of HANPP in the Yangtze River Delta. The results showed that the total HANPP of the Yangtze River Delta increased from 102.3 Tg C yr−1 to 142.2 Tg C yr−1, during 2005–2015, with an average of 121.3 Tg C yr−1. NPP changes induced by harvest (HANPPharv) made the dominant contribution of 79.9% to the total HANPP, and the increase of HANPPharv in cropland was the main driver of total HANPP growth, which was significantly correlated with the improvement in agricultural production conditions, such as total agricultural machinery power and effective irrigation area. The proportion of HANPP ranged from 59.3% to 72.4% of potential NPP during 2005–2015 in the Yangtze River Delta, and distinguishable differences in the proportions were found among the four provinces in the Yangtze River Delta. Shanghai had the largest proportion of 84.3%, while Zhejiang had the lowest proportion of 32.0%.