Facing the challenge of NDVI dataset consistency for improved characterization of vegetation response to climate variability.

Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.

Stage response of vegetation dynamics to urbanization in megacities: A case study of Changsha City, China.

Urban vegetation affects urban microclimate and maintains biodiversity, which is vital to the social-ecological system. However, there is a lack of research on quantitatively identifying urbanization stage impact on vegetation dynamics, and the stage difference in the response of vegetation dynamics to urbanization characteristics is not clear. In this study, taking Changsha City as an example, we explored the response of vegetation dynamics to urbanization, and identified the impact stages of urbanization on vegetation dynamics as well as their social-ecological characteristics. The results showed that the vegetation dynamics in Changsha City presented spatial pattern of "increase-decrease-increase" from downtown to outside in the past 20 years. The population density, GDP density and construction land proportion firstly inhibited vegetation growth, and then promoted it, with the turning points of 141.58 million yuan/km2, 1205 person/km2, and 19.80 %, respectively. Then, the urbanization impact on vegetation dynamics was quantitatively divided into three stages according to the vegetation change speed, and in different stages, urbanization impacts on vegetation dynamics were compared. This study illustrated the typical stage feature of the urbanization impact on vegetation dynamics.

Responses of spatial relationships between ecosystem services and the Sustainable Development Goals to urbanization.

Ecosystem services (ES) are the important component supporting the United Nations Sustainable Development Goals (SDGs) realization. In recent decades, rapid urbanization has strongly affected the relationship between ES and SDGs, resulting in the decoupling of ES and SDGs. However, the key urbanization factors dominating the relationship between ES and SDGs are still unclear. In this study, a structural equation model was constructed to explore the impact path and its change of urbanization structure and scale factors on the relationship between ES and SDGs. The results showed that the economic urbanization structure indicator (Engel coefficient) under the influence of technology import significantly impacted the relationship between ES and SDGs in different periods. Under the influence of changes in urban and rural population, population urbanization structure indicator (labor force population proportion) had significant impact on the relationship between ES and economic SDGs, which was significantly stronger in the period of 2010-2015 than in the period of 2000-2005. Land urbanization scale indicators (construction land proportion, and protected natural area proportion) also significantly impacted the relationship between ES and SDGs. Especially for ecological SDGs, the negative impact of construction land on protected natural area increased significantly in the period of 2010-2015, which might further weaken the ES's contribution to SDGs. This study highlighted that along with the continuous transformation of China's society, the key impacts on the relationship between ES and SDGs resulted from the urbanization indicators of scale as well as structure, which provided an extensive support for the sustainable development and social transformation of developing countries and regions.

Distinguishing the impacts of land use change in intensity and type on ecosystem services trade-offs.

Changes in land use intensity and types can affect the structure and function of ecosystems, and thus ecosystem services (ESs) as well as their interactions. However, the impacts of changes in land use intensity on ESs remain poorly understood. Through four different land use scenarios, we distinguished the independent contribution of changes in agricultural land use intensity and types to grain production (GP), water purification (WP), and their trade-offs in the Dongting Lake Basin. The results showed that from 1990 to 2015, GP increased across 58.07% of the total area, but WP decreased across 64.81% of the study area. The two ESs simultaneously increased or decreased across 41.93% of the total area. Watersheds covering 48.72% of the study area where GP increased and WP decreased were mainly distributed in areas with increased land use intensity. The other regions where GP decreased and WP increased were mainly distributed in areas with decreased land use intensity. The scenario analysis of GP, WP, and their trade-offs showed that the areas where agricultural land use intensity was the dominant factor were as large as 1.95 times, 2.38 times, and 2.43 times those dominated by land use type respectively, under the same climate conditions. This study highlighted the importance of changes in agricultural land use intensity on ES, which provided further supporting to ES-based land use management.

How can massive ecological restoration programs interplay with social-ecological systems? A review of research in the South China karst region.

Exploring the cost-effective pathways for restoring ecosystems is a fundamental aspect for scientific communities and policy-makers aiming for a sustainable future. The South China karst region has experienced severe environmental degradation because of unsustainable management practices in this vulnerable social-ecological context. However, it has also become one of the most stunning areas following its remarkable vegetation recovery over recent decades as a result of large-scale ecological restoration programs. There is an extensive body of literature focusing on how ecological restoration programs have altered the degraded environment in this region. By searching and comparing the published peer-reviewed articles, we reviewed the studies related to the effects of ecological restoration programs from the point of view of ecological, socio-economic, and integrated social-ecological impacts, as well as influencing factors and restoration approaches. We found independent evidence to support that large-scale ecological restoration programs increased biomass and carbon sequestration since 2000 across this region. The farmers' livelihoods have spontaneously transited from agriculture into forestry or non-farming sectors without financial compensation or incentive schemes, which coincided with a positive correlation between the implementation of ecological restoration programs and poverty alleviation. However, due to a lack of clear "before and after" comparisons, many studies have indirectly determined the impacts of ecological restoration with non-negligible uncertainties. In addition, considering the critical interactions between belowground and aboveground processes in karst regions, special attention should be given to the selection of tree species and restoration measures according to different bedrock types. In the future, to better understand the impacts of ecological restoration on social-ecological systems, research could be advanced by considering data access, context-based analysis, measurement-targeted assessment, and cross-scale integration.

Human activity vs. climate change: Distinguishing dominant drivers on LAI dynamics in karst region of southwest China.

Understanding the impacts of climate change and human activities on vegetation is of great significance to the sustainable development of terrestrial ecosystems. However, most studies focused on the overall impact over a period and rarely examined the time-lag effect of vegetation's response to climatic factors when exploring the driving mechanisms of vegetation dynamics. In this study, we identified key areas driven by either positive or negative human activities and climate change. Taking the three karst provinces of southwest China as the case study area, a Leaf Area Index (LAI)-climate model was constructed by quantifying the time-lag effect. Then the associated residual threshold was calculated to identify the vegetation change areas dominated by human activities and climate change. The results showed that, during the implementation period of ecological restoration projects from 1999 to 2015, positive impact areas of human activities were mainly distributed among the implementation areas of ecological restoration projects, accounting for 5.61% of the total area. For another, the negative impact areas were mainly distributed across the mountainous area of Yunnan Province, accounting for 1.30% of the total area. Karst landform had the greatest influence on the areas dominated by positive human activities, whereas both topography and karst landform significantly affected the areas dominated by negative human activities. Urban development level had the greatest impact on the areas dominated by climate change. The outcomes of this study provided scientific supports for the sustainable development of ecological restoration projects in China's karst region.

Using the modified i-Tree Eco model to quantify air pollution removal by urban vegetation.

Fine particulate matter (PM2.5) can pose health problems for humans following urbanization. Because urban vegetation has a large surface area to filter PM2.5 out of the air, it can be an effective long-term way to mitigate air pollution. Various studies have quantified PM2.5 removal by vegetation in cities, but the spatial variability of removal within cities and future scenarios have not been well documented. To ensure more reasonable and effective urban tree planting regimes, we used the spatiotemporal i-Tree Eco model combined with the vertical distribution of vegetation in a case study in Shenzhen City, China. The results indicated that the PM2.5 removal by urban vegetation in 2015 was 1000.1 tons, and the average removal rate by vegetation was 1.6 g m-2 year-1. A maximum hourly local air quality improvement of up to 3% could be achieved, with an average of 1%, which differed significantly with elevation. In terms of vegetation type, evergreen shrubs, evergreen broadleaved forests, and evergreen needle-leaved forests had the highest removal efficiency within <100, 100-300, and >300 m, respectively. For five future planting scenarios, by increasing vegetation cover by 5% in different elevation zones (<100, 100-300, and >300 m), an annual amount of 1220.6-1308 tones could be achieved. Specifically, it was estimated that an increase in evergreen shrubs cover in the developed area (<100 m) would have the best removal potential.

Multifunctional landscapes identification and associated development zoning in mountainous area.

Multifunctional landscape has become a new discipline growth point in landscape ecology. Globally mountainous areas occupy about one fifth of Earth's surface. However, few studies focused on landscape multifunctionality in mountainous areas. Taking Dali Bai Autonomous Prefecture, China, as a case study area, five typical landscape functions (net primary productivity, soil retention, water yield, crop production, and residential support) were quantified and mapped. Hotspots of multiple landscape functions were identified using spatial overlap tools, interaction between each landscape function pair was discussed through Spearman's rank correlation analysis, and development zoning was conducted based on landscape function bundle. The results showed that, about 61% of the study area had at least one kind of landscape function hotspot, with only 2.7% covering three or more kinds of landscape function hotspots. Significant trade-offs or synergies existed between all pairs of landscape functions, except the pair of net primary productivity and residential support. With the application of Self-Organizing Feature Maps (SOFM) method, the study area was divided into four types of development zones (i.e. ecological shelter area, ecological transition area, suburban development area, and urban agglomeration area) which were all corresponding to different landscape function bundles. This study could provide spatial guidance for differentiated sustainable developing in mountainous areas according to local conditions of landscape multifunctionality.

Linking ecosystem services and circuit theory to identify ecological security patterns.

The rapid process of urbanization, accompanied by the sharp increase of urban population and expansion of artificial surface, has resulted in the loss of natural ecosystems and the degradation of ecosystem services. Identifying and protecting key places that have high importance for ecological sustainability are great challenges. Ecological security patterns are such an integrated approach to protecting regional ecological sustainability. In this study, taking Yunnan Province, China as a case study area, ecological sources were identified through ecosystem services, and circuit theory was used to model ecosystem processes in heterogeneous landscapes via calculating the 'resistance' or 'current', and thus to identify ecological corridors and key ecological nodes. The results showed that, ecological security patterns included 66 ecological sources, 186 ecological corridors, 24 pinch-points and 10 barriers. In details, the ecological sources were mainly distributed in the southwest and northwest of Yunnan Province, with the ecological corridors locating along the high mountains, and both ecological sources and corridors were mostly covered with forest land. Pinch-points covered by forest land and cultivated land, were distributed in the middle of Yunnan Province along the rivers. Approximately 75.9% nature reserves were located in the identified ecological sources, and the remainings were mainly distributed in eastern Yunnan Province with small area, showing the effectiveness in identifying ecological security patterns. Among 81 projects of low-slope hill development carried out in Yunnan Province, 46.9% showed potential human stress on regional ecological security. Based on ecosystem services and circuit theory, this study provides a new approach to identifying the spatial range of ecological corridors and the specific location of key nodes for effective ecological conservation and restoration.