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A comprehensive quantification of the relationships between forest vegetation changes and forest landscape fragmentation is urgently required to provide valuable insights for informed forest management decisions. Previous efforts often focused on these two aspects separately, overlooking their intricate relationships, restricts the formulation of precise forest protection and recovery measures. To address this gap, taking Southeast China as a case, this study employed the Google Earth Engine (GEE) platform to calculate the annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2020, a forest fragmentation comprehensive index (FFCI) was constructed to evaluate the static and dynamic forest landscape fragmentation over the study period, then the Theil-Sen and Mann-Kendall, the two-dimensional framework and the bivariate spatial autocorrelation, and a machine learning algorithm (random forest, RF) were employed to explore the forest vegetation change and forest landscape fragmentation, and their spatial coupling relationships and driving patterns, respectively. The results showed that the temporal trend of the forest vegetation can be categorized into three distinct phases, with the pattern of “decreasing-rising-decreasing”, 78.2% of the area improved in forest vegetation, while 11.0% degraded. Approximately 6.0% of forest landscape showed a decline in fragmentation, while 9.3% experienced increased fragmentation trends, including 3.8% of forest landscape with moderate to high static fragmentation and 5.5% of forest landscape with low to very low static fragmentation. Throughout the study period, approximately 66.8% of forest landscape remained stability with the improved forest vegetation; however, there were cases (more than 7% of forests) with improved forest vegetation, but experienced severe landscape fragmentation. The RF outcome revealed that the forest vegetation change dynamics were influenced by the multiple factors, including distance to forest edge, distance to road, and elevation; while distance to forest edge emerged as the overwhelming dominant factor driving the forest landscape fragmentation. This research offers significant insights into the intricate interconnections between the forest vegetation changes and the forest landscape fragmentation dynamics.

Vegetation resilience (VR), providing an objective measure of ecosystem health, has received considerable attention, however, there is still limited understanding of whether the dominant factors differ across different climate zones. We took the three national parks (Hainan Tropical Rainforest National Park, HTR; Wuyishan National Park, WYS; and Northeast Tiger and Leopard National Park, NTL) of China with less human interference as cases, which are distributed in different climatic zones, including tropical, subtropical and temperate monsoon climates, respectively. Then, we employed the probabilistic decay method to explore the spatio-temporal changes in the VR and their natural driving patterns using Geographically Weighted Regression (GWR) model as well. The results revealed that: (1) from 2000 to 2020, the Normalized Difference Vegetation Index (NDVI) of the three national parks fluctuated between 0.800 and 0.960, exhibiting an overall upward trend, with the mean NDVI of NTL (0.923) ​> ​HTR (0.899) ​> ​WYS (0.823); (2) the positive trend decay time of vegetation exceeded that of negative trend, indicating vegetation gradual recovery of the three national parks since 2012; (3) the VR of HTR was primarily influenced by elevation, aspect, average ​annual temperature change (AATC), and average annual precipitation change (AAPC); the WYS’ VR was mainly affected by elevation, average ​annual precipitation (AAP), and AAPC; while the terrain factors (elevation and slope) were the main driving factors of VR in NTL; (4) among the main factors influencing the VR changes, the AAPC had the highest proportion in HTR (66.7%), and the AAP occupied the largest area proportion in WYS (80.4%). While in NTL, elevation served as the main driving factor for the VR, encompassing 64.2% of its area. Consequently, our findings indicated that precipitation factors were the main driving force for the VR changes in HTR and WYS national parks, while elevation was the main factors that drove the VR in NTL. Our research has promoted a deeper understanding of the driving mechanism behind the VR.

To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O3, the annual average concentration of NO2, PM2.5 and SO2 showed a decreasing trend during 2016 and 2019; NO2, PM2.5 and SO2 show a trend of “low in summer and high in winter”; the diurnal variation of NO2 concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO2 and NO2 had four primary periods, while O3 only had two primary periods. The high NO2 concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO2 and SO2 decreased considerably during lockdown, whereas PM2.5 and O3 increased dramatically. The contribution rates of transportation activities to the NO2, O3, PM2.5 and SO2 were estimated be 9.4 % ∼ 17.2 %, −76.4 % ∼ −42.9 %, −39.5 % ∼ –22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and −10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.

The phenomenon of polarized development among regional cities has sparked extensive contemplation and indicated a need for research on multi-source regional networks. However, such research faces two obstacles: the absence of quantitative measurement of differences in network structures and the lack of a thorough examination of the degree of city clustering and the dynamics of community composition in hierarchical networks. Thus, we identified 16 cities in the Chengdu–Chongqing Economic Circle (CCEC) as the spatial units to examine the spatial network structures of population, resources, and transportation and the integrated spatial network structure. Using social network analysis, this paper describes the structural characteristics of the three networks (population, resource, and transportation), followed by an analysis of their collective and hierarchical network clustering characteristics, and explores the driving mechanisms and factors that make up each network model. Our results show the following: (1) All three networks exhibit an “east dense, west sparse” characteristic, but there are differences in the layouts of the core cities in terms of the three networks. (2) The clustering characteristics of the hierarchical networks are more pronounced than those of the overall network. The results of the analysis combined with the network formation mechanisms can help effectively plan the future coordinated development of the CCEC.

Urbanization has rapidly increased, leading to a wide range of significant disruptions to the global ecosystem. Road construction has emerged as the primary catalyst for such ecological degradation. As a result, it is imperative to develop efficient technological approaches for quantifying and tracking alterations in the ecological environment. Additionally, it is crucial to delve deeper into the spatial correlation between the quality of the ecosystem and the urban road network. This is of crucial importance in promoting sustainable development within the region. In this study, the research area selected was the Minjiang River Basin (MRB). We made optimal use of the Google Earth Engine (GEE) cloud platform to create a long-term series of remote sensing ecological index (RSEI) data in order to assess the quality of the ecological environment in the area. Additionally, we integrated digital elevation data (DEM) and OpenStreetMap (OSM) road network data to investigate the response mechanisms of RSEI with regard to elevation, slope, and the road network. The findings were as follows: (1) There were two distinct phases observed in the average value of RSEI: a slow-rising phase (2000–2010) with a growth rate of 1.09% and a rapidly rising phase (2010–2020) with a growth rate of 5.36%; the overall 20-year variation range fell between 0.575 and 0.808. (2) During the period of 2000 to 2010, approximately 41.6% of the area exhibited enhanced ecological quality, whereas 17.9% experienced degradation. Subsequently, from 2010 to 2020, the proportion of the region with improved ecological quality rose to 54.0%, while the percentage of degraded areas declined to 3.8%. (3) With increasing elevation and slope, the average value of RSEI initially rose and then declined. Specifically, the regions with the highest ecological quality were found in the areas with elevations ranging from 1200 to 1500 m and slopes ranging from 40 to 50°. In contrast, areas with an elevation below 300 meters or a slope of less than 10° had the poorest ecological quality. (4) The RSEI values exhibited a rapid ascent within the 1200 m buffer along the road network, while beyond this threshold, the increase in RSEI values became more subdued. (5) The bivariate analysis found a negative correlation between road network kernel density estimation (KDE) and RSEI, which grew stronger with larger scales. Spatial distribution patterns primarily comprised High–Low and Low–High clusters, in addition to non-significant clusters. The southeastern region contained concentrated High–Low clusters which covered approximately 10% of the study area, while Low–High clusters accounted for around 20% and were predominantly found in the western region. Analyzing the annual changes from 2000 to 2020, the southeastern region experienced a decrease in the number of High–Low clusters and an increase in the number of High–High clusters, whereas the northwestern region showed a decline in the number of Low–High clusters and an increase in the number of non-significant clusters. This study addresses a research gap by investigating the spatial correlation between road distribution and RSEI, which is vital for comprehending the interplay between human activities and ecosystem services within the basin system.

Although many prior studies have found that landscape pattern significantly affects urban heat environment globally, the spatially heterogeneous in the cooling effects of landscape pattern remains poorly understood. In addition, most previous studies have only employed a single landscape metric separately, without holistic consideration of the composition and configuration of different landscapes. Taking one of the new “stove” cities in China-Fuzhou City, Fujian Province, as an example, we employed the principal component analysis (PCA) to synthesize a landscape pattern comprehensive index (LPCI) composed of the four common landscape metrics (i.e., aggregation index, AI; mean patch area, Area mn; largest patch index, LPI; and percentage of landscape, PLAND) of the three major land surfaces (i.e., water, vegetation, and impervious surface). Then, the local model (geographically weighted regression, GWR) was proposed to explore the spatially heterogeneous in the cooling effects of urban landscape. The results revealed that: (1) from 2000 to 2016, the land surface temperature (LST) increased by 4.262 °C, and the proportion of the urban heat island region showed an upward trend, while the urban-heat-island ratio index (URI) increased from 0.328 to 0.457; (2) the cooling effect of different land surfaces ranked from high to low was: water (29.69 °C), vegetation (38.56 °C), and impervious surface (41.82 °C); (3) compared with vegetation patches, water patches had a more obvious cooling effect on the surrounding environment, with the cooling distance within 60–90 m for the vegetation, while reaching 120–150 m for water body; (4) the proposed LPCI could explain more than 80% of the information for all of the landscape metrics for all of the landscape types, and presented a patchy distribution in the study area; (5) the GWR results revealed that the cooling effect of the landscape pattern varied spatially across the study area, indicating that the configuration of landscapes is more important in an urban center in alleviating urban heat environment than in an urban fringe area. The proposed approach provides a new understanding of the interaction between the landscape patterns and urban heat environments, providing a strong basis for landscape planning strategies for specific local sites.

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.

An in-depth analysis of urban road network distribution plays a critical role in understanding the urbanization process. However, effective ways to quantitatively analyze the spatial paradigms of road networks are still lacking, and few studies have utilized road networks to rapidly identify urban areas of a region. Thus, using a fast-developing region in the south-eastern costal region of China, Fuzhou City, as a case, we introduced kernel density estimation (KDE) to characterize road networks and quantified the area’s spatial heterogeneity using exploratory spatial data analysis (ESDA) and semivariance analysis (SA). The results show that there is an uneven spatial distribution of the networks both at the regional and downtown levels. At the regional level, there is a conspicuous polarization in the road distribution, with the KDE being much higher in the urban areas than in the rural areas; at the downtown level, the KDE gradually decreases from the center to the periphery. Quantitatively, the ranges of the spatial dependence of the networks are approximately 25 km for the entire study region and 12 km for the downtown area. Additionally, the spatial variations vary among different directions, with greater variations in the northeast–southwest and the southeast–northwest directions compared with the other directions, which is in line with the urban sprawl policy of the study area. Both the qualitative and quantitative results show that the distribution of road networks has a clear urban–rural dual structure, which indicates that road networks can be an active tool in identifying the urban areas of a region. To this end, we propose a quick and easy method to delimit urban areas using KDE. The extraction results of KDE are better than those of the index-based built-up index (IBI), indicating the effectivity and feasibility of our proposed method to identify the urban areas in the region. This research sheds new light on urbanization development research.

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann−Kendall test and Theil−Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

The rapid increase in the livestock industry in China in recent two decades has played an important role in the livelihoods of people and has become a very significant issue in terms of sustainable animal food supply chains. Knowledge gaps in the geographic distribution may hinder the sustainable development of livestock industry. This paper investigates the spatial distribution in the outputs of livestock products (meat, milk and egg, respectively) in China using exploratory spatial data analysis. This method is a set of GIS spatial statistical techniques that are useful in describing and visualizing the spatial distribution, detecting patterns of hot-spots, and suggesting spatial regimes. The global Moran’s I statistics for the three products reveal strong positive and significant spatial autocorrelation. Furthermore, the Moran significance maps indicate four hot-spots (North-eastern cluster, Northern Coast cluster, Central Inland cluster and Southern cluster) and one cold-spot (Western cluster) in the meat product distribution, one large hot-spot (Northern cluster) and one large cold-spot (South-central cluster) for the milk product, four relatively small hot-spots (North-eastern cluster, Northern Coast cluster, Eastern Coast cluster and Central Inland cluster) and one large cold-spot (Western cluster) for the egg product. Based on the results, we show that livestock products are polarized into clusters and the outputs of the products tend to be reducing from east to west and from north to south China. Implications are drawn, such as priority of resource allocations for hot-spot area in terms of animal-source food security and the utilization of spillover effects from hot-spots. [ABSTRACT FROM AUTHOR]

The mixed Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Masson's pine (Pinus massoniana Lamb.), and hardwood forest is a major forest type in China and of national and international importance in terms of its provision of both timber and ecosystem services. However, over-harvesting has threatened its long-term productivity and sustainability. We examined the impacts of timber harvesting intensity on soil physical and chemical properties 10 and 15 years after cutting using the research plots established with a randomized block design. We considered five treatments, including clear cutting and low (13.0% removal of growing stock volume), medium (29.1%), high (45.8%), and extra-high (67.1) intensities of selective cutting with non-cutting as the control. The impact on overall soil properties derived from principal component analysis showed increasing with a rise in cutting intensity, and the most critical impact was on soil nutrients, P and K in particular. Soil nutrient loss associated with timber harvesting even at a low cutting intensity could lead to nutrient deficits in this forest although most of the soil physical properties could be recovered under the low and medium intensities of cutting. These results indicate that clear cutting and the selective cutting of extra-high and high intensities should be avoided in this type of forest in the region. [ABSTRACT FROM AUTHOR]