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To explore the response relationship of vegetation dynamic change characteristics, clearness vegetation and precipitation changes in the Hexi Corridor, China. This paper analyzed the NDVI between 2000-2019 using linear regression slope method based on long time series MODIS data. The annual NDVI values of the Hexi Corridor range from 0.15 to 0.22. The vegetation overall showed an upward trend in the last 20 years, with an average slope from 0.0125/10a. But there was significant variability in the trend of inter annual changes, as compared from 2000 to 2009, the rate of exponential increases in vegetation in the Hexi Corridor slowed in 2010-2019, and the average slope of changes in vegetation cover decreased from 0.0139/10a to -0.006 8/10a. The variation trend of NDVI is basically consistent with precipitation. The positive and negative correlation between NDVI and precipitation account for 61.58% and 38.42% respectively. The positive correlation region is mainly located in the central and southwest regions with relatively rich vegetation. The regions with negative correlation are mainly in the northern and northeastern desert areas, where there is no vegetation cover. It shows that the relationship between vegetation index and precipitation in Hexi Corridor is relatively close in areas with abundant vegetation. [ABSTRACT FROM AUTHOR]

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region's net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance. [ABSTRACT FROM AUTHOR]

Street greening, an indispensable element of urban green spaces, has played an important role in beautifying the environment, alleviating the urban heat island effect, and improving residents' comfort. Vegetation coverage is a common index used for measuring street greening. However, there are some shortcomings in the traditional evaluation methods of vegetation coverage. Part of the vegetation coverage cannot be determined from a two-dimensional perspective, such as shrubs and green walls. In this paper, the Sentinel-2 image was used to extract the street fractional vegetation cover (SFVC) and the Baidu street view panoramas were used to extract the green view index (GVI). To overcome the lack of a single perspective from the street vegetation coverage evaluation, the above two indices were merged to construct a comprehensive street greening evaluation index (CSGEI). The research area is the Longhua District of Shenzhen city in Southern China. All three indices were divided into five classes using natural breakpoint methods based on previous research experience. The results showed that Baidu street view panoramas could effectively identify shrubs and green walls that were deficient in the Sentinel-2 image. The GVI is a supplement to the street vegetation coverage. The SFVC and GVI were divided into five classes, from L1 to L5 implying a gradual increase in the percentage of the vegetated area. The result has shown that the SFVC was in the L1, accounting for 53.68%. After index merging, the process of accounting for the L1 decreased to 31.29%. The multiperspective integrated CSGEI could comprehensively measure the distribution information of street greening and guide the planning and management of urban green landscapes. [ABSTRACT FROM AUTHOR]

Quantify the relative effects of climate change and human activities on vegetation cover can help us understand the vegetation cover change and its drivers. Residual analysis, defined as the difference between potential normalized difference vegetation index (NDVIpot) and observed normalized difference vegetation index (NDVIact), was used to solve this problem. The estimation of NDVIpot is one of the important issues faced by ecologists. Due to the strictly protection of environment and ecosystems, the ecosystems in nature reserves are rarely disturbed by human activities. Therefore, establishing NDVIpot estimation model based on nature reserves can improve the estimation accuracy of NDVIpot. However, the estimation of NDVIpot based on nature reserves has not yet been reported. In this paper, the NDVIpot estimation model was established based on nature reserves, and residual analysis was used to quantify the impact of human activities on NDVIact. The results show that the NDVIact in Nanjing metropolitan area (NJMA) showed a significant upward trend from 2000 to 2019, and 74.20% of NJMA showed greening trends, while 24.47% showed browning trends. The temperature and precipitation were positively correlated with normalized difference vegetation index (NDVI), and the impact of the temperature on NDVI was greater than that of precipitation. The promotion of human activities on NDVI had become more and more significant. There was 51.88% of NJMA where the human activities had a positive promotion effect on NDVI, while there was 46.29% of the regional where the human activities had a negative inhibitory effect on NDVI. The population density (POP) and gross domestic product (GDP) were mainly positively correlated with NDVI, and the impact of the POP on NDVI was greater than that of GDP. The conversion of cropland to forests and the increase of the sown area of crops also caused the NDVI to increase, while the disorderly expansion of urban land caused the NDVI to decrease. [ABSTRACT FROM AUTHOR]

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The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate the C factor at a regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiments, and needs the coverage ratio data of croplands, woodlands, and grasslands at a standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. The results show that: (1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands, and grasslands on a km-sized grid, and takes the C factor formula obtained from the standard plot experiment and applies it to a regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas; (2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = −0.03, df = 0.12, 2-tail significance p < 0.05) and significantly correlated with the measured vegetation coverage; (3) The C factor of the study area is smaller in the middle, southern, and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands, and the valleys affected by human activities; (4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on a large scale and provide strong support for regional soil and water conservation planning. [ABSTRACT FROM AUTHOR]

This study aims to understand the factors and mechanisms influencing the spatio-temporal changes of fractional vegetation cover (FVC) in the northern slopes of the Tianshan Mountains. The MOD13Q1 product data between June and September (peak of plants growing) during the 2001–2020 period was incorporated into the pixel dichotomy model to calculate the vegetation cover changes. Then, the principal component analysis method was used to identify the primary driving factors affecting the change in vegetation cover from the natural, human, and economic perspectives. Finally, the partial correlation coefficients of FVC with temperature and precipitation were further calculated based on the pixel scale. The findings indicate that (1) FVC in the northern slopes of the Tianshan Mountains ranged from 0.37 to 0.47 during the 2001–2020 period, with an obvious inter-annual variation and an overall upward trend of about 0.4484/10 a. Although the vegetation cover had some changes over time, it was generally stable, and the area of strong variation only accounted for 0.58% of the total. (2) The five grades of vegetation cover were distributed spatially similarly, but the area-weighted gravity center for each vegetation class shifted significantly. The FVC under different land use/land cover types and elevations was obviously different, and as elevation increased, vegetation coverage presented a trend of a "∩"-shape change. (3) According to the results of principal component analysis, human activities, economic growth, and natural climate were the main driving factors that caused the changes in vegetation cover, and the cumulative contribution of the three reached 89.278%. In addition, when it came to climatic factors, precipitation had a greater driving force on the vegetation cover change, followed by temperature and sunshine hours. (4) Overall, precipitation and temperature were correlated positively with FVC, with the average correlation coefficient values of 0.089 and 0.135, respectively. Locally, the correlations vary greatly under different LULC and altitudes. This research can provide some scientific basis and reference for the vegetation evolution pattern and ecological civilization construction in the region. [ABSTRACT FROM AUTHOR]

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%. [ABSTRACT FROM AUTHOR]

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies. [ABSTRACT FROM AUTHOR]

In the context of global climate change and ecological restoration projects, significant changes have been observed in the fractional vegetation cover (FVC) in the Yellow River basin. The increased vegetation growth accelerates water consumption, exacerbating drought and water scarcity issues, thereby heightening regional water resource shortage risks. This study targets the Yellow River basin in China, employing a pixel-based model to convert NDVI into FVC datasets. We establish a pixel-wise mathematical model for annual runoff and environmental factors based on residual analysis and methods like multiple linear regression. Using climate model data from CMIP6 as independent variables, in conjunction with the statistical model, we elucidate the spatiotemporal characteristics of annual runoff in the Yellow River basin under future climate scenarios. Our results indicate that, under four different climate scenarios, the average annual runoff in the Yellow River basin is projected to increase. The increases are quantified as 0.008 mm/a, 0.065 mm/a, 0.25 mm/a, and 0.24 mm/a for SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. From 2022 to 2040, the spatial distribution of the runoff change rates under the SSP245 and SSP370 scenarios show an increasing trend in upstream areas such as the Qinhe and Longmen regions, with rates ranging from 6.00 to 8.61 mm/a. During the period from 2041 to 2060, all four climate scenarios indicate minimal changes in the runoff depth in the northern part of the Yellow River basin. From 2061 to 2080, under the SSP126 and SSP245 scenarios, the spatial distribution of the runoff shows significant increases in the river source area and a decreasing trend in the middle reaches, with rates ranging from 4.52 to 11.39 mm/a. For the period from 2081 to 2100, the runoff change rates vary significantly under the four climate scenarios. These findings provide a detailed understanding of how future climate scenarios could impact water resource distribution in the Yellow River basin, offering critical insights for regional water management and policy making to mitigate potential water scarcity challenges. [ABSTRACT FROM AUTHOR]

Forest ecosystems are essential to the global carbon cycle because they are the biggest terrestrial carbon reserves. In the management of forests, thinning is a commonly employed strategy, impacting the respiration and biomass loss of trees, thereby modifying forest carbon dynamics. However, there is a lack of scientific research to confirm the impacts of thinning intensities on carbon storage in trees, soil layers, shrubs, and ground vegetation layers as well as its impact on wood production and growth rate. The goal of this study was to find the optimal thinning levels for increasing carbon sequestration during the growth stage of the Korean pine (Pinus koraiensis) middle-aged plantations in Northeast China. In this study, thinning intensity (0, 10, 11, 16, 18, and 22%) affected the carbon storage of trees, tree growth, volume, and, we suspected, soil layer, shrubs, and vegetation (herbs, litter, and grass) also. Specifically, after four years of thinning, the 18% treatment significantly increased total carbon storage, individual organ storage, growth, and tree volume (p < 0.05). These results give us abundant information about how thinning affects the dynamics of carbon storage, wood production, and the interactions between soil and plants in P. koraiensis plantations, contributing to multi-objective management strategies for optimizing carbon sequestration, wood production, and ecosystem health. [ABSTRACT FROM AUTHOR]

Vegetation to some extent can reflect the overall state of the ecological environment, and increasing vegetation cover can improve the quality of the ecological environment. The southwest karst region of China is a typical ecologically fragile area and an important ecological barrier. Against the backdrop of intensified human activities, the vegetation dynamics in the karst region have attracted widespread attention. Analyzing vegetation cover changes and their responses to human activities is important for regional ecological development. This study is based on SPOT-VGT NDVI data from 1998 to 2020 and employs trend analysis, "baseline" determination, multiple regression, residual analysis, and principal component analysis to investigate the characteristics of vegetation cover changes in the southwest karst region of China. Additionally, it quantifies the specific impacts of various human activities on these changes. The research findings indicate that (1) over the past 23 years, the inter-annual variability and seasonal differences in the NDVI of the southwest karst region have been significant, exhibiting an overall increasing trend. Spatially, the vegetation in this region demonstrates a decreasing trend from south to north and southwest to northeast. (2) Over time, the areas where vegetation has improved gradually expand, and the effectiveness of vegetation restoration increases with time. (3) Overall, human activities influence vegetation growth in the southwest karst landscape region of China. (4) The socioeconomic factors affecting vegetation cover changes in the southwest karst region of China primarily include economic development level, population and labor migration, urbanization development, and vegetation construction. These results provide a scientific basis for formulating reasonable ecological environment protection strategies by humans. [ABSTRACT FROM AUTHOR]

Dexing City is an important mining city in China, abounding in copper ore, lead ore, zinc ore, and other metal resources, and there are two large open-pit mines in its territory, Dexing Copper Mine and Yinshan Mine. The two open-pit mines have been expanding their mining production scale since 2005, with frequent mining activities; and the expansion of the pits and the discharge of solid waste will undoubtedly increase the land use and cause the destruction of vegetation. Therefore, we plan to visualize the change in vegetation cover in Dexing City from 2005 to 2020 and the expansion of the two open-pit mines by calculating changes of the Fractional Vegetation Cover (FVC) in the mining area using remote sensing technology. In this study, we calculated the FVC of Dexing City in 2005, 2010, 2015 and 2020 using data from NASA Landsat Database via ENVI image analysis software, plotted the FVC reclassified maps via ArcGIS, and conducted field investigations in the mining areas of Dexing City. In this way, we can visualize the spatial and temporal changes of vegetation cover in Dexing City from 2005 to 2020, and appreciate the situation of mining expansion and its solid waste discharge in Dexing City. The results of this study showed that the vegetation cover of Dexing City remained stable from 2005 to 2020, as the expansion of mining scale and mine pits was accompanied by active environmental management and land reclamation, setting a positive example for other mining cities. • The change in FVC from 2005 to 2020 was used to visualize the impact of mining expansion in Dexing City, China. • The paper presented Dexing City as an example of a mining city that has achieved "healthy" development. • This paper described the environmental hazards associated with open-pit mining. [ABSTRACT FROM AUTHOR]

Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases. [ABSTRACT FROM AUTHOR]

Many soil and water conservation (SWC) measures have been applied in the Jinghe River Basin to decrease soil erosion and restore degraded vegetation cover. Analysis of historical streamflow records suggests that SWC measures may have led to declines in streamflow, although climate and human water use may have contributed to observed changes. This paper presents an application of a watershed-scale, physically-based eco-hydrological model--the Regional Hydro-Ecological Simulation System (RHESSys)--in the Jinghe River Basin to study the impacts of SWC measures on streamflow. Several extensions to the watershed-scale RHESSys model were made in this paper to support the model application at larger scales (>10,000 km2) of the Loess Plateau. The extensions include the implementation of in-stream routing, reservoir sub-models and representation of soil and water construction engineering (SWCE). Field observation data, literature values and remote sensing data were used to calibrate and verify the model parameters. Three scenarios were simulated and the results were compared to quantify both vegetation recovery and SWCE impacts on streamflow. Three scenarios respectively represent no SWC, vegetation recovery only and both vegetation recovery and SWCE. The model results demonstrate that the SWC decreased annual streamflow by 8% (0.1 billion m3), with the largest decrease occurring in the 2000s. Model estimates also suggest that SWCE has greater impacts than vegetation recovery. Our study provides a useful tool for SWC planning and management in this region. [ABSTRACT FROM AUTHOR]

[Objective】 In this paper, the vegetation coverage of the research area for the period 2000 - 2017 was calculated by using the veg¬ etation index product (MODIS13QI) with spatial resolution of 250 meters and 16 days synthesis.【Method】 The spatial and temporal charac¬teristics of vegetation cover in the northern foothills of the central part of Tianshan, China were studied by using differential analysis in the past 18 years, and the annual and quarterly vegetation cover area was evaluated using the quantitative evaluation model of change trend.【Re- suit】 The results showed that the spatial characteristics of vegetation coverage in the northern foot of the central Tianshan Mountains gradually increased from the north to the middle to the south % The intensity of spatial changes in vegetation growth was gradually reduced fromsummer to autumn to spring. In the quantitative evaluation of annual dynamic changes in vegetation coverage, the degree of degradation was: summer spring > autumn % the annual degraded area accounted for 0. 27 % of tlie total area% The stable area of -0. 1 -0. 1 was： spring > au¬tumn > summer, annual stable area covered 84.46 % of tlie whole area% The improvement areas greater than 0. 1 were： summer > autumn spring, and the annual improvement area accounts for 15 . 27 % of the entire area. The change of the non-significant area was as follows: spring > summer > autumn, and the annual non-significant vegetation area was 64.39 % % the apparent increase trend was： summer autumn > spring, and the annual increase in area accounts for 29 . 13 %. 【Conclusion 】Through quantitative evaluation of the temporal and spatial changes of vegetation cover， the 3 value and the corresponding degree of vegetation change are defined. The seasonal vegetation cover area decreases from strong to weak and appears as ： spring > autumn > summer % The investigation of annual and quarterly vegetation dynamic changes in the study area provides a certain theoretical basis and data support for vegetation and ecological environmental protection in the northem foot of tlie central Tianshan mountains [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century in Shaanxi Province, China, ecological restoration has increased fractional vegetation cover (FVC) and decreased soil and water erosion. The climate and topography will be critical factors for maintaining vegetation coverage in the future. Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. The results indicated that 73.86% of the area in Shaanxi Province exhibited an increasing FVC with a growth rate of 0.0026 year−1 from 2000 to 2018. The FVC in the three subregions varied, as QBM (87.24–91.47%) > GZP (47.45–66.93%) > LOP (36.33–49.74%), which displayed a significant increase, slight increase, and slight decrease, respectively. The variation of FVC was significantly positively correlated with climate factors (precipitation, temperature, sunshine duration) at monthly and seasonal scales. The time-lag duration between FVC and climate factors was 1–3 months except for the conjunctional areas of GZP with the LOP and QBM, which exhibited a time-lag of 5–6 months. Topographically, the landform of hills had the highest FVC increase at an altitude of 500–1500 m and a slope of 2°–6°. The dominant driving factors affecting FVC variation in Shaanxi Province and LOP area were climatic factors. In the QBM area, the dominant factors were related to topography (relief, elevation, slope), whereas in the GZP area, they were relief and sunshine duration. We can conclude that local topography characteristics are important in implementing revegetation projects because they strongly influence water, temperature, and sunshine redistribution. [ABSTRACT FROM AUTHOR]

Due to the lower canopy height at the maximum crown width at the bottom of young Picea crassifolia trees, they are mixed with undergrowth. This makes it challenging to accurately determine crown size using CHM data or point cloud data. UAV imagery, on the other hand, incorporates rich color information and, after processing, can effectively distinguish between spruce trees and ground vegetation. In this study, the experimental site was an artificial young forest of Picea crassifolia in Shangshan Village, Qinghai Province, China. UAV images were used to obtain normalized saturation data for the sample plots. A marker-controlled watershed segmentation algorithm was employed to extract tree parameters, and the results were compared with those obtained via point cloud clustering segmentation and the marker-controlled watershed segmentation algorithm based on Canopy Height Model (CHM) images. The research results showed that the single tree recognition capabilities of the three types of data were similar, with F-measures of 0.96, 0.95, and 0.987 for the CHM image, UAV imagery, and point cloud data, respectively. The mean square errors of crown width information extracted from the UAV imagery using the marker-controlled watershed segmentation algorithm were 0.043, 0.125, and 0.046 for the three sample plots, which were better than the values of 0.103, 0.182, and 0.074 obtained from CHM data, as well as the values of 0.36, 0.461, and 0.4 obtained from the point cloud data. The point cloud data exhibited better fitting results for tree height extraction compared to the CHM images. This result indicates that UAV-acquired optical imagery has applicability in extracting individual tree feature parameters and can compensate for the deficiencies of CHM and point cloud data. [ABSTRACT FROM AUTHOR]

Vegetation significantly influences human health in the Yellow River basin and the plant cover is vulnerable to people. Typical types of erosion in the Yellow River basin include that caused by water, wind and freeze-thaw. In this paper, vegetation cover change from 1982 to 2006 was studied for a number of different erosion regions. The Global Inventory Monitoring and Modeling Studies Normalized Difference Vegetation Index (GIMMS NDVI) data were employed, while climatic data were also used for analysis of other influencing factors. It was shown that: (1) generally the vegetation cover in different erosion regions displayed similar increasing trends; (2) spatially the vegetation cover was highest in the water erosion region, the second highest was in the freeze-thaw region and the lowest in the wind erosion region; and (3) vegetation cover in the Yellow River basin is influenced by climate factors, especially by temperature. In water erosion regions, the temporal change of vegetation cover seemed complicated by comprehensive climatic and human influences. In wind erosion regions, the vegetation cover had close relations to precipitation. In freeze-thaw erosion regions, the vegetation cover was primarily altered by temperature. In all the three erosion regions, significant change of the vegetation cover occurred from 2000 just after the 'Grain for Green' (GFG) programme was implemented throughout China. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Three-North Shelter Forest Programme (TNSFP) covers 551 Chinese counties and an area of 4,069,000 km mostly in arid and semi-arid regions. In this paper, we discuss the temporal and spatial changes in value of the normalized-difference vegetation index (NDVI) in this region, and the relationships between NDVI and climatic factors (temperature and precipitation) based on NOAA Advanced Very High Resolution Radiometer Global Inventory Modeling and Mapping Studies NDVI data with 8-km resolution from 1982 to 2006. During the past 25 years, the vegetation cover has generally increased in eastern regions of China and the oasis in the north piedmont of Tianshan Mountains, but has decreased northwest of Xinjiang and in the Hulunbeier Plateau. The multi-year monthly average NDVI distribution map showed that NDVI increased from April to August, but in the western and northern plateau areas, the lower temperatures and high altitude created a shorter growing season (1 or 2 months). The vegetation of the study area has generally increased in the regions covered by the TNSFP. Linear regression analysis of the vegetation cover showed an increasing trend over large areas. The largest annual growth rate per pixel (the slope of the regression) was 0.009; the largest negative annual change was −0.004. The correlation between NDVI and precipitation was higher than that between NDVI and temperature, suggesting that precipitation is the most important factor that affects NDVI changes in the study area, especially for temperate desert vegetation in northwestern China. [ABSTRACT FROM AUTHOR]

It appears that two main factors hinder the effective incorporation of ecological information derived from computer modeling in resource management at large spatial scales: (1) some models are not ecologically sound and (2) most models based on ecological principles are often too sophisticated or detailed, having complex structures and requiring a large range of input data. This paper employs a generic model based on well-established ecological principles, but with appropriate detail, to better serve decision-making in sustainable resource management. The model was used to simulate aboveground net primary productivity (ANPP), foliage projective cover (FPC), and the evaporation coefficient (k) for large arid areas in northwest China, where there has been serious desertification recently. Observation data of ANPP and FPC in the study area were used to validate the model, and the model results were in good agreement with observation data and other published data. The model was then used to simulate the evaporation coefficient, FPC, and ANPP for the study area. The simulation results indicated that except for several sites, the k parameter was lower than 0.35 × 10-2 and implied a typical arid climate in the study area. Estimated FPC of the plant community was lower than 50% at most sites, and the annual ANPP was very low-less than 1 tons · ha-1 · yr-1 at 93.8% of sites. These simulation results could serve as references for vegetation restoration and livestock husbandry management in arid areas of China. [ABSTRACT FROM AUTHOR]

How urban vegetation was influenced by three decades of intensive urbanization in China is of great interest but rarely studied. In this paper, we used satellite derived Normalized Difference Vegetation Index (NDVI) and socioeconomic data to evaluate effects of urbanization on vegetation cover in China's 117 metropolises over the last three decades. Our results suggest that current urbanization has caused deterioration of urban vegetation across most cities in China, particularly in East China. At the national scale, average urban area NDVI (NDVI) significantly decreased during the last three decades ( P < 0.01), and two distinct periods with different trends can be identified, 1982-1990 and 1990-2006. NDVI did not show statistically significant trend before 1990 but decrease remarkably after 1990 ( P < 0.01). Different regions also showed difference in the timing of NDVI turning point. The year when NDVI started to decline significantly for Central China and East China was 1987 and 1990, respectively, while NDVI in West China remained relatively constant until 1998. NDVI changes in the Yangtze River Delta and the Pearl River Delta, two regions which has been undergoing the most rapid urbanization in China, also show different characteristics. The Pearl River Delta experienced a rapid decline in NDVI from the early 1980s to the mid-1990s; while in the Yangtze River Delta, NDVI did not decline significantly until the early 1990s. Such different patterns of NDVI changes are closely linked with policy-oriented difference in urbanization dynamics of these regions, which highlights the importance of implementing a sustainable urban development policy. [ABSTRACT FROM AUTHOR]

Influencing factor analysis is required for assessing the performance of vegetation fractional coverage (VFC) models with remote sensing imagery. This paper analyses influences of the radiometric correction level (RL), vegetation index (VI) and polynomial exponential power (EP) choice on VI-VFC relationship models. A SPOT 5 HRG image of Nanjing, China was chosen, and three RLs (digital number, top of atmosphere reflectance and post atmospheric correction reflectance) were used to derive six VIs, such as normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Fifty-four models describing the VI-VFC relationship were established, and the influences of the RL, VI and EP choice on the VI-VFC models were analysed based on a statistical analysis of determination coefficients (R 2) of these models. The results showed that model robustness was jointly influenced by the three factors, so these three factors should be synthetically taken into account in VI-VFC modelling. It is recommended to establish different models between the VFC and various VIs derived from different RLs, and then to select the better ones. [ABSTRACT FROM AUTHOR]

Green vegetation plays a vital role in urban ecosystem services. Rapid urbanization often tends to induce urban vegetation cover fragmentation (UVCF) in cities and suburbs. Mapping the changes in the structure (aggregation) and abundance of urban vegetation cover helps to make improved policies for sustainable urban development. In this paper, a new distance-based landscape indicator to UVCF, Frag, was proposed first. Unlike many other landscape indicators, Frag measures UVCF by considering simultaneously both the structure and abundance of vegetation cover at local scales, and thus provides a more comprehensive perspective in understanding the spatial distribution patterns in urban greenness cover. As a case study, the urban greenness fragmentation indicated by Frag was demonstrated in Wuhan metropolitan area (WMA), China in 2015 and 2020. Support vector machine (SVM) was then designed to examine the impact on the Frag changes from the associated factors, including urbanization and terrain characteristics (elevation and slope). The Frag changes were mapped at different scales and modeled by SVM from the selected factors, which reasonably explained the Frag changes. Sensitivity analysis for the SVM model revealed that urbanization showed the most dominant factor for the Frag changes, followed by terrain elevation and slope. We conclude that Frag is an effective scale-dependent indicator to UVCF that can reflect changes in the structure and abundance of urban vegetation cover, and that modeling the impact of the associated factors on UVCF via the Frag indicator can provide essential information for urban planners. [ABSTRACT FROM AUTHOR]

To enable grasslands to serve a larger ecological and service role against the backdrop of climate change and human activity, management is essential following long-term fencing. Using desert grassland that had been fenced for 20 years, we conducted experiments on different mowing frequencies (once, twice, and three times a year) and different amounts of vegetation coverage (mowing removal, mowing, and in situ mulch; and mowing, in situ mulch, and additional coverage) and control (no mowing, no removal, and no cover). In 2021 and mid-August 2022, the aboveground biomass, species diversity, and vegetation cover of each species were assessed, and the impacts of species complementarity and competition on the productivity of the fenced grassland were examined. Our findings revealed several significant outcomes: (1) Two and three times of mowing per year promoted growth and development of annual or biennial plants and increased species diversity of the community. (2) The interaction impact of mowing and covering two and three times a year was the most important, as it encouraged compensatory development of plants, particularly the dominant species, and increased aboveground biomass of the fenced grassland (p < 0.05). (3) The number of complementary species groups was c. 11% lower for mowing and covering than for mowing removal, indicating that the interspecific relationship for the mowing and covering interaction was dominated by competition, which was more beneficial to the increase in community productivity. (4) Lespedeza potaninii Vass. (which dominated the semi-shrubs) and Agropyron mongolicum Keng (which dominated the tufted grasses) exhibited compensatory growth, which alleviated the change of mowing disturbance and precipitation fluctuation on the productivity of this grassland ecosystem. (5) Adding vegetation cover after mowing positively influenced community production but did not significantly impact litter decomposition. Our research provides data support for the sustainable development of long-term fenced desert grasslands. [ABSTRACT FROM AUTHOR]

Coal is the most prevalent energy source in China and plays an important role in ensuring energy security. The continuous monitoring of coal mining activities is helpful to clarify the incremental space of coal production and establish a rational framework for future coal production capacity. In this study, a multi-source remote sensing approach utilizing SPOT 4, GF, and Landsat data is employed to monitor land cover and vegetation changes in the Juhugeng mining area of the Muli coalfield over a span of nearly 20 years. The analysis incorporates an object-oriented classification method and a vegetation parameter to derive insights. The findings reveal that the mining operations can be divided into two periods, since their initiation in 2003 until their cessation in 2021, with a dividing point around 2013/2014. The initial phase witnessed rapid and even accelerated expansion of the mine, while the subsequent phase was characterized by more stable development and the implementation of some restorative measures for the mine environment. Although the vegetation parameter, Fractional Vegetation Cover (FVC), indicates some reclamation efforts within the mining area, the extent of the reclaimed land remains limited. This study demonstrates the effective application of object-oriented classification in conjunction with the vegetation parameter FVC for monitoring coal mining areas. [ABSTRACT FROM AUTHOR]

The forest fire burned area is one of the most basic factors used to describe forest fires and plays a vital role in damage assessment. The development of the NSSI-NDVI vegetation index triangular space method enables simultaneous calculation of the flammable non-photosynthetic vegetation (NPV), combustible photosynthetic vegetation (PV), and incombustible bare soil (BS) fractional cover in forest areas. This can be used to compensate for the calculation method that was based on NDVI vegetation index only by comparing vegetation cover before and after forest fires, with the omission of the NPV burned area. To this end, the NSSI-NDVI triangular space shape consistency before and after forest fires was elucidated through combustion and ash wetting experiments. In addition, the feasibility of the NSSI-NDVI triangular space method for the accurate calculation of the post-fire vegetation damage area was verified. Finally, the applicability and accuracy of this research method were verified based on 10 m spatial resolution satellite hyperspectral images from before and after the forest fire in Lushan, Sichuan Province, China. The NSSI-NDVI triangular space method was used to calculate the PV, NPV, and BS coverage simultaneously, and component transformation was used to calculate the burned area and burned site separately. [ABSTRACT FROM AUTHOR]

Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a. [ABSTRACT FROM AUTHOR]

Geo-environmental factors are believed to be major determinants of rural poverty. However, few studies have quantified the effects of these factors on rural poverty in China. In this paper, we used county-level poverty incidence data and geo-environmental factors to explore spatial patterns of the incidence of poverty using global and local spatial autocorrelation analysis and to investigate the effect of geo-environment factors on rural poverty using a geo-detector model. Our results demonstrated that there was spatial clustering of the incidence of poverty in the study area. The incidence of poverty decreased from south to north and from the east and west to the central area. The incidence of high–high poverty areas was mainly distributed in the southeast of Guizhou Province and the incidence of low–low poverty areas was distributed in the northeast. The results also demonstrated that percentage of effective irrigation on arable land, slope, elevation and vegetation cover were the dominant factors explaining the spatial pattern of poverty. Interaction analysis demonstrated that the slope non-linearly enhanced the percentage of effective irrigation on arable land. Our findings suggested that geo-environment is the fundamental control factor explaining the spatial pattern of rural poverty in China. Through analysis of the impact of the geo-environment on the spatial pattern of poverty, this study provides a reference for effectively implementing targeted alleviation of poverty. [ABSTRACT FROM AUTHOR]

The objective of this study was to map and discriminate variations of vegetation net primary production (NPP) from the effects of precipitation variability and thereby identify the effects of grazing on NPP variations in the Xilingol Grassland. Satellite-based simulations of NPP collected over 14-year period and residuals method were used to normalize the effects of interannual precipitation on NPP. A fixed-size moving-window method was developed in this paper to quantify the annual grazing pressure (defined as grazing pressure index, GPI) on the regional scale, using vegetation cover images from 2001 to 2014. Temporal and spatial analysis methods were conducted to show patterns and trends of GPI and NPP residuals (NPP res ), and further to examine the effects of grazing pressure changes on variations of NPP res . Our results showed 80.7% of the study area experienced increasing NPP res and 18.5% with significantly increasing trends ( p  < 0.05). 76.7% of the study area showed decreasing grazing pressure, and 11.8% of areas experienced significantly decreasing trends ( p  < 0.05), with a small patches scattered distribution pattern. The negative correlations between NPP res and GPI were found in 94.6% of the study area, and 41.9% with significant correlation ( p  < 0.05); and long-term decreasing grazing pressure had positive impacts on vegetation NPP. They indicate grazing is a main driving factor of variations in vegetation NPP other than precipitation in Xilingol Grassland. The spatial patterns and temporal trends of NPP res , grazing pressure and responses of NPP res to grazing pressure changes varied among three main grassland types. [ABSTRACT FROM AUTHOR]

The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001–2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (RH) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future. [ABSTRACT FROM AUTHOR]

The wetlands in the middle and lower reaches of the Yangtze River are the main overwintering and perching places for cranes. To examine the habitat selection mechanism of cranes in this area, two natural wetland reserves, Shengjin Lake and Poyang Lake, which are the main habitats of typical cranes, were selected as the study area. Using 20 years of Landsat satellite image data (between 1999 and 2019), the vegetation cover index was calculated from a pixel dichotomy model, and the landscape pattern index was obtained through Fragstats. The entropy method was adopted to determine the weight of the landscape index, and then, the habitat suitability index was calculated. Combined with the number of typical crane populations in the reserve, the selection mechanism of overwintering habitat of cranes was revealed. On the change of land-use type, the crane habitat of Shengjin Lake transferred more to non-crane habitat, and other land types increased, resulting in the decrease of crane habitat area. However, the change of crane habitat in Poyang Lake Reserve was small, so it can accommodate more cranes to overwintering here. In terms of vegetation coverage, most of the vegetation cover areas of Shengjin Lake were woodland near or far from the lake, but the woodland was not the habitat of cranes. Most of the vegetation-covered areas of Poyang Lake are grassland near the lake, which provide rest and foraging places for cranes. In the landscape pattern, the number of landscape patches in Shengjin Lake was large, the degree of landscape fragmentation was higher than that in Poyang Lake, the landscape complexity was higher, and the landscape diversity was simpler. This is not conducive to the maintenance of crane habitat, but also reduces the attractiveness of overwintering cranes, while the landscape suitability of crane habitat in Poyang Lake was higher than that in Shengjin Lake, and cranes were more likely to choose Poyang Lake as their overwintering habitat. [ABSTRACT FROM AUTHOR]

Understory vegetation plays an important ecological role in maintaining the diversity of the ecosystem, the stability of ecosystem services, and the accumulation of nutrient elements, as an important part of a forest ecosystem. In this study, a new method of recognizing areas without understory vegetation is proposed. The method makes full use of the advantages of spectral characteristics, spatial structure information and temporal resolution of UAV images, and can quickly and simply distinguish understory, without vegetation cover. Combined with fractional vegetation coverage (FVC) and vegetation dispersion, understory, with no vegetation area, can be successfully recognized, and the Pr, Re and F1 are all above 85%. The proportion of bare soil under forest in our study area is 20.40%, 19.98% and even 41.69%. The study area is located in Changting County, Fujian Province, which is a typical red soil area in China where serious soil erosion is taking place in the forest. The method provides a promising, quick and economic way of estimating understory vegetation coverage with high spatial accuracy. [ABSTRACT FROM AUTHOR]

As one of the most intensively eroded regions worldwide, soil erosion assessment is currently gaining further momentum in the Loess Plateau (LP), China. However, there exist very few review studies that assess soil erosion in relation to land use and land cover (LULC), climate, and ecosystem services (ESs) at multiple temporal and spatial scales in the LP. Thus, this review provides a comprehensive review of the interactions among soil erosion assessment, climate change, LULC change, and ESs at different scales. We focused on the academic literature published in the English language and analyzed the coverage of soil erosion across 109 publications. Our results highlighted three aspects: (1) studies dealing with the effects of LULC change on soil erosion are abundant. A recent surge in research interest is to explore a threshold of vegetation cover to support a sustainable eco-hydrological environment in the LP. However, the impacts of multiple stressors on soil erosion have received limited attention. (2) The short-term soil erosion assessment dominated the published studies. Cross-spatial-scale and long-term erosion assessments have received little attention. (3) 43% of reviewed papers used a field measurement method to assess soil erosion with the validation of measurement methods. 41% of reviewed papers used empirical models to assess soil erosion, but the modeling validation remains an unsolved problem. Then, three recommendations are listed as follows: (1) the combined impacts of LULC and climate change on soil erosion need to be further examined. (2) Long-term and cross-spatial-scale soil erosion assessments need to be established. (3) Soil erosion modeling accuracy on a large scale needs to be developed. • The assessment of multiple stressors on soil erosion has received limited attention. • Poor coverage of multi-spatial scale and long-term soil erosion assessment. • Large scale soil erosion modeling validation remains an unsolved problem. • A recent surge in academic interest is to explore thresholds of vegetation cover in the Loess Plateau. [ABSTRACT FROM AUTHOR]

[Display omitted] • The vegetation cover status of 9 provinces in the Yellow River basin was analyzed. • The water poverty status of 9 provinces in the Yellow River basin is analyzed. • There is a U-shaped relationship between vegetation cover and water poverty. • There were differences in the effects of vegetation cover on WPI components. Vegetation plays an important role in improving the water resource environment. Faced with the serious water poverty problem in various regions of China, whether vegetation restoration can further alleviate water poverty needs to be studied urgently. Taking 9 provinces in the Yellow River basin in China as an example, this paper first uses the framework of the existing water poverty index (WPI) to calculate the WPI of each province over the years from 1999 to 2019 and evaluate the water poverty status of each province. Later, the normalized difference vegetation index (NDVI) was used to analyze the vegetation restoration status of each area. Finally, using provincial panel data for the study area from 1999 to 2019, a comprehensive feasible generalized least squares (FGLS) model was used to analyze the impact of vegetation cover on water poverty. It was found that there was no pure linear relationship between vegetation cover and water poverty; instead, there was a significant U-shaped relationship. Moreover, the influence of vegetation cover on each component of the WPI has certain differences. This research advances knowledge on the relationship between vegetation cover and water poverty and provides ideas for effectively alleviating regional water poverty. [ABSTRACT FROM AUTHOR]

As China has recently launched the GaoFen-1 satellite (GF-1) carrying on the wide-field view (WFV) sensor, it is a challenging task to make full use of its observations to produce the fractional vegetation cover (FVC). In light of this, our study presents a comprehensive algorithm to generate a 16 m/10-day FVC product by considering the vegetation types characteristics. For forests, considering the foliage clumping effect, FVC was estimated from the gap probability theory using GF-1 leaf area index (LAI) and clumping index (CI) as a priori knowledge; for non-forests, FVC was estimated from the dimidiate pixel model using GF-1 normalized difference vegetation index (NDVI). The performance of GF-1 FVC from 2018 to 2020 was evaluated using FVC ground measurements obtained from 7 sites for crops, grasslands, and forests in China. The direct validation indicated that the performance of the FVC product was satisfactory, as evidenced by R2 = 0.55, RMSE = 0.15 and BIAS = 0.01 for all vegetation types. Furthermore, the GF-1 FVC exhibited better performance compared to the GEOV3 FVC at a spatial resolution of 300 meters. Moreover, the 10-day temporal interval of GF-1 FVC product successfully facilitated the extraction of regional phenological information at a spatial resolution of 16 meters. [ABSTRACT FROM AUTHOR]

Soil erosion is a global environmental problem. The rapid monitoring of the coverage changes in and spatial patterns of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at regional scales can help improve the accuracy of soil erosion evaluations. Three deep learning semantic segmentation models, DeepLabV3+, PSPNet, and U-Net, are often used to extract features from unmanned aerial vehicle (UAV) images; however, their extraction processes are highly dependent on the assignment of massive data labels, which greatly limits their applicability. At the same time, numerous shadows are present in UAV images. It is not clear whether the shaded features can be further classified, nor how much accuracy can be achieved. This study took the Mu Us Desert in northern China as an example with which to explore the feasibility and efficiency of shadow-sensitive PV/NPV classification using the three models. Using the object-oriented classification technique alongside manual correction, 728 labels were produced for deep learning PV/NVP semantic segmentation. ResNet 50 was selected as the backbone network with which to train the sample data. Three models were used in the study; the overall accuracy (OA), the kappa coefficient, and the orthogonal statistic were applied to evaluate their accuracy and efficiency. The results showed that, for six characteristics, the three models achieved OAs of 88.3–91.9% and kappa coefficients of 0.81–0.87. The DeepLabV3+ model was superior, and its accuracy for PV and bare soil (BS) under light conditions exceeded 95%; for the three categories of PV/NPV/BS, it achieved an OA of 94.3% and a kappa coefficient of 0.90, performing slightly better (by ~2.6% (OA) and ~0.05 (kappa coefficient)) than the other two models. The DeepLabV3+ model and corresponding labels were tested in other sites for the same types of features: it achieved OAs of 93.9–95.9% and kappa coefficients of 0.88–0.92. Compared with traditional machine learning methods, such as random forest, the proposed method not only offers a marked improvement in classification accuracy but also realizes the semiautomatic extraction of PV/NPV areas. The results will be useful for land-use planning and land resource management in the areas. [ABSTRACT FROM AUTHOR]

We present the first gridded and temporally continuous quantitative pollen-based plant-cover reconstruction for temperate and northern subtropical China over the Holocene (11.7 ka to present) obtained by applying the Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) model. The objective is to provide a dataset of pollen-based land cover for the last ca. 12 millennia that is suitable for palaeoclimate modelling and for the evaluation of simulated past vegetation cover from dynamic vegetation models and anthropogenic land-cover change (ALCC) scenarios. The REVEALS reconstruction was achieved using 94 selected pollen records from lakes and bogs at a 1 ∘ × 1 ∘ spatial scale and a temporal resolution of 500 years between 11.7 and 0.7 ka and in three recent time windows (0.7–0.35 ka, 0.35–0.1 ka, and 0.1 ka to present). The dataset includes REVEALS estimates of cover and their standard errors (SEs) for 27 plant taxa in 75 1 ∘ × 1 ∘ grid cells distributed within the study region. The 27 plant taxa were also grouped into 6 plant functional types and 3 land-cover types (coniferous trees CT, broadleaved trees BT, and C 3 herbs/open land (C 3 H/OL)), and their REVEALS estimates of cover and related SEs were calculated. We describe the protocol used for the selection of pollen records and the REVEALS application (with parameter settings) and explain the major rationales behind the protocol. As an illustration, we present, for eight selected time windows, gridded maps of the pollen-based REVEALS estimates of cover for the three land-cover types (CT, BT, and C 3 H/OL). We then discuss the reliability and limitations of the Chinese dataset of Holocene gridded REVEALS plant cover, and its current and potential uses. The dataset is available at the National Tibetan Plateau Data Center (TPDC; Li, 2022; 10.11888/Paleoenv.tpdc.272292). [ABSTRACT FROM AUTHOR]

Decades of reckless deforestation have caused serious soil erosion and land desertification issues in the Loess Plateau (LP). "Grain for Green" Program (GFGP), one of the world's largest ecological restoration projects, is crucial to improve the ecological environment. Previous studies have demonstrated that GFGP lowers soil erosion in the LP. However, there are trade-offs and synergies between ecological services. Does strengthening soil conservation prevent enhancing other ecosystem services? Consequently, can the GFGP improve many ecological services simultaneously? This study compares changes in NDVI prior to and following the implementation of the GFGP in LP to the enhancement of ecosystem services. During the research period, the LP's overall vegetation cover rose significantly, particularly in the GFGP's major counties. Significant improvements were made to ecosystem services such as carbon sequestration, soil conservation, and habitat quality. The GFGP enhanced the synergistic linkages between ecological services. The implementation of the GFGP decreased water yield, suggesting trade-offs with other ecosystem services. Additionally, we investigate regional trade-offs/synergies between ecosystem services and their influencing factors, which were influenced by topographic and climatic variables. To maximize the benefits of ecological restoration efforts, we need a deeper understanding of the relationships between ecosystem services and the mechanisms that drive them. Thus, policymakers can scientifically exert control over local influences on ecosystem services, either by boosting the provision of specific services or by limiting specific influences in order to maintain ecosystem stability. [ABSTRACT FROM AUTHOR]

• Spatiotemporal variation in vegetation on the Loess Plateau was investigated. • Precipitation was the greatest driving factor. • There was a bivariate or nonlinear enhancement between all factors. • Key factors' adaptation range or type suitable for vegetation cover was identified. Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVI ymax) and growing season mean NDVI (NDVI gsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVI ymax and NDVI gsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVI ymax and NDVI gsmean. However, precipitation was the most critical factor both on NDVI ymax and NDVI gsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies. [ABSTRACT FROM AUTHOR]

High-resolution Corona imagery acquired by the United States through spy missions in the 1960s presents an opportunity to gain critical insight into historic land cover conditions and expand the timeline of available data for land cover change analyses, particularly in regions such as Northern China where data from that era are scarce. Corona imagery requires time-intensive pre-processing, and the existing literature lacks the necessary detail required to replicate these processes easily. This is particularly true in landscapes where dynamic physical processes, such as aeolian desertification, reshape topography over time or regions with few persistent features for use in geo-referencing. In this study, we present a workflow for georeferencing Corona imagery in a highly desertified landscape that contained mobile dunes, shifting vegetation cover, and a few reference points. We geo-referenced four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery with an overall accuracy of 11.77 m, and the workflow is documented in sufficient detail for replication in similar environments. [ABSTRACT FROM AUTHOR]

The Mu Us Sandy Land is a key region of man-made desert control and farmland to forest (grass) return in China. Despite global change and the strong influence of human activities, the vegetation in this region has been significantly improved and restored. In this study, multisource remote sensing data and multiple indicators were used to quantitatively monitor and evaluate the vegetation restoration status in this area. The driving factors were also analysed. The results show that in the past 20 years, nearly the entire Mu Us Sandy Land significantly and substantively recovered. The regional fractional vegetation cover increased, with an average annual growth rate of 0.59% and obvious spatial heterogeneity. The nine most important driving factors could comprehensively account for 58.38% of the spatial distribution of the vegetation coverage. Factors such as land use and land cover, the aridity index, and gross domestic product had the most significant impact, followed by precipitation and temperature. The results confirmed that the vegetation was restored and improved in the Mu Us Sandy Land and determined the main driving factors, which is helpful for vegetation restoration and ecological improvement on sandy land similar to the Mu Us Sandy Land. [ABSTRACT FROM AUTHOR]

Through field investigation and comprehensive analysis, this paper classified green space system on campus of Southwest University of Science & Technology into points, lines and surfaces, and used basic theories of ecology, botany, garden aesthetics, and plant landscape assessment to analyze ornamental characteristics, seasonal changes, spatial layers and cultural connotations of its typical plant landscapes, so as to assess campus plant landscape comprehensively in the near future. [ABSTRACT FROM AUTHOR]

Taking natural grassland on the northern slope of the Qilian Mountain for example, this paper investigated and compared above-ground and bclowground biomass of grassland in multi-year enclosure (20 years), one-year enclosure, control areas (natural grazing areas). The results showed that coverage and height of the enclosure sample plots were significantly higher than that of natural grazing area (P<0.05); mean aboveground biomass of grassland: multi-year enclosure (316.58 g/m2) > one-year enclosure area (299.07 g/m2) > multi-year enclosure control area (254.39 g/m2) > one-year enclosure control area (187.37 g/m2); bclowground biomass: multi-year enclosure (2,906.90 g/m2) > one-year enclosure area (2,587.26 g/m2) > multi-year enclosure control area (2,378.93 g/m") > one-year enclosure control area (2,029.17 g/m"); mean aboveground biomass of natural grassland was 263.60 g/m2, mean belowground biomass 2,225.56 g/m2; ratio of belowground biomass to aboveground biomass varied between 6.79 and 12.90, distribution of bclowground biomass and aboveground biomass in each plot showed significant differences (P <0.05). Enclosure was favorable for improving the coverage and biomass of natural grassland plant communities in the Qilian Mountains. [ABSTRACT FROM AUTHOR]

Urban squares are an important part of a city's overall spatial environment. However, many urban squares lack rational designs, causing the thermal environment to deteriorate. To ensure sustainable urban development, urban square microclimates should be improved. Given that, this study investigates the effects of three coverages of three landscape elements of urban squares through modeling and simulation using the ENVI-met model validated by field measurements. The correlation between physiological equivalent temperature (PET) and different amounts of landscape elements is investigated using Spearman analysis. This study presents a case study of a typical urban square in a cold climate area. Design strategies in the area are proposed. The results show that the microclimate and thermal comfort of the urban square can be improved by expanding water bodies, modest increasing buildings and optimizing vegetation. Vegetation is the most important landscape element affecting thermal comfort in the urban square. The PET can be reduced by about 1.5 °C by increasing the vegetation cover from 40% to 70%. However, the degree of microclimate regulation by vegetation is disturbed by water bodies and buildings (|ρ| ≥ 0.5). Therefore, to achieve a more comfortable thermal environment, a combination of landscape elements should be considered. [ABSTRACT FROM AUTHOR]

Differences among regions in the meteorological, surface, and vegetation cover characteristics lead to differences in sediment transport. In this paper, field data were measured above five surface types in the arid zone of northern China to analyze the vertical distribution of sediments in the near-surface transport layer (0-50 cm) and total sediment transport. We were able to express the sediment flux in this zone as an exponential function for all surface types. Sediment transport differed among the surfaces, with the greatest transport above mobile dunes, followed by cultivated land, semi-fixed dunes, fixed dunes, and a shrub community. Vegetation coverage is important for reducing wind erosion and increasing the proportion of the creep sediment transport. With increasing vegetation cover, the soil erosion decreased; the amount for the mobile dunes was 659 times that of the shrub community, and 8 times for fixed dunes. The proportion of sediment transported by creeping differed among the five surface types, with a minimum of 33% for the mobile dunes, and maximum of 44% for the shrub community. [ABSTRACT FROM AUTHOR]

Larix chinensis Beissn., as a native, dominant and climate-sensitive coniferous species at Mount Taibai timberline, Qinling mountains, is rarely disturbed by anthropogenic activities; thus, it is an ideal proxy for the investigation of climate change or vegetation evolution. In this study, we applied dendrochronological methods to the L. chinensis tree-ring series from Mt. Taibai and investigated the relationships between tree-ring widths and NDVI/climate factors using Pearson correlation analysis. On the basis of the remarkable positive correlations (r = 0.726, p < 0.01, n = 23) between local July normalized difference vegetation indices (NDVI) and tree-ring width indices, the regional 146-year annual maximum vegetation density was reconstructed using a regression model. The reconstructed NDVI series tracked the observed data well, as the trans-function accounted for 52.8% of observed NDVI variance during AD 1991–2013. After applying an 11-year moving average, five dense vegetation coverage periods and six sparse vegetation coverage periods were clearly presented. At a decadal scale, this reconstruction was reasonably and negatively correlated with a nearby historical-record-based dryness/wetness index (DWI), precisely verifying that local vegetation cover was principally controlled by hydrothermal variations. Spectral analysis unveiled the existence of 2–3-year, 2–4-year, 5–7-year and 7–11-year cycles, which may potentially reflect the connection between local NDVI evolution and larger-scale circulations, such as the El Niño–Southern Oscillation (ENSO) and solar activity. This study is of great significance for providing a long-term perspective on the dynamics of vegetation cover in the Qinling mountains, and could help to guide expectations of future forest variations. [ABSTRACT FROM AUTHOR]

It is known that evapotranspiration (ET) differs before and after vegetation change in watersheds. However, impacts of vegetation change on ET remain incompletely understood. In this paper, we investigated the process-specific, nonclimatic contribution (mainly vegetation coverage changes) to ET at grid, sub-basin, and basin scales using observation and remote sensing data. The Poyang Lake Basin was selected as the study area, which experienced a fast vegetation restoration from 1983 to 2014. Our results showed that vegetation cover change produced contrasting effects on annual ET in magnitude and direction during shifts from a less covered to a more covered stage. At the early stage (1983–1990), with vegetation cover of 30%, vegetation cover change produced negative effects on ET over the basin. At the middle stage (1990–2000), the vegetation coverage increased at a fast pace and the negative effects gradually shifted to positive. At the late stage (2000–2014), the vegetation coverage remained high (over 60%) and maintained a positive relationship with ET. In summary, the vegetation effects are collaboratively influenced by both vegetation coverage and its change rate. Our findings should be helpful for a comprehensive understanding of complicated hydrological responses to anthropogenic revegetation. [ABSTRACT FROM AUTHOR]

Summary: Soil moisture is an effective water source for plant growth in the semi-arid Loess Plateau of China. Characterizing the response of deep soil moisture to land use and afforestation is important for the sustainability of vegetation restoration in this region. In this paper, the dynamics of soil moisture were quantified to evaluate the effect of land use on soil moisture at a depth of 2m. Specifically, the gravimetric soil moisture content was measured in the soil layer between 0 and 8m for five land use types in the Longtan catchment of the western Loess Plateau. The land use types included traditional farmland, native grassland, and lands converted from traditional farmland (pasture grassland, shrubland and forestland). Results indicate that the deep soil moisture content decreased more than 35% after land use conversion, and a soil moisture deficit appeared in all types of land with introduced vegetation. The introduced vegetation decreased the soil moisture content to levels lower than the reference value representing no human impact in the entire 0–8m soil profile. No significant differences appeared between different land use types and introduced vegetation covers, especially in deeper soil layers, regardless of which plant species were introduced. High planting density was found to be the main reason for the severe deficit of soil moisture. Landscape management activities such as tillage activities, micro-topography reconstruction, and fallowed farmland affected soil moisture in both shallow and deep soil layers. Tillage and micro-topography reconstruction can be used as effective countermeasures to reduce the soil moisture deficit due to their ability to increase soil moisture content. For sustainable vegetation restoration in a vulnerable semi-arid region, the plant density should be optimized with local soil moisture conditions and appropriate landscape management practices. [Copyright &y& Elsevier]

Abstract: For the fragile-ecosystem arid land, the macro and integral information about the land cover types and area is important for the restoration and the improvement of the ecological environment. In this paper, the arid Gaotai County of Gansu Province, China, was taken as a study case to discuss classified method of the arid land based on various indexes. Taking the advantages of different temporal images into account, two TM images acquired in October and June respectively were selected to conduct the research. Firstly, a series of indexes such as NDVI, vegetation coverage, MNDWI, Albedo, Wetness and SI were calculated. Secondly, combining Robert algorithms and artificial experience methods, the integrated index threshold for distinguishing vegetation from other land types was calculated. Thirdly, using the threshold and decision tree classifier, the middle-high and low coverage vegetation were extracted from both two images. Fourthly, the vegetation areas from two images were combined to form the total vegetation cover types (Oasis), which was used to mask October images, and the outside of mask area was continually classified into the types such as the salinization land, water, bare land and wetlands and other land cover types with decision tree classifier. At last, combining all cover types together, the land cover classification map of whole county was produced. Compared with the land cover types interpreted from the high resolution images and field investigation points, this method was found achieving the accuracy of 91.5%. [Copyright &y& Elsevier]