NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China

Vulnerability and driving mechanism of four typical grasslands in China under the coupled impacts of climate change and human activities

Understanding of how different grasslands types respond to climate change and human activities across different spatial and temporal dimensions is crucial for devising effective strategies to prevent grasslands degradation. In this study, we developed a novel vulnerability assessment model for grasslands that intricately evaluates the combined impact of climate change and human activities. We then applied this model to analyze the vulnerability and driving mechanism of four representative Chinese grasslands to climate change and human activities. Our findings indicate that the vulnerability of the four grasslands would show a pattern of higher in the west and lower in the east under the influence of climate change alone. However, when human activities are factored in, the vulnerability across the four grasslands tends to homogenize, with human activities notably reducing the vulnerability of alpine grasslands in the west and, conversely, increasing the vulnerability of grasslands in the east. Furthermore, our study reveals distinct major environmental drivers of grasslands vulnerability across different regions. The two western alpine grasslands exhibit higher vulnerability to annual mean temperature and isothermality compared to the eastern temperate grasslands, while their vulnerability to precipitation of the coldest quarter is lower than that of the eastern temperate grasslands. These findings are helpful for understanding the multifaceted causes and mechanisms of grasslands degradation, providing a scientific foundation for the sustainable management and conservation of grassland resources.

Influence of climate variability and land cover dynamics on the spatio-temporal NDVI patterns in western hydrological regions of Bangladesh

Analyzing vegetation greenness considering climate and land cover changes is crucial for Bangladesh given the historically drier North-West and South-West regions of Bangladesh have shown prominent climatic and hydrological variations. Therefore, this study assessed the spatial and temporal variation of NDVI and its relationship with climate and land cover changes from 2000 to 2022 for these regions. In this study, Moran's I and Getis Ord Gi* were employed for spatial autocorrelation and Mann-Kendall, Sen's slope test along with Innovative Trend Analysis were deployed to identify temporal trends of NDVI. RMSE, MAE and R-squared values were assessed between computed and observed PET. Correlation of NDVI with climate variables were assessed through multivariate correlation analysis and correlation mapping. Additionally, Pearson product moment correlation was applied between different types of land cover and NDVI. Spatial autocorrelation outcomes showed that NDVI values have been clustered in distinct hotspots and cold-spots over the years. Temporal trend detection results indicate that NDVI values are rising significantly all over the regions. Multivariate correlation analysis identified no climate variable to be the limiting factor for NDVI changes. Similarly, the precipitation-NDVI correlation map displayed no significant correlation. Nonetheless, temperature-NDVI correlation map illustrated varying degrees of mostly moderate and strong positive correlations with distinct negative correlation results in the Sundarbans of South-West region. Land cover pattern analysis with NDVI showed a positive correlation to forest, cropland and vegetation area increasing and negative correlation to grassland and barren area decreasing. In this regard, Rangpur division exhibited stronger correlations than Rajshahi division in North-West. The findings indicate that NDVI changes in the regions are largely dependent on land cover changes in comparison to climate trends. This can instigate further research in other hydrological regions to explore the natural and man-made factors that can affect the greenery and vegetation density in specific regions.

Assessing the effects of climate and human activity on vegetation change in Northern China

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990-2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

Water turbidity dynamics using random forest in the Yangtze River Delta Region, China

Turbidity is a water quality indicator that is essential for the sustainable development of aquatic ecosystems and the protection of biodiversity. The turbidity of different water surfaces and its response mechanisms to regional climatic factors and human activities in the Yangtze River Delta Region (YRDR), an important rapid economic development region in China, remain poorly understood. To enhance the knowledge of turbidity variations and dominant drivers of YRDR water surfaces, a complete long-term turbidity series was obtained using Landsat images from 1990 to 2020. The results show that the turbidity trend differed from -1.3 NTU/yr to 0.7 NTU/yr in different water surfaces. Turbidity decreased significantly in the mainstream of the Yangtze River (MYR), aquaculture ponds (AP) and other water bodies, whilst increasing significantly in the medium lakes (ML) and mainstream of the Qiantang River (MQR). Meanwhile, no significant changes in turbidity were observed in the great lakes (GL) and small lakes (SL). Rather than climatic factors, urbanisation and decreasing wastewater discharge were the dominant drivers of turbidity trends during the study period. In addition, ecological engineering in AP increased water transparency. The Three Gorges Dam also decreased turbidity in MYR. Increasing turbidity in the downstream of MQR was driven by increasing seasonal water surfaces and reclamation projects near Hangzhou Bay. GL faced no significant increase in turbidity due to the offset of afforestation to urbanisation-induced turbidity increase. These findings provide important information for government decision-making for subsequent aquatic environmental protection and restoration in the YRDR.

Considering Climatic Factors, Time Lag, and Cumulative Effects of Climate Change and Human Activities on Vegetation NDVI in Yinshanbeilu, China

Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area's normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area.

Wetland vegetation cover changes and its response to climate changes across Heilongjiang-Amur River Basin

The Heilongjiang-Amur River Basin is one of the largest and most complex aquatic systems in Asia, comprising diverse wetland resources. The wetland vegetation in mid-high latitude areas has high natural value and is sensitive to climate changes. In this study, we investigated the wetland vegetation cover changes and associated responses to climate change in the Heilongjiang-Amur River Basin from 2000 to 2018 based on the growing season (May to September) climate and LAI data. Our results indicated that the wetland LAI increased at 0.014 m2·m-2/yr across Heilongjiang-Amur River Basin with the regional climate showed wetting and warming trends. On a regional scale, wetland vegetation in China and Russia had positive partial correlation with solar radiation and minimum air temperature, with precipitation showing a slight lag effect. In contrast, wetland vegetation in Mongolia had positive partial correlation with precipitation. These correlations were further investigated at different climate intervals. We found the precipitation is positively correlated with LAI in the warm regions while is negatively correlated with LAI in the wet regions, indicating an increase in precipitation is beneficial for the growth of wetland vegetation in heat sufficient areas, and when precipitation exceeds a certain threshold, it will hinder the growth of wetland vegetation. In the cold regions, we found solar radiation and minimum air temperature are positively correlated with LAI, suggesting SR and minimum air temperature instead of mean air temperature and maximum air temperature play more important roles in affecting the wetland vegetation growth in the heat limited areas. The LAI was found to be negatively correlated with maximum air temperature in the arid areas, indicating excessive temperature would inhibit the wetland vegetation growth when the water is limited. Our investigation can provide a scientific foundation for the trilateral region in wetland ecosystem protection and is beneficial for a more comprehensive understanding of the responses of wetlands in the middle and high latitudes to climate change.

Spatiotemporal change and driving factors of ecological status in Inner Mongolia based on the modified remote sensing ecological index

Remote sensingmonitoring of regional ecological quality has advanced significantly with the rapid developments of remote sensing technology. At present, remote sensing ecological index (RSEI) has been widely used in ecological status monitoring. However, RSEI was proposed for urban environments, and the rationality and accuracy of its applicability to desert-dominated arid region ecosystems need to be demonstrated. Therefore, in this study, we incorporated desertification monitoring index (DMI) and salinity monitoring index (SMI) to RSEI and developed the modified remote sensing ecological index (MRSEI) for arid regions. Moreover, we analyzed the stability of MRSEI in ecological status monitoring for arid regions. The MRSEI was then used to evaluate the ecological quality of Inner Mongolia from 2000 to 2020 and exploring its causes. The results show that (1) Although the evaluation results of RSEI and MRSEI are more consistent in areas with high ecological status grades, the MRSEI results are more cautious and reliable in extreme conditions (e.g., desertification, salinization) than the RSEI. (2) Approximately 87.66% of ecological quality have improved or remain stable from 2000 to 2020, but the remaining areas (accounting for 12.34% of the whole area) are still under degraded conditions. This demonstrates that although local governments have made some progress in ecological conservation, the areas that are fluctuating or degraded still require protection or management. (3) In Inner Mongolia, the ecological quality which drove by precipitation (P) & temperature (T) accounting for 26.67% of the study area, population density (D) and GDP per capita (G) affected 13.23% of regional ecological quality. Overall, this research is crucial for evaluating spatial and temporal changes in arid region ecology and establishing conservation strategies.

A novel framework for vegetation change characterization from time series landsat images

Understanding terrestrial ecosystem dynamics requires a comprehensive examination of vegetation changes. Remote sensing technology has been established as an effective approach to reconstructing vegetation change history, investigating change properties, and evaluating the ecological effects. However, current remote sensing techniques are primarily focused on break detection but ignore long-term trend analysis. In this study, we proposed a novel framework based on a change detection algorithm and a trend analysis method that could integrate both short-term disturbance detection and long-term trends to comprehensively assess vegetation change. With this framework, we characterized the vegetation changes in Zhejiang Province from 1990 to 2020 using Landsat and landcover data. Benefiting from combining break detection and long-term trend analysis, the framework showcased its capability of capturing a variety of dynamics and trends of vegetation. The results show that the vegetation was browning in the plains while greening in the mountains, and the overall vegetation was gradually greening during the study period. By comparison, detected vegetation disturbances covered 57.71% of the province's land areas (accounting for 66.92% of the vegetated region) which were mainly distributed around the built-up areas, and most disturbances (94%) occurred in forest and cropland. There were two peak timings in the frequency of vegetation disturbances: around 2003 and around 2014, and the proportions of more than twice disturbances in a single location were low. The results illustrate that this framework is promising for the characterization of regional vegetation growth, including long-term trends and short-term features. The proposed framework enlightens a new direction for the continuous monitoring of vegetation dynamics.

Spatial and temporal variations of grassland vegetation on the Mongolian Plateau and its response to climate change

The Mongolian Plateau is an arid and semi-arid region with grassland as its main vegetation. It has a fragile ecosystem and is a sensitive area for global warming. The study is based on MODIS NDVI data and growth season meteorological data from 2000 to 2018, this study examined the spatial and temporal variation characteristics of grassland vegetation on the Mongolian Plateau during the growing season using trend analysis, partial correlation analysis, and residual analysis, and it explores the dual response of NDVI changes to climate and human activities. The study’s findings demonstrated that the growing season average NDVI of grassland vegetation on the plateau gradually increased from southwest to northeast during the growing season; the growing season average NDVI demonstrated a significant overall increase of 0.023/10a (p < 0.05) from 2000 to 2018, with an increase rate of 0.030/10a in Inner Mongolia and 0.019/10a in Mongolia; the area showing a significant increase in NDVI during the growing season accounted for 91.36% of the entire study area. In Mongolian Plateau grasslands during the growing season of 2000–2018, precipitation and downward surface shortwave radiation grew significantly at rates of 34.83mm/10a and 0.57 W/m2/10a, respectively, while average air temperature decreased slightly at a rate of −0.018°C/10a. Changes in meteorological factors of grassland vegetation varied by region as well, with Inner Mongolia seeing higher rates of precipitation, lower rates of average air temperature, and lower rates of downward surface shortwave radiation than Mongolia. On the Mongolian Plateau, the NDVI of grassland vegetation in the growing season showed a significant positive correlation with precipitation (0.31) and a significant negative correlation with average air temperature (−0.09) and downward surface shortwave radiation (−0.19), indicating that increased in NDVI was driven by an increase in precipitation paired with a decrease in air temperature and a decrease in surface shortwave radiation. The overall increase in NDVI caused by human activity in the grasslands of the Mongolian Plateau was primarily positive, with around 18.37% of the region being beneficial. Climate change and human activity both affect NDVI variations in Mongolian Plateau grasslands, which are spatially heterogeneous. Moderate ecological engineering and agricultural production activities are crucial for vegetation recovery. This work is crucial to further understanding surface–atmosphere interactions in arid and semi-arid regions in the context of global climate change.

The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years. Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets (GLC2000, GlobCover, CCI-LC, MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10⁴ to 332.46×10⁴ km², with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of east-central Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products, with an overall accuracy (OA) reaching 77.51% and a kappa coefficient of 0.51; CLCD is slightly less accurate than MCD12Q1 (OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its 30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China's grasslands and call for researchers and the government to actively produce a new generation of grassland maps.

Ecological restoration programs dominate vegetation greening in China

Many ecological restoration programs have been implemented in China during the last two decades. At the same time, the vegetation has turned green significantly in China. However, few studies have directly evaluated the contribution of the ecological restoration programs to vegetation greening in comparison with the contribution of climate change using high-resolution data of afforestation areas at the national scale. We used newly compiled high-resolution data on yearly forest plantation and mountain closure, the daily climate data from the 2480 meteorological stations and GIMMS 3g NDVI data. We used a multiple linear regression model to compare the influence of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We then used the hierarchical variance partitioning method to evaluate the relative contribution of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We found a significant greening trend in China from 1999 to 2015 with an annual increase rate of 0.0017 yr^-1 in the mean growing season NDVI. The ecological restoration programs dominated the vegetation greening in northern China and the southern coastal regions, indicating a good performance of restoration programs in these regions. In contrast, temperature or precipitation dominated the vegetation greening in southwestern China, Inner Mongolia and the implementation regions of several ecological restoration programs in northeastern China. Among the ecological restoration programs except the Three-North Shelterbelt Forest Program, the effect of ecological restoration programs on vegetation greening was stronger than the total effects of temperature and precipitation changes. Our study presents a systematic assessment on the contribution of ecological restoration programs to the vegetation greening in China, accessed the role on vegetation greening of different ecosystem restoration programs. We analyzed the reasons for the differences in the contribution of different ecological restoration programs to vegetation greening and provided insights facilitating policy makers to prioritize future restoration planning.

Vegetation dynamics and their relationships with climatic factors in the "Golden Triangle" region

The "Golden Triangle" is located on the border between Myanmar, Laos, and Thailand, and slash-and-burn cultivation is an ancient and typical land type in this region. With the development of the "The Belt and Road" strategy of China and the climate change, the vegetation information is bound to change intensively under the combined influence of alternative plantation projects and economic policies. Here we used MOD13Q1-normalized differential vegetation index (NDVI) and meteorological data to analyze the variation of vegetation coverage and its correlation with climatic factors (temperature and precipitation) during the period of 2000-2018 by using trend analysis, stability analysis, and partial correlation analysis. The results showed that the overall vegetation coverage of this region exerted the trend of improvement and became more stable over time. Spatially, the agglomeration degree became weaker as time goes during 2000-2018. The precipitation was more closely correlated with NDVI than temperature, indicating that precipitation could be the main limiting factor influencing vegetation change in this area. The correlation between NDVI and climatic factors exhibited differences among different seasons, with NDVI being less correlated with temperature and precipitation in spring and summer and more correlated with them in autumn and winter. Investigating the long-term vegetation coverage of this region and analyzing the trend of climate change is beneficial to understand the development trend of the ecological environment and resource potential in this region. Simultaneously, it can provide a favorable ecological evaluation for The Belt and Road strategy and provide important scientific suggestions and guidance for the sustainable development of ecosystems and human society under the drastic environmental changes.

Monitoring Desertification Using Machine-Learning Techniques with Multiple Indicators Derived from MODIS Images in Mu Us Sandy Land, China

Mu Us Sandy Land is a typical semi-arid vulnerable ecological zone, characterized by vegetation degradation and severe desertification. Effectively identifying desertification changes has been a topical environmental issue in China. However, most previous studies have used a single method or remote sensing index to monitor desertification, and lacked an efficient and high-precision monitoring system. In this study, an optimal monitoring scheme that considers multiple indicators combination and different machine learning methods (Classification and Regression Tree-Decision Tree, CART-DT; Random Forest, RF; Convolutional Neural Networks, CNN) was developed and used to analyze the spatial–temporal patterns of desertification from 2000 to 2018 in Mu Us Sandy Land. The results showed that: (a) The random forest model performed best for monitoring desertification based on medium and low-resolution remote sensing images, and the four-index combination (Albedo, NDVI, LST and TGSI) obtained the highest classification accuracy (OA = 87.67%) in Mu Us Sandy Land. Surprisingly, the model accuracy of the three-index combination (NDVI, LST and TGSI) (OA = 85.74%) is comparable to the four-index combination. (b) The TGSI index used to characterize soil information performs well, while the LST is not conducive to the extraction of desertified land in several desertification monitoring indicators. (c) Since 2000, the area of extremely severe desertified land has shown a reversal trend; however, there is significant interannual fluctuation in the total and light desertification land area affected by extreme climate. This research provides a novel approach and a valuable reference for monitoring the evolution of desertification in regional studies, and the results improve the research system of desertification and provide a data basis for desertification cause analysis and prevention.

Does the recent afforestation program in Ethiopia influenced vegetation cover and hydrology? A case study in the upper awash basin, Ethiopia

The Ethiopian government planned afforestation programs in the past decades whereas more attention was given to tree plantation since the 2010 year. However, the effectiveness of the afforestation programs and its impacts on vegetation cover and hydrology has not been well studied. This study aims to assess the recent campaigned afforestation program and its impact on vegetation cover and hydrology in the upper Awash basin, Ethiopia. Landsat 8 images of 2013–2020 years were used to calculate the NDVI for the upper Awash basin to assess trends in vegetation greenness for the basin. Moreover, observed streamflow and precipitation datasets of the basin were collected and used for assessing the impact of the afforestation on hydrology. The study result showed decreasing NDVI values despite the afforestation programs in the upper Awash basin. This shows either afforestation rate was less than the deforestation rate or the tree plantation campaign was not effective in the basin. In addition, the campaign based tree plantation focused on the number of tree planted not on how many trees are grown. On the other hand, mean annual precipitation and streamflow were generally increased from 2013 to 2020 in the upper Awash basin. Declining NDVI values but increasing mean annual precipitation in the Awash basin indicated that the declining vegetation was attributed to anthropogenic effects. The increasing streamflow during the same time could be due to the increasing mean annual precipitation. Moreover, the decreasing vegetation cover might have contributed for the increasing streamflow through increasing surface runoff and decreasing transpiration. However, further research is required to assess the precise impacts of afforestation on vegetation cover and hydrologic processes. Generally, the study result showed that the focus of afforestation should be on tree growing than on tree plantation alone.

Spatial-Temporal Evolution and Driving Forces of NDVI in China's Giant Panda National Park

Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan-Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities.

Quantitative Effects of Climate Change on Vegetation Dynamics in Alpine Grassland of Qinghai-Tibet Plateau in a County

Alpine grassland in the Qinghai-Tibet Plateau is known to be sensitive to climate change. To quantify the impacts of climate change on alpine ecosystems at small scale, Wulan County in Qinghai Province was taken as the research object, and the relationships between vegetation dynamics and climate changes and the direct and indirect effects of climate factors on vegetation dynamics were analyzed using the methods of ordinary least squares, Pearson correlation analysis and path analysis, on the basis of MOD13A3 data and meteorological data from 2001 to 2020. The results showed that the Normalized Difference Vegetation Index (NDVI) in the growing season of the county and 5 vegetation types showed similar fluctuation processes and relationships with climate factors during the study period. The growing season NDVI (GSN) of shrubland and desert steppe respectively were the highest and lowest. The yearly mean values of GSN over the county ranged from 0.151 to 0.264, and increased extremely significantly with years at a rate of 0.0035 yr−1. Spatially, GSN gradually decreased from northeast to southwest, and 97.2% of the county area showed an increasing trend in GSN. With years, growing season evaporation (GSE) decreased extremely significantly at a rate of 29.6 mm yr−1, while growing season average relative humidity (GSARH) showed a significant increasing trend at a rate of 0.16% yr−1. The correlations and effects of GSE, GSARH, and growing season precipitation (GSP) on vegetation dynamics were weakened in turn. GSE was the main direct effect factor, and the latter two were the indirect effect factors through GSE. The total contribution rates of GSE, GSARH and GSP to vegetation dynamics was about 78.0%. However, growing season average temperature (GSAT) had little effect on vegetation dynamics. This study provides information for understanding the characteristics of vegetation dynamics of alpine grassland in the Qinghai-Tibet Plateau at a small scale.

Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.

Influences of Climate Change and Human Activities on NDVI Changes in China

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin.

Quantification of Natural and Anthropogenic Driving Forces of Vegetation Changes in the Three-River Headwater Region during 1982–2015 Based on Geographical Detector Model

The three-river headwater region (TRHR) supplies the Yangtze, Yellow, and Lantsang rivers, and its ecological environment is fragile, hence it is important to study the surface vegetation cover status of the TRHR to facilitate its ecological conservation. The normalized difference vegetation index (NDVI) can reflect the cover status of surface vegetation. The aims of this study are to quantify the spatial heterogeneity of the NDVI, identify the main driving factors influencing the NDVI, and explore the interaction between these factors. To this end, we used the global inventory modeling and mapping studies (GIMMS)-NDVI data from the TRHR from 1982 to 2015 and included eight natural factors (namely slope, aspect, elevation, soil type, vegetation type, landform type, annual mean temperature, and annual precipitation) and three anthropogenic factors (gross domestic product (GDP), population density, and land use type), which we subjected to linear regression analysis, the Mann-Kendall statistical test, and moving t-test to analyze the spatial and temporal variability of the NDVI in the TRHR over 34 years, using a geographical detector model. Our results showed that the NDVI distribution of the TRHR was high in the southeast and low in the northwest. The change pattern exhibited an increasing trend in the west and north and a decreasing trend in the center and south; overall, the mean NDVI value from 1982 to 2015 has increased. Annual precipitation was the most important factor influencing the NDVI changes in the TRHR, and factors, such as annual mean temperature, vegetation type, and elevation, also explained the vegetation coverage status well. The influence of natural factors was generally stronger than that of anthropogenic factors. The NDVI factors had a synergistic effect, exhibiting mutual enhancement and nonlinear enhancement relationships. The results of this study provide insights into the ecological conservation of the TRHR and the ecological security and development of the middle and lower reaches.

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

Temporal and spatial changes in vegetation and their influencing factors are of great significance for the assessment of climate change and sustainable development of ecosystems. This study applied the Asymmetric Gaussians (AG) fitting method, Mann-Kendall test, and correlation analysis to the Global Inventory Monitoring and Modeling System (GIMMS) third-generation Normalized Difference Vegetation Index and gridded climate and drought data for 1982–2015. The temporal and spatial changes to NDVI for natural grassland and forest during the growing season were analyzed. Relationships among NDVI, climate change, and droughts were also analyzed to reveal the influence of vegetation change. The results showed that: (1) Land use/cover change (LUCC) in China was mainly represented by increases in agricultural land (Agrl) and urban and rural land (Uril), and decreases in unutilized land (Bald), grassland, forest, and permanent glacier and snow (Snga). The increase in agricultural land was mainly distributed in the western northwest arid area (WNW) and northern North China (NNC), whereas regions with severe human activities such as southern South China (SNC), western South China (WSC), and eastern South China (ESC) showed significant decreases in agricultural land due to conversion to urban and rural land. (2) The start of the growing season (SOS) was advanced in WNW, SNC, WSC, and ESC, and the end of growing season (EOS) was delayed in WNW, NNC, and SNC. The growing season length (GSL) of natural vegetation in China has been extended by eight days over the last 34 years. However, the phenology of the Qinghai-Tibet Plateau (TP) was opposite to that of the other regions and the GSL showed an insignificant decreasing trend. (3) The NDVI increased significantly, particularly in the SNC, WSC, ESC, and the grassland of the WNW. Precipitation was found to mainly control the growth of vegetation in the arid and semi-arid regions of northwest China (WNW and ENW), and precipitation had a much greater impact on grassland than on forests. Temperature had an impact on the growth of vegetation throughout China, particularly in SNC, ESC, and WSC. (4) The Standardized Precipitation Evapotranspiration Index (SPEI) showed a downward trend, indicating an aridification trend in China, particularly in ENW, NNC, and WNW. Similar to precipitation, the main areas affected by drought were WNW and ENW and grassland was found to be more sensitive to drought than forest. The results of this study are of great significance for predicting the response of ecosystem productivity to climate change under future climate change scenarios.

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.