Human disturbance caused stronger influences on global vegetation change than climate change

Night lights observations significantly improve the explainability of intra-annual vegetation growth globally

Understanding the underlying mechanism of vegetation growth is of great significance to improve our knowledge of how vegetation growth responds to its surrounding environment, thereby benefiting the prediction of future vegetation growth and guiding environmental management. However, human impacts on vegetation growth, especially its intra-annual variability, still represent a knowledge gap. Night Lights (NL) have been demonstrated as an effective indicator to characterize human activities, but little is known about the potential improvement of intra-annual vegetation growth using seasonal NL observations. To address this gap, we investigated and quantified the explainability improvement of intra-annual vegetation growth by establishing a multiple linear regression model for vegetation growth (indicated by Normalized Difference Vegetation Index, NDVI) with human factor (indicated by NL observations here) and three climatic factors, i.e., temperature, water availability, and solar radiation using the Principal Components Regression (PCR) method. Results indicate that NL observations significantly improve our understanding of intra-annual vegetation growth globally. Model explainability, i.e., adjusted R2 metric of the PCR model, was comparatively improved by 54 % on average with a median value of 11 % when taking NL observations into consideration. Such improvement occurred in 82 % of the whole investigation pixels. We found that the improvement of model explanatory power was significant in regions where both NL and NDVI trends were large, except for the case where both of their trends were negative. At the country-level, the improvement of model explanatory power increases as GDP decreases, illustrating a greater improvement in a lower middle-income country than that in a high-income country. Our findings emphasize the importance of considering human activities (indicated by NL here) in vegetation growth, offering novel insights into the explanation of intra-annual vegetation growth.

Vegetation growth enhancement modulated by urban development status

Cities are natural laboratories for studying the vegetation response to global change due to their own climatic, atmospheric, and biological conditions. However, whether the urban environment promoted vegetation growth is still uncertain. Using the Yangtze River Delta (YRD), an economic powerhouse of modern China, as a case study, this paper investigated the impact of urban environment on vegetation growth at three scales: cities, sub-cities (rural-urban gradient) -pixels. Based on the satellite observations of vegetation growth indicated during 2000-2020, we explored the direct (replacement of original land by impervious surfaces) and indirect impact (e.g., climatic environment) of urbanization on vegetation growth and their trends with urbanization level. We found that significant greening accounted for 43.18 %, and significant browning accounted for 3.60 % of the pixels in the YRD. Urban area was turning green faster than suburban area. Moreover, land use change intensity (D) was a representation of the direct impact ω_d of urbanization. The direct impact of urbanization on vegetation growth was positively correlated with the intensity of land use change. Furthermore, vegetation growth enhancement due to indirect impact ω_i occurred in 31.71 %, 43.90 % and 41.46 % of the YRD cities in 2000, 2010 and 2020. And vegetation enhancement occurred in 94.12 % of highly urbanized cities in 2020, while in medium and low urbanization cities, the averaged indirect impact was near zero or even negative, proving that vegetation growth enhancement was modulated by urban development status. Also, the growth offset (τ) was most pronounced in high urbanization cities (4.92 %), but there was no growth compensation in medium urbanization cities (-4.48 %) and low urbanization cities (-57.47 %). When urbanization intensity reached a threshold value of 50 % in highly urbanized cities, the growth offset (τ) tended to saturate and remained unchanged. Our findings have important implications for understanding the vegetation response to continuing urbanization process and future climate change.

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.

Revealing the dominant factors of vegetation change in global ecosystems

In the context of climate change, revealing the causes of significant changes in ecosystems will help maintain ecosystem stability and achieve sustainability. However, the dominant influencing factors of different ecosystems in different months on a global scale are not clear. We used Ordinary Least Squares Model and Mann–Kendall test to detect the significant changes (p < 0.05) of ecosystem on a monthly scale from 1981 to 2015. And then multi-source data, residual analysis and partial correlation method was used to distinguish the impact of anthropogenic activities and dominant climate factors. The result showed that: (1) Not all significant green areas in all months were greater than the browning areas. Woodland had a larger greening area than farmland and grassland, except for January, May, and June, and a larger browning area except for September, November, and December. (2) Anthropogenic activities are the leading factors causing significant greening in ecosystems. However, their impact on significant ecosystem browning was not greater than that of climate change on significant ecosystem greening in all months. (3) The main cause of the ecosystem’s significant greening was temperature. Along with temperature, sunshine duration played a major role in the significant greening of the woodland. The main causes of significant farmland greening were precipitation and soil moisture. Temperature was the main factor that dominated the longest month of significant browning of grassland and woodland. Temperature and soil moisture were the main factors that dominated the longest month of significant browning of farmland. Our research reveals ecosystem changes and their dominant factors on a global scale, thereby supporting the sustainable ecosystem management.

NDVI-Based Greening of Alpine Steppe and Its Relationships with Climatic Change and Grazing Intensity in the Southwestern Tibetan Plateau

Alpine vegetation on the Southwestern Tibetan Plateau (SWTP) is sensitive and vulnerable to climate change and human activities. Climate warming and human actions (mainly ecological restoration, social-economic development, and grazing) have already caused the degradation of alpine grasslands on the Tibetan Plateau (TP) to some extent. However, it remains unclear how human activities (mainly grazing) have regulated vegetation variation under climate change and ecological restoration since 2000. This study used the normalized difference vegetation index (NDVI) and social statistic data to explore the spatiotemporal changes and the relationship between the NDVI and climatic change, human activities, and grazing intensity. The results revealed that the NDVI increased by 0.006/10a from 2000 to 2020. Significant greening, mainly distributed in Rikaze, with partial browning, has been found in the SWTP. The correlation analysis results showed that precipitation is the most critical factor affecting the spatial distribution of NDVI, and the NDVI is correlated positively with temperature and precipitation in most parts of the SWTP. We found that climate change and human activities co-affected the vegetation change in the SWTP, and human activities leading to vegetation greening since 2000. The NDVI and grazing intensity were mainly negatively correlated, and the grazing caused vegetation degradation to some extent. This study provides practical support for grassland use, grazing management, ecological restoration, and regional sustainable development for the TP and similar alpine areas.

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

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

Spatial Variations in Vegetation Greening in 439 Chinese Cities From 2001 to 2020 Based on Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index Data

Vegetation is essential for maintaining urban ecosystems, climate regulation, and resident health. To explore the variations in city-level vegetation greening (VG) and its relationship to urban expansion, VG in 439 Chinese cities was extracted using the Theil–Sen and Mann–Kendall algorithms based on Moderate Resolution Imaging Spectroradiometer EVI (enhanced vegetation index) data from 2001 to 2020. The spatial variations in VG and its patterns, as well as its relationship with urban expansion, were then analyzed. The following results were obtained: (1) cities with larger greening areas were primarily located in the central and eastern provinces of China, followed by the southeastern, southwestern, and western provinces. The 48 cities with the largest greening areas accounted for 60.47% of the total greening area. (2) VG patches in northern China exhibited better integrity. (3) The centralization trend of VG was evident; the location of VG patterns was influenced by the form of urban expansion. (4) The intensity of artificial impervious area expansion had a weak negative correlation with the VG. Therefore, we must enhance vegetation in new urban areas to improve the spatial balance of VG. The present results of this study can provide a foundation for developing effective policies for the construction and management of urban greenery projects.

Global Forest Types Based on Climatic and Vegetation Data

Forest types are generally identified using vegetation or land-use types. However, vegetation classifications less frequently consider the actual forest attributes within each type. To address this in an objective way across different regions and to link forest attributes with their climate, we aimed to improve the distribution of forest types to be more realistic and useful for biodiversity preservation, forest management, and ecological and forestry research. The forest types were classified using an unsupervised cluster analysis method by combining climate variables with normalized difference vegetation index (NDVI) data. Unforested regions were masked out to constrict our study to forest type distributions, using a 20% tree cover threshold. Descriptive names were given to the defined forest types based on annual temperature, precipitation, and NDVI values. Forest types had distinct climate and vegetation characteristics. Regions with similar NDVI values, but with different climate characteristics, which would be merged in previous classifications, could be clearly distinguished. However, small-range forest types, such as montane forests, were challenging to differentiate. At macroscale, the resulting forest types are largely consistent with land-cover types or vegetation types defined in previous studies. However, considering both potential and current vegetation data allowed us to create a more realistic type distribution that differentiates actual vegetation types and thus can be more informative for forest managers, conservationists, and forest ecologists. The newly generated forest type distribution is freely available to download and use for non-commercial purposes as a GeoTIFF file via doi: 10.13140/RG.2.2.19197.90082).

The contribution of forest and grassland change was greater than that of cropland in human-induced vegetation greening in China, especially in regions with high climate variability

Vegetation growth is strongly affected by both human activities and climate change. The contribution of land use change caused by human activities to vegetation growth may correlate with climate change, whereas climate variability has often been overlooked. To quantify vegetation growth during 1982-2017 in China, we used the Leaf Area Index (LAI). We also introduced climate variability to divide climate regimes using assignment entropy and built a relative greening performance indicator to identify the contribution of land use (forest, grassland, and cropland) changes to vegetation growth. The results showed that climate variability increased based on precipitation classification, and the regions with low and high climate variability accounted for 33.38%-34.41% and 12.18%-32.38% of China before and after 2000, respectively. Areas of vegetation growth affected by human activities accounted for 7.71%-19.31% and were located mainly in low variability regimes. The contribution of forest and grassland change was greater than that of cropland to vegetation greening in China, especially in high variability regimes. However, the contribution of cropland change was greater than that of forest and grassland in low variability regimes. These results imply the importance of forest and grassland change in human-induced vegetation greening, and this information can provide guidance for regional ecosystem management.