Normalized Difference Vegetation Index Trends Research Articles

Investigating the Relationship between Topographic Factors and Vegetation Spatial Patterns in the Alpine Plateau: A Case Study in the Southwestern Tibetan Plateau

Vegetation on the Southwestern Tibetan Plateau (SWTP) is critical to ensuring ecological security and promoting regional economic and social development. Here, we explored the relationship between topographic factors (elevation, slope, and aspect) and the spatial patterns in the normalized difference vegetation index (NDVI) in the SWTP over the past 20 years. The results found that the NDVI in the SWTP was primarily influenced by elevation and slope. The regions with significant variations in NDVI were concentrated between 4500 m to 5500 m and slopes ranging from 0° to 15°. Although the influence of aspect on NDVI was small, there was a decreasing trend in NDVI on sunny slopes and an increasing trend on shady slopes. Dominant topographic conditions were identified by considering 230 different combinations of elevation, slope, and aspect. The combination of topographic parameters indicated stronger patterns in NDVI variability, notably within sections of 0°–25°slopes and below 5000 m elevation. These findings highlight the relevance of topography, notably slope and aspect, for vegetation in alpine settings. The information gathered from this study about the prevailing topographic distribution and vegetation growth state in the SWTP can help with future ecological restoration and conservation efforts in the Tibetan Plateau and other comparable regions worldwide.

Impact of freshwater diversions on vegetation in coastal wetlands based on remote sensing derived vegetation index

There exist contrasting results on the impact of large-scale freshwater and sediment diversions on land gain/loss. To improve understanding on the efficacy of diversion projects in restoring coastal wetlands, we aim to evaluate the long-term impacts of diversion-altered salinity and water level on vegetation productivity in coastal wetlands. Two freshwater diversion projects Caernarvon and Davis Pond in Louisiana, U.S. and associated reference sites were selected for inclusion in this study. We implemented multi-level Bayesian models to evaluate 1) how vegetation productivity approximated by Landsat-derived Normalized Difference Vegetation Index (NDVI) in peak biomass season changed over time (pre- and post-construction and operation of the freshwater diversions), and 2) how peak-season NDVI responded to mean and variability of water level and salinity among a suite of factors that could affect vegetation productivity. Analysis showed difference in temporal trends of NDVI between the reference and diversion site for the Caernarvon project with a significant increase at the diversion site although NDVI negatively responded to diversion at the beginning. Diversion did not seem to negatively affect NDVI for the Davis Pond project and NDVI only marginally increased at the diversion site. For both projects, NDVI negatively responded to water level, while salinity negatively affected NDVI or showed quadratic relation with NDVI. At the Caernarvon diversion project, the negative impact of water level on NDVI was greater at the diversion site than at the reference site. At the Davis Pond project, it was the salinity that showed different impact between the diversion and reference site. The temporal increase of NDVI at the Caernarvon diversion site can be explained by the lower salinity driven by diversion. The quadratic relation between salinity and NDVI at the Davis Pond diversion project led to only marginal increase at the diversion site. This study provided uncertainty estimates in temporal trend of NDVI and the impact of two key abiotic drivers on NDVI. The improved understanding on vegetation productivity will help predict landscape change in response to freshwater diversions.

Remote Sensing Classification of Temperate Grassland in Eurasia Based on Normalized Difference Vegetation Index (NDVI) Time-Series Data

The Eurasian temperate grassland is the largest temperate grassland ecosystem and vegetation transition zone globally. The spatiotemporal distribution and changes of grassland types are vital for grassland monitoring and management. However, there is currently a lack of a unified classification method and standard distribution map of Eurasian temperate grassland types. The Normalized Difference Vegetation Index (NDVI) from remote sensing data is commonly used in grassland monitoring. In this paper, the Accumulated Rate of NDVI Change Index (ARNCI) was proposed to characterize the annual NDVI trend of different temperate grassland types, and four transitional categories were introduced to account for the overlap between them. Based on survey data on the distribution of Eurasian temperate grassland types in the 1980s, the study area was divided into three sub-regions: Northern China, Central Asia, and Mongolia. Regionally, pixel-based ARNCI maps in the 1980s and 1990s were successfully calculated from using NOAA’s AVHRR NDVI time-series products. The ARNCI classification thresholds for different sub-regions were determined, and classification experiments and validation were conducted for each sub-region. The overall accuracies of grasslands types classification for Northern China, Central Asia, and Mongolia in the 1980s were 75.3%, 64.2%, and 84.6%, respectively, which demonstrated that there were variations in classification accuracy in the three sub-regions, and the overall performance was favorable. Finally, distribution maps of Eurasian temperate grassland types in the 1980s and 1990s were obtained, and the spatiotemporal changes of grassland types were analyzed and discussed. The ARNCI method is simple to operate and easy to obtain data, and it can be conveniently used in grassland type classification. The maps firstly address the lack of remote sensing classification maps of Eurasian temperate grassland types, and provide a promising tool for monitoring grassland degradation, management, and utilization.

Quantification of Urban Greenspace in Shenzhen Based on Remote Sensing Data

Rapid urbanization has led to the expansion of Shenzhen’s built-up land and a substantial reduction in urban greenspace (UG). However, the changes in UG in Shenzhen are not well understood. Here, we utilized long-time-series land cover data and the Normalized Difference Vegetation Index (NDVI) as a proxy for greenspace quality to systematically analyze changes in the spatio-temporal pattern and the exposure and inequality of UG in Shenzhen. The results indicate that the UG area has been decreasing over the years, although the rate of decrease has slowed in recent years. The UG NDVI trend exhibited some seasonal variations, with a noticeable decreasing trend in spring, particularly in the eastern part of Shenzhen. Greenspace exposure gradually increased from west to east, with Dapeng and Pingshan having the highest greenspace exposure regardless of the season. Over the past two decades, inequality in greenspace exposure has gradually decreased during periods of urban construction in Shenzhen, with the fastest rate of decrease in spring and the slowest rate of decrease in summer. These findings provide a scientific basis for a better understanding of the current status of UG in Shenzhen and promote the healthy development of the city. Additionally, this study provides scientific evidence and insights for relevant decision-making institutions.

Time lag effect of vegetation response to seasonal precipitation in the Mara River Basin

BackgroundMara River Basin is an ecologically fragile area in East Africa, with a pattern of alternating wet and dry seasons shaped by periodic precipitation. Considering the regional biological traits and climatic change, the vegetation's response to seasonal variation is complicated and frequently characterized by time lags. This study analyzed the variation of the Normalized Difference Vegetation Index (NDVI) and investigated its time lag to precipitation at the monthly scale. NDVI characteristic peaks were proposed from the perspective of seasonal mechanisms and were quantified to assess the lag effect.ResultsThe results showed that the Anomaly Vegetation Index could identify low precipitation in 2006, 2009, and 2017. The NDVI showed an increasing trend in 75% of areas of the basin, while showed a decreased significance in 3.5% of areas, mainly in savannas. As to the time lag, the 1-month lag effect dominated most months, and the spatiotemporal disparities were noticeable. Another method considering the alternations of wet and dry seasons found that the time lag was approximately 30 days. Based on the time distribution of NDVI characteristic peaks, the average time lag was 35.5 days and increased with the range of seasons.ConclusionsThe findings confirmed an increasing trend of NDVI in most regions from 2001 to 2020, while the trends were most obvious in the downstream related to human activities. The results could reflect the time lag of NDVI response to precipitation, and the 1-month lag effect dominated in most months with spatial heterogeneity. Four NDVI characteristic peaks were found to be efficient indicators to assess the seasonal characteristics and had a great potential to quantify vegetation variation.

Анализ восстановления растительного покрова в пределах Шуруповских карьеров на основе данных дистанционного зондирования

The article presents an analysis of restoration processes within two quarries located in the northwest of the Shurupovsky Khutor in the Frolovsky district of the Volgograd region. The described methods of revegetation analysis within the quarry-dump complexes included images from the USGS Earth Explorer portal (US Geological Survey). The Landsat-5 TM satellite images were used in July 2007, Landsat 8 OLI_TIRS was used in July 2015, and Landsat 9 OLI_TIRS described the status in July 2022. Using the ArcGIS geoinformation system 10.3.1, we considered multispectral images of different times and calculated the Normalized Difference Vegetation Index (NDVI) for 2007, 2015, and 2022. As a second index, we calculated the NDVI trend for 2007–2015 and 2015–2022. Based on the results of interpretation, the assessment of changes in the vegetation cover of quarry-dump complexes was carried out. As a result, it was revealed that the vegetation cover of quarries demonstrated different restoration pathways despite their close territorial location. By 2022, the NDVI of the eastern quarry did not exceed 0.2, and the NDVI of the western quarry increased to 0.5. However, in general, there is a positive NDVI trend in the territories of both quarries. Resulting from the data obtained, NDVI maps and NDVI trend maps for the studied quarries were prepared. Tables for calculating changes in the areas of flooding and vegetation cover of the Shurupov quarries for 2007, 2015, and 2022 were created in MS Excel.

20 years of change in tundra NDVI from coupled field and satellite observations

In 2022 we resampled normalized difference vegetation index (NDVI) along a 100 m transect in tundra near Utqiagvik, AK that had been previously measured through the 2000–2002 growing seasons, providing an opportunity to examine a 20 year NDVI change at a 1 m resolution in a region that is experiencing increased warming and precipitation over this period. Multidecadal NDVI change was spatially variable across the transect with nearly half of the transect showing greening, about a third not showing conclusive change, and about 20% browning. In wet areas, greening (increased NDVI) was associated with increased green leaf area index, while in drier areas greening was related to changes in species cover. Browning was not related to change in species cover and appeared to be due to increased coverage of standing dead material in graminoid dominated canopies. These types of detailed observations provide insights into the interpretation of satellite based NDVI trends and emphasize the importance of microtopography and hydrology in mediating vegetation change in a warming Arctic.

The imprint of urbanization on vegetation in the ecologically fragile area: A case study from China’s Loess Plateau

Rapid urbanization significantly affects vegetation growth in urban environments. Responses of vegetation to urbanization at high spatial resolutions and long temporal ranges are still unclear, especially in ecologically fragile areas. Here, we explored urbanization impacts on vegetation across China’s Loess Plateau (CLP) from 2000 to 2020 at a spatial resolution of 30 m. Urbanization and annual maximum Normalized Difference Vegetation Index (NDVI) in urban agglomerations, provincial capital cities, also the whole CLP experienced a rapid increase over this period; the annual maximum NDVI (NDVImax) in urban areas decreased before 2011 but increased afterward to reach its maximum value in 2018, and in non-urban areas, a significant increase was found between 2000 and 2020. NDVImax with significant reduction areas were mostly distributed in the urbanization area. A total of 97.56%, 53.66%, 80.49%, and 100% of all cities’ urban core, urbanization, urban, and non-urban areas, respectively, indicated an increase in NDVImax during 2000–2020. For 97.56% (40/41) of the cities in the CLP, the increases in NDVImax in the non-urban areas were stronger than the NDVImax increases detected in the urban areas of these cities. In general, the urban areas of most cities (29/41) in the CLP had positive NDVI trends of greater than 50%. The NDVImax generally decreased with increasing urbanization development intensity (UDI) in 2000 and 2020 in the CLP along with five urban agglomerations. These findings have important implications for understanding urbanization impacts on vegetation, urbanization management, and development in ecologically fragile areas.

Analysis of NDVI Trends and Driving Factors in the Buffer Zone of the Aral Sea

A buffer zone can be used to analyze the influence of the lake on the surrounding spatial elements, which is of great significance for discussing the problems of lake retreat, vegetation degradation, and overall environmental deterioration in the Aral Sea. Taking the 3 km buffer zone of the Aral Sea as the research area, the spatiotemporal variation characteristics and main influencing factors of the Normalized Difference Vegetation Index (NDVI) in the Aral Sea research area were studied using remote sensing over 31 years (1987, 1997, 1992, 2007, 2010, 2014, 2015, 2017, and 2018). The results showed that the vegetation growth in the Aral Sea buffer zone deteriorates with the retreat of the lake; the vegetation of the small Aral Sea began to recover due to the stable water volume and salt content of the lake; vegetation began to grow in the west coast of the West Aral Sea; the shrinkage of the Aral Sea caused by human activities is an important factor affecting the growth of the vegetation. This study provides a reference for the restoration and reconstruction of regional vegetation.

Analysis of Growing Season Normalized Difference Vegetation Index Variation and Its Influencing Factors on the Mongolian Plateau Based on Google Earth Engine.

Frequent dust storms on the Mongolian Plateau have adversely affected the ecological environmental quality of East Asia. Studying the dynamic changes in vegetation coverage is one of the important means of evaluating ecological environmental quality in the region. In this study, we used Landsat remote sensing images from 2000 to 2019 on the Mongolian Plateau to extract yearly Normalized Difference Vegetation Index (NDVI) data during the growing season. We used partial correlation analysis and the Hurst index to analyze the spatiotemporal characteristics of the NDVI before and after the establishment of nature reserves and their influencing factors on the GEE cloud platform. The results showed that (1) the proportion of the region with an upwards trend of NDVI increased from 52.21% during 2000-2009 to 67.93% during 2010-2019, indicating a clear improvement in vegetation due to increased precipitation; (2) the increase in precipitation and positive human activities drove the increase in the NDVI in the study region from 2000 to 2019; and (3) the overall trend of the NDVI in the future is expected to be stable with a slight decrease, and restoration potential is greater for water bodies and grasslands. Therefore, it is imperative to strengthen positive human activities to safeguard vegetation. These findings furnish scientific evidence for environmental management and the development of ecological engineering initiatives on the Mongolian Plateau.

Impacts of Climate Change and Human Activities on NDVI Change in Eastern Coastal Areas of China

Based on the datasets of normalized difference vegetation index (NDVI), temperature, precipitation, and solar radiation and the methods of trend, partial correlation, and residual analyses, this study explored the spatiotemporal variation in NDVI and its response to climate change from 1982 to 2019 in eastern coastal areas of China. Then, the effects of climate change and non-climatic factors (e.g., human activities) on NDVI trends were analyzed. The results showed that:① the NDVI trend varied greatly in different regions, stages, and seasons. On average, the growing season NDVI increased faster during 1982-2000 (stage I) than that during 2001-2019 (stage Ⅱ) in the study area. Moreover, NDVI in spring showed a more rapid increase than that in other seasons in both stages. ② For a given stage, the relationships between NDVI and each climatic factor varied in different seasons. For a given season, the major climatic factors associated with NDVI change were different between the two stages. The relationships between NDVI and each climatic factor showed great spatial differences in the study period. In general, the increase in growing season NDVI in the study area from 1982 to 2019 was closely related to the rapid warming. The increase in precipitation and solar radiation in stage Ⅱ also played a positive role. ③ In the past 38 years, climate change played a greater role in the change in growing season NDVI than non-climatic factors, including human activities. Whereas non-climatic factors dominated the increase in growing season NDVI during stage I, climate change played a major role during stage Ⅱ. We suggest that more attention should be paid to the impacts of various factors on vegetation cover variation during different periods to promote the understanding of terrestrial ecosystem changes.

Attribution of climate change and human activities to vegetation NDVI in Jilin Province, China during 1998–2020

Vegetation is among the key elements of ecosystems, and the normalized difference vegetation index (NDVI) is among the most frequently used tools for studying changes in regional vegetation dynamics. Studying these changes and their drivers is essential for understanding the interactions between vegetation and ecosystems; therefore, here, we analyzed the spatial and temporal patterns of NDVI in Jilin Province and their influencing factors. The correlation between NDVI and climatic factors was analyzed using Pearson’s correlation analysis. Additionally, we explored the correlation between NDVI and human activity using geographically weighted regression analysis. The results were as follows: (1) From 1998 to 2020, although the temporal trends of NDVI for different vegetation types in Jilin Province showed some differences in general, they all showed an increasing trend. (2) The spatial trends of NDVI in Jilin Province ranged from − 0.031 to 0.046. The NDVI values exhibited a trend toward growth in most regions. (3) Climate change factors are more important than human activity factors for improving vegetation. (4) Among the climatic factors, the negative correlation between temperature and NDVI was stronger than the positive correlation, while the positive and negative correlations between precipitation and NDVI were equally strong. (5) Human activity factors such as GDP and population have mostly negative effects on regional NDVI, whereas land-use type shifts have mostly positive effects. The results of this study contribute to a deeper understanding of the drivers of vegetation change. Additionally, some discussions can provide theoretical references for ecological management and sustainable development in Jilin Province.

Response Time of Vegetation to Drought in Weihe River Basin, China

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales.

Divergent vegetation variation and the response to extreme climate events in the National Nature Reserves in Southwest China, 1961–2019

Southwest China (SWC) is a critical hotspot of biodiversity and highly sensitive to climate change. To better protect biodiversity, many National Nature Reserves have been established to maintain viable populations of species and minimize their habitat loss. However, the response of the normalized difference vegetation index (NDVI) in National Nature Reserves to extreme climate events has not been described. We analyzed NDVI changes and made several key observations: (1) from 1998 to 2019, annual average NDVI trends of 0.00421 yr−1, 0.00514 yr−1, and 0.00529 yr−1 were significantly increasing and NDVI values more than 0.8 were mainly concentrated at 1049–2349 m, 947–2547 m, 617–2817 m, in the National Nature Reserves of Hengduan Mountain (NNRHDM), Sichuan basin (NNRSCB), and Yunnan-Guizhou Plateau (NNRYGP), respectively. The change trends in different altitude bins were more significant in the NNRYGP; (2) extreme cold (warm) indices significantly decreased (increased) in the NNRHDM, NNRSCB and NNRYGP, extreme precipitation indices increased in the NNRHDM, and decreased in the NNRSCB and NNRYGP; (3) the combination of extreme temperature indices can better explain NDVI change in the NNRSCB and NNRYGP, and the combination of extreme temperature and precipitation indices can better explain the NDVI change in the NNRHDM on the monthly scale. Overall, the NDVI green trend is more obvious in the NNRYGP than in the NNRHDM and NNRSCB, and is more sensitive to extreme climate change, especially extreme temperature. Our findings showed that although the National Nature Reserves have achieved remarkable results in conservation, future work should focus on the development of adaptation and alleviation strategies to prevent extreme climate events (especially extreme temperature events) that could promote vegetation degradation in these sensitive areas.

Climate factors rather than human activities controlled NDVI trends across wet meadow areas in the Andes Centrales of Argentina

In the Andes of southern South America, wet meadows are much more productive than nearby arid areas. Human activities, along with climate variations may reduce the productivity of these ecosystems. However, we do not know what the relative effect of climate and human activities on their degradation status is. Here, we first quantified the degradation extent through the temporal trend of normalized difference vegetation index (NDVI), a proxy of productivity, from 1999 to 2017. We did it in 736 wet meadow areas inserted in three nearby zones with contrasting land management. Second, we analyzed the relationship between NDVI and a large-scale mode of climate variability (El Niño Southern Oscillation, ENSO) and regional climate variables (precipitation and temperature) through a structured climate model. Finally, we evaluated human activities’ effects through the temporal trends in the residuals of the climate model. Independently of land management, NDVI trends were mainly negative and affected 56% of the wet meadow areas. The climate model indicated that NDVI increased under La Niña, cool and wet years. Regarding land management, the degradation level was similar across the contrasted land management. Our results suggest that climate factors rather than human activities influenced the degradation process in these areas.

Spatiotemporal trends of NDVI and its response to climate variability in the Abbay River Basin, Ethiopia

Woody vegetation plays a vital role in regulating the water budget and energy exchange in the Earth's system. This study aimed at analyzing the spatiotemporal variability of Normalized Difference Vegetation Index (NDVI) and its response to Potential Evapotranspiration (PET), rainfall (RF), soil moisture (SM), and temperature (TEM) in the study area. The trends, correlations, and relationships between NDVI and climate variables were executed using Mann-Kendall monotonic trend (MKMT), partial correlation coefficients (PCC), and multiple linear regression (MLR) methods, respectively. Over the last 26 years, the interannual NDVI increased by 0.0065 yr−1 (R2 = 0.159, p = 0.157). The spatiotemporal MKMT and Theil-Sen slope analysis showed that interannual NDVI increased significantly in 78% of the basin's total area. Of the 78% of the basin, 31%, and 47%, of the total area showed extremely significant increasing (Zmk = 4.706, p ≤ 0.01), and significant increasing trends (Zmk = 2.378, p ≤ 0.05) respectively. The interannual variation of NDVI was well explained (R2 = 0.88, Adjusted R2 = 0.84) by the climate variables in the eastern, southeastern, and central sub-basins where agriculture, grass, sparse vegetation and barelands are the predominant land use land cover (LULC) classes. The main climatic factors that control vegetation growth and greenness during the rainy season were found to be PET, SM, and RF with 0.91, 0.99, and 0.86 PCC with NDVI respectively. The current study broadens the scientific community's understanding of the relationship between climate variables and vegetation growth in highland ecosystems. Understanding the seasonal and long-term relationship between climate and NDVI contributes to the scientific knowledge of highland ecosystems, which are extremely vulnerable to climate change.

Characteristics of NDVI Changes in the Altay Region from 1981 to 2018 and Their Relationship to Climatic Factors

Vegetation growth and its response to climatic factors have become one of the most pressing issues in ecological research. However, no consensus has yet been reached on how to resolve this problem in arid areas with a high-elevation gradient and complex underlying surface. Here, NOAA CDR AVHRR NDVI V5 for 1981–2018 and China’s regional surface meteorological faction-driven datasets were used. General linear regression, the Mann-Kendall test and sliding t-test, Pearson correlations, and the Akaike information criterion (AIC), on a grid-scale, were applied to analyze the annual normalized difference vegetation index (NDVI) and its relationship with temperature and precipitation in the Altay region. Results revealed that the temporal trend of NDVI for most grid cells was non-significant. However, mountains, coniferous forests, grasslands, and meadows in the high-elevation zone displayed a slow increasing trend in NDVI. Further, NDVI was positively correlated with the mean annual temperature and total annual precipitation, the latter playing a more significant role. Yet, for desert and shrub vegetation and coniferous forest, their NDVI had insignificant negative correlations with the mean annual temperature. Hence, both the trends and drivers of NDVI of high elevation are highly complex. This study’s findings provide a reference for research on vegetation responses to climate change in arid areas having a high-elevation gradients and complex underlying surfaces.

Spatio-Temporal Development of Vegetation Carbon Sinks and Sources in the Arid Region of Northwest China.

Drylands, which account for 41% of Earth's land surface and are home to more than two billion people, play an important role in the global carbon balance. This study analyzes the spatio-temporal patterns of vegetation carbon sinks and sources in the arid region of northwest China (NWC), using the net ecosystem production (NEP) through the Carnegie-Ames-Stanford approach (CASA). It quantitatively evaluates regional ecological security over a 20-year period (2000-2020) via a remote sensing ecological index (RSEI) and other ecological indexes, such as the Normalized Difference Vegetation Index (NDVI), fraction of vegetation cover (FVC), net primary productivity (NPP), and land use. The results show that the annual average carbon capacity of vegetation in NWC changed from carbon sources to carbon sinks, and the vegetation NEP increased at a rate of 1.98 gC m-2 yr-1 from 2000 to 2020. Spatially, the annual NEP in northern Xinjiang (NXJ), southern Xinjiang (SXJ) and Hexi Corridor (HX) increased at even faster rates of 2.11, 2.22, and 1.98 gC m-2 yr-1, respectively. Obvious geographically heterogeneous distributions and changes occurred in vegetation carbon sinks and carbon sources. Some 65.78% of the vegetation areas in NWC were carbon sources during 2000-2020, which were concentrated in the plains, and SXJ, the majority carbon sink areas are located in the mountains. The vegetation NEP in the plains exhibited a positive trend (1.21 gC m-2 yr-1) during 2000-2020, but this speed has slowed since 2010. The vegetation NEP in the mountain exhibited only intermittent changes (2.55 gC m-2 yr-1) during 2000-2020; it exhibited a negative trend during 2000-2010, but this trend has reversed strongly since 2010. The entire ecological security of NWC was enhanced during the study period. Specifically, the RSEI increased from 0.34 to 0.49, the NDVI increased by 0.03 (17.65%), the FVC expanded by 19.56%, and the NPP increased by 27.44%. Recent positive trends in NDVI, FVC and NPP have enhanced the capacity of vegetation carbon sinks, and improved the eco-environment of NWC. The scientific outcomes of this study are of great importance for maintaining ecological stability and sustainable economic development along China's Silk Road Economic Belt.

Precipitation, Vegetation, and Groundwater Relationships in a Rangeland Ecosystem in the Chihuahuan Desert, Northern Mexico

For this study, conducted in a semiarid (318 mm) rangeland setting in the Chihuahuan Desert region in northern Mexico, we evaluated the seasonal and interannual variability of precipitation, vegetation, and groundwater relations. Between 2012 and 2014, a series of soil and water conservation practices (e.g., land imprinting, contour furrows, and planting of native shrub species) were conducted in several areas within the 2500 ha study site. Since 2014, the site has been gradually instrumented to monitor several hydrologic variables, including rainfall, soil water content, and groundwater. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) vegetation indices were used to evaluate vegetation conditions between 2007 and 2021, before and after the treatment. Soil water content and groundwater began to be monitored in 2014 and 2016, respectively. Study results show that NDVI and NDII values were higher in the years following the treatment. A negative trend in NDVI values was observed in the years before restoration and reversed in the post-treatment years. The relatively low levels of soil water content obtained every year followed a seasonal response to precipitation inputs characterized by a quick rise and decline at the 0.2 m depth and a more gradual rise and decline for sensors at 0.5 m and 0.8 m depths. A positive trend in groundwater levels has been observed since the onset of monitoring in 2016, with seasonal groundwater levels rising between 0.7 m and 1.3 m for most years, except for 2020, when levels dropped 1 m. The yearly recharge of the aquifer ranged between 102 mm and 197 mm. The conservation practices employed have positively affected the state of the rangeland ecosystem. The upward trends in NDVI, NDII, and groundwater levels observed in the post-treatment years were partly attributed to improved land conditions. The findings of this study contribute to the improved understanding of land use and environmental relations in summer precipitation-dominated rangeland ecosystems.