Changes In Normalized Difference Vegetation Index Research Articles

Multi-Year Hurricane Impacts Across an Urban-to-Industrial Forest Use Gradient

Coastal forests in the eastern United States are increasingly threatened by hurricanes; however, monitoring their initial impacts and subsequent recovery is challenging across scales. Understanding disturbance impacts and responses is essential for sustainable forest management, biodiversity conservation, and climate change adaptation. Using Sentinel-2 imagery, we calculated the annual Normalized Difference Vegetation Index change (∆NDVI) of forests before and after Hurricane Michael (HM) in Florida to determine how different forest use types were impacted, including the initial wind damage in 2018 and subsequent recovery or reactive management for two focal areas located near and far from the coast. We used detailed parcel data to define forest use types and characterized multi-year impacts using sampling and k-means clustering. We analyzed five years of timberland logging activity up to the fall of 2023 to identify changes in logging rates that may be attributable to post-hurricane salvage efforts. We found uniform impacts across forest use types near the coast, where winds were the most intense but differences inland. Forest use types showed a wide range of multi-year responses. Urban forests had the fastest 3-year recovery, and the timberland response was delayed, apparently due to salvage logging that increased post-hurricane, peaked in 2021–2022, and returned to the pre-hurricane rate by 2023. The initial and secondary consequences of HM on forests were complex, as they varied across local and landscape gradients. These insights reveal the importance of considering forest use types to understand the resilience of coastal forests in the face of potentially increasing hurricane activity.

An Assessment of Vegetation Changes in the Three-River Headwaters Region, China: Integrating NDVI and Its Spatial Heterogeneity

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems.

Impact of Extreme Drought on Vegetation Greenness in Poyang Lake Wetland

The Poyang Lake Wetland, an internationally significant ecosystem, frequently experiences drought during the flood season. However, the total impact of extreme drought on wetland vegetation remains poorly understood. This study determined the standardised precipitation evapotranspiration index (SPEI) and analysed drought trends within the Poyang Lake Basin. Additionally, spatiotemporal variations in wetland vegetation under drought conditions were examined by analysing the mean normalised difference vegetation index (NDVI) values and categorising NDVI classifications. The key factors affecting wetland vegetation and its respective thresholds were determined. The Poyang Lake Basin has experienced increasing aridity over the past 3 years. In response to this trend, the wetland vegetation area in Poyang Lake expanded, whereas vegetation greenness declined. Notably, in the year following an extreme drought, Poyang Lake’s vegetation greenness was lower than that during the same period in previous years. Regardless, the correlation analysis showed no significant relationship between the SPEI values and the wetland vegetation greenness; however, water level changes significantly impacted the wetland vegetation, with a correlation coefficient of −0.89 (p < 0.001). A critical water level of 14 m was identified as the threshold at which sudden changes in the mean NDVI were observed. This research offers valuable insights into hydrological management strategies to protect Poyang Lake Wetland’s vegetation under drought conditions. Future studies should enhance the differentiation of drought tolerance among different wetland plant species, thereby achieving differentiated hydrological management.

Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M-K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69% of vegetation will degrade, that is, NDVI will degrade instead of improvement.. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had > 70% explanatory power (73.5, 79.9, 79.0, and 74.9%, respectively). The explanatory power of edaphic factors was > 30%, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of < 30%. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.

Permafrost Degradation Induces the Abrupt Changes of Vegetation NDVI in the Northern Hemisphere

AbstractPermafrost, widely distributed in the Northern Hemisphere, plays a vital role in regulating heat and moisture cycles within ecosystems. In the last four decades, due to global warming, permafrost degradation has accelerated significantly in high latitudes and altitudes. However, the impact of permafrost degradation on vegetation remains poorly understood to date. Based on active layer thickness (ALT) monitoring data, meteorological data and normalized difference vegetation index (NDVI) data, we found that most ALT‐monitored sites in the Northern Hemisphere show an increasing trend in NDVI and ALT. This suggests an overall increase in NDVI from 1980 to 2021 while permafrost degradation has been occurring. Permafrost degradation positively influences NDVI growth, with the intensity of the effects varying across land cover types and permafrost regions. Furthermore, based on Mann‐Kendall trend test, we detected abrupt changes in NDVI and environmental factors, further confirming that there is a strong consistency between the abrupt changes of ALT and NDVI, and the consistency between the abrupt change events of ALT and NDVI is stronger than that of air temperature and precipitation. These findings work toward a better comprehending of permafrost effects on vegetation growth in the context of climate change.

Analysis of changes before and after forest fires with LAI, NDVI and ET time series: Focusing on major forest fires in Korea

This study investigates the changes caused by forest fires and the following recovery processes, targeting four forest fire sites in Korea. The time series of two vegetation indices, leaf area index (LAI) and normalized difference vegetation index (NDVI) are investigated along with evapotranspiration (ET) time series for the study objectives. The analysis results show that LAI is most sensitive to burn severity and burned area with its change ranging from 40 % to 70 %, while the changes of NDVI and ET remain 30 % and 20 %, respectively, regardless of forest fire sites. The recovery time from forest fire also varies according to indices: the recovery time is estimated to be about 15 years when considering LAI and NDVI, while just 5 years when considering ET. Overall, LAI seems better to analyze the change in vegetation before and after forest fires. Different vegetation shift patterns after the forest fire are also noticed, mostly from evergreen needleleaf trees to deciduous broadleaf trees. However, it is also found that bad soil fertility condition and artificial afforestation help to maintain the evergreen needleleaf trees after the forest fire.

Quantitative assessment of Hurricane Ian’s damage on urban vegetation dynamics utilizing Landsat 9 in Fort Myers, Florida

Florida’s unique climatic and geographical features have profoundly influenced its hurricane history. This study quantitatively examines the effects of Hurricane Ian on urban vegetation in Fort Myers, Florida, using remote sensing data. We analyzed pre- and post-hurricane vegetation indices, including NDVI (Normalized Difference Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index). Our findings reveal varied spatial impacts, with NDVI changes ranging from −0.03 to 0.333, ARVI changes from −0.016 to 0.25, and SAVI changes from −0.04 to 0.5. Negative values indicate vegetation damage, while positive values suggest resilience or recovery. The study area experienced a 63.75% reduction in vegetation cover, from 67.10 km2 before Hurricane Ian to 24.325 km2 after. Pre-hurricane NDVI ranged from −0.2298 to 0.5663, while post-hurricane values ranged from −0.189 to 0.521, indicating overall vegetation stress. ARVI maxima decreased from 0.379 to 0.352, and SAVI maxima from 0.849 to 0.782, further confirming vegetation damage. Support Vector Machine classification achieved 89% accuracy (Kappa = 0.85) for pre-hurricane and 87% (Kappa = 0.83) for post-hurricane vegetation mapping. These findings enhance our understanding of hurricane impacts on urban green infrastructure, with significant implications for urban planning and disaster preparedness in coastal cities prone to extreme weather events. The outcomes enhance damage assessment methodologies and provide valuable insights into the ecological consequences of hurricanes on urban ecosystems.

Contribution of Climatic Change and Human Activities to Vegetation Dynamics over Southwest China during 2000–2020

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems.

Pattern and change of NDVI and their environmental influencing factors for 1986–2019 in the Qinling-Daba Mountains of central China

Previous studies have shown that climate change and human activities play an important role in the vegetation dynamics in the Qinling-Daba Mountains of central China. However, which environmental factors including climate, topography, soil and human activities play an important role in the vegetation dynamics and its spatial pattern in the Qinling-Daba Mountains remains to be further clarified. Based on the normalized difference vegetation index (NDVI) data of the growing season from 1986 to 2019 synthesized by Landsat series satellite data on Google Earth Engine, this study aimed to further investigate the spatial pattern of NDVI and its dynamics, and clarify its environmental controlling factors in the Qinling-Daba Mountains using the methods of spatial analysis and Geodetector. The results showed that: (1) the spatial pattern of NDVI in the study area had a U-shaped NDVI distribution in latitude, anti-U-shaped patterns in longitude and with increasing altitude. (2) 2005 was the year of NDVI breakthrough increase, and the vegetation dynamics was divided into two periods according to the result of MK mutation test: the slow increasing period with an increasing rate of 0.25%/a from 1986 to 2004 (R2 0.74), and the rapid increasing period with an increasing rate of 0.30%/a from 2005 to 2019 (R2 0.92). (3) Topography regulating local hydrothermal conditions and soil enriching nutritions played more important influence on NDVI spatial pattern than climate factors (temperature and precipitation) at the regional scale. The effect of land use on NDVI change was stronger than that of climate warming (temperature), and the climate warming in recent decades played a more important role than precipitation on the NDVI dynamics. Research on vegetation patterns, changes and their environmental influencing factors will help the government and other related agencies to formulate plans or policies for infrastructure development and land management, ecological restoration.

A dataset of NDVI with medium to high spatio-temporal resolutions in Tajikistan using spatio-temporal data fusion method (2010&amp;ndash;2020)

Normalized Difference Vegetation Index (NDVI) is one of the most commonly used remote sensing indices to study the condition of vegetation. The long time series data of NDVI is of great significance to the study of vegetation change. However, due to the limitations of sensors, remote sensing data cannot obtain both high temporal resolution and high spatial resolution. Therefore, among the widely used NDVI data products, the data with high spatial and temporal resolution is still scarce. Based on the Cubist model, we integrated MODIS data with Landsat and Sentinel remote sensing data, and obtained the MODIS-Landsat fusion data of Tajikistan from 2010 to 2020, and the MODIS-Sentinel fusion data of 2020. This dataset can reflect the temporal and spatial changes of NDVI in Tajikistan from 2010 to 2020, and provide long-term data support for vegetation change analysis and ecological environment monitoring in Tajikistan.

Changes in Land Use and Land Cover, Normalized Difference Vegetation Index and Land Surface Temperature in the Narsingdi District During 2001 to 2021

Rapid urbanization and industrialization cause land use changes, reduce green spaces, and increase the land surface temperature. Green spaces of a city area maintain environmental quality by absorbing air pollutants and reducing land surface temperature. The present study aimed to detect the changes in land use and land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) in the Narsingdi district including six Upazilas from 2001 to 2021. The Landsat 7- Enhanced Thematic Mapper Plus (ETM+), the Landsat 8- Operational Land Imager (OLI) imageries and the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to analyze the LULC, NDVI, and LST by using Google Earth Engine (GEE) and ArcGIS 10.8.2. The images were analyzed into four land use classifications – agricultural land, built-up area, forest vegetation, and water body. Among all the Upazilas, Narsingdi Sadar was the vulnerable area, where agricultural land, forest coverage, and water body decreased significantly by 6.67%, 2.2%, and 4.66%, respectively. The built-up area increased by a considerable amount of 13.53% during the last twenty years. The forest coverage of Narsingdi Sadar Upazila was calculated at only 8.91% in 2021 and decreased from 11.11% in 2001. The NDVI values surprisingly increased in all the Upazilas, but in Narsingdi Sadar, it is comparatively low (0.08) than in other Upazilas. The increasing trend in LST in the Narsingdi Sadar Upazila is alarming, 1.14oC from 2001 to 2021 (0.57oC/decade). The significant changes in LULC, NDVI, and LST made the Narsingdi Sadar a more critical area in the Narsingdi district. The findings of the study will be helpful to policymakers in making appropriate decisions in future city development. J. of Sci. and Tech. Res. 5(1): 93-118, 2023

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p &lt; 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

An insight into effect of soil salinity on vegetation dynamics in the exposed seafloor of the Aral Sea

The desiccation of the Aral Sea has precipitated significant ecological degradation, resulting in the progressive development of vegetation on the exposed seafloor. Soil salinity emerges as a pivotal determinant in this ecological succession process. Employing a comprehensive methodology integrating multi-source datasets spanning from 1986 to 2023, this study elucidates the temporal changes in vegetation dynamics and soil salinity levels. Satellite imagery (Landsat-4/5/7/8), field soil samplings, hydrological and topographic data were analyzed to understand their interactions with regression analysis. The results reveal a consistent increasing trend in the Normalized Difference Vegetation Index (NDVI) across the exposed seabed since 1986. However, NDVI demonstrates a non-linear relationship with elevation in the North Aral Sea region. Interestingly, NDVI levels near an elevation of 42 m on the exposed seabed approximate those observed during the pre-recession period in the 1960s. Conversely, in the South Aral Sea region, NDVI demonstrates a linear upward trend with increasing elevation. Furthermore, the spatial distribution of soil salinity on the exposed seabed was delineated with linear regression analysis. It revealed water salinity levels at the time of sea recession can serve as a proxy for soil salinity in cases where direct soil data is unavailable. Through establishing a robust correlation between NDVI and soil salinity, the range of stable NDVI values on the exposed seabed was delineated. Lastly, three hypothetical scenarios of rising water levels were considered to evaluate changes in stable NDVI across different elevation gradients. If the water level returns to 45 m, the salt-desert area would decrease by 4.5 × 104 km2, accounting for 23 % of the total area in 1960. At this water level, it is anticipated that lake hydrological conditions and ecological environments may restore to those observed in 1981. This study provides a long-term perspective on environmental changes in the Aral Sea region by integrating multiple data sources and analytical methods. The predictive insights from the scenario analysis offer valuable guidance for future water management and ecological restoration efforts. Compared with previous studies, it presents a detailed and comprehensive picture of the interplay between vegetation dynamics and soil salinity, highlighting the critical impact of water level changes on the region's ecosystem.

Spatiotemporal change characteristics of NDVI and response to climate factors in the Jixi Wetland, Eastern China.

Exploring the spatiotemporal variation characteristics of vegetation in the confluent area of water systems in western Jinan and its response mechanism to climatic factors is of great significance for the scientific evaluation of the benefits of the water system connectivity project and eco-environmental protection and can provide a reference for ecotourism development in the Jixi wetland park. Based on the Landsat series of images and meteorological data, this study used ENVI to interpret the normalized difference vegetation index (NDVI) of the confluent area from 2010 to 2021, and the spatiotemporal change characteristics and trends of NDVI were quantitatively analyzed. The response of the growing-season NDVI (GSN) to climate factors and its time-lag effect were explored. The results showed that the overall change in the interannual NDVI in the confluent area from 2010 to 2021 was stable. The GSN in the confluent area was significantly positively correlated with precipitation, average temperature, and relative humidity in 37.64%, 25.52%, and 20.87% of the area respectively, and significantly negatively correlated with sunshine hours in 15.32% of the area. There was a time-lag effect on the response of the GSN to climate factors; the response to precipitation and sunshine hours lagged by 1month, and the response to average temperature and relative humidity was longer.

Simulating the impact of greenspace exposure on metabolic biomarkers in a diverse population living in San Diego, California: A g-computation application.

Growing evidence exists that greenspace exposure can reduce metabolic syndrome risk, a growing public health concern with well-documented inequities across population subgroups. We capitalize on the use of g-computation to simulate the influence of multiple possible interventions on residential greenspace on nine metabolic biomarkers and metabolic syndrome in adults (N = 555) from the 2014-2017 Community of Mine Study living in San Diego County, California. Normalized difference vegetation index (NDVI) exposure from 2017 was averaged across a 400-m buffer around the participants' residential addresses. Participants' fasting plasma glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, systolic and diastolic blood pressure, hemoglobin A1c (%), waist circumference, and metabolic syndrome were assessed as outcomes of interest. Using parametric g-computation, we calculated risk differences for participants being exposed to each decile of the participant NDVI distribution compared to minimum NDVI. Differential health impacts from NDVI exposure by sex, ethnicity, income, and age were examined. We found that a hypothetical increase in NDVI exposure led to a decrease in hemoglobin A1c (%), glucose, and high-density lipoprotein cholesterol concentrations, an increase in fasting total cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, and minimal changes to systolic and diastolic blood pressure, waist circumference, and metabolic syndrome. The impact of NDVI changes was greater in women, Hispanic individuals, and those under 65 years old. G-computation helps to simulate the potential health benefits of differential NDVI exposure and identifies which subpopulations can benefit most from targeted interventions aimed at minimizing health disparities.

Examining the Land Use and Land Cover Impacts of Highway Capacity Expansions in California Using Remote Sensing Technology

Highway capacity expansion has wide impacts on land use and land cover (LULC). Cost–benefit analyses and environmental review processes for roadway capacity expansion and maintenance decisions do not comprehensively consider the LULC impacts. This study examined land cover changes directly associated with highway expansion in California, and the relationship between land use and the vegetation impacts of highway projects using satellite remote sensing data. The methodology involves a geospatial analysis of normalized difference vegetation index (NDVI) data in 18 sites across California before and after highway expansion project completion. We accounted for seasonality and included a set of control sites. Findings indicate that the impacts of highway expansion on changes in NDVI are diverse, stressing the importance of the environmental context around each individual project site. Sites that are located near less-developed areas with more extensive natural vegetation (e.g., sprawled areas or exurbs), show a significant decline in NDVI values. Virtually all sites with insignificant changes in NDVI after highway expansion are located in areas that already exhibit heavy urban development (e.g., Los Angeles, San José) or are otherwise located near large expanses of bare, non-vegetated earth. Also, project sites that experienced multiple types of construction (i.e., adding more lanes, widening sections, bridge renovation, etc.) were more likely to exhibit decreasing NDVI values compared with project sites that only experienced one type of construction. Decisions about highway construction and capacity expansion should consider the context and the full environmental impacts, including land use and land cover changes over time.

Spatiotemporal Evolution Disparities of Vegetation Trends over the Tibetan Plateau under Climate Change

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions.

Elevated CO2 concentrations contribute to a closer relationship between vegetation growth and water availability in the Northern Hemisphere mid-latitudes

The Northern Hemisphere mid-latitudes, with large human populations and terrestrial carbon sinks, have a high demand for and dependence on water resources. Despite the growing interest in vegetation responses to drought under climate change in this region, our understanding of changes in the relationship between vegetation growth and water availability (referred to as Rvw) remains limited. Here, we aim to explore the Rvw and its drivers in the Northern Hemisphere mid-latitudes between 1982 and 2015. We used the satellite-derived normalized difference vegetation index (NDVI) and the fine-resolution Palmer drought severity index (PDSI) as proxies for vegetation growth and water availability, respectively. The trend analysis results showed that changes in NDVI and PDSI were asynchronous over the past three decades. Moreover, we analyzed the spatiotemporal patterns of the correlation coefficient between NDVI and PDSI. The results indicated that the Rvw was getting closer in more areas over the period, but there were differences across ecosystems. Specifically, most croplands and grasslands were primarily constrained by water deficit, which was getting stronger; however, most forests were primarily constrained by water surplus, which was getting weaker. Furthermore, our random forest regression models indicated that the dominant driver of changes in the NDVI-PDSI correlation was atmospheric carbon dioxide (CO2) in more than 45% of grid cells. In addition, the partial correlation analysis results demonstrated that elevated CO2 concentrations not only boosted vegetation growth through the fertilizer effect but also indirectly enhanced water availability by improving water use efficiency. Overall, this study highlights the important role of atmospheric CO2 in mediating the Rvw under climate change, implying a potential link between vegetation greening and drought risk.

Spatio-Temporal Analysis of Vegetation Index Change on Land Surface Temperature in Yogyakarta Special Region Using MODIS Imagery (2000-2023)

Yogyakarta has become a province renowned for its abundance of tourist destinations and learning opportunities for students from all over Indonesia. Temperature and vegetation density are key factors contributing to comfort in both academic pursuits and recreational activities. This study utilizes NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) to determine these crucial parameters. MODIS imagery serves as a vital data source for analyzing NDVI and LST. The aim of this research is to comprehend the environmental dynamics of DIY (Yogyakarta Special Region) by exploring the relationship between NDVI and LST. Through the Pearson correlation method, the relationship between these variables is deeply evaluated. The analysis reveals a significant correlation between changes in NDVI and LST, indicating a complex interaction between vegetation and land surface temperature. However, these significant findings have minimal impact on Yogyakarta City and Sleman, which serve as the focal points of community activities in Yogyakarta. These changes can be attributed to various factors, including changes in land use, urbanization, and climate variability, which show weak correlation values when associated with vegetation. Another finding is that Yogyakarta City exhibits the most distinct response in vegetation density and surface temperature compared to the surrounding four cities. This is attributed to Yogyakarta City being the administrative center and economic hub of DIY. These findings provide a deeper understanding of the environment's response to these factors in DIY. By employing spatial and temporal analysis, this study offers valuable insights for climate change mitigation efforts and adaptation to the evolving environmental dynamics.

Long-Term Response of Floodplain Meadow Normalized Difference Vegetation Index to Hydro-Climate and Grazing Pressure: Tamir River Plains, Mongolia

The greenery of floodplain meadows in arid regions, such as Mongolia, is influenced by climate, hydrology, and land use. In this study, we analyzed the NDVI (Normalized Difference Vegetation Index) of two floodplain meadows located along the South Tamir and Tamir Rivers using LANDSAT images. Our goal was to observe NDVI spatial changes, variations, and mean values in mid-August every six years from 1991 to 2015 and to identify the factors driving these differences. To achieve this, we conducted variance analysis to identify changes in NDVI and implemented Principal Component Analysis to determine the influence of hydro-meteorological factors and grazing intensity. Our findings indicate a significant decrease in greenness, as measured by pixel-scale NDVI, during the late summer period. This decrease was consistently observed, except for a series of harsh winters that followed relatively dry summers, resulting in a disastrous event called dzud, which led to the death of livestock. The decrease in NDVI was amplified by lower precipitation in June, higher temperatures and wind speed in July, and increased precipitation in August, along with a higher frequency of days with convective rain. Our findings have important implications for managing grazing in Mongolia’s grasslands, promoting sustainable land use, and mitigating sandstorms. The variance and average values of NDVI at the pixel level can serve as reliable markers of sustainable pasture management in areas where other vegetation measures are limited.