Normalized Difference Vegetation Index Variables Research Articles

Mammal diversity responses to anthropic, environmental, and seasonal changes within Caatinga seasonal dry forest landscapes

AbstractCaatinga's conservation and biodiversity are threatened due to the intensification of anthropic activities and climate change. The mammals have different responses to seasonal and anthropic changes, however particularly in Caatinga, these effects are still poorly understood. We assessed the influence of anthropic (distance from urban areas and wind farms), environmental (distance from water), and seasonal (Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST)) variables on the number of records and richness of medium and large‐sized mammals in Brazilian Caatinga. We used camera traps in 2016–2017 and 2018–2019, estimated the variation (cv) of NDVI and LST, and generated Euclidean distance maps to anthropic and environmental variables at 250, 500, and 1000 m spatial scales. We performed Generalized Linear Models, used the Akaike information criterion, and calculated model averaging to assess the strength and direction of effect and the uncertainties of the winner models, respectively. The distance from wind farms and maximum LST had a noticeable effect on the number of records and total richness. The distance from wind farms had a negative effect on the records of Dicotyles tajacu and a positive effect on the records of Leopardus pardalis and richness. The maximum LST had a negative effect on the records of L. pardalis and a positive effect on the records of Puma concolor and Cerdocyon thous. Our results emphasize that an unsustainable expansion of wind farms is likely to compromise mammal diversity. We found an opposite pattern for some species regarding LST. However, it is important to highlight that the conservation of vegetation areas on the top of mountains and springs, and the installation of artificial water sources are important strategies to mitigate the impacts of high temperatures on mammals' biodiversity in Caatinga.Abstract in Portuguese is available with online material.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Analysis of Spatial Differentiation of NDVI and Climate Factors on the Upper Limit of Montane Deciduous Broad-Leaved Forests in the East Monsoon Region of China

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m−2/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10−3/a). NDVI’s lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation’s vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation’s diverse adaptability to climatic variations.

Influence on the surrounding vegetation’s spatiotemporal changes by Jinping II Hydropower Station

The Jinping II Hydropower Station, located near Jinping Mountain, is a strategically significant hydropower project. The construction and operation of this project have impacted the surrounding ecological environment. The Normalized Difference Vegetation Index (NDVI) is widely used in remote sensing and serves as a crucial parameter for assessing vegetation health and ecological vitality. Through quantitative analysis using Sen’s slope and the Mann-Kendall non-parametric test on NDVI, we gained a deeper understanding of the impact of hydropower station construction on the surrounding vegetation. The primary objective of this study was to systematically investigate the spatiotemporal variations in NDVI before, during, and after the construction of the Jinping II Hydropower Station. The research findings contribute to the formulation of strategies aimed at mitigating potential adverse effects on the environment while promoting the region’s sustainable development goals.

Habitat heterogeneity diversity: A simple animal biodiversity surrogate in Spain

The lack of wall-to-wall biotic inventories poses a significant challenge for conservation biology. Therefore, a primary goal in conserving the unknown (species and their distribution) is finding biodiversity surrogates. This study uses remote sensing techniques and animal distribution in Spain to propose a new surrogate - habitat heterogeneity diversity (HHD). HHD was defined using measures of NDVI (Normalized difference vegetation index) and maximum dispersion approach to identify areas that spam habitat heterogeneity among sites. The efficacy of HHD in selecting sites to represent species was compared to the biotic surrogate rarity-weighted richness (RWR), a single NDVI measure, and a null solution. This study also investigated the spatial patterns of RWR and whether the methodology used to estimate HHD influences its efficiency as a surrogate. RWR patterns generally show high values in the north. While the correlations among RWR values were generally moderate, HHD successfully represented species compared to NDVI and null solutions and, in some cases, RWR. HHD is a robust alternative for complementarity-based solutions for solving problems that require the representation of at least one occurrence per species. Since HHD could be calculated using NDVI variables, the cost of conservation planning could be reduced since remote sensing variables are widely available and accessible most of the time. When validated, HHD could be incorporated into spatial conservation prioritization plans alongside other abiotic-based solutions at the same scale that conservation is practiced.

Monitoring long‐term vegetation dynamics over the Yangtze River Basin, China, using multi‐temporal remote sensing data

AbstractVegetation plays a crucial role in nature, with intricate interactions between it and the geographical environment. The Yangtze River Basin (YRB) refers to the third largest river basin globally and an essential ecological security barrier in China. Monitoring vegetation dynamics in the basin is of profound significance for addressing climate change, soil erosion, and biodiversity loss in the basin's ecosystems. Here, we investigate the spatiotemporal variations of vegetation at both the basin and land cover scales in the YRB from 2000 to 2020. We elucidate the determinants driving the changes and explore future normalized difference vegetation index (NDVI) trends. The results indicate that NDVI in the YRB increased at a rate of 0.0032 year−1 (p &lt; 0.01) over the past 21 years, and it is anticipated to maintain an upward trend in the future. Regions in the upper and middle reaches of the YRB demonstrated higher NDVI, whereas regions in the headwater area and the lower reaches showed lower NDVI. Significant vegetation improvement was primarily concentrated in the central part of the basin, while noticeable vegetation degradation was observed in the eastern region. Temperature and wind speed were identified as the primary controlling factors affecting vegetation greenness. Global‐scale climate oscillations played a significant role in driving periodic variations in NDVI, with La Niña events tending to increase NDVI, while El Niño events hindered its rise. Land cover types were influenced by long‐term interactions between natural factors and human activities, although short‐term vegetation variations might be more affected by the latter. Our findings provide valuable insights into the mechanisms behind vegetation variability driven by multiple variables, and the strong vegetation carbon sink capacity advances the conservation and development of ecosystems.

Assessing the influence of elevation on satellite derived normalized difference vegetation index and land surface temperature in Rajasthan

Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r2 = 0.26) and negative correlation with LST (r2 = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.

Irrigation expansion has kept pace with the CO2 fertilization effect on vegetation growth in a typical arid region

Dynamics of vegetation in arid areas have drawn worldwide attention. The expansion of irrigated cropland (ICE) in arid regions contributes to increased food security and impacts on the extent and development of regional vegetation. However, the quantitative attribution of vegetation growth variation from ICE and biogeochemical factors (e.g., atmospheric CO2 concentration, climatic factors) is still lacking. Here, we assessed key drivers of vegetation growth in the inland arid region of Northwest China (IANC) from 1982 to 2018, including ICE, increased nitrogen rates, elevated atmospheric CO2 concentration (eCO2) and climate drivers, using normalized difference vegetation index (NDVI) and ecosystem gross primary productivity (GPP) as measures. These variables were quantified through trend decomposition, machine learning algorithms, and a satellite-based model. The results show that vegetation growth was increased in IANC mainly due to eCO2 and ICE. After 1995, as the regional climatic aridity intensified, the CO2 fertilization effect on vegetation growth decreased, as the atmospheric CO2 concentration continued to increase. Meanwhile, irrigated cropland area increased sharply, and ICE-driven GPP variation exceeded that driven by eCO2 in the whole region, while the ICE-driven NDVI variation exceeded that due to eCO2 when the ICE reached 6.38%. The ICE effect on regional vegetation growth rather than the CO2 fertilization effect has mitigated the slowdown of the rate of vegetation growth caused by climate changes. Although the ICE is conducive to food security and continuous greening of arid areas, further reclamation will exacerbate water scarcity. Our results provide research base for identifying the scale of sustainable agricultural development.

Susceptibility of Landslide Debris Flow in Yanghe Township Based on Multi-Source Remote Sensing Information Extraction Technology (Sichuan, China)

The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to geological hazards. Yanghe Township is located in the mountainous area of western Sichuan and lacks geological survey data. Therefore, it is important predict the spatial and temporal development law of landslide debris flow in this area and improve the effectiveness and accuracy of monitoring changes in landslide debris flow, this article proposes a method for extracting information on the changes in landslide debris flows combined with NDVI variation, which is based on short baseline interferometry (SBAS-InSAR) and optical remote sensing interpretation. In this article, we present relevant maps based on six main factors: vegetation index, slope, slope orientation, elevation, topographic relief, and formation lithology. At the same time, different remote sensing images were compared to improve the accuracy of landslide debris flow sensitivity assessments. The research showed that the highest altitude of the region extracted by multi-source remote sensing technology is 2877 m, and the lowest is 630 m, which can truly reflect the topographic relief characteristics of the region. The pixel binary model’s lack of regional restrictions enables a more accurate estimation of the Normalized Difference Vegetation Index (NDVI), bringing it closer to the actual vegetation situation. The study uncovered a bidirectional relationship between vegetation coverage changes and landslide deformation in the study area, revealing spatial–temporal evolution patterns. By employing multi-source remote sensing technology, the research effectively utilized changes in multi-period imagery and feature extraction methods to accurately depict the development process and distribution characteristics of landslide debris flow. This approach not only offers technical support but also provides guidance for evaluating the vulnerability of landslide debris flow in the region.

Environmental characterization and cartographic modeling of wild plant habitats at the northern coastal zone of Egypt

Assessing natural vegetation through conventional methods faces considerable constraints, such as limited geographical scope, reduced precision, a lack of historical data, high expenses and time demands. The study aims to use environmental and spectral data to identify and map natural vegetation and plant species along the Mediterranean coast of Egypt. This involves employing spatial analyses and cartographic modeling techniques, marking an initial effort in this endeavor. To fulfill this aim, a total number of 70 wild plant habitats were surveyed and sampled for further laboratory identification of plant species. Multispectral and thermal bands of Landsat imagery were processed to generate land cover map, as well as calculate Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to be integrated with the naturally grown plants in a cartographic model to predict the wild plant habitats. Coastal wild plant habitats were sparsely located and associated with a vegetation density ranging from 0.096 to 0.280 with an average of 0.167. Besides, the LST of these habitats fluctuated from 30.559 to 38.652 with an average of 34.361°C. Although the wild plant habitats at the Northern coastal region of Egypt are similar in environmental and climatic conditions, there are variability in NDVI and LST of each single habitat (pure or mixed). On the other hand, the lowest LST were associated with Erodium laciniatum/Rumex pictus and Erodium laciniatum habitats reporting 30.559 and 30.741°C, respectively. However, Lotus halophilus mixed with Ifloga spicata habitats reported the highest LST (38.652°C). The high vegetation density is mainly characterized by low LST which indicates the mixed wild plant habitats. The developed cartographic model showed a narrow strip along the Mediterranean region as highly suitable habitats for wild plant growth. The model can be used to map pure and mixed habitats of various wild plants with an accuracy exceeding 90%. The model was applied for mapping Arthrocnemum macrostachyum showing that the middle coastal region is most suitable for its growth. It’s recommended to integrate remotely sensed data with spatial analyses for the environmental analyses of natural plants. Present findings support researchers and scientists interested in environmental, botanic, and medical studies.

Spatial and temporal variation in normalized difference vegetation index of vegetation in Liaoning Province from the perspective of ecogeographic zoning

Normalized difference vegetation index (NDVI) is an important indicator reflecting vegetation cover and growth status. It is of significance for regional ecological conservation and natural resource management to investigate its spatial and temporal variation trends and response to ecological factors. We divided Liaoning Province into three ecological geographical regions, including northwest agro-pastoral zone, central agricultural zone, and eastern agroforestry zone. Based on remote sensing, vegetation, climate, topography and human activities, we used trend analysis and geographic probe model to examine the spatial and temporal trends of NDVI in Liaoning Province, and analyzed the intensity and interaction mechanism of each driver on the spatial distribution pattern of NDVI. The results showed that the annual average NDVI in Liaoning Province from 2001 to 2020 was 0-0.92, showing a distribution pattern of high in the east and low in the west, high in the inland and low in the coastal land. The overall trend of vegetation cover was increasing, and the NDVI increasing areas were mainly concentrated in the northwest agro-pastoral zone and the eastern agroforestry zone, the NDVI reduction areas were mainly concentrated at the border between the central agricultural zone and the eastern agroforestry zone, as well as in the coastal area of the eastern agroforestry zone. The annual average NDVI change varied among the three ecological-geographic zones. The NDVI of the northwest agro-pastoral zone from 2001 to 2020 were generally low, but showed a fluctuating trend of slow increase. The NDVI of the eastern agroforestry zone was high overall, and the interannual variation of NDVI was generally stable. The distribution of high and low NDVI in the central agricultural zone was staggered, and the interannual variation of NDVI showed a decreasing trend. Natural factors were the key drivers of NDVI changes in the three ecogeographic zones, with cumulative temperature and precipitation having the greatest influence. The interactions between the factors were all mutually and nonlinearly enhanced.

Temperate grassland vegetation restoration influenced by grazing exclusion and climate change

Grasslands are one of the most important terrestrial biomes, supporting a wide range of ecological functions and services. Grassland degradation due to overgrazing is a severe issue worldwide, especially in developing regions. However, observations from multiple sources have shown that temperate grasslands in China have significantly increased during the past two decades. It remains controversial what factors have driven the vegetation restoration in this region. In this study, we combined remote-sensing images and field survey datasets to quantify the contributions of different factors to vegetation restoration in six temperate grasslands in northern China. Across the six grasslands, the Normalized Difference Vegetation Index (NDVI) increased by 0.003–0.0319 year−1. The average contributions of grazing exclusion and climate change to the NDVI increase were 49.23 % and 50.77 %, respectively. Precipitation change was the primary climate factor driving vegetation restoration, contributing 50.76 % to the NDVI variance. By contrast, climate warming tended to slow vegetation restoration, and atmospheric CO2 concentration change contributed little to the NDVI increase in the temperate grasslands. These results emphasize the significant contributions of both climate change and human management to grassland vegetation restoration.

Spatio-temporal linkage variations between NDVI and tree rings on the leeward side of the northern Patagonian Andes

The Normalized Difference Vegetation Index (NDVI) and tree rings provide different approaches that allow evaluating the productivity of ecosystems. Following this premise, we evaluated the relationship between NDVI and tree-ring widths of Austrocedrus chilensis forests at sectors of the Argentinian northwestern foothills of the Patagonian Andes. To proceed with this study, wood core samples were taken from 153 trees at 6 sites located at different latitudes and along a strong west-east precipitation gradient. The NDVI presented a significant and positive spatial correlation with precipitation, the Standardized Precipitation-Evapotranspiration Index (SPEI-3), soil moisture, and a negative correlation with air temperature at large geographical coverages. Stand location in the latitudinal and meridian coordinates showed a species-specific effect, with greater sensitivity to NDVI in sites located at higher latitudes and under drier conditions. We found stronger associations between stem biomass (tree rings) and canopy biomass (NDVI) in drier sites, assuming a closer link to stressful water conditions. This behavior was accentuated since 1988, which coincides with the increase in air temperature as a result of regional warming processes. These results suggest that site environmental conditions influence how NDVI and ring width are linked, showing the complex dynamics between climate and growth. The results indicate that ring widths of A. chilensis trees have the potential to assess current NDVI variations in the study area, allowing reconstructions of past productivity and potential predictive models of future changes.

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.

Evaluation of Urban Sprawl and Land Surface Temperature along with Vegetation and Built-up Index for Nagpur City, Maharashtra

Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. background: The LULC classification is performed based on four classes Bare land, Built-up, Vegetation and Waterbodies. Method: The study aims to provide a detailed analysis of land and temperature change with variation in Normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI) for the study area using a geospatial technique. The LULC classification is performed based on four classes which are Bare land, Built-up, Vegetation, and Waterbodies from the year 2000 to 2020. The classified data is further used to extract the Land Surface Temperature (LST) data from the thermal band to generate LST maps. The NDVI and NDBI maps are also generated using the land sat imageries. From the above-mentionedanalysis, it is found that Nagpur city temperature has risen by 3.67 °C in two decades. Whereas, LULC results show that bare land and vegetation decreased by 11.88% and 14.93% respectively, while an increase is seen for built-up and water bodies by 25.62% and 0.19% respectively. objective: The classified data is further used to extract the LST data from thermal band to generate LST maps. Result: Regression analysis between temperature and NDVI, NDBI shows that temperature and NDVI have a negation relation and NDBI has a positive relation with temperature (Pearson’s r: between -0.89 to -0.81and between 0.90 to 0.81respectively) for both the years. The increased temperature is a result of urbanization in the study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. method: The NDVI and NDBI maps are also generated using the land sat imageries. From the analysis of above mentioned it is found that Nagpur city temperature is raised by 3.67 °C in two decades. Conclusion: This study recommends that policymakers develop policies that should minimize the transition of different classes and check the outcome of industries and the temperature of the surroundings. conclusion: The increase temperature is a result of urbanization for study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. other: .

The Impact of Dam Construction on Downstream Vegetation Area in Dry Areas Using Satellite Remote Sensing: A Case Study

The assessment of ecosystem quality and the maintenance of optimal ecosystem function require understanding vegetation area dynamics and their relationship with climate variables. This study aims to detect vegetation area changes downstream of the Hali dam, which was built in 2009, and to understand the influence of the dam as well as climatic variables on the region’s vegetation areas from 2000 to 2020. The case study is located in an arid area with an average rainfall amount from 50 to 100 mm/year. An analysis of seasonal changes in vegetation areas was conducted using the Normalized Difference Vegetation Index (NDVI), and supervised image classification was used to evaluate changes in vegetation areas using Landsat imagery. Pearson correlation and multivariate linear regression were used to assess the response of local vegetation areas to both hydrologic changes due to dam construction and climate variability. The NDVI analysis revealed a considerable vegetation decline after the dam construction in the dry season. This is primarily associated with the impoundment of seasonal water by the dam and the increase in cropland areas due to dam irrigation. A significantly stronger correlation between vegetation changes and precipitation and temperature variations was observed before the dam construction. Furthermore, multivariant linear regression was used to evaluate the variations in equivalent water thickness (EWT), climate data, and NDVI before and after the dam construction. The results suggested that 85 percent of the variability in the mean NDVI was driven by climate variables and EWT before the dam construction. On the other hand, it was found that only 42 percent of the variations in the NDVI were driven by climate variables and EWT from 2010 to 2020 for both dry and wet seasons.

Tracing climatic and human disturbance in diverse vegetation zones in China: Over 20 years of NDVI observations

Over the past two decades (2000–2021), China has experienced a significant increase in vegetation cover. However, the intricate spatial and temporal variations in vegetation cover, arising from the interaction of climate change and human activities, remain enigmatic, particularly across diverse vegetation zones. This research endeavors to unravel the dynamics of vegetation cover using the MOD13A1 Normalized Difference Vegetation Index (NDVI) dataset, delving into China's seven distinct vegetation zones (VZ1-VZ7). This research employs residual analysis, relative analysis, and geo-detector techniques to investigate the drivers of NDVI variation and spatial heterogeneity across China's seven distinct vegetation zones. The findings reveal a compelling upward trajectory in NDVI, predicted NDVI, and residual NDVI across China during the 2000–2021 period. Notably, significant and extremely significant increases are discernible in approximately 53.90%, 50.13%, and 23.29% of China's NDVI, respectively. Among the vegetation zones, VZ3 displays the highest change rates, yet the proportion with a significant increase in VZ1 surpasses other zones. The relative analysis underscores climate change as the predominant catalyst for vegetation enhancement, although an exception is noted in VZ3. Furthermore, the spatial heterogeneity of China's NDVI is primarily influenced by the interactions between land use types and relative humidity, sunshine duration, and precipitation, especially the interaction between relative humidity and land use types. Significant changes in climate can lead to marked alterations in the structure and function of terrestrial ecosystems, while changes in land use patterns at the regional level also alter the type of land cover, which together exert a significant impact on the vegetation NDVI. These results accentuate the importance of formulating tailored ecosystem management strategies tailored to the distinct characteristics of each vegetation zone to ensure the sustainable development of terrestrial ecosystems across China.

Study of Changes in the Ulan Buh Desert under the Dual Impacts of Desert Farmland Development and Climate Change.

Desert farmland provides food for desert areas, but water is the main limiting factor of this region, thus desert farmland has an extremely fragile ecological environment. This study investigated the temporal and spatial variations of vegetation NDVI (Normalized Difference Vegetation Index) in the Ulan Buh Desert, China, from 1990 to 2022, using long-term Landsat satellite data obtained from the Google Earth Engine platform and local statistical data. The results showed that from 1990 to 2022, the NDVI exhibited relatively small fluctuations and a steady increase. Furthermore, the study analyzed the impact of climate factors, namely precipitation and temperature, on NDVI, and collected the groundwater lever changes under irrigation and farmland development. The results demonstrated a positive correlation between NDVI and both precipitation and temperature from 1990 to 2006. The study area experienced an overall trend of increasing humidity. Specifically, from 1990 to 2006, significant positive correlations with precipitation and temperature were observed in 4.4% and 5.5% of the region, respectively. From 2007 to 2022, significant positive correlations were observed in 5.4% and 72.8% of the region for precipitation and temperature, respectively. These findings suggest that temperature has become increasingly influential on vegetation NDVI, while the impact of precipitation remains relatively stable. Moreover, the study assessed the impact of human activities on vegetation NDVI. The results revealed that from 1990 to 2006, human activities contributed to 43.1% of the promotion of local vegetation NDVI, which increased to 90.9% from 2007 to 2022. This study provides valuable insights into the dynamics of vegetation in the Ulan Buh Desert and its response to climatic changes and human activities. The findings highlight the significance of climate conditions and human interventions in shaping the vegetation dynamics in the region, offering essential information for ecological restoration and conservation efforts.

REMOTE SENSING AND GEOSTATISTICS APPLIED TO NDVI, KC AND PRODUCTIVE PARAMETERS IN IRRIGATED CORN CROP

Remote sensing (SR) has contributed to digital agriculture, associated with the use of geostatistical techniques that allow data acquisition, analysis and decision making. In view of this, the objective of this work was to evaluate the spatial variability of soil physical attributes, NDVI and second crop corn productivity in the territory of Santa Helena de Goiás, and to estimate values of crop coefficient (KC) through the NDVI, and to evaluate the spatiality. Soil samples were collected in a georeferenced way using a regular grid. The images were from the Sentinel 2A satellite. The Normalized Difference Vegetation Index (NDVI) was calculated, the Kc values of the crop were estimated, using two methodologies Toureiro et al., (2017) and Kamble et al. (2013), and later the values were extracted through a regular GRID, with which the geostatistical analyzes and correlations with productivity were submitted. The NDVI showed an increase up to 69 days after planting, in addition to proving the spatial variability and spatial dependence. With the geostatistics it was possible to generate a map of the spatial distribution of productivity. The NDVI allowed to obtain the Kc corresponding to the different phases of cultivation, sensitive to the methodologies used. The Kc obtained presents a high response and potential to the management of irrigated corn..

Assessing the Vegetation Dynamics and Its Influencing Factors in Central Asia from 2001 to 2020

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.