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Grasslands are one of the most common land-cover types, providing important ecosystem services globally, yet few studies have examined grassland critical-zone functioning throughout hillslopes. This study characterised a coastal grassland over a small hillslope at Point Reyes National Seashore, California, using multidisciplinary techniques, combining remotely-sensed, geophysical, plant, and soil measurements. Clustering techniques delineated the study area into four landscape zones, up-, mid-, and down-slope, and a bordering riparian ecotone, which had distinct environmental properties that varied spatially across the site, with depth, and time. Soil moisture increased with depth and down slope towards a bordering riparian zone, and co-varied with soil CO2 flux rates both spatially and temporally. This highlighted three distinct controls of soil moisture on soil respiration: CO2 fluxes were inhibited by high moisture content in the down-slope during the wet winter months, and converged across landscape positions in the dry summer months, while also displaying post-rain pulses. The normalised difference vegetation index (NDVI) ranged from 0.32 (September)–0.80 (April) and correlated positively with soil moisture and aboveground biomass, moving down slope. Yet, NDVI, aboveground biomass, and soil moisture were not correlated to soil organic carbon (SOC) content (0.4%–4.5%), which was highest in the mid-slope. The SOC content may instead be linked to shifts in dominant grassland species and their rhizosphere properties with landscape position. This multidisciplinary characterisation highlighted significant heterogeneity in grassland properties with landscape position, and demonstrated an approach that could be used to characterise other critical-zone environments on hillslopes.

This poster was presented at the 2024 UCSB EEMB Research symposium.This project investigates how water quality metrics correlate with seasonal changes in algal and aquatic vegetation abundance via close-range and satellite imagery analysis at the Devereux Slough. The Devereux Slough, part of UCSB’s North Campus Open Space (NCOS), is a temporary open/closed estuary undergoing restoration efforts following its previous use as a golf course. The Devereux Slough provides buffer area for storm surges and sea level rise, and it functions as a wildlife corridor linking protected lands. The Aquatic Invertebrate Lab at the NCOS evaluates wetland function in terms of nutrient filtering and support for wildlife. We analyze the effects of restoration on the ecosystem, abiotic factors on aquatic food webs, and the relationship between nutrient pulses and algal blooms. We deployed camera stands at bridges across NCOS to research algal growth through photography where Coastsnap is used to catalog images taken by citizen scientists. We then use ENVI and Adobe Photoshop to divide photographs into quadrats and analyze the algal cover based on pixel cover. We have Solinst leveloggers deployed near each site which collect water level, temperature, and in some cases, conductivity. We also investigate how aquatic vegetation density shifts spatially and temporally at the Devereux Slough from 2022-2024. We isolate spectral bands from monthly PlanetLabs satellite imagery and apply the normalized difference aquatic vegetation index function in R. We are interested in how plant health varies between longer- and shorter-term restoration sites and with water quality metrics taken by Solinst leveloggers.

Woody vegetation restoration projects are an important feature of landscape function in Indonesian karst savannas. Understanding the relationship between available moisture and vegetation condition can assist with the planning and implementation of revegetation efforts. Working at vegetation restoration sites in East Nusa Tenggara, Indonesia, we applied a windowed cross-correlation method to mean values of NDVI to examine the lag between moisture input and NDVI response for both rainfall and soil moisture between 1999 and 2018. To test for increasing or decreasing trends in NDVI and rainfall time series, we undertook Mann-Kendall trend analyses. We identified increasing trends in Landsat 7 NDVI at two of four restoration sites, with annual increases in NDVI of 2.7 and 3.74 × 10-4 respectively. We found that rainfall dependent sites had significant Pearson's correlations with NDVI ranging from 0.52 to 0.71, while NDVI was not correlated with rainfall at shallow groundwater sites. There was a clear negative effect of the very dry period on all sites, and this was less pronounced at shallow groundwater sites. Wet years resulted in a positive response to NDVI across all sites, while the response was lower in very wet years with annual rainfall above 1,200 mm. We found that between 2 and 4 months of antecedent rainfall gave the highest correlation with NDVI, while for soil moisture the closest relationship was found with no lag and 1 month lag. Through this study, we demonstrated the applicability of using NDVI, rainfall, and soil moisture trend analyses to identify groundwater-dependent vegetation patches and monitor the effectiveness of vegetation restoration. [ABSTRACT FROM AUTHOR]

To reveal the evolution characteristics of vegetation cover in Nanchang City and identify the main climate drivers affecting its change trends, in order to provide guidance for its long-term stable and benign development and active response to the subsequent climate change. In this paper, NDVI monthly data and ten meteorological driving factors such as temperature, precipitation and pressure of Nanchang City from 2000 to 2020 were used to study the importance of different drivers by using single sample K-S test, Friedmann test, Kendall harmony coefficient test and random forest analysis. The results showed that, 1) the NDVI of Nanchang City showed a fluctuating downward trend from 2000 to 2020, the peak appeared in 2000, while the trough occurred in 2010. 2) The overall vegetation cover of the city showed a spatial distribution pattern of high perimeter and low center, with the NDVI values in the north and the west being relatively high and decreasing at a slow rate, and the NDVI values of the central areas of the city such as the East Lake District and the Castle Peak Lake District being relatively low and with an obvious downward trend. 3) The great average value of the vegetation cover appeared in August each year, which was most significantly affected by temperature and least affected by wind direction, with a certain time lag influenced by precipitation, so the study of vegetation cover change should be determined by combining the driving effect of multiple elements. [ABSTRACT FROM AUTHOR]

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. [ABSTRACT FROM AUTHOR]

The Greater Mekong Subregion (GMS) economic cooperation program is an effective and fruitful regional cooperation initiative for socioeconomic development in Asia; however, the vegetation change trends and directions in the GMS caused by rapid development remain unknown. In particular, there is a current lack of comparative studies on vegetation changes in various countries in the GMS. Based on the MODIS normalized difference vegetation index (NDVI) time series data, this study analyzed the spatiotemporal patterns of vegetation coverage and their trends in the GMS from 2000 to 2022 using the Theil–Sen slope estimation, the Mann–Kendall mutation test, and the gravity center migration model. The key findings were as follows: (1) the NDVI in the GMS showed an overall upward fluctuating trend over the past 23 years, with an annual growth rate of 0.11%. The NDVI changes varied slightly between seasons, with the greatest increases recorded in summer and winter. (2) The spatial distribution of NDVI in the GMS varied greatly, with higher NDVI values in the north–central region and lower NDVI values in the south. (3) A total of 66.03% of the GMS area showed increments in vegetation during the studied period, mainly in south–central Myanmar, northeastern Thailand, Vietnam, and China. (4) From 2000 to 2022, the gravity center of vegetation greenness shifted northward in the GMS, especially from 2000 to 2005, indicating that the growth rates of vegetation in the north–central part of the GMS were higher than those in the south. Furthermore, the vegetation coverage in all countries, except Cambodia, increased, with the most pronounced growth recorded in China. Overall, these findings can provide scientific evidence for the GMS to enhance ecological protection and sustainable development. [ABSTRACT FROM AUTHOR]

Climate change has altered the frequency and severity of extreme weather, which can affect vegetation condition and habitat quality for wildlife. Declines in vegetation productivity during droughts and heatwaves can negatively impact animals that depend on vegetation for water and nutrition. We used the normalised difference vegetation index (NDVI) to look at relationships between vegetation productivity and the presence of koalas (Phascolarctos cinereus) in potential habitat throughout much of their range. Using a large, long-term koala presence dataset, we tested the hypothesis that locations where koalas had been observed would exhibit higher NDVI values than a random, representative sample from the same vegetation group. We also identified the minimum NDVI threshold at which koalas occurred across time for each vegetation group and compared these to the minimum NDVI values across potential koala habitat before and during the Millennium Drought, one of the worst recorded in Australia. Additionally, we investigated whether vegetation above the minimum NDVI thresholds was significantly closer to perennial water than unsuitable vegetation. We found that koalas tend to occur at locations with higher NDVI than average for all vegetation groups. Prior to the drought, 49% of potential koala habitat maintained a minimum NDVI above the koalas' threshold, equating to 190,227 km2, which declined to 166,746 km2 during drought (i.e., a 12% reduction). We also found that unsuitable vegetation tended to occur farther from perennial water than suitable vegetation for some vegetation groups. Areas that remained above the NDVI thresholds during the drought should be considered potential refugia for populations during an event of similar magnitude and could indicate future habitat extent. [ABSTRACT FROM AUTHOR]

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality N D V I time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality N D V I time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original N D V I time series and producing high-quality N D V I profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an R M S E of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality N D V I time series data could produce timely and spatially explicit phenological information at the field and crop levels. [ABSTRACT FROM AUTHOR]

The increase in atmospheric and soil temperatures in recent decades has led to unfavorable conditions for plants in many Mediterranean coastal environments. A typical example can be found along the coast of the Campania region in Italy, within the "Volturno Licola Falciano Natural Reserve", where a pine forest suffered a dramatic loss of trees in 2021. New pines were planted in 2023 to replace the dead ones, with a larger tree layout and interspersed with Mediterranean bushes to replace the dead pine forest. A direct (in situ) monitoring program was planned to analyze the determinants of the pine salinity stress, coupled with Sentinel-2 L2A data; in particular, multispectral indices NDVI and NDMI were provided by the EU Copernicus service for plant status and water stress level information. Both the vadose zone and shallow groundwater were monitored with continuous logging probes. Vadose zone monitoring indicated that salinity peaked at a 30 cm soil depth, with values up to 1.9 g/L. These harsh conditions, combined with air temperatures reaching peaks of more than 40 °C, created severe difficulties for pine growth. The results of the shallow groundwater monitoring showed that the groundwater salinity was low (0.35–0.4 g/L) near the shoreline since the dune environment allowed rapid rainwater infiltration, preventing seawater intrusion. Meanwhile, salinity increased inland, reaching a peak at the end of the summer, with values up to 2.8 g/L. In November 2023, salts from storm-borne aerosols ("sea spray") deposited on the soil caused the sea-facing portion of the newly planted pines to dry out. Differently, the pioneer vegetation of the Mediterranean dunes, directly facing the sea, was not affected by the massive deposition of sea spray. The NDMI and NDVI data were useful in distinguishing the old pine trees suffering from increasing stress and final death but were not accurate in detecting the stress conditions of newly planted, still rather short pine trees because their spectral reflectance largely interfered with the adjacent shrub growth. The proposed coupling of direct and remote sensing monitoring was successful and could be applied to detect the main drivers of plant stress in many other Mediterranean coastal environments. [ABSTRACT FROM AUTHOR]

This study analyzed satellite data from 2000 to 2022 in the Tian Shan ecoregions of northwestern China to investigate relationships between climate, environment, soil conditions, and human activities on constantly changing vegetation fluctuations. The NDVI index outperformed EVI over the period, with peak values of 0.56 and 0.33, respectively. NDVI trends indicated a higher slope of 5.15 compared to 4.91 for EVI. Analysis showed that vegetation area had expanded over time, with the lowest coverage between 2000 and 2005, but then, it spread due to varying degree of human activities' impacts from 0 to 63. Tests revealed significant negative correlations between soil moisture and EVI/NDVI indexes attributed to natural water phenomena causing vegetation stress. Positive correlations were found between EVI/NDVI with actual evapotranspiration and snow, while negative with wind speed and radiation. The study also found a positive correlation between NDVI and measures of human activity, indicating restoration efforts, project implementations, and soil management preventing erosion expanded vegetation. Overall, the study concluded that human activities had a greater influence than climate through water and soil preservation, resulting in more vegetation expansion over time. Indigenous resource concepts also significantly contributed to long-term preservation efforts evidently maintaining and strengthening vegetation in the ecoregions. These findings highlight the efficacy and resilience of human activity in overcoming climatic and environmental challenges. While human factors can potentially harm the environment and renewable resources, long-term planning and preservation efforts, as evident in the Tian Shan ecoregions, have successfully preserved and strengthened the vegetation. [ABSTRACT FROM AUTHOR]

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 1 month, and the response to average temperature and relative humidity was longer. [ABSTRACT FROM AUTHOR]

The process of determining whether a specific portion of land is suitable for a specific purpose is known as land suitability analysis (LSA). In order to promote sustainable development in semi-arid regions, the objective of this study is to analyse, evaluate, and identify the land for green growth based on topography, climate, and soil characteristics. Twelve thematic maps are prepared by using remote sensing satellite data. The Landsat 8 OLI/TIRS is used for the preparation of the thematic maps like land use land cover (LULC), normalized difference vegetation index (NDVI), top soil grain size index (TGSI), and geomorphology (GM), and DEM data is used for the preparation slope, and drainage density (DD). The collateral data is used to prepare geology and soil thematic maps. From the field work, we have collected soil samples for the compulsory physicochemical parameters such as soil EC and soil N-P-K which were taken into consideration and prepared thematic maps. The analytical hierarchy process (AHP) was used to generate the LSA of the research region, by assigning the appropriate weights to each criterion and sub-criterion for the thematic maps. Geographic information systems (GIS) and the multicriteria decision-making (MCDM) approach were used in the study's methodology. The LSA of the study area has been categories in to four types, i.e., highly suitable, moderately suitable, marginally suitable, and not suitable. The results revealed that 421.31 sq.km (40.09%) is not suitable for agriculture green growth in the study region, whereas 89.58 sq.km (8.52%) is moderately suitable, 267.66 sq.km (25.47%) is marginally suitable, and 266.54 sq.km (25.36%) is highly suitable. Accuracy assessment has validated the LSA map's accuracy (AA). The AA of LSA is 84.22%, which demonstrates a strong connection with the actual data. The research's results could be helpful in locating productive agricultural areas in various parts of the world. The decision-making AHP tool paired with GIS provides a novel method. [ABSTRACT FROM AUTHOR]

Climate change and anthropogenic activities (changes in rainfall and temperature pattern) activities have significantly affected agricultural activity and crop production. So, studying cropland greenness and crop yield trends is essential to understand their impacts and to ensure food security. The present study attempted to depict the cropland greenness and wheat yield trends in Uttar Pradesh (UP) state, India, during 2001–2019. The Moderate Resolution Imaging Spectroradiometer (MODIS) based Normalised Difference Vegetation Index (NDVI) dataset was used to decipher the cropland greenness trends through the Mann-Kendall (MK) test. Furthermore, the wheat yield dataset from Baghel and Sharma (2022) was used to depict the wheat yield trends using the Thiel-Sen slope test from 2001 to 2019. The study's results showed that ~ 37.3% (82,041 km2) area of the total agricultural land was with positive cropland greenness (NDVI) trend, and 1.67% (3673 km2) area was under a negative cropland greenness trend. On the other hand, ~ 8463 km2 (at 99% significance level) and 160,557 km2 (90% significance level) area corresponding to the ~ 4% and 73%, respectively, of the total agricultural land has shown positive (increasing) wheat yield trends during 2001–2019. In conclusion, the findings of this study emphasize the need for ongoing monitoring and a comprehensive understanding of cropland greenness and wheat yield trends over different agroclimatic zones. By gaining insights into these trends, policymakers and stakeholders can develop and implement effective mitigation and adaptation strategies to minimize the adverse effects of climate change and anthropogenic activities on agricultural systems. [ABSTRACT FROM AUTHOR]

With global climate change and increased human activities, landslides increasingly threaten human safety and property. Precisely extracting large-scale spatiotemporal information on landslides is crucial for risk management. However, existing methods are either locally based or have coarse temporal resolution, which is insufficient for regional analysis. In this study, spatiotemporal information on landslides was extracted using multiple remote sensing data from Emilia, Italy. An automated algorithm for extracting spatial information of landslides was developed with NDVI datasets. Then, we established a landslide prediction model based on a hydrometeorological threshold of three-day soil moisture and three-day accumulated rainfall. Based on this model, the locations and dates of rainfall-induced landslides were identified. Then, we further matched these identified locations with the extracted landslides from remote sensing data and finally determined the occurrence time. This approach was validated with recorded landslides events in Emilia. Despite some temporal clustering, the overall trend matched historical records, accurately reflecting the dynamic impacts of rainfall and soil moisture on landslides. The temporal bias for 87.3% of identified landslides was within seven days. Furthermore, higher rainfall magnitude was associated with better temporal accuracy, validating the effectiveness of the model and the reliability of rainfall as a landslide predictor. [ABSTRACT FROM AUTHOR]

In 2022, a severe drought and heatwave occurred in the middle and lower reaches of the Yangtze River Basin. Previous studies have highlighted the severity of this event, yet the relevance of soil moisture (SM), as well as vapor pressure deficit (VPD) and vegetation damage, remained unclear. Here, we utilized solar-induced chlorophyll fluorescence (SIF) and various flux data to monitor the impact of drought on vegetation and analyze the influence of different environmental factors. The results indicated a severe situation of drought and heatwave in the Yangtze River Basin in 2022 that significantly affected vegetation growth and the ecosystem carbon balance. SIF and NDVI have respective advantages in reflecting damage to vegetation under drought and heatwave conditions; SIF is more capable of capturing the weakening of vegetation photosynthesis, while NDVI can more rapidly indicate vegetation damage. Additionally, the correlation of SM and SIF are comparable to that of VPD and SIF. By contrast, the differentiation in the severity of vegetation damage among different types of vegetation is evident; cropland is more vulnerable compared to forest ecosystems and is more severely affected by drought and heatwave. These findings provided important insights for assessing the impact of compound drought and heatwave events on vegetation growth. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa subsp. pauca ST53 (XFP), the causal agent of olive quick decline syndrome (OQDS), was thoroughly investigated after a 2013 outbreak in the Salento region of Southern Italy. Some trees from Ogliarola Salentina and Cellina di Nardò, susceptible cultivars in the Gallipoli area, the first XFP infection hotspot in Italy, have resprouted crowns and are starting to flower and yield fruits. Satellite imagery and Normalized Difference Vegetation Index analyses revealed a significant improvement in vegetation health and productivity from 2018 to 2022 of these trees. Lipid molecules have long been recognized as plant defense modulators, and recently, we investigated their role in XFP-positive hosts and in XFP-resistant as well as in XFP-susceptible cultivars of olive trees. Here, we present a case study regarding 36 olive trees (12 XFP-positive resprouting, 12 XFP-positive OQDS-symptomatic, and 12 XFP-negative trees) harvested in 2022 within the area where XFP struck first, killing millions of trees in a decade. These trees were analyzed for some free fatty acid, oxylipin, and plant hormones, in particular jasmonic and salicylic acid, by targeted LC-MS/MS. Multivariate analysis revealed that lipid markers of resistance (e.g., 13-HpOTrE), along with jasmonic and salicylic acid, were accumulated differently in the XFP-positive resprouting trees from both cultivars with respect to XFP-positive OQDS symptomatic and XFP-negative trees, suggesting a correlation of lipid metabolism with the resprouting, which can be an indication of the resiliency of these trees to OQDS. This is the first report concerning the resprouting of OQDS-infected olive trees in the Salento area. [ABSTRACT FROM AUTHOR]

Air pollution poses a substantial risk not only to human health but also to the health of ecosystems. Despite the increasing air pollutants in the North Africa region, there are limited studies that identify the factors of air pollution. Further, no study devoted to identify the interaction between air pollution and environmental variables in Algeria. Thus, the primary aim of this study is to determine the interactions between air pollutants and environmental factors in Aures, Algeria. A spatial–temporal distribution was analyzed to determine the air pollutant concentration in the study area for 2019–2021. To this end, we used Sentinel-5P TROPOMI time series data from the Google Earth Engine (GEE) cloud platform to estimate the concentration of air pollutants. A trend analysis with a linear regression model was used to determine the interrelationship between NDVI and air pollutant concentration. Findings show that throughout 2019–2021, air pollutants are highly concentrated in the northeast and the southernmost portion of this area, and during the winter and spring seasons. The regression results show that vegetation is negatively associated with PM2.5 (β = − 0.23, p = 0.04) and SO2 concentration (β = − 0.65, p = 0.01), while showed a positive association with NO2 (β = 0.31, p = 0.08) and CH4 concentration (β = 1.02, p = 0.00) in this region. These outcomes give quantitative decision-making benchmarks for the rational development, usage, and management of land resources, as well as the attainment of regionally coordinated controls of PM2.5 pollution. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems are vital to arid environments like Saudi Arabia, offering crucial ecological services and enhancing biodiversity. This study investigates the spatial distribution and temporal dynamics of mangrove coverage in the Al Wajh lagoon area, crucial for crafting effective conservation and management strategies. Employing high-resolution Landsat and Sentinel imagery, the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were analysed across 35 mangrove clusters. By 2023, mangroves covered approximately 24.5 km2. Kendall Tau-b Correlation Coefficients confirmed a significant positive trend. The study further explored the relationship between NDVI and surface temperature, revealing an inverse correlation with slope values ranging from -4.03 to -44.1, suggesting that increased mangrove coverage significantly contributes to local cooling, thereby underscoring the climate regulation benefits of mangrove conservation. This positive change in NDVI post-2020 is directly attributable to the conservation efforts embedded within the Red Sea Project, Vision 2030, development phases, demonstrating the critical role of designated conservation zones in mitigating the environmental impacts of large-scale development projects. The findings underscore the need for targeted policies to protect and expand mangrove habitats as part of broader environmental sustainability goals in the region. [ABSTRACT FROM AUTHOR]

In order to analyze the ecological management effect and future vegetation restoration potential of Ejina Oasis at the tail of the Heihe River, Normalized Difference Vegetation Index (NDVI) was extracted based on the MOD13Q1 data set from 2000 to 2020. This paper used Sen trend and M-K test to analyze the spatiotemporal variation characteristics of vegetation in the Ejina Oasis. Considering the factors of topography, geomorphology, hydrology, soil and vegetation type, the similar habitat method was used to explore the potential and spatial pat- tern of oasis vegetation restoration. The results show that NDVI shows an increasing trend from 2000 to 2020, and the annual growth rate is 0.001 3/a. The proportion of areas with a significant increase in NDVI in spring on a seasonal scale is the highest. The vegetation coverage near the Ejina Banner government is relatively high and the vegetation coverage in the middle reaches of Xihe River increases significantly during the study period. In the future, the vegetation will still show a spatial distribution pattern of high around the two rivers and low at the edge of Ejina Oasis. The lower reaches of the surrounding areas of the tail lake are close to or have reached the maximum recovery potential. The vegetation enhancement potential of Badaohe to Swan Lake channel, Qidaohe tail channel, Sidaohe and Liudaohe tail channel and the west of Xihe lower section are great. [ABSTRACT FROM AUTHOR]

Efficient drought monitoring in the Urmia Lake basin (ULB) is imperative to protect its ecosystem, agriculture, and the livelihoods of local communities relying on its water resources, considering the lake's susceptibility to changes in water availability. This study presents a novel approach to address the pressing issue of precise drought monitoring in regions with limited and unevenly distributed weather stations. By utilizing Tropical Rainfall Measuring Mission (TRMM) satellite-derived data and a Geographically Weighted Regression (GWR) model, we have significantly refined the spatial resolution of TRMM-3B43 precipitation (5 km) compared with the original TRMM data (about 25 km). This innovative methodology, which uses the globally available MODIS data of Normalized Difference Vegetation Index (NDVI) and day-night difference in land surface temperature (LSTdn) as independent variables, achieves spatial downscaling, enhancing the spatial resolution to 5 km for the period 2001–2019. The downscaled precipitation data from three models (M1: GWR_NDVI, M2: GWR_LSTdn, and M3: GWR_NDVI-LSTdn) were applied for drought assessment based on the Standardized Precipitation Index (SPI) and modified Rainfall Anomaly Index (mRAI). The findings demonstrate that the M2 model is more accurate than the other models for spatial downscaling of TRMM 3B43 data, with reductions in RMSE and MAE values by 6.04 and 4.16 mm, respectively. Additionally, this downscaling model significantly improves KGE values from 0.56 to 0.92 while achieving the lowest percentage bias. Monthly downscaled TRMM data based on LST across 12 synoptic stations in the basin reveals enhanced accuracy post-downscaling. Spatial analysis of average monthly precipitation maps illustrates a descending rainfall trend from June to September, followed by an ascending trend from October, with peak rainfall in November and December, notably in the western and southwestern regions. This analysis of precipitation trends offers valuable insights into the spatial distribution of rainfall within the basin, revealing variations across regions crucial for effective water resource management. The concentration of higher precipitation levels in the western and southwestern sectors underscores the significance of targeted water conservation and storage efforts. Drought severity analysis unveiled persistent and escalating drought conditions, causing significant impacts across the basin. A comparative assessment of severity classes using SPI and mRAI indices at 12 synoptic stations demonstrated strong agreement. Drought occurrences were a near-annual affair, attaining severe levels during specific years, notably 2008, 2011, and 2019. Spatial analysis revealed widespread drought events affecting nearly half of the basin area, with a noticeable worsening in severity during critical years. This highlights the practical need for adopting drought mitigation strategies and water resources management. [ABSTRACT FROM AUTHOR]

Armed conflicts can lead to environmental degradation, thereby threatening the basis of people's livelihoods and wellbeing. Identifying areas where conflicts drive environmental degradation is important for designing effective recovery strategies, but this is inherently challenging in insecure contexts. We use a case study in Tigray, Ethiopia to illustrate how open-source satellite data can be used to support the identification of woody vegetation loss during armed conflicts in situations where ground-based assessments are difficult or impossible. Areas of potential woody vegetation loss extend across 930 km2 (approximately 4% of the area occupied by forest and other woody vegetation in Tigray) and appear to be concentrated mostly along major roads; however, vegetation recovery has continued during the war across a significantly larger area (approximately 2600 km2). Spatial patterns of woody vegetation loss appear to be unrelated to drought conditions and large-scale wildfires. Based on these observations and anecdotal evidence of deforestation, we propose that it may be conflict-driven deforestation, caused by increases in fuel wood demands, that are driving the woody vegetation losses in some areas of Tigray. Eventual recovery efforts will have to consider the loss in landscape health during the war in areas where woody vegetation has declined, and include efforts to restore this vegetation to ensure both food security and livelihoods. Open access satellite data, together with ground-based data collection, could inform such post-war restoration efforts by helping identify degraded areas at a regional scale. [ABSTRACT FROM AUTHOR]

Fire is one of the most significant agents of disturbance in forest ecosystems, with implications for their structure and composition. An understanding of its dynamics is essential for the delineation of forest management policies in the context of predicted climate scenarios. Based on the monthly monitoring of greenness index (NDVI) values recorded over one year at the individual crown level, this study aimed to analyze the dynamics of NDVI values for four different genera, growing in a Mexican mixed forest and subjected to a prescribed burn, relative to those of a control (unburned) treatment. The results demonstrated the general effect of burning over time on NDVI values among the genera, with Pinus showing the most significant effect, while the effect on Quercus was not significant. Tree height was related to NDVI values for Pinus and Juniperus in the burned area, where low-growing individuals responded negatively in terms of greenness index values. Further studies are still required, but we can conclude that fire plays a differential role in the dynamics of canopy activity and that tree size is an important variable. The results also contribute to our understanding of forest responses to fire disturbance, providing indicators with which to assess ecosystem stability under the threat of extreme climatic variations. [ABSTRACT FROM AUTHOR]

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability. [ABSTRACT FROM AUTHOR]

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) RNDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while RNDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of RNDVI-GP and RNDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in RNDVI-GP but an insignificant increase in RNDVI-GT, while no significant temporal changes in RNDVI-GP or RNDVI-GT were observed in the barren land. (3) The fluctuations in RNDVI-GP and RNDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in RNDVI-GP but an increase in RNDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both RNDVI-GP and RNDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations. [ABSTRACT FROM AUTHOR]

In light of recently accelerating global warming, the changes in vegetation trends are vital for the monitoring of the dynamics of both whole ecosystems and individual species. Detecting changes within the time series of specific forest ecosystems or species is very important in the context of assessing their vulnerability to climate change and other negative phenomena. Hence, the aim of this paper was to identify the trend change points and periods of greening and browning in multi-annual time series of the normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) of four main forest-forming tree species in the temperate zone: pine, spruce, oak and beech. The research was conducted over the last two decades (2002–2022), and was based on vegetation indices data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). To this end, several research approaches, including calculating the linear trends in the moving periods and BEAST algorithm, were adapted. A pattern of browning then greening then constant was detected for coniferous species, mostly pine. In turn, for broadleaved species, namely oak and beech, a pattern of greening then constant was identified, without the initial phase of browning. The main trend change points seem to be ca. 2006 and ca. 2015 for coniferous species and solely around 2015 for deciduous ones. [ABSTRACT FROM AUTHOR]

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP. [ABSTRACT FROM AUTHOR]

The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets. [ABSTRACT FROM AUTHOR]

Waterlogging increasingly challenges crop production, affecting 10% of global arable land, necessitating the development of pragmatic strategies for mitigating the downside risk of yield penalty. Here, we conducted experiments under controlled (tank) and field conditions to evaluate the efficacy of nitrogenous fertiliser in alleviating waterlogging stress. Without intervention, we found that waterlogging reduced grain yields, spike numbers and shoot biomass, but had a de minimus impact on grain number per spike and increased grain weight. Soil fertiliser mitigated waterlogging damage, enhancing yields via increased spike numbers, with crop recovery post-waterlogging catalysed via improved tiller numbers, plant height and canopy greenness. Foliar nitrogen spray has little impact on crop recovery, possibly due to stomatal closure, while modest urea application during and after waterlogging yielded similar results to greater N application at the end of waterlogging. Waterlogging-tolerant genotypes (P-17 and P-52) showed superior growth and recovery during and after waterlogging compared to the waterlogging-sensitive genotypes (Planet and P-79). A comparison of fertiliser timing revealed that field fertilizer treatment two (F2: 90 kg·ha−1 at 28 DWL, 45 kg·ha−1 at sowing and 45 kg·ha−1 at 30 DR) yielded the highest and fertilizer treatment three (F3: 45 kg·ha−1 at sowing and 45 kg·ha−1 at 30 DR) recovered the lowest yield and spike number, while fertilizer treatment one (F1: 45 kg·ha−1 at 28 DWL, 45 kg·ha−1 at 0 DR, 45 kg·ha−1 at sowing and 45 kg·ha−1 at 30 DR) and four (F4: 90 kg·ha−1 at 0 DR, 45 kg·ha−1 at sowing and 45 kg·ha−1 at 30 DR) had the highest shoot biomass in the field. Treatment five (T5: 180 kg·ha−1 at 0 DR, 30 kg·ha−1 at sowing and 90 kg·ha−1 at 30 DR) presented the most favourable results in the tank. Our results provide rigorous evidence that long periods of waterlogging caused significant yield penalty, mainly due to decreased spike numbers. We contend that increasing fertiliser rates during waterlogging up to 90 kg·ha−1 can provoke crop growth and mitigate waterlogging-induced grain yield losses, and is more beneficial than applying nitrogen post-waterlogging. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to understand the vegetation ecological status and dissect the factors affecting vegetation change. [Methods] Based on the MOD13A3 NDVI data and meteorological data, the spatiotemporal variation of NDVI and its response to hydrothermal conditions were analyzed by using the Theil-Sen median trend analysis. Mann-Kendall test. Hurst index and correlation analysis in order to provide a scientific basis for ecological conservation and rational resource allocation in this region. [Results] During the study period. NDVI in Inner Mongolia increased significantly (p<0.01), and its spatial distribution was higher in the east and lower in the west. In most regions, NDVI showed an increasing trend. The growth rate of cultivated land was the largest and the proportion of cultivated land area with increasing trend was also the largest. The change of NDVI was relatively stable with low average coefficient variation, and the stability of forestland was the best. In the future, the change trend of NDVI in Inner Mongolia will be dominated by reverse sustainability, and the proportion of reverse sustainability and sustainability area will be 76.51% and 23.49%. The areas with negative correlation between NDVI and temperature accounted for 78.25% of the regions, and the areas with extremely significant and significant negative correlation between NDVI and temperature accounted for 19.92% of the regions. The areas with positive correlation between NDVI and precipitation and soil moisture accounted for 93.16% and 93.53%, and the areas with extremely significant and significant positive correlation between NDVI and precipitation and soil moisture accounted for 51.37% and 55.85%, respectively. [Conclusion] The overall vegetation ecology in Inner Mongolia showed a tendency towards improvement during the study period, and the vegetation ecology may deteriorate in the future. Soil moisture was the major effective factor on vegetation growth in Inner Mongolia. [ABSTRACT FROM AUTHOR]

In this study, a multilevel drought-induced resistance and resilience analysis (MDRRA) approach was developed to investigate the stability of vegetation in the Yellow River Basin (YRB). MDRRA was quantified by utilizing the Normalized Difference Vegetation Index (NDVI). It was applied to YRB to assess vegetation resistance and resilience to various levels of drought by utilizing precipitation and NDVI data from 2000 to 2019. The results reveal that vegetation resistance and resilience in YRB are affected by drought severity. Monthly and annual changes in SPI over the warm–temperate humid zone of the YRB show a decreasing trend, with rates of 0.001 per decade and 0.034 per decade, respectively; however, the other climatic subregions exhibit an increasing trend, with rates ranging from 0.002 per decade to 0.82 per decade. Over 77.56% of the downstream areas show increases in the annual SPI averages. Drought severity differs across subregions in the YRB. More severe drought events occur in its upper and middle reaches, while less severe ones happen in its lower reaches. As the drought severity increases, the arid and semiarid regions of the mesothermal zone exhibit a decrease in the resistance and resilience indices. MDRRA can help improve the stability and resilience of the ecosystem in the YRB. [ABSTRACT FROM AUTHOR]

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices. [ABSTRACT FROM AUTHOR]

Dry spells strongly influence biomass production in forest ecosystems. Their effects may last several years following a drought event, prolonging growth reduction and therefore restricting carbon sequestration. Yet, our understanding of the impact of dry spells on the vitality of trees' above-ground biomass components (e.g., stems and leaves) at a landscape level remains limited. We analyzed the responses of Pinus sylvestris and Picea abies to the four most severe drought years in topographically complex sites. To represent stem growth and canopy greenness, we used chronologies of tree-ring width and time series of the Normalized Difference Vegetation Index (NDVI). We analyzed the responses of radial tree growth and NDVI to dry spells using superposed epoch analysis and further explored this relationship using mixed-effect models. Our results show a stronger and more persistent response of radial growth to dry spells and faster recovery of canopy greenness. Canopy greenness started to recover the year after the dry spell, whereas radial tree growth remained reduced for the two subsequent years and did not recover the pre-drought level until the fourth year after the event. Stem growth and canopy greenness were influenced by climatic conditions during and after drought events, while the effect of topography was marginal. The opposite responses of stem growth and canopy greenness following drought events suggest a different impact of dry spells on trees´ sink and source compartments. These results underscore the crucial importance of understanding the complexities of tree growth as a major sink of atmospheric carbon. [ABSTRACT FROM AUTHOR]

Monitoring agriculture by remote sensing enables large‐scale evaluation of biomass production across space and time. The normalized difference vegetation index (NDVI) is used as a proxy for green biomass. Here, we used satellite‐derived NDVI of arable farms in the Netherlands to evaluate changes in biomass following conversion from conventional to organic farming. We compared NDVI and the stability of NDVI across 72 fields on sand and marine clay soils. Thirty‐six of these fields had been converted into organic agriculture between 0 and 50 years ago (with 2017 as reference year), while the other 36 were paired control fields where conventional farming continued. We used high‐resolution images from the Sentinel‐2 satellite to obtain NDVI estimates across 5 years (January 2016–October 2020). Overall, NDVI did not differ between conventional and organic management during the time series, but NDVI stability was significantly higher under organic management. NDVI was lower under organic management in sandy, but not in clay, soils. Organic farms that had been converted less than ~19 years ago had lower NDVI than conventional farms. However, the difference diminished over time and eventually turned positive after ~19 years since the conversion. NDVI, averaged across the 5 years of study, was positively correlated to soil Olsen‐P measured from soil samples collected in 2017. We conclude that NDVI in organic fields was more stable than in conventional fields, and that the lower biomass in the early years since the transition to organic agriculture can be overcome with time. Our study also indicates the role of soil P bioavailability for plant biomass production across the examined fields, and the benefit of combining remote sensing with on‐site soil measurements to develop a more mechanistic understanding that may help us navigate the transition to a more sustainable type of agriculture. [ABSTRACT FROM AUTHOR]

The Carpathian region, to which the research area belongs, is one of the most forested areas of Ukraine, and at the same time the most anthropogenically loaded. Studying the vegetation cover of the territories is currently one of the most important parameters that allows us to assess the state of the environment. The paper analyzes the change in the state of vegetation based on the NDVI index using Google Earth Engine (GEE) for the period between 2018 and 2023, with the construction of a difference index. The satellite image interpretation data allowed us to summarise the dynamics of the vegetation index change for two similar periods, in particular, for 2018 it is 0.464, for 2023 it is 0.330. The average difference value for the study area is 0.229. Taking into account dynamic climate change and its parameters such as precipitation, air temperature, surface temperature, wind speed, NDVI and NDWI indices, a correlation analysis was performed to identify the relationship between changes in vegetation cover and weather conditions. Statistical analysis of the data allowed us to identify the main groups of factors that have the greatest impact on the NDVI index, namely temperature and precipitation. However, the NDWI moisture index is in a clear inverse correlation, which confirms the significant impact of global temperatures on the unevenness of precipitation, especially during the 2018 floods. It is worth noting that the movement of air masses, which was determined through the average annual wind speed, gives an understanding of how changes in temperature above the ground affect the moisture content of vegetation. [ABSTRACT FROM AUTHOR]

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While open-pit mining activities represent one of the human-derived most impactful land cover changes, these changes and the linked restoration processes can be challenging to assess. This article presents a reproducible methodology carried out with cloud processing of satellite images (Google Earth Engine (GEE)) to evaluate the evolution of open-pit mining activities and their restoration in a Mediterranean landscape. For this purpose, the calculation of the normalized differential vegetation index (NDVI) was used to obtain a quantitative parameter to monitor vegetation presence in each extractive area. To validate these results, confusion matrices were performed between the classification obtained in the study and the official land cover mapping, using randomly selected mining areas as test points, with an average accuracy of 88%. According to the methodology used, the surface of areas denuded by mining in the period 1984–2023 has fluctuated over time, with a maximum in 2005 coinciding with the peak of the Spanish construction boom, and a subsequent decrease towards the present. From these results, it can be concluded that Landsat-type data processed using GEE provide a quick and useful tool for monitoring the evolution of mining activity, including restoration trends, becoming particularly valuable for public bodies' inspections or decision making. [ABSTRACT FROM AUTHOR]

In recent years, with the sustained attention from academia and media to urban air quality and environmental issues, governments and scholars worldwide have been devoted to studying the relationship between air quality and green infrastructure (GI), seeking effective measures to address urban air pollution. This study aims to explore the impact of GI on urban air quality, focusing on analyzing data from Ulsan and Junpo cities in South Korea. Significant statistical significance has been found through correlation analysis between GI area and air pollutants such as nitrogen dioxide, carbon monoxide, particulate matter, and ozone. Specifically, when calculating GI using the Normalized Difference Vegetation Index (NDVI) data, for every 1% increase in GI area, nitrogen dioxide (NO2) decreases by approximately 0.000000001925 ppm. This finding offers important insights for future air quality research and provides empirical evidence for urban planning, emphasizing the significance of green space planning in improving air quality. Additionally, the study suggests that future research should be more diverse, incorporating data from more cities and conducting an in-depth analysis of GI distribution to understand its impact on air quality comprehensively. [ABSTRACT FROM AUTHOR]

Ecosystem resilience, the ability of an ecosystem to recover from disturbances, is a critical indicator of environmental health and stability, particularly under the impacts of climate change and anthropogenic pressures. This study focuses on the Three-River Headwater Region (TRHR), a critical ecological area for East and Southeast Asia, often referred to as the "Water Tower of China". We used the Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation growth and productivity and calculated Critical Slowing Down (CSD) indicators to assess the spatiotemporal dynamics of grassland ecosystem resilience in the TRHR from 1984 to 2021. Our research revealed a sustained improvement in ecosystem resilience in the TRHR starting in the late 1990s, with a reversal in this trend observed after 2011. Spatially, ecosystem resilience was higher in areas with greater precipitation and higher vegetation productivity. Temporally, changes in grazing intensity were most strongly correlated with resilience dynamics, with explanatory power far exceeding that of NDVI, temperature, and precipitation. Our study underscores the importance of incorporating ecosystem resilience into assessments of ecosystem function changes and the effectiveness of ecological conservation measures, providing valuable insights for similar research in other regions of the world. [ABSTRACT FROM AUTHOR]

Exploring NDVI variation and what drives it on the Qinghai–Tibet Plateau can strategically inform environmental protection efforts in light of global climate change. For this analysis, we obtained MODIS NDVI data collected during the vegetative growing season, vegetation types for the region, and meteorological data for the same period from 2001 to 2020. We performed Theil–Sen trend analysis, Mann–Kendall significance testing, spatial autocorrelation analysis, and Hurst index calculation to review the spatiotemporal changes in NDVI characteristics on the plateau for various vegetation types. We used the correlation coefficients from these analyses to investigate how the NDVI responds to temperature and precipitation. We found the following: (1) Overall, the Qinghai–Tibet Plateau NDVI increased throughout the multi-year growing season, with a much larger area of improvement (65.68%) than of degradation (8.83%). (2) The four main vegetation types were all characterized by improvement, with meadows (72.13%) comprising the largest portion of the improved area and shrubs (18.17%) comprising the largest portion of the degraded area. (3) The spatial distribution of the NDVI had a strong positive correlation and clustering effect and was stable overall. The local clustering patterns were primarily low–low and high–high clustering. (4) The Hurst index had an average value of 0.46, indicating that the sustainability of vegetation is poor; that is, the trend of vegetation change in the growing season in a large part of the Qinghai–Tibet Plateau in the future is opposite to that in the past. (5) The plateau NDVI correlated positively with air temperature and precipitation. However, the correlations varied geographically: air temperature had a wide influence, whereas precipitation mainly influenced meadows and grassland in the northern arid zone. The overall temperature-driven effect was stronger than that of precipitation. This finding is consistent with the current research conclusion that global warming and humidification promote vegetation growth in high-altitude areas and further emphasizes the uniqueness of the Qinghai–Tibet Plateau as a climate-change-sensitive area. This study also offers a technical foundation for understanding how climate change impacts high-altitude ecosystems, as well as for formulating ecological protection strategies for the plateau. [ABSTRACT FROM AUTHOR]

Several studies have been carried out in the West Africa Sahel; however, there is limited data on agroforestry studies in two distinct ecosystems. Moreover, livelihoods are facing growing uncertainties due to climate change and extensive anthropogenic activities. The majority of the population relies heavily on ecosystem services. The aim of this study was to determine the services provided by agroforestry parklands in two distinct villages. Initially, a survey involved 131 individual farmers (68 in Dan Saga and 63 in Sekoukou village) to highlight the benefits derived from agroforestry parklands for rural communities. Subsequently, a forest inventory employed a systematic random sampling approach along transects, including 40 plots in Dan Saga and 32 plots in Sekoukou (each. Finally, the study utilized the Normalized Difference Vegetation Index (NDVI). The study documented 25 different woody species in Dan Saga and 24 in Sekoukou village. There was a greater prevalence of species from the Fabaceae and Balanitaceae family, followed by Anacardiaceae and Combretaceae. Dendrometric surveys revealed a relatively low tree density (23 stems/ha in Dan Saga and 14 stems/ha in Sekoukou), a bell-shaped diameter distribution indicating a significant dominance of intermediate diameter trees ([20 cm; 30 cm[), and a limited representation of trees with extreme diameters ([0; 10 cm[ and [70 cm; → [). The analysis of variances revealed a significant distinction (P-value < 0.05) in all dendrometric parameters and carbon content, excluding tree density and diameter. The Shannon–Wiener index (reflecting intra-ecosystem diversity) and the Pielou equitability index were relatively low but comparable to the mean Sørensen index. The NDVI of both areas fluctuated between 0 and 1 from 2001 to 2013, illustrating evolving vegetation (biomass) dynamics. This evolution was characterized by minimal vegetation cover in 2001, which subsequently increased by 2013 in both villages. The importance of land restoration in Niger has become a crucial and beneficial activity for the population in recent years. [ABSTRACT FROM AUTHOR]

In Kerala, human-elephant conflicts (HEC) are reported in almost all parts of the Western Ghats region, exceptionally high in Wayanad. In recent years, there has been a notable surge in HEC occurrences throughout the state, leading to the loss of property, animals, and life. The dependency of humans on enclosures, the transformation of natural areas into plantations, and the fragmentation of natural ecosystems are the main reasons for the escalating conflict between people and elephants. As a result, it is necessary to study the suitability of habitat for elephants and ensure their conservation. The study aims to identify suitable elephant habitats in the Wayanad Wildlife Sanctuary using geospatial data based on their known association with environmental variables. This study employs the Analytic Hierarchy Process (AHP), an approach to determining the weightings of individual parameters in assessing habitat suitability. The analysis findings indicated that the central portion of the study area within the Sultan Bathery (43.63 sq. km), range represents the most suitable area for elephant habitat followed by Kurichat (33.59 sq. km) and Tholpetti (19.45 sq. km). The final output of the study, the Habitat Suitability Map was validated by adding the human-elephant conflict locational data, which illustrates that the Kurichiat range recorded the highest HEC conflict (50%), followed by Muthanga (29%), Sulthan Bathery (12%), and Tholpetti (9%). The map could assist in managing, conserving, and protecting forest resources and aid in decision-making activities. [ABSTRACT FROM AUTHOR]

The landscape of the Brazilian Cerrado has been modified by human actions in the last decade with a significant increase in fires. Despite efforts to delimit an area for biodiversity conservation, human pressure on the landscape surrounding these units can affect the local environmental condition. We assessed the effect of landscape factors and fire occurrence on necrophagous Diptera diversity in different vegetation physiognomies of the Cerrado region in Chapada das Mesas National Park. We sampled in 35 sites in five physiognomies of the Cerrado biome (Open Forest or Cerradão, Dense Woodland, Gallery Forest, Park Woodland, and Woodland) using 350 traps to capture the Diptera specimens. We observed lower dipteran abundance and richness in the Gallery Forest compared to other physiognomies, and the species composition differed from all other physiognomies. Specifically, five species of Diptera were predominantly found in Open Forest or Cerradão, two species in Park Woodland, and one in Gallery Forest. The presence of species with generalist habits in forested physiognomies and the low abundance of expected forest-associated species in forest habitats suggests a decline in environmental quality in these areas. This serves as a warning sign for the ecological conditions of the Park's predominantly forested physiognomies. Implications for insect conservation: Our results highlight that despite the biodiversity of necrophagous dipterans observed in CMNP, ongoing fire frequency and anthropic activities within the park could severely undermine its ability to preserve Diptera biodiversity. Consequently, environmental agencies must intensify monitoring efforts and implement effective measures to mitigate human-induced impacts in this Brazilian Cerrado conservation unit. [ABSTRACT FROM AUTHOR]

Urban forests face multiple human-mediated pressures leading to compromised ecosystem structure and functioning. Therefore, understanding ecosystem structure in response to ongoing pressures is crucial for sustaining ecological integrity and human well-being. We aim to assess the disturbance and its effects on the vegetation structure of urban forests in Chandigarh using a combination of remote sensing techniques and vegetation surveys. The disturbance was evaluated as a change in NDVI (Normalised Difference Vegetation Index) from 2001 to 2021 by applying the BFAST (Breaks For Additive Season and Trend) algorithm to the MODIS satellite imagery data. A vegetation survey was conducted to compare the species composition, taxonomic and phylogenetic diversity as measures of forest vegetational structure. While signals of disturbance were evident, the changes in vegetation structure were not well established from our study. Further, this analysis indicated no significant differences in vegetation composition due to disturbance (F1,12 = 0.91, p = 0.575). However, the phylogenetic diversity was substantially lower for disturbed plots than undisturbed plots, though the taxonomic diversity was similar among the disturbed and undisturbed plots. Our results confirmed that disturbance effects are more prominent on the phylogenetic than taxonomic diversity. These findings can be considered early signals of disturbance and its impact on the vegetation structure of urban forests and contribute to the knowledge base on urban ecosystems. Our study has implications for facilitating evidence-based decision-making and the development of sustainable management strategies for urban forest ecosystems. [ABSTRACT FROM AUTHOR]

Multispectral data were used to estimate the above-ground forest biomass in the newly established carbon polygon in Bashkortostan. Field measurements were compared with Normalised Difference Vegetation Index (NDVI) and Normalised Green-Red Difference Index (NGRDI) obtained from the Global Inventory Monitoring and Modelling System (GIMMS) dataset. The relationship between measured biomass values and vegetation indicators was linear; NDVI has the highest correlation coefficient with above-ground biomass and greatest significance at all sites, so regression relationships with NDVI were used to map the above-ground biomass. [ABSTRACT FROM AUTHOR]

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change. [ABSTRACT FROM AUTHOR]

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition. [ABSTRACT FROM AUTHOR]

China's vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use. [ABSTRACT FROM AUTHOR]

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. This study used the annual mean Normalized Difference Vegetation Index (NDVI) as the vegetation greenness indicator. The variation in NDVI on Hainan Island was analyzed using the Theil–Sen median trend analysis and Mann–Kendall test during 2000–2019. The influence of natural and anthropogenic factors on the driving mechanism of the spatial pattern of NDVI was explored by the Multiscale Weighted Regression (MGWR) model. Additionally, we employed the Boosted Regression Tree (BRT) model to explore their contribution to NDVI. Then, the MGWR model was utilized to predict future greenness patterns based on precipitation and temperature data from different Shared Socioeconomic Pathway (SSP) scenarios for the period 2021–2100. The results showed that: (1) the NDVI of Hainan Island forests significantly increased from 2000 to 2019, with an average increase rate of 0.0026/year. (2) the R2 of the MGWR model was 0.93, which is more effective than the OLS model (R2 = 0.42) in explaining the spatial relationship. The spatial regression coefficients of the NDVI with temperature ranged from −10.05 to 0.8 (p < 0.05). Similarly, the coefficients of Gross Domestic Product (GDP) with the NDVI varied between −5.98 and 3.28 (p < 0.05); (3) The natural factors played the most dominant role in influencing vegetation activities as a result of the relative contributions of 83.2% of forest NDVI changes (16.8% contributed by anthropogenic activities). (4) under SSP119, SSP245, and SSP585 from 2021 to 2100, the NDVI is projected to have an overall decreasing pattern under all scenarios. This study reveals the trend of greenness change and the spatial relationship with natural and anthropogenic factors, which can guide the medium and long-term dynamic monitoring and evaluation of tropical forests on Hainan Island. [ABSTRACT FROM AUTHOR]

Deserts are unique ecosystems that provides suitable habitats to many floral and faunal species and that are beneficial to human beings in many ways. Desert ecosystems are affected by several natural and anthropogenic factors, resulting in the degradation of ecosystem goods and services provided by them. Thus, there is a need to monitor them. Accordingly, the ecological status of 34 major non-polar deserts of the world have been monitored for a period of four decades. We have used (i) vegetation cover and NDVI (vegetation density/vigour) as indicators of ecological conditions, and (ii), long term rainfall and temperature patterns to monitor the extent and the effect of climatic variations. Among the 34 deserts, Taklimakan has consistently the lowest NDVI, while Tanami has the highest NDVI during the entire monitoring period. The Asian Kavir and Kharan deserts have the lowest vegetation cover; Tanami has the highest vegetation cover. Out of 34 deserts, Gobi, Kalahari, Margo, Mu Us, Simpson, Strzelecki, Taklimakan and Thar deserts have shown an increasing trend in vegetation cover. While, Chalbi, Patagonian and Sonoran deserts have shown a decreasing trend. Thar, Sechura and Sahara have shown an increasing trend in precipitation, while Namib has shown an opposite trend. 31 deserts have shown an increasing trend in the temperature. Present study is important as changes in the ecological conditions of the deserts have a profound impact on the land surface albedo, surface energy balance, regional climate, carbon sequestration, biodiversity, and global dust emissions. [ABSTRACT FROM AUTHOR]

Although much attention has been paid to improve the representation of the urban spatial complexity, most efforts have been limited to the inclusion of green cover data to describe the biological component of cities, leaving the measurement of crucial urban ecological properties out of the picture. To tackle this, the Urban Ecosystem Integrity Index (UEII) was recently proposed to spatially represent the interplay between the intensity of urbanization and the biological components of cities, including urban biodiversity. The UEII has shown to be effective in measuring the ecosystem integrity of a Neotropical city; however its generalized applicability remains to be tested. Thus, here we tested the suitability of the UEII when applied in a boreal city and also evaluated if it adds more valuable information than the NDVI (also contrasting it with a UEII previously published for a tropical city), which is often suggested as a proxy of urban greenness and therefore the ecology of cities. We calculated the UEII by considering land surface temperature, built-up cover, and native plants and bird species richness of the city of Lahti, Finland (Palearctic; boreal). The UEII showed to be robust, comparable, and effective in capturing the intra and inter-urban spatial heterogeneity of both cities. Moreover, although the UEII and NDVI values correlated, the variance in such correlation showed the detail that the UEII brings to the quantificaiton of the ecological integrity of the cities, representing a more integral indicator. Thus, our findings indicate that the UEII is a valuable tool to inform on areas requiring ecological enhancement, as well as identifying those that are thriving, in cities ranging from tropical and highly biodiverse to boreal and with considerable less biodiversity. Highlights: We tested the suitability of the UEII in a boreal city, contrasting it with that of a tropical one. The index effectively captures the spatial heterogeneity of the city. The UEII allows to identify the diverse ecosystem integrity scenarios of cities. It provides a broader ecological representation of cities than the NDVI. The UEII represents a quantitative and spatially explicit tool for spatial planning. [ABSTRACT FROM AUTHOR]