Normalized Difference Vegetation Index Research Articles

Research on Key Influencing Factors of Ecological Environment Quality in Barcelona Metropolitan Region Based on Remote Sensing

With the rapid development of urbanization, the ecological environment is being degraded. Taking the Barcelona Metropolitan Region as an example, this paper developed an ecological environment quality-assessment system suitable for different times and regions, based on remote sensing, to evaluate the quality of the ecological environment from 2006 to 2018. We also built various ordinary least squares models to analyze multiple variables affecting the ecological environment. Finally, the characteristic triangular spatial structure was used to explain the interaction between the two key variables. The results showed that the ecological quality was unevenly distributed. The largest green space contributed the most benefits but was decreasing and becoming fragmented. NDVI (normalized difference vegetation index) was the most significant natural variable related to the distribution of green space. Precipitation was the most closely related climate factor to NDVI. There was a complex two-way interaction mechanism between the two, and its boundary value was getting higher and higher. In conclusion, the environmental quality of the BMR needs improvement. The characteristic triangle can effectively explain the interaction mechanism between precipitation and NDVI. This study deeply analyzes how various factors affect environmental quality from both the global and internal perspectives and provides a scientific basis for urban ecological management and sustainable development.

Spatial (Mis)Matches Between Biodiversity and Habitat Quality Under Multi-Scenarios: A Case Study in Shandong Province, Eastern China

Despite declines in biodiversity and habitat quality (HQ) at a global scale, our understanding of the HQ and matches between HQ and biodiversity under management scenarios is incomplete. To address this deficiency, the study examined trends in HQ and (mis)matches between biodiversity and HQ over four decades in Shandong province, China, identified the key drivers, and assessed the effectiveness of ecological policies, including Ecological Redlines (ERLs) and the Grain for Green (GG) program. During the 40-year period, HQ and matching degrees (indicated by related coefficients) between biodiversity and HQ decreased obviously. Correlation analysis showed that related coefficients between HQ and four biodiversity indices (vertebrate, vascular plant, and vegetation formation type richness, and comprehensive biodiversity index) were all significant (p < 0.01), and coefficients were highest for the biodiversity composite index. An analysis of relative importance by the random forest algorithm indicated significant variation in driving factors for spatial distribution of HQ, biodiversity, and matches between them. The key determinants of biodiversity distribution were biophysical factors, such as NDVI (normalized difference vegetation index), DEM (digital elevation model), and temperature. However, the main drivers of HQ distribution were social factors, such as the accessibility of anthropogenic activities, urbanization, and population density. Ecological policy scenarios, ERLs and GG, are clearly effective and could improve HQ and the matching degree between HQ and biodiversity significantly. Furthermore, the improvement in HQ under ERLs was less than that under GG, while the increase in the matching degree was opposite. The results of this study can be integrated by ecological managers and planners for biodiversity conservation.

Time series analysis from 1984 to 2023 of Earth Observation Satellites data for evaluating changes in vegetation cover and health at flaring sites in the Niger Delta, Nigeria.

Normalized Difference Vegetation Index (NDVI) is the most popular vegetation index used to clarify the difficulties of multi-spectral imagery, for example evaluation of vegetation.The data (11 Landsat 5 TM, 49 Landsat 7 ETM+, 27 Landsat 8 OLI-TIRS, and 15Landsat 9 OLI-TIRS)dated from 10/10/1984 to 17/12/2023 with < 3 % cloud cover wereused to study 11 flaring sites in Rivers State, Nigeria. Data processing and analysis were carried out using MATLAB codes. NDVI For Landsat 5 and Landsat 7, NDVI was determined from the atmospherically corrected multispectral bands (1-4) and for Landsat 8 and Landsat 9 are bands (2-5) in the N, E, S and W directions at distances 60 m, 90 m, 120 m and 240 m respectively from the flare. Generally, the results show that the NDVI at 60 m are the lowest. NDVI increases as distance increases to 90 m, 120 m and 240m from the flare for all the 11 sites. NDVI for all sites decreases as each year passes away however, Onne Flow Station gives an unsteady pattern for the years 1984 to 2007 before the flow station was built. The lowest mean NDVI (0.290) obtained from all the 11 sites is recorded at Umudioga 60 m East from the flare stack, followed by Obigbo with (0.300) at 60 m East from the flare. SD for each site is small with a range value (5.0786 ×10-5- 2.0689 × 10-4). Therefore, it can be concluded that Landsat sensors can be used to evaluate the changes in vegetation cover and its health at the flaring sites in the Niger Delta.

Remote sensing of cyanobacterias with Sentinel-2 in the Salto Grande

Abstract. The Salto Grande Reservoir of the Uruguay River experiences recurrent increases of cyanobacteria bloom, which negatively impacts water quality, with adverse consequences for public health and tourism. This study uses images from the Sentinel-2 satellite to investigate different methods for detecting and monitoring cyanobacteria. Two indices are calculated and two models are applied for the detection of cyanobacteria: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI) and Chlorophyll-a models M1 and M2, which have not previously been evaluated with images from Sentinel-2. NDVI is commonly used to identify areas with vegetation cover. The NDWI, using green and near-infrared (NIR) bands, is used as a filter for the application of Chlorophyl-A models since it is very useful for the identification of aquatic areas. Both M1 and M2 estimate the concentration of chlorophyll in the water, with model M2 demonstrating greater efficiency in the detection of cyanobacteria compared to model M1. Sentinel-2 images allow the correct observation of variations in the concentration of cyanobacteria in the riverbed, facilitating the monitoring of seasonal changes and cyanobacteria blooms. The results of our investigation underlines the effectiveness of NDWI when used with the Sentinel-2 images for identifying surface water areas and the M2-Chlorophyll-a model for detecting cyanobacteria in them. Combining these indices with on-site measurements is likely to offer a robust approach to monitoring and managing of the Salto Grande Reservoir.

On the use of meteorological parameters and satellite image-based indices for improving solar radiation estimation.

Solar radiation (Rs) is a major renewable energy source and also a crucial factor in designing solar panels, determining water requirement, and irrigation scheduling. In this study, meteorological parameters (air temperature, average air temperature, and relative humidity; Scenario 1), satellite image-based indices (normalized difference vegetation index: NDVI and land surface temperature: LST; Scenario 2), and their combination (Scenario 3) were used as predictors of Rs simulator models in Mashhad watershed (2005-2015). To this end, three different transfer function algorithms of the multi-layer perceptron (MLP), namely Levenberg-Marquardt backpropagation (MLP-LVM), gradient descend backpropagation (MLP-GDB), and batch training with weight and bias learning rules (MLP-BTWB), as well as two other machine learning models, M5 Tree and XGB (eXtreme Gradient Boosting), were employed. In addition, hybrid models obtained through coupling the best MLP transfer function with genetic algorithm (MLP-LVM-GA) and coupling Variational Mode Decomposition with M5 Tree and XGB (VMD-M5 Tree and VMD-XGB, respectively) were also investigated. In the case of MLP-based models, the third scenario proved to be more efficient, with RMSEs equal to 404.2, 424.5, and 423.8J.cm-2.day-1 for the aforementioned transfer functions, respectively. The results showed that GA coupled with the best transfer function algorithm of MLP improved Rs estimates, decreasing estimation error by 14.7, 30.65, and 32.67% in the three defined scenarios, respectively. In training set, VMD-M5 Tree and VMD-XGB hybrid models outperformed corresponding base models under all three scenarios; but in testing set, estimation error was decreased only in the third scenario (by 37 and 42%, respectively). Overall, all models (base and hybrid) had the least Rs estimation errors in the third scenario (application of both meteorological parameters and satellite image-based data).

Normalized Difference Vegetation Index Prediction for Blueberry Plant Health from RGB Images: A Clustering and Deep Learning Approach

In precision agriculture (PA), monitoring individual plant health is crucial for optimizing yields and minimizing resources. The normalized difference vegetation index (NDVI), a widely used health indicator, typically relies on expensive multispectral cameras. This study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative. To identify individual plant bushes, K-means and Gaussian Mixture Model (GMM) clustering were applied. RGB images were transformed into the HSL (hue, saturation, lightness) color space, and the hue channel was constrained using percentiles to exclude extreme values while preserving relevant plant hues. Further refinement was achieved through adaptive pixel-to-pixel distance filtering combined with the Davies–Bouldin Index (DBI) to eliminate pixels deviating from the compact cluster structure. This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. The model achieved strong performance with mean squared losses of 0.0074, 0.0044, and 0.0021 for training, validation, and test datasets, respectively. The test dataset also yielded a mean absolute error of 0.0369 and a mean percentage error of 4.5851. These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.

A 30-Year Analysis of Forest Cover and Land Surface Temperature in Attapeu Province, Lao PDR

Changing forest areas can have a complex range of impacts on ground temperatures, from increasing temperatures due to loss of shade to creating drier and hotter local climate environments. This study aims to identify spatiotemporal changes in forest cover and retrieve land surface temperature (LST) using thermal infrared sensor (TIRS) data in Attapeu province, Lao PDR from 1994 to 2024. Geographic information system (GIS) techniques were employed to extract spatiotemporal changes in land use and land cover (LULC) and Normalized Difference Vegetation Index (NDVI). The analysis of LULC change revealed a notable decrease of 18.43% in dense forest areas, accompanied by an increase of 7.87% in sparse forest and 11.35% in cropland, indicating a transition from dense forest to sparse forest and cropland for the study area. The analysis of LST utilizing TIRS revealed a consistent negative correlation with NDVI. The coefficient of determination (R2) indicated values of 0.5896 in 1994, 0.5691 in 2009, and 0.4344 in 2024. By correlating remotely-sensed thermal data with in situ observations, this research delineated the prolonged alterations in LST due to fluctuations in forest cover. The urgency of enacting policies is underscored to mitigate the ongoing loss of forest cover. These findings emphasize the need for immediate policy actions, such as enhanced forest conservation and reforestation programs, to mitigate rising temperatures and ensure ecological sustainability in Attapeu province. The insights garnered from this investigation hold significant implications for the conservation endeavors aimed at preserving the forests of Attapeu province.

Assessing intertidal sediment photopigment content from spectral reflectance with an UAV-mounted 10-band multispectral sensor

ABSTRACT Multispectral sensors mounted to unoccupied aerial vehicles (UAVs) can be leveraged to quantify microphytobenthos (MPB) biomass in intertidal mudflats, providing data products with cm-scale pixels. However, no standard protocol currently exists for calibrating UAV-acquired spectral information to sediment MPB content. Here, we present a new protocol for calibrating data from a UAV-mounted multispectral sensor to sediment MPB biomass as measured by photopigment content. To do so, we developed a methodology for acquiring and analyzing UAV imagery and sediment photopigment field data. We then implemented the protocol in the Fraser River Estuary, Canada to build a statistically valid calibration equation, testing the effectiveness of several spectral indices and photopigment measurements. Calibrated spectral index values can provide a very accurate measurement of MPB biomass, able to achieve 90 % correlation between the normalized difference vegetation index (NDVI) and sediment chlorophyl-a (chl-a) concentration. This high performance was achieved by closely pairing georeferenced sediment samples to corresponding multispectral imagery and minimizing the lag between sediment sample collection and UAV imagery acquisition. This protocol can facilitate the use of calibrated UAV-acquired multispectral imagery for investigating ecologically-important fine-scale spatial heterogeneity of MPB biomass.

Soil Moisture Content Prediction in Loam Soil with RFR Model

Soil moisture content (SMC) is an important factor in agricultural productivity; it has an impact on crop growth, water use efficiency, and soil health. However, accurately predicting SMC, especially at deeper soil layers, remains challenging due to high variability and limited spatiotemporal data resolution. This study developed and evaluated a Random Forest Regression (RFR) model to predict SMC in loam soil at five different depths (5, 20, 40, 60, and 80 cm) utilizing meteorological data (temperature, humidity, precipitation, wind speed, and solar radiation) and vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Data were collected during the maize vegetation season in 2023 in Mosonmagyaróvár, Hungary. The results showed that the mean SMC ranged from 12.61% to 16.19%. Correlation analysis demonstrated that precipitation and NDMI had the strongest positive correlation with SMC, especially at shallower depths r = 0.78 at 5 cm depth, Solar radiation had a moderate correlation with SMC, especially at the deeper depths. The RFR model performed well at all depths, achieving an R² of 0.86 at 5 cm depth; the model accuracy enhanced at deeper layers, achieving R² values of 0.91 and 0.94 at 60 and 80 cm depths, respectively. The most significant predictors according to the feature importance analysis were precipitation, humidity, and NDMI, with NDMI playing a crucial role in subsurface moisture retention at deeper depths. These findings highlight the potential for machine learning algorithms to optimize irrigation approaches and improve water management in precision agriculture.

Influence of urban landscape pattern on summer surface temperature and its spatial scale effects

Urban planning is pivotal in understanding how urban landscapes influence land surface temperature (LST) across various spatial scales. Using Landsat 8 OLI_TIRS images, we studied the spatial heterogeneity of LST in Hefei city of Anhui province, China. The results show that landscape pattern index (LSPI) can explain the spatial distribution of LST, remote sensing indices such as modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), play dominant roles in LST, and the explanatory power is stronger at small scales. As the spatial scale decreased, the explanatory power of NDVI and Built-land proportion on LST increases. The combined explanatory power of water and woodland on LST is more significant than the individual effect. When the proportion of Built-up land exceeds 60%, increasing the proportion of water and woodland can reduce the LST. This study provides insights to mitigate urban heat island (UHI) effect in urban planning.

Influence of War on the Forest Plantations of the Left Bank Kherson Region

Mapping burnt areas of forest plantations is needed to understand the scale of the violation and plan the post-fire activities to save the biggest artificially made forest in the world. The military actions due to the ongoing war in Ukraine complicate the monitoring by traditional and standard methods. Given the complexities of military actions, our study during this period is heavily reliant on remote sensing, marking a significant shift from traditional methods. The article is dedicated to the impact of hostilities resulting from Russian armed aggression against Ukraine on the state of forest plantations and shelterbelts of the left bank Kherson region. The research was conducted using images of Sentinel-2 L2A satellite, focusing on the study of forest burnt areas from the beginning of hostilities on the territory of the occupied Kherson region in February 2022 to March 2024. The pre-war year 2021 was used as a benchmark for comparing the state of forest plantations. Satellite images were used to generate the Normalized Difference Vegetation Index (NDVI) and Normalized Burn ratio (NBR) index to determine and monitor forest cover changes and burnt areas in forest plantations as a result of hostilities on the research territory using ArcGIS Pro. The area of study is 44 657 hectares, and the burnt area of Oleshky forest in March 2024 was 7 833 hectares (17.5 %). The conducted studies have proved the critical situation with forest plantations on the left bank of the Dnipro River within the Kherson region. This situation necessitates immediate and collaborative intervention to save the ecosystem of Oleshky forest.

In-Season Nitrogen Management in the Root Zone of Wheat in a Calcareous Soil Using the Response Index

ABSTRACT Excessive and inappropriate nitrogen (N) fertilizer applications on calcareous soils result in significant losses, necessitating a targeted approach for improved efficiency and yield. Field experiments were conducted over two seasons to develop a strategy for root-zone (RZ) N fertilization for wheat. In the first season, three methods of N fertilizer application were used: surface (SF), in the RZ at a depth of 10 cm (RZF10), and in the RZ at a depth of 20 cm (RZF20). The N fertilizer was applied in a range of 0–280 kg N ha−1. In the second season, another experiment was carried out to validate the results. The data showed that to achieve maximum yield levels, N fertilizer rates of 264 kg ha−1 for SF, 218 kg ha−1 for RZF10, and 183 kg ha−1 for RZF20 were required. The findings also revealed that relocating the application from the surface to depths of 10 and 20 cm resulted in an increase in recovery efficiency by 11.2% and 31.2%, respectively. A linear equation established the relationship between the response index at harvest (RI-Harvest) and the corrective N rate for RZF20. The in-season response index of the normalized difference vegetation index effectively predicted RI-Harvest, leading to the development of an empirical equation to predict the corrective N rate. The validation experiment demonstrated the effectiveness of the proposed strategy in adjusting N rates based on crop-specific needs. This approach allows growers to optimize profits by carefully applying N in the RZ, eliminating both excessive and inadequate use.

Near Real Time Crop Health Status Monitoring and Mapping using Sentinel Satellite Image in Menjar Shenkora District, Ethiopia

Agricultural monitoring systems must provide timely and standardized information on crop production, status, and yield, from sub-regional to national scales. Accurate monitoring and mapping of vegetation condition and health are vital for managing crops, assessing damage, and predicting yields. Crop health monitoring is one of the important items for tracking the general health status of any crop. In this regard, remote sensing and GIS play a crucial role for monitoring crop health, providing current information that traditional methods like field surveys and sampling questionnaires struggle to obtain. Effective cropland mapping techniques are essential for regular crop monitoring. This type of monitoring demands frequents continuous data with high time and space resolution. Near real-time crop monitoring uses technologies like the Sentinel-2 satellite mission, offering a consistent 5-day revisit cycle and freely accessible data. This opens new doors for delivering timely updates and monitoring parcel-based crop health and conditions in real-time. Therefore, this study used satellite images, Global Positioning System (GPS) collected data, and parcel-based socioeconomic data. GPS and socioeconomic data were employed to validate the satellite-based near real-time crop monitoring results. Vegetation Condition Index (VCI) and the Normalized Difference Vegetation Index (NDVI) were used to evaluate crop health at different stages of the growing season and to generate time series data for crop phenology respectively. NDVI time series data was used to generate crop phenology information for four main crops: Teff, wheat, onion, and sorghum. The crop type maps for these crops at the study sites were validated with an overall accuracy of 79.26% and a Kappa value of 0.737. Additionally, the results from the current research and the field-collected data were consistent in providing information about the onset, greening, maturity, and senescence dates of each crop. These findings highlight the effectiveness of the satellite-based system for real-time agricultural crop monitoring using Sentinel-2 observations across various sites and time frames. Moreover, it helps to fill the gaps of traditional crop monitoring methods with those based on satellite technology. This system is particularly valuable for early warning purpose in areas like the current study site, where conventional crop monitoring methods and inputs are limited.

Drought Index Assessment Using Thermal Vegetation Index (TVI) Method and Soil Physic Approaches (A Case study: Manyaran District, Central Java, Indonesia)

The early detection of areas that have the potential to experience drought is necessary to minimize the hazards of land drought. The study aimed to identify the current conditions of drought by TVI with actual soil physic characteristic approaches, and to find the determinant factors related to the dynamics of agricultural land drought conditions so as to determine land management strategies that are considered appropriate and efficient to prevent agricultural land drought. The method for analyzing the drought index was the Thermal Vegetation Index (TVI) method, which involved the calculation of the ratio between the Land Surface Temperature (LST) and the vegetation index using Normalized Difference Vegetation Index (NDVI). The method in this research was modified by adding soil physical indicators, including soil moisture content, bulk density, and pF value. The results showed that the land drought classes were normal drought (46.89% study area), mild drought (20.62% study area), moderate drought (14.10% study area), severe drought (8.58% study area), and extreme drought (9.81% study area). Land drought was highly correlated to the soil's physical condition, negatively correlated with the moisture content (r -0.403) and bulk density (r -0.317), and positively correlated with the pF (r 0.429). Enhancing the soil’s capacity to absorb water and retain moisture through the addition of organic matter is one of the required techniques as a suitable recommendation to the issue after determining the determinant factors and present conditions.

Study on the Response of Net Primary Productivity to Vegetation Phenology and Its Influencing Factors in Karst Ecologically Fragile Regions

Net primary productivity (NPP) is a crucial indicator of ecosystem function and sustainability. Quantifying the response of NPP to phenological dynamics is essential for understanding the impact of climate change on ecosystem processes. In this study, vegetation phenology data for Guizhou Province were extracted from the MCD12Q2 dataset, and NPP was estimated using the Normalized Difference Vegetation Index (NDVI) combined with meteorological data. Linear regression, trend analysis, and structural equation modeling were employed to clarify the spatiotemporal patterns of NPP and phenology as a basis for exploring the role of climatic factors in the NPP’s response to phenological changes. The results indicate that 72.15% of Guizhou Province shows an increasing trend in vegetation NPP (slope = 5.0981, p = 0.002). The start of the growing season (measured as SOS) tends to advance (slope = −0.4004, p = 0.0528), while the end of the growing season (measured as EOS) tends to delay (slope = 0.2747, p = 0.1011), resulting in an overall extension of the increasing length of the season (LOS) (slope = 0.64549, p = 0.0065). The spatiotemporal patterns of SOS, EOS, LOS, and NPP varied with elevation changes. For every 500 m increase in altitude, NPP decreased by 25.3 gC/m2, SOS was delayed by 7.1 days, EOS advanced by 1.25 days, and LOS decreased by 8.36 days. These findings suggest that the response of NPP to phenological changes is primarily controlled by local climatic and topographical conditions. Additionally, the indirect effects of climate on NPP through phenological changes were more significant than the direct effects. Climatic factors play varying roles in the NPP response to phenological dynamics, highlighting the profound influence of climate in regulating the mechanisms by which NPP responds to phenological changes.

Roles of host and environment in shift of primary anthrax host species in Kruger National Park.

Environmental and climatic factors, as well as host demographics and behaviour, significantly influence the exposure of herbivorous mammalian hosts to pathogens such as Bacillus anthracis, the causative agent of anthrax. Until the early 1990s in Kruger National Park (KNP), kudu (Tragelaphus strepsiceros) was the host species most affected by anthrax, with outbreaks occurring predominantly in the dry season, particularly during drought cycles. However, the most affected host species has shifted to impala (Aepyceros melampus), with more frequent anthrax outbreaks during the wet season. This study investigates the roles of environmental variation and other host species in this shift. Temporal trends in environmental variables such as precipitation, soil moisture, temperature, and normalised difference vegetation index (NDVI) were analyzed in relation to anthrax occurrence (presence/ absence and counts). Additionally, correlations between host species' densities and anthrax mortalities over time were examined. Anthrax cases in 1990 were concentrated in the central and northern regions of KNP(excluding Pafuri), primarily affected kudus; while subsequent mortalities affected mostly impala and were restricted to the far north, in Pafuri. Significant correlations were found between kudu anthrax mortality and a decrease in NDVI, average temperature, SPI-6 and SPI-12 (Standardised Precipitation Index in various time intervals. Conversely, anthrax occurrence in impalas was associated with a decline in SPI-3, and temperature rise, with increased mortality during the rainy season. Elephant density correlated negatively with kudu mortality, but a positive correlation with both impala mortality and impala density. The study concludes that environmental variables and species' densities may alter the diversity and frequency of hosts exposed to B. anthracis. Climate extremes and alterations therein may exacerbate anthrax severity by modifying species susceptibility and their probability of exposure over time.

Estimation of Vegetation Carbon Sinks and Their Response to Land Use Intensity in the Example of the Beijing–Tianjin–Hebei Region

Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region.

Satellite-Based and Street-View Green Space and Adiposity in US Children

Green space may be associated with obesity outcomes in children by promoting physical activity and reducing stress. However, previous studies have mostly used a single measure of green space and body mass index (BMI) as the sole indicator of adiposity. To examine associations of the satellite-based normalized difference vegetation index (NDVI) and street-view imagery-based green space metrics with comprehensive adiposity measures in children. The Project Viva cohort study observed participants from mid-childhood (mean [SD] age, 8.0 [0.8] years) and early adolescence (mean [SD] age, 13.3 [1.0] years) through late adolescence (mean [SD] age, 17.8 [0.7] years) between January 1, 2007, and December 31, 2021. This analysis included children with data on green space and adiposity measures. Data were analyzed from January 1 to June 30, 2024. Green space metrics (ie, percentage of trees, grass, and other green space) using street-view imagery and deep learning algorithms along with the NDVI were linked with participants' home addresses during mid-childhood and early adolescence. Body mass index (calculated as weight in kilograms divided by height in meters squared) and waist circumference z scores were calculated from anthropometric measurements during early and late adolescence. The total fat mass index (FMI; calculated as fat mass in kilograms divided by height in meters squared) and trunk FMI were obtained using dual-energy x-ray absorptiometry. Separate linear regression models adjusted for a child's age, sex, individual and neighborhood socioeconomic status, and population density estimated prospective associations of the NDVI and street-view green space (in mid-childhood or early adolescence) with 4 adiposity measures (in early or late adolescence). The sample included 843 children (mean [SD] age at mid-childhood, 7.9 [0.8] years; 423 girls [50.2%]) at baseline. After adjustment for confounders, a higher NDVI (per 1-SD increase) within 270 m in early adolescence was associated with lower z scores for BMI (-0.16 [95% CI, -0.27 to -0.04]) and waist circumference (-0.13 [95% CI, -0.23 to -0.03]), as well as a lower total FMI (-0.62 [95% CI, -1.02 to -0.23]) and trunk FMI (-0.33 [95% CI, -0.55 to -0.12]) in late adolescence. Higher percentages of street-view other green space (flowers, plants, and fields), but not street-view trees and grass, within 500 m in early adolescence were associated with lower BMI z scores (-0.09 [95% CI, -0.18 to -0.01) in late adolescence. The NDVI and street-view green space exposure at mid-childhood were not associated with adiposity measures in early adolescence. This cohort study of US children suggests that higher levels of satellite-based NDVI greenness and percentages of street-level green space components (flowers, plants, and fields) were associated with lower adiposity. The findings support the exploration of increasing residential green space levels and adding specific green space components as an urban planning and public health intervention strategy to combat the prevalence of childhood obesity in the US.

Threshold changes in the production potential of bush clumps along piosphere gradients in arid thicket mosaics

Various studies have noted threshold changes in vegetation composition and structure and soil physical and chemical properties in the Albany Thicket biome of South Africa. The aim of this study is to assess if these changes to the environment have transformed the ecosystem’s forage production potential. To estimate forage production potential we compare three models that make use of the mean and variance of the Normalized Difference Vegetation Index (NDVI) obtained from two WorldView-2 satellite images. For the models, NDVI values were sampled along four independent piosphere gradients that vary in their intensity of use and rest from herbivory. Our results indicate that the model that makes use of NDVI variance after a good rainfall year is able to discern changes in the forage production potential that corroborate threshold changes in the vegetation and soil environment. Our results support the growing body of evidence that increases in the variance of ecosystem processes and services are important indicators of impending threshold changes in social-ecological systems.

Old-growth beech forests in Germany as cool islands in a warming landscape

The climate crisis seriously threatens Central European forests and their ecosystem functions. There are indications that old-growth forests are relatively resilient and efficient in micro-climatic regulation during extreme climatic conditions. This study evaluates five well-protected old beech forests in Germany, part of a UNESCO World Heritage Site. We examined temperature dynamics and vitality in core, buffer, and border zones during hot days from 2017 to 2023, using Landsat 8 and 9 imageries to assess Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), alongside on-site Air Temperature (AT) measurements. Our findings reveal that all five forests were impacted by recent extreme heat events, with core zones remaining cooler and more vital, followed by buffer zones. Temperature-regulating patterns varied with landscape characteristics and the surrounding matrixes. We observed a site-dependent cooling effect of the forest interior that increased with higher LST. Our study highlights the value of old-growth forests and recommends increasing effective protection around mature forests, establishing corridors between isolated patches, and creating mosaics in managed landscapes that include unmanaged areas capable of developing into old-growth ecosystems.