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This study explored macroinvertebrate diversity and its relationship with macrophyte cover in Boussedra, an urban pond in north-eastern Algeria subject to high anthropogenic pressure. Monthly macroinvertebrate samples and physico-chemical parameters were collected from five sampling sites, each characterised by distinct vegetation types or the absence thereof: Typha angustifolia, Tamarix africana, Bolboshoenus maritimus–Juncus maritimus, Phragmites australis, and open water. Analysis of the Normalised Difference Vegetation Index (NDVI) from 2003 to 2020 revealed a significant decline in macrophyte density over time. A total of 7,004 macroinvertebrates, representing 38 taxa, were collected, with Diptera, Hemiptera, and Coleoptera emerging as the most abundant and diverse groups. Sites dominated by T. angustifolia exhibited the highest macroinvertebrate richness and abundance, while open water areas had the lowest diversity. Relationships between taxon richness, abundance, species composition, environmental factors, and vegetation strata were investigated using univariate and multivariate analyses. Generalised Linear Modelling (GLM), vector fitting, and PERMANOVA demonstrated that vegetation type was strongly associated with macroinvertebrate assemblage composition. The findings underscore the ecological importance of small urban wetlands and highlight the critical role of aquatic vegetation in fostering macroinvertebrate diversity. Furthermore, the study emphasises the need to protect these habitats and calls for additional research to develop effective strategies for aquatic vegetation restoration and phytoremediation on a broader scale. [ABSTRACT FROM AUTHOR]

With the advance of industrialisation and urbanisation, land use change and climate change have significant impacts on the global ecosystem. Focusing on Chongqing Municipality, a municipality that plays a central role in regional economic development and national strategies, this study explores the impacts of land use and climate change on the evolution of its NDVI spatial and temporal patterns between 2000 and 2020 and reveals the driving mechanisms behind them. By analysing remote sensing image data and climate data, it was found that Chongqing Municipality experienced significant land use changes during the study period, especially urban expansion and the reduction of agricultural land, which led to the reduction of vegetation cover. Meanwhile, precipitation in climate change positively affected vegetation growth and coverage, while the increase in surface temperature during urbanisation negatively affected vegetation cover and exacerbated the urban heat island effect. NDVI was positively correlated with precipitation and negatively correlated with air temperature, suggesting that moderate precipitation promotes vegetation growth, while high temperatures may adversely affect vegetation activities. The results of this study can provide a scientific basis for urban planning and ecological conservation, especially in formulating effective urban management and land management strategies to protect the ecological environment and rationally utilise land resources. [ABSTRACT FROM AUTHOR]

Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China's main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R2) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas. [ABSTRACT FROM AUTHOR]

The Caspian Sea and the Hyrcanian forest rely on each other to maintain balanced hydrological and climatological conditions and preserve the local wildlife and natural resources. Climate variation impacts this balance and the ecological status of the forest. In this study, thermal and phenological seasonality in three Hyrcanian protected areas is evaluated over a period of 33 years (1987–2019). The results suggest extensions of 3–18 days per decade in growing seasons. This could be mitigated by applying policies such as the expansion of protected areas, stringent endangered species conservation, updated silviculture practices, social awareness and cross-sectoral management. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm. [ABSTRACT FROM AUTHOR]

In the face of excessive urban growth, urban green spaces face the challenge of efficiently providing ecosystem and environmental services benefits. While public green spaces (PGS) stand out for their different environmental and social benefits, their efficiency depends on the vegetated cover, which can be evaluated in relation to the area, type of polygon, and degree of equipment. In this study, the effective green cover (EGC) assessed from a geographic information system, and the level of equipment–infrastructure were evaluated in different green spaces in the metropolitan area of San Luis Potosí, Mexico. The PGS categories included park with hydrological potential, urban park, linear park, neighborhood park, local garden, residual green space, and sports area. In our results, the urban park and the park with hydrological potential indicated 69.5 and 79.5% of EGC, respectively, and a value of 0.3 of Normalized Difference Vegetation Index (NDVI). Specifically, only in urban park, the land surface temperature (LST) decreased with the increase in the NDVI. The total green coverage of PGS was 6.7 m2 and the EGC was 5.8 m2, which is largely due to the large-sized parks. Furthermore, the provision of parks is insufficient compared to other metropolitan areas on national and international scales. In the spaces with the highest score of equipment, the outstanding elements were urban furniture, children's playgrounds, and exercise areas, which varied according to the type of green space, indicating a differentiated social use. This study suggests that EGC can be a parameter to assess the green proportion of PGS in cities. Integrating this variable in PGS planning and design can enhance ecosystem services provision in metropolitan areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately estimating gross primary productivity (GPP) is essential for understanding and managing carbon dynamics within an ecosystem. This study investigates the potential of imagery approaches, specifically utilizing Landsat‑8 and Sentinel‑2 data, to model GPP in tea ecosystem within the subtropical region of China. While extensive research has focused on cereal crop ecosystems, tea plantations, despite their global significance as a cash crop, have received limited attention regarding GPP modeling. To address this gap, a field campaign was carried out using the eddy covariance (EC) system to monitor net ecosystem exchange (NEE) within tea plantations at an ecosystem scale. Pruning is recognized as a crucial management practice in the growth of tea plants, leading to significant variations in NEE and its components (ecosystem respiration (RES)). Consequently, we selected the pruning period, from February to June for modeling GPP. Traditionally, vegetation photosynthesis models (VPMs) based on imagery data have required extensive parameterization, posing challenges for data-limited scenarios. In this study, we developed a parametric model based on vegetation indices such as normalized difference vegetation index (NDVI) and scaled photochemical reflectance index (sPRI), which describe both plant structure and physiology using EC and Landsat-8/Sentinel‑2 imagery data. Results indicate that while NDVI partially captures GPP variation using Landsat‑8 (R2 = 0.60) and Sentinel‑2 (R2 = 0.71) imagery, incorporating sPRI significantly enhances the agreement between modeled and observed GPP (Landsat-8 : R2 = 0.77, Sentinel-2 : R2 = 0.80). Furthermore, comparing GPP estimates derived from EC (GPPEC) with those from Sentinel (GPPSentinel) and Landsat (GPPLandsat) imagery reveals that GPPSentinel closely aligns with GPPEC (R2 = 0.80), outperforming GPPLandsat based on various evaluation indices (index of Agreement, Kling-Gupta efficiency, mean bias error, relative bias in percent). [ABSTRACT FROM AUTHOR]

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth. [ABSTRACT FROM AUTHOR]

Flooded forests are rare and highly dynamic ecosystems, yet they can store a significant amount of carbon because of their ability to produce biomass rapidly. Estimation and mapping of the carbon that is stored in flooded forests are challenging tasks through the use of optical remote sensing because these ecosystems are often located in moist regions where clouds can interfere with data acquisition and image interpretation. This study models the aboveground carbon (AGC) stocks of a flooded forest in Turkish Thrace with synthetic aperture radar (SAR) data, which are less affected by weather and illumination conditions compared to optical imagery. Forest management plan data, including inventory records of 229 sample plots, a detailed forest cover map, and stand tables of the 2.119-ha Igneada Longoz Forest, were used to calculate AGC and to develop spatially explicit models based on ALOS/PALSAR-2 (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) and Landsat-8 images. The results indicated that the horizontally transmitted and horizontally received (HH) and cross-polarization ratio (CPR) bands of ALOS/PALSAR were the most influential variables in the linear and nonlinear regression models. The models did not include any variables from either radar- or optical-based vegetation indices. While the estimation accuracies of the two models were similar (root mean square percentage error ≈ 26%), the linear model yielded negative estimations in several land cover classes (e.g., dune, forest opening, degraded forest). AGC stock was estimated and mapped using the nonlinear model in these cases. The density map revealed that Igneada Longoz Forest stored 279,258.9 t AGC, with a mean and standard deviation of 124 ± 115.4 t C ha-1. AGC density varied significantly depending on stand types and management units across the forest, and carbon hotspots accumulated in the northern and southern sites of the study area, primarily composed of ash and alder seed stands. The models and maps that this study developed are expected to help in the rapid and cost-effective assessment of AGC stored in flooded forest ecosystems across the temperate climate zone.

Abstract Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology.

The middle reaches of the Yellow River Basin (MYRB) are known for their significant soil erosion and fragile ecological environment, where vegetation growth is important. However, the vegetation's reaction to climate change (CC) and human activity (HA), and the potential driving mechanisms underlying such changes in the MYRB, have not yet been clarified. Thus, based on remote sensing data, combined with trend analysis and the Hurst method and supplemented by the structural equation model (SEM) and residual analysis method, we aimed to conduct an analysis of the spatio-temporal evolution of the normalized difference vegetation index (NDVI) in the MYRB from 2000 to 2020. Additionally, we explored how climate and human factors together affect the NDVI and quantified the proportion of their respective contributions to NDVI change. The NDVI exhibited a fluctuating upward trend in the MYRB. Moreover, approximately 97.7% of the area showed an improving trend, with nearly 50% of the area continuing to maintain an improving trend. Precipitation and temperature had positive effects on the NDVI, while vapor pressure deficit (VPD) and land use intensity (LUI) had negative effects. HA played a pivotal role in the vegetation improvement area with a contribution rate of 67.53%. The study revealed NDVI variations and emphasized the influence of HA on the NDVI in the MYRB. The findings are vital in comprehending the response mechanism of ecosystems and guiding reasonable environmental protection policies, which is beneficial for the sustainable development of the region. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The interaction between humans and their environment significantly impacts the health of urban residents. However, there is limited research on assessing the spatiotemporal variations of human thermal exposure as well as inequalities and their relationships with greenspace based on remote sensing data, especially in developing countries. This study offers a framework utilizing pixel-wise data on land surface coolness index (LSCI), normalized difference vegetation index (NDVI), and population metrics to evaluate population-weighted thermal and greenspace exposures in China's Greater Bay Area (GBA) over eleven years (2010–2020). It further examines thermal exposure inequality using the Gini index and explores both natural and socio-economical drivers of thermal exposure and its inequality. The results indicate an increasing trend of cities within the GBA being experiencing detrimental thermal conditions. Over the study period, the proportion of cities with high exposure to LSCI and NDVI exhibited a general decline. Approximately 63.6% of major cities observed growing imbalances in LSCI and NDVI exposures over eleven years. Notably, there were positive correlations between NDVI exposure and LSCI exposure, along with their associated inequalities. Moreover, coastal cities characterized by lower elevations, higher precipitation rates, and rapid economic growth experienced higher levels of LSCI and NDVI exposures yet these cities also demonstrated greater disparities. [ABSTRACT FROM AUTHOR]

Remote sensing has proven to be a vital tool for monitoring and forecasting the quality and yield of crops. The utilization of innovative technologies such as Solar-Induced Fluorescence (SIF) and satellite measurements of column-averaged CO2 (xCO2) can enhance these estimations. SIF is a signal emitted by crops during photosynthesis, thus indicating photosynthetic activities. The concentration of atmospheric CO2 is a critical factor in determining the efficiency of photosynthesis. The aim of this study was to investigate the correlation between satellite-derived Solar-Induced Chlorophyll Fluorescence (SIF), column-averaged CO2 (xCO2), and Normalized Difference Vegetation Index (NDVI) and their association with sugarcane yield and sugar content in the field. This study was carried out in south-central Brazil. We used four localities to represent the region: Pradópolis, Araraquara, Iracemápolis, and Quirinópolis. Data were collected from orbital systems during the period spanning from 2015 to 2016. Concurrently, monthly data regarding tons of sugarcane per hectare (TCH) and total recoverable sugars (TRS) were gathered from 24 harvest locations within the studied plots. It was observed that TRS decreased when SIF values ranged between 0.4 W m−2 sr−1 μm−1 and 0.8 W m−2 sr−1 μm−1, particularly in conjunction with NDVI values below 0.5. TRS values peaked at 15 kg t−1 with low NDVI and xCO2 values, alongside SIF values lower than 0.4 W m−2 sr−1 μm−1 and greater than 1 W m−2 sr−1 μm−1. These findings underscore the potential of integrating SIF, xCO2, and NDVI measurements in the monitoring and forecasting of yield and sugar content in sugarcane crops. [ABSTRACT FROM AUTHOR]

Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The condition of the Bengkulu watershed area is outlined in the Indonesia Integrated Watershed Management Plan. Adverse conditions in the watershed have been linked to a range of natural calamities, such as floods and droughts. Moreover, the process of converting forests into plantations or agricultural lands has resulted in environmental degradation. Therefore, there is a pressing need for an evaluation of vegetation density and plant analysis within the watershed. This is crucial for comprehending ecological conditions, devising restoration measures, and implementing conservation efforts. Hence, the aim of this study is to analyze vegetation density and track plant diversity, specifically focusing on tree characteristics, throughout. METHODS: The study techniques utilized in this investigation encompass the gathering of normalized difference vegetation index data from satellite imagery, followed by its analysis through the utilization of geographic information system software. Sentinel satellite imagery from 2021 is utilized due to its efficacy in monitoring environmental conditions and managing natural resources. Spatial data encompass maps and field data. FINDINGS: By employing normalized difference vegetation index data, the study pioneers a novel approach to environmental monitoring, setting an example for effective resource management and ecological conservation in watershed regions. The study findings indicate that 29 percent of the watershed area exhibits moderately steep topography with a dendritic flow pattern. The assessment of the normalized difference vegetation index demonstrates that the watershed is comprised of multiple sections abundant in high-density vegetation, primarily dedicated to plantations. Within the Bengkulu watershed area, a total of 49 tree species from 22 families were identified, with diversity indices falling within the moderate category. CONCLUSION: An in-depth knowledge of the ecological factors and plant preservation initiatives in the Bengkulu watershed can greatly aid in sustainable environmental management and help policymakers develop more effective policies for ensuring environmental sustainability. The findings of this study contribute significantly to the Global Journal of Environmental Science and Management's goals of promoting sustainable environmental management and biodiversity preservation, offering actionable insights for policymakers and conservationists. [ABSTRACT FROM AUTHOR]

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) is a crucial indicator of ecological environment quality. To better understand the carbon absorption and carbon cycling capabilities of Henan Province, this study investigates the trends and driving factors of NPP across different climatic zones and land use types. The Theil–Sen Median trend analysis method and the Mann–Kendall trend test are employed to monitor NPP changes from 2001 to 2020. The average annual NPP in Henan Province during this period was 414.61 gC·m−2·year−1, showing a significant increasing trend with a growth rate of 3.73 gC·m−2·year−1. Spatially, both the annual average NPP and its increase rate were higher in the western part of Henan compared to the eastern part, and NPP variability was more stable in the southern region than in the northern region. By classifying climatic zones and using the Geodetector method to assess NPP sensitivity to natural factors, the results show that climate and vegetation factors jointly influence NPP variations, with annual precipitation being the primary natural factor affecting NPP trends in Henan Province from 2001 to 2020. By analyzing the NPP gain and loss matrix, the impact of land use changes on NPP was evaluated. Forests had the highest average annual NPP at 483.52 gC·m−2·year−1, and the conversion of arable land to urban areas was identified as the primary land change type leading to NPP reductions. In the subtropical zone of Henan, forests, croplands, and grasslands exhibited higher NPP values and increase rates compared to those in the warm belt. This study provides new insights into the spatial variation of NPP caused by changes in climatic zones and land use types. [ABSTRACT FROM AUTHOR]

Agroecosystems are facing the adverse effects of climate change. This study explored how the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) can give new insight into irrigation allocation and plant health. Leveraging the global coverage and 70-m spatial resolution of the Evaporative Stress Index (ESI) from ECOSTRESS, we processed over 200 overpasses and examined patterns over 3 growing seasons across the Maipo River Basin of Central Chile, which faces exacerbated water stress. We found that ECOSTRESS ESI varies substantially based on the overpass time, with ESI values being systematically higher in the morning and lower in the afternoon. We also compared variations in ESI against spatial patterns in the environment. To that end, we analyzed the vegetation greenness sensed from Landsat 8 and compiled the referential irrigation allocation from Chilean water regulators. Consistently, we found stronger correlations between these variables and ESI in the morning time (than in the afternoon). Based on our findings, we discussed new insights and potential applications of ECOSTRESS ESI in support of improved agricultural monitoring and sustainable water management. [ABSTRACT FROM AUTHOR]

For the needs of precision agriculture, it is important to create a database on the trends of changes in vegetation indices. The correct interpretation of the data for a specific region is useful for reading and planning subsequent treatments. The research was conducted with a small UAV equipped with a NIR camera in the period 2019-2022, in three fields in southern Dobrudja. The aim is to track the dynamics of NDVI changes in wheat. To create a database on the trends of change in NDVI in the specific agrarian climatic conditions of southern Dobrudja. The dynamics of the index during the period of extreme drought 2019-2020 have been tracked. Maximum values are recorded in the spindle phase - grading at the beginning of May. Returning frosts at the beginning of March lower the value sharply. The dynamics of NDVI during the phenological development of wheat was tracked, as a maximum of 0.5 was reached during the grading period. Humidity analysis gives a direct link to NDVI change trends with a week lag of the changes that occurred. [ABSTRACT FROM AUTHOR]

Ecological protection in the Yellow River Basin (YRB) is a major strategy for China's sustainable development. Amid global warming, droughts have occurred more frequently, severely affecting vegetation growth. Based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) at different time scales from 2003 to 2020, this study employed the linear trend method and the Spearman correlation coefficient method to calculate the trends and correlation coefficients of NDVI and SPEI at different scales at the pixel scale and explored the spatial distribution pattern of the sensitivity of vegetation growth in the YRB to drought. The results show that: (1) NDVI and SPEI are positively correlated in 77% of the area, negatively correlated in 9%, and are positively correlated in the arid and semi-arid areas, while negatively correlated in the humid and subhumid areas. The significant negative correlation between NDVI and drought at high altitudes may be due to the fact that Gramineae vegetation is more sensitive to drought, with heat being more affected than water. (2) Urbanization has a relatively obvious impact on the distribution of drought. Extreme drought mainly occurs in the middle and upper reaches of the Wei River; severe drought mainly occurs in the central area of the Guanzhong Plain centered on Xi'an; the central area of the Loess Plateau; and the surrounding areas of the Zhengzhou-centered Central Plains City Group. (3) The NDVI showed an upward trend from 2003 to 2020, indicating an increase in vegetation density or an expansion of vegetation coverage. From the temporal trend, SPEI decreased at a rate of −0.17/decade, indicating that the entire watershed has a drought trend on an annual scale. (4) Spring NDVI is more sensitive to the water supply provided by SPEI-1, while the positive correlation between SPEI and NDVI begins to rise in June and reaches its peak in July, then starts to decline in August. In autumn and winter, NDVI is more sensitive to 3–6-month accumulated drought. (5) From the dynamic transmission laws of different levels of positive correlation, the positive impact of the 3-month accumulated drought on NDVI is most significant, and the influence of SPEI-1 on the negative correlation between SPEI and NDVI is most significant. This paper aims to clarify the sensitivity of vegetation to different time-scale droughts, provide a basis for alleviating drought in the YRB, and promote sustainable development of ecological environmental protection. The research findings enable us to gain a profound insight into the responsiveness of vegetation growth to drought in the context of global warming and offer a valuable theoretical foundation for devising pertinent measures to alleviate stress on vegetation growth in regions prone to frequent droughts. [ABSTRACT FROM AUTHOR]

Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas. [ABSTRACT FROM AUTHOR]

Satellite-based remote sensing approaches provide a cost-efficient means to collect information on the world’s forests and to repeatedly survey large, or inaccessible, forest areas. However, it may not always be possible to ground truth–associated findings using direct ecological field surveys conducted by trained forest scientists. Local ecological knowledge (LEK) is an alternative form of data which could be used to complement, interpret and verify information from satellite data. Using a case study on the Democratic People’s Republic of Korea (North Korea), we evaluate the potential for integrating remote sensing and LEK data, gathered with non-specialist former residents, to understand patterns and drivers of forest cover change. LEK reports often concurred with, or provided key information to enable interpretation of, satellite data. This revealed that between 1986 and 2021, North Korea experienced high, but uneven, rates of deforestation. There was a pronounced northwards deforestation shift in the mid-1990s, coinciding with a period of extreme hardship and famine (the “Arduous March”), and associated with clearance of trees in more forested northern provinces as an economic and fuel resource, and conversion of forest to agricultural cropland. Loss of forest cover in North Korea has continued and recently accelerated, to a rate of > 200 km2 per annum between 2019 and 2021. This increases the vulnerability of North Korean socio-ecological systems to future environmental change and is an obstacle to the recovery of threatened species across the Korean Peninsula. We recommend that LEK- and remote sensing–based approaches are considered within a suite of complementary techniques to analyse forest changes where ecological field surveys cannot be conducted. [ABSTRACT FROM AUTHOR]

Effects of climate change in semi-arid areas occur in drought events, which affect aquifers whose recharge depends essentially on precipitation. The objective of this study is to evaluate the relationship between depth to groundwater (DTW), precipitation, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), in the alluvial aquifer of Mostaganem Plateau, Algeria over 2000, 2005, 2010-2011 and 2014-2015. This is caried out through an adaptive methodology, using remote sensing, Geographic Information Systems (GIS), and statistical analysis: correlation analysis and Multiple Linear Regression (MLR). The results indicate a 62 mm decline in precipitation from 2000 to 2015, inducing shifts in spatial patterns. This resulted in an increase of DTW (4 m to 10 m). The strong negative correlation between decreased precipitation and increased DTW, supported by an R2 value of -0.80, is evident. Moreover, NDVI and LST values increased notably by 0.034 and 3.38°C, respectively. The relationship between DTW, NDVI, and LST showed a diminishing negative correlation. The MLR reaffirmed the influence of precipitation and highlighted the impact of human activity on DTW and drought indicators effectiveness. High NDVI values indicated intensive groundwater pumping, while elevated LST contributed to DTW decrease due to increased evaporation rates caused by changes in crop types resulting from human actions. This study contributes to the understanding of the dynamic interactions between DTW, precipitation, and anthropogenic activities and gives insight to decision makers regarding irrigation strategies.

In the face of excessive urban growth, urban green spaces face the challenge of efficiently providing ecosystem and environmental services benefits. While public green spaces (PGS) stand out for their different environmental and social benefits, their efficiency depends on the vegetated cover, which can be evaluated in relation to the area, type of polygon, and degree of equipment. In this study, the effective green cover (EGC) assessed from a geographic information system, and the level of equipment–infrastructure were evaluated in different green spaces in the metropolitan area of San Luis Potosí, Mexico. The PGS categories included park with hydrological potential, urban park, linear park, neighborhood park, local garden, residual green space, and sports area. In our results, the urban park and the park with hydrological potential indicated 69.5 and 79.5% of EGC, respectively, and a value of 0.3 of Normalized Difference Vegetation Index (NDVI). Specifically, only in urban park, the land surface temperature (LST) decreased with the increase in the NDVI. The total green coverage of PGS was 6.7 m2 and the EGC was 5.8 m2, which is largely due to the large-sized parks. Furthermore, the provision of parks is insufficient compared to other metropolitan areas on national and international scales. In the spaces with the highest score of equipment, the outstanding elements were urban furniture, children’s playgrounds, and exercise areas, which varied according to the type of green space, indicating a differentiated social use. This study suggests that EGC can be a parameter to assess the green proportion of PGS in cities. Integrating this variable in PGS planning and design can enhance ecosystem services provision in metropolitan areas.

BACKGROUND AND OBJECTIVES: Mangroves play a crucial role in mitigating climate change by absorbing carbon stocks. However, there is a lack of information on mangrove distribution and their carbon absorption abilities. Therefore, this study aimed to bridge this gap by gathering data on the ability of mangrove forest areas to absorb carbon stocks. Specifically, this study aims to assess the carbon absorption potential of the Lantebung mangrove ecosystem through field surveys, allometric calculations, and unmanned aerial vehicle imagery.METHODS: The methodology employed in this study consisted of field surveys, allometric calculations, and multispectral aerial imagery processing along the coastal of Makassar City, South Sulawesi, within the Lantebung mangrove ecosystem. Field surveys were conducted to determine the species composition of each mangrove stand and measure their diameter at breast height. The allometric formula was then used to calculate mangrove biomass, which was subsequently converted into carbon stock values. Aerial imagery was processed using the normalized difference vegetation index, followed by a regression analysis between normalized difference vegetation index and carbon stock values to obtain a carbon stock estimation model.FINDINGS: The results of the analysis of red-green-blue aerial imagery from the multispectral unmanned aerial vehicle has provided valuable insights into the extent of mangrove vegetation cover in the Lantebung mangrove forest area, revealing it to be 14.18 hectares. The normalized difference vegetation index results indicated that mangrove objects fall within a value range of 0.21–1, categorized into three density classes: high-, medium-, and low-density mangroves. The field surveys confirmed the presence of three types of mangroves in Lantebung Makassar, namely Rhizophora apiculata, Rhizophora mucronata, and Avicennia sp. The regression analysis conducted to assess the relationship between the normalized difference vegetation index value and carbon stocks yielded the equation model carbon stock = 474.61, vegetation Index value + 17.238, with a linear regression value of 0.7945. The carbon stock values for low-density class mangrove areas were predicted to range between 17.24 and 288.64 tons carbon per hectare, medium-density mangroves' carbon stocks to be between 126.04 and 391.14 tons carbon per hectare, and high-density mangrove areas' carbon stocks to range from 258.04 to 491.85 tons carbon per hectare.CONCLUSION: The utilization of drones as a technique for monitoring carbon stocks has offered significant benefits. Drones equipped with multispectral sensors enable the collection of precise and comprehensive data on vegetation and elevation in many ecological systems. The survey and subsequent analysis highlighted the wide variation in the density of mangrove forests in the Lantebung mangrove ecosystem. This study demonstrated a strong correlation between the normalized difference vegetation index extracted using unmanned aerial vehicle and mangrove carbon levels obtained from actual field measurements.

Abstract African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate urban heat and how air pollution can increase temperatures. Recognizing the predictive value of LST for urban heat island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, and Phoenix—each characterized by unique climate and urban planning features. This study investigates the relationship between atmospheric pollutants (SO2, NO2, CO, O3) and the Normalized Difference Vegetation Index (NDVI) with land surface temperature (LST) using regression and correlation analyses. The analysis aims to elucidate how changes in atmospheric pollutants and NDVI affect variations in land surface temperature. Regression analysis is employed to estimate the coefficients of independent variables and quantify their impact on LST. Correlation analysis assesses the linear relationships between variables, providing insights into their pairwise associations. The study also examines multicollinearity between independent variables to identify potential confounding factors. The results reveal significant associations between atmospheric pollutants, NDVI, and land surface temperature, contributing to our understanding of the environmental factors influencing LST dynamics and informing climate change mitigation strategies. The observed inconsistencies in correlations across cities highlight the importance of the local context in environmental studies. Understanding these variations can aid in developing tailored urban planning policies that consider unique city characteristics for more effective climate resilience. Furthermore, a positive association was consistently obtained between pollutants and LST, indicating that increased pollution levels contribute to higher surface temperatures across different urban settings. [ABSTRACT FROM AUTHOR]

Changes in vegetation status generally also represents changes in the ecological health of islands and reefs (IRs). However, studies are limited of drivers and trends of vegetation change of Nansha Islands, China and how they relate to climate change and human activities. To resolve this limitation, we studied changes to the Normalized Difference Vegetation Index (NDVI) vegetation-greenness index for 22 IRs of Nansha Islands during normal and extreme conditions. Trends of vegetation greenness were analyzed using Sen' s slope and Mann-Kendall test at two spatial scales (pixel and island), and driving factor analyses were performed by time-lagged partial correlation analyses. These were related to impacts from human activities and climatic factors under normal (temperature, precipitation, radiation, and Normalized Difference Built-up Index (NDBI)) and extreme conditions (wind speed and latitude of IRs) from 2016 to 2022. Results showed: 1) among the 22 IRs, NDVI increased/decreased significantly in 15/4 IRs, respectively. Huayang Reef had the highest NDVI change-rate (0.48%/mon), and Zhongye Island had the lowest (−0.29%/mon). Local spatial patterns were in one of two forms: dotted-form, and degradation in banded-form. 2) Under normal conditions, human activities (characterized by NDBI) had higher impacts on vegetation-greenness than other factors. 3) Under extreme conditions, wind speed (R2 = 0.2337, P < 0.05) and latitude (R2 = 0.2769, P < 0.05) provided limited explanation for changes from typhoon events. Our results provide scientific support for the sustainable development of Nansha Islands and the United Nations 'Ocean Decade' initiative. [ABSTRACT FROM AUTHOR]

Urban heat poses significant challenges in rapidly developing cities, particularly in countries like Bangladesh. This study investigates the cooling effects of urban green spaces in Rajshahi city, addressing a critical research gap in developing urban contexts. We examined the relationships among urban vegetation, heat mitigation, and temperature variables using the InVEST Urban Cooling Model and spatial analysis techniques. This study focused on three key relationships: Normalized Difference Vegetation Index (NDVI) and Heat Mitigation Index (HMI), HMI and Land Sur face Temperature (LST), and HMI and Air Temperature (AT). Analysis revealed a strong positive correlation between NDVI and HMI, indicating the effectiveness of vegetation in enhancing urban cooling. A robust inverse relationship between HMI and LST was observed (R2 = 0.78, r = −0.88), with every 0.1 unit increase in HMI corresponding to a 0.53 °C decrease in LST. The HMI−AT relationship showed an even stronger correlation (R2 = 0.84, r = −0.87), with each unit increase in HMI associated with a 2.80 °C decrease in air temperature. These findings quantify the significant role of urban green spaces in mitigating heat and provide valuable insights for urban planning in developing cities, underscoring the importance of integrating green infrastructure into urban-development strategies to combat urban heat and improve livability. [ABSTRACT FROM AUTHOR]

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons' NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced "greening" process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant "browning" trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of "greening in the east and browning in the west" of Central Asia. During spring, the dominant theme was the "greening" of vegetation NDVI, although there was noticeable "browning" observed in southwest region of Central Asia. During summer, the "browning" of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the "browning" trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the "greening" trend of vegetation NDVI in arid regions, which further aggravates the "browning" trend of vegetation NDVI. [ABSTRACT FROM AUTHOR]

Amidst escalating global energy demands, the advancement and utilization of renewable energy sources have emerged as critical strategies for addressing environmental concerns and alleviating energy crises. Among them, wind power, as a renewable and clean energy source, has been widely applied and developed in China. However, the construction of wind farms may have some impact on vegetation cover and soil properties. This study aims to assess the impact of wind farm construction on vegetation cover and soil characteristics, thereby offering a scientific foundation for the sustainable management of wind farm development sites. The present study was carried out in the area of Jingzhushan wind farm in Linxiang City, Hunan Province, to examine the trends of the normalized difference vegetation index (NDVI), the fractional vegetation cover (FVC), and the indexes expressing the physicochemical properties of the soil in this area. The results showed the following: (1) The NDVI of the wind farm for the three periods was 0.742 in 2013, 0.770 in 2016, and 0.758 in 2023, respectively. According to the analysis of the index of FVC, it can be seen that the trend of the FVC of the study area for the three periods was basically the same as that of the NDVI. The average value of FVC was 0.754 in 2013, 0.791 in 2016, and 0.769 in 2023. This indicated that the vegetation cover in the early stage of wind farm construction (2013) was lower than that in the late stage of operation (2016, 2023), and it also suggested that the vegetation cover gradually recovered over time. (2) Compared with natural ecosystems, both altitude and wind farm construction significantly affected the organic carbon, the total nitrogen, the effective phosphorus, and the rapidly available potassium in the soil. At the same altitude, these four soil indicators in the area where the wind turbines were constructed had significantly lower levels compared with the control (CK), which indicated a decrease in soil fertility—the closer to the turbine construction area, the lower the levels of each indicator. In addition, soil pH did not change significantly during the construction of the wind farm. The analysis and comparison of various data showed that the construction and operation of wind farms can have an impact on local vegetation cover, and it had a significant negative impact on soil properties. Reasonable measures are needed to protect vegetation and soil to achieve the sustainable development of the ecological environment. [ABSTRACT FROM AUTHOR]

Investigation of cities' spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite images and the Getis-Ord Gi* statistic, this research investigated how vegetation and urbanization impact SUHIs. Based on the main results, mean NDVI, UI, and LST values for 2009 and 2017 were analyzed, revealing significant vegetation loss in 37 of Istanbul's 39 districts, with substantial urbanization, especially in the north, due to new infrastructure development. On the other hand, hotspot analysis was conducted on normalized NDVI, UI, and LST images by analyzing 977 neighborhoods. Results showed a significant transformation of green areas to non-significant classes in NDVI, high urbanization in UI, and the formation of new hot areas in LST. SUHIs were found to cluster in areas with increasing residential and industrial activities, highlighting the role of urban development on SUHI formation. This research can be applied to any region since it offers crucial perspectives for decision-makers and urban planners aiming to mitigate SUHI effects through targeted greening strategies and sustainable urban development. By integrating environmental metrics into urban planning, this study underscores the need for comprehensive and sustainable approaches to enhance urban resilience, reduce environmental impact, and improve livability in Istanbul. [ABSTRACT FROM AUTHOR]

The Moderate Solution Imaging Spectroradiometer (MODIS) is a multispectral sensor that has been actively researched in various fields using diverse land and atmospheric products. MODIS was first launched over 20 years ago, and the demand for novel sensors that can produce data comparable to that obtained using MODIS has continuously increased. In this study, land products obtained using the visible infrared imaging radiometer suite (VIIRS) of the Suomi National Polar-orbiting Partnership satellite launched in 2011 were introduced, including land surface temperature and vegetation indices such as the normalized difference vegetation index and enhanced vegetation index. These land products were compared with existing data obtained using MODIS to verify their local applicability in South Korea. Based on spatiotemporal monitoring of an extreme drought period in South Korea and the application of VIIRS land products, our results indicate that VIIRS can effectively replace MODIS multispectral sensors for agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

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