Search Results (4,396)

Understanding the phenology of urban trees can help mitigate the heat island effect by strategically planting and managing trees to provide shade, reduce energy consumption, and improve urban microclimates. In this study, we carried out the first evaluation of high spatial resolution satellite images from Landsat-8, Sentinel-2, and PlanetScope images to quantify urban street tree phenology in downtown Beijing. The major research goals are to evaluate the consistency in pixel-level spring–summer growth period phenology and to investigate the capacity of high-resolution satellite observations to distinguish phenological transition dates of urban street trees. At the city scale, Landsat-8, Sentinel-2, and PlanetScope show similar temporal NDVI trends in general. The pixel-level analysis reveals that green-up date consistency is higher in areas with medium (NDVI > 0.5) to high (NDVI > 0.7) vegetation cover when the impacts of urban surfaces on vegetation reflectance are excluded. Similarly, maturity date consistency significantly increases in densely vegetated pixels with NDVI greater than 0.7. At the street scale, this study emphasizes the efficacy of NDVI time series derived from PlanetScope in quantifying the phenology of common street tree genera, including Poplars (Populus), Ginkgos (Ginkgo), Chinese Scholars (Styphnolobium), and Willows (Salix), in downtown Beijing to improve urban vegetation planning. Based on PlanetScope observations, we found that the four street tree genera have unique phenological patterns. Interestingly, we found that the trees along many major streets, where Chinese Scholars are the major tree genus, have later green-up dates than other areas in downtown Beijing. In conclusion, the three satellite observation datasets prove to be effective in monitoring street tree phenology during the spring–summer growth period in Beijing. PlanetScope is effective in monitoring tree phenology at the street scale; however, Landsat-8 may be affected by the mixture of land covers due to its relatively coarse spatial resolution. Full article

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of an interactive assessment of their distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest area but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of the forests has been increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development. Full article

Hydrological processes and the sustainable use of water resources in a river basin are altered by climate change and changes in human variables. This study examined the significant effects of vegetation and hydrological, climatic, and human activity changes on the basin’s biological environment and usage of water resources. The Min River Basin (MRB) in the upper Yangtze River served as the study location. Mann–Kendall and Pettitt mutation test techniques were used to examine the features of runoff changes in the basin. The effects of meteorological and anthropogenic factors on runoff and vegetation changes in the MRB from 1982 to 2020 were quantitatively evaluated using the expanded Budyko equation. Following this, spatial and temporal variations in land use and the NDVI in the basin were studied. The results of the research demonstrated the following: (1) The MRB yearly runoff trended downward and that an abrupt change in runoff happened in 1994. (2) Precipitation (Pr) showed a decreasing tendency from the base period (S1) to the change period (S2), but potential evapotranspiration (ET₀) showed an increasing trend. (3) From 1985 to 2020, the land use area of the MRB changed rapidly, and the construction land and water area increased by 322% and 58.85%, respectively, while the cultivated land area decreased by 11.72%. (4) From S1 to S2, there was a rising trend in both the NDVI and the Budyko parameter n. The contributions of Pr, ET₀, NDVI, and n to the runoff change were 32.41%, 9.43%, 27.51%, and 30.65%, respectively. Full article

The main objective of protecting ecosystems and enhancing the supply of ecosystem services (ESs) is to quantify the value of ecological services. This article calculates the ecological service value (ESV) of the Tarim Basin over the past 40 years using the improved benefits transfer method of satellite remote sensing data, such as Landsat, analyzes the spatiotemporal evolution characteristics of ESV, and studies the driving mechanism of ESV changes using GeoDetector. Finally, the FLUS model was selected to predict the ecosystem service value until 2030, setting up three scenarios: the Baseline Scenario (BLS), the Cultivated Land Protection Scenario (CPS), and the Ecological Protection Scenario (EPS). The results indicate that (1) the ESV in the Tarim Basin decreased by USD 1248.21 million (−2.29%) from 1980 to 2020. The top three contributors are water bodies, wetlands, and grassland. (2) Waste treatment and water supply functions had the highest service value, accounting for 44.53% of the total contribution. The rank order of ecosystem functions in terms of their contribution to the total value of ESV was as follows, refining from high to low importance: water supply, waste treatment, biodiversity protection, climate regulation, soil formation, recreation and culture, gas regulation, food production, raw material. (3) The spatial differentiation driving factors of ESV were detected, with the following Q-values in descending order: net primary productivity (NPP) > normalized difference vegetation index (NDVI) > precipitation > aspect > temperature > slope > soil erosion > GDP > land use intensity > per capita GDP > population > human activity index. (4) The ESVs simulated under the three scenarios (BLS, CPS, and EPS) for 2030 were USD 51,133.9 million, USD 53,624.99 million, and USD 54,561.26 million, respectively. Compared with 2020, the ESVs of the three scenarios decreased as follows: BLS (USD 4209.33 million), CPS (USD 1718.24 million), and EPS USD (−781.97 million). These findings are significant for maintaining the integrity and sustainability of the large-scale ecosystem, where socioeconomic development and the fragile features of the natural ecosystem interact. Additionally, the study results provide a crucial foundation for governmental decision-makers, local residents, and environmental researchers in northwest China to promote sustainable development. Full article

Since 2002, numerous sustainable land management (SLM) interventions have been implemented in Ethiopia, such as agroforestry, area closure, forage development, gully rehabilitation, and conservation agriculture. In addition, watershed-based developments contributed comprehensively to a better use of existing natural resources. This study determined the impact of Integrated Watershed Management (IWM) on land use/land cover for the Yesir watershed in Northern Ethiopia. Supervised image classification algorithms were applied to a time series of Landsat 5 (2002) and Landsat 8 (2013 and 2022) images to produce land use/land cover maps. A Geographic Information System was applied to analyze and map changes in land use/land cover for settlements, agricultural land, grazing land, and land covered with other vegetation. In focus group discussions, the time series maps were analyzed and compared with the integrated watershed management practices to analyze their impacts. The results document that integrated watershed management practices have contributed to a significant change in land use/land cover in the study area over the past 20 years. The quantitative analysis of land use/land cover between the years 2002 and 2022 only revealed a downward trend in agricultural land. Considering the value of the Normalized Difference Vegetation Index (NDVI) as a biophysical feature for the increase of green mass, this indicator also documents an improvement in land use/land cover with regard to sustainable land management and consequently poverty alleviation. Full article

Ecological zonation research is typically conducted in the eastern margin of the Tibetan Plateau. In order to enhance the structure and function of regional ecosystems and monitor their quality, it is crucial to investigate shifts in the coverage of vegetation and the factors that contribute to these shifts. The goal of this study is to assess the spatial and temporal variations in vegetation covering and the partitioning of its drivers in the Minjiang River Basin on the eastern edge of the Tibetan Plateau between 2000 and 2022. The Mann-Kendall test, Hurst index, Theil-Sen median trend analysis, and other techniques were used to look at the features of temporal and geographical changes in regional vegetation coverage as well as potential development trends. The climatic influences leading to the spatial differentiation of vegetation NDVI (Normalized Difference Vegetation Index) were quantified through partial and complex correlation analyses of NDVI with temperature and precipitation. The results of the study showed that (1) the NDVI of the watershed performed well with a stable upward trend, indicating that the vegetation growth was generally good; (2) the spatial analysis showed that the coefficient of variation of the NDVI reached 0.092, which highlighted the stability of the vegetation change in the region; (3) the future development trend of the vegetation coverage in the watershed is low, and there is a certain degree of ecological risk; and (4) the main driver of the vegetation coverage is the non-climate factor, distributed in most parts of the watershed; (5) the climate driver shows localized influence, especially concentrated in the southwest, downstream and part of the upstream areas of the watershed. Full article

In response to novel and complex uncertainties, the present research is conducted to characterize the most significant indicators of watershed health including drought, water quality, and vegetation for the Tajan watershed, Mazandaran, Iran. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are, respectively, used to quantify the meteorological and hydrological droughts in the present (1993–2020) and future (2023–2050) employing optimistic RCP2.6 and pessimistic RCP8.5 scenarios. To concoct discharge data for the future, IHACRES v1.0 software is used with a Nash–Sutcliffe coefficient (NSE) of 0.48 and a coefficient of determination (R²) of 0.58. Maps of land use and Normalized Difference Vegetation Index (NDVI) are also prepared using Landsat images. Subsequently, the surface water quality is assessed using AqQA v1.1.0 software. The results show the difference in the severity of future meteorological droughts in different stations. In addition, the predominance of non-drought (SDI≥0) or mild drought (−1 ≤ SDI < 0) is indicated for future hydrology. The land use changes show a decrease in rangeland (−5.47%) and an increase in residential land (9.17%). The water quality analysis also indicates an increase in carbonate ions in the watershed outlet. Communicating the relationships between study indicators, which is a big gap in the current watershed management approach, avoids future failures and catastrophes. Full article

The invasion of Spartina alterniflora (S. alterniflora) has posed serious threats to the sustainability, quality and biodiversity of coastal wetlands. To safeguard coastal ecosystems, China has enacted large-scale S. alterniflora removal projects, which set the goal of effectively controlling S. alterniflora throughout China by 2025. The accurate monitoring of S. alterniflora with remote sensing is urgent and requisite for the scientific eradication, control and management of this invasive plant. In this study, we combined multi-temporal WorldView-2/3 (WV-2/3) and Sentinel-1/2 imagery to monitor the S. alterniflora dynamics before and after the S. alterniflora removal projects in Zhangjiang Estuary. We put forward a new method for S. alterniflora detection with eight-band WV-2/3 imagery. The proposed method first used NDVI to discriminate S. alterniflora from water, mud flats and mangroves based on Ostu thresholding and then used the red-edge, NIR1 and NIR2 bands and support vector machine (SVM) classifier to distinguish S. alterniflora from algae. Due to the contamination of frequent cloud cover and tidal inundation, the long revisit time of high-resolution satellite sensors and the short-term S. alterniflora removal projects, we combined Sentinel-1 SAR time series and Sentinel-2 optical imagery to monitor the S. alterniflora removal project status in 2023. The overall accuracies of the S. alterniflora detection results here are above 90%. Compared with the traditional SVM method, the proposed method achieved significantly higher identification accuracy. The S. alterniflora area was 115.19 hm² in 2015, 152.40 hm² in 2017 and 15.29 hm² in 2023, respectively. The generated S. alterniflora maps clearly show the clonal growth of S. alterniflora in Zhangjiang Estuary from 2015 to 2017, and the large-scale S. alterniflora eradication project has achieved remarkable results with a removal rate of about 90% in the study area. With the continuous implementation of the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)” which aims to eliminate more than 90% of S. alterniflora in all provinces in China by 2025, the continual high-spatial resolution monitoring of S. alterniflora is crucial to control secondary invasion and restore coastal wetlands. Full article

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. Full article

The study of Land Use and Landscape Patterns (LULPs) changes and their ecological quality effects in Haikou city under the background of the Hainan Free Trade Port Plan (HFTPP) helps to promote coordinated development between cities and the environment. To date, most research on ecological quality has focused on areas with extremely fragile ecology and/or is related to LULP analysis. There are few studies in the literature focusing on the impact of high-intensity human activities caused by relevant policies on urban LULPs. The purpose of this research was to design a framework that monitors urban ecological security by considering the effect of the developing free trade port. The proposed framework was constructed by integrating multi-temporal Sentinel-2 remote sensing images, night light remote sensing data, digital elevation model (DEM) data, and spectral index features such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), bare soil index (BSI), and normalized intertidal mangrove index (NIMI), as well as analytical approaches such as the land use transfer matrix, land use dynamic degree, land use degree and transfer matrix, land use gravity center measurement, and landscape pattern index. The framework takes advantage of the Google Earth Engine (GEE) cloud platform and was applied to a highly developed Haikou city, the capital of Hainan province. Maps of brightness (SBI), greenness (GVI), and humidity (WET) were created annually from 2016 to 2021, enabling detailed ecological environment quality evaluation and analysis. The advantages of this study are (1) reliable land cover results obtained automatically and quickly; (2) the strong objectivity of the quantitative research on landscape patterns and land use; and (3) deep integration with free trade port policies. Through the research on the ecological quality problems caused by the change in LULP in the study area, the research results show that, from 2016 to 2021, the spatial distribution of land use and landscape pattern in Haikou city had been constantly changing; the area of construction land has decreased, with most of it having been converted into forest land and farmland; the gravity center of the building land has moved to the northwest; the degree of landscape fragmentation has decreased and the heterogeneity of landscape distribution has increased; the free trade port policies have promoted Haikou’s economic development and ecological civilization construction; and finally, Haikou’s ecological environmental quality has improved significantly. Full article

Alpine grassland is one of the most fragile and sensitive ecosystems, and it serves as a crucial ecological security barrier on the Tibetan Plateau. Due to the combined influence of climate change and human activities, the degradation of the alpine grassland in Gannan Prefecture has been increasing recent years, causing increases in ecological risk (ER) and leading to the grassland ecosystem facing unprecedented challenges. In this context, it is particularly crucial to construct a potential grassland damage index (PGDI) and assessment framework that can be used to effectively characterize the damage and risk to the alpine grassland ecosystem. This study comprehensively uses multi-source data to construct a PGDI based on the grassland resilience index, landscape ER index, and grass–livestock balance index. Thereafter, we proposed a feasible framework for assessing the comprehensive ER of alpine grassland and analyzed the responsive relationship between the comprehensive ER and comprehensive ecosystem services (ESs) of the grassland. There are four findings. The first is that the comprehensive ER of the alpine grassland in Gannan Prefecture from 2000–2020 had a low distribution in the southeast and a high distribution trend in the northwest, with medium risk (29.27%) and lower risk (27.62%) dominating. The high-risk area accounted for 4.58% and was mainly in Lintan County, the border between Diebu and Zhuoni Counties, the eastern part of Xiahe County, and the southwest part of Hezuo. Second, the comprehensive ESs showed a pattern of low distribution in the northwest and high distribution in the southeast. The low and lower services accounted for only 9.30% of the studied area and were mainly distributed in the west of Maqu County and central Lintan County. Third, the Moran’s index values for comprehensive ESs and ER for 2000, 2005, 2010, 2015, and 2020 were −0.246, −0.429, −0.348, −0.320, and −0.285, respectively, thereby indicating significant negative spatial autocorrelation for all aspects. Fourth, ER was caused by the combined action of multiple factors. There are significant differences in the driving factors that affect ER. Landscape index is the first dominant factor affecting ER, with q values greater than 0.25, followed by DEM and NDVI. In addition, the interaction between diversity index and NDVI had the greatest impact on ER. Overall, this study offers a new methodological framework for the quantification of comprehensive ER in alpine grasslands. Full article

In Indonesia, where the agricultural insurance system has been in full operation since 2016, a new damage assessment estimation formula for rice diseases was created through integrating the current damage assessment method and unmanned aerial vehicle (UAV) multispectral remote sensing data to improve the efficiency and precision of damage assessment work performed for the payments of insurance claims. The new method can quickly and efficiently output objective assessment results. In this study, UAV images and bacterial leaf blight (BLB) rice damage assessment data were acquired during the rainy and dry seasons of 2021 and 2022 in West Java, Indonesia, where serious BLB damage occurs every year. The six-level BLB score (0, 1, 3, 5, 7, and 9) and damage intensity calculated from the score were used as the BLB damage assessment data. The relationship between normalized UAV data, normalized difference vegetation index (NDVI), and BLB score showed significant correlations at the 1% level. The analysis of damage intensities and UAV data for paddy plots in all cropping seasons showed high correlation coefficients with the normalized red band, normalized near-infrared band, and NDVI, similar to the results of the BLB score analysis. However, for paddy plots with damage intensities of 70% or higher, the biased numbering of the BLB score data may have affected the evaluation results. Therefore, we conducted an analysis using an average of 1090 survey points for each BLB score and confirmed a strong relationship, with correlation coefficients exceeding 0.9 for the normalized red band, normalized near-infrared band, and NDVI. Through comparing the time required by the current assessment method with that required by the assessment method integrating UAV data, it was demonstrated that the evaluation time was reduced by more than 60% on average. We are able to propose a new assessment method for the Indonesian government to achieve complete objective enumeration. Full article

Urban vegetation has an essential role in maintaining the hydrological and energy balance. These processes in urban areas have been long overlooked due to the fragmentation and uneven feature of land use and vegetation distribution. Recent advances in remote sensing and the ease of data acquisition have allowed a more precise mapping of vegetation and land cover, making it possible to simulate the above processes at micro scales. This research selects a small typical residential catchment in Japan as the study area and the purpose of this research is to investigate the impact of urban vegetation on mitigating urban runoff and the heat island effect. The remote-sensed Normalized Difference Vegetation Index (NDVI) data were used to represent vegetation spatial distribution and seasonal variation. A single layer canopy model and the Storm Water Management Model were coupled to simulate interception, evapotranspiration, and runoff generation processes. The effects of vegetation amount and landscape patterns on the above processes were also considered. The results showed that the coupled model had a satisfactory performance in the modeling of these processes. When the vegetation amount was set to 1.4 times its original value, the summer total runoff had a 10.7% reduction and the average surface temperature had a 2.5 °C reduction. While the vegetation amount was 0.8 times its original value, the total runoff increased by 6%, and the average surface temperature in summer increased by 1.5 °C. The combination of green roof and dense street trees showed the best mitigation performance among the different landscape patterns. The results of this study could be used as a reference for future green infrastructure development in areas with similar climate and vegetation characteristics. Full article

Two species of Psilochalcis wasps (P. minuta and P. quadratis) were recently described from Utah’s eastern Great Basin. The extent of their known distributions is extremely limited, based on few data points. We developed species distribution models (SDMs) using Maxent modeling software for each Psilochalcis species to identify areas of probable suitable habitat for targeted collecting to improve our knowledge of their distributions. We used six occurrence data points for P. minuta and eight occurrence data points for P. quadratis, along with ten environmental variables as inputs into the Maxent modeling software. Model-predicted areas with a potential suitable habitat value greater than 0.69 were mapped using ArcGIS Pro to help select locations for model accuracy assessment. Employing Malaise traps, eighteen sites were sampled to evaluate each SDM’s ability to predict the occurrence of Psilochalcis species. Psilochalcis minuta occurred at eight of nine juniper-dominated sample sites that were predicted as having high suitability by the model for this species. Likewise, P. quadratis occurred at two of four cheatgrass-dominated sample sites predicted by the model. Psilochalcis minuta occurred at three of nine sampled sites that were not predicted by the model, and P. quadratis occurred at seven of fourteen non-predicted sites. The Maxent SDM results yielded an AUC value of 0.70 and p-value of 0.02 for P. minuta and 0.68 and 0.02. for P. quadratis. These results were reflected in our model accuracy assessment. Of the selected environmental variables, aspect, historic fire disturbance, and elevation yielded the greatest percent contributions to both species’ models. Sympatric distributions were observed for P. minuta and P. quadratis. Elevation, vegetation type, NDVI, and soil type are the most important environmental variables in differentiating areas of optimal suitable habitat for the two species. Full article

Identifying impact factors and spatial variability of pollutants is essential for understanding environmental exposure and devising solutions. This research focused on PM_2.5 as the target pollutant and developed land use regression models specific to the Shenyang metropolitan area in 2020. Utilizing the Least Absolute Shrinkage and Selection Operator approach, models were developed for all seasons and for the annual average, explaining 62–70% of the variability in PM_2.5 concentrations. Among the predictors, surface pressure exhibited a positive correlation with PM_2.5 concentrations throughout most of the year. Conversely, both elevation and tree cover had negative effects on PM_2.5 levels. At a 2000 m scale, landscape aggregation decreased PM_2.5 levels, while at a larger scale (5000 m), landscape splitting facilitated PM_2.5 dispersion. According to the partial R² results, vegetation-related land use types were significant, with the shrubland proportion positively correlated with local-scale PM_2.5 concentrations in spring. Bare vegetation areas were the primary positive factor in autumn, whereas the mitigating effect of tree cover contrasted with this trend, even in winter. The NDVI, an index used to assess vegetation growth, was not determined to be a primary influencing factor. The findings reaffirm the function of vegetation cover in reducing PM_2.5. Based on the research, actionable strategies for PM_2.5 pollution control were outlined to promote sustainable development in the region. Full article