Search Results (1,488)

The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to quantitatively assess the impact of natural and human factors, such as temperature, precipitation, soil type, and land use, on vegetation growth. It aims to reveal the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau over the past 26 years. The results indicate that from 1995 to 2020, the vegetation coverage on the Loess Plateau shows an increasing trend, with a fitted slope of 0.01021 and an R² of 0.96466. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use. The highest average vegetation coverage is achieved when the temperature is between −4.8 and 2 °C or 12 and 16 °C, precipitation is between 630.64 and 935.51 mm, the soil type is leaching soil, and the land use type is forest. And the interaction between all factors has a greater effect on the vegetation cover than any single factor alone. This study reveals the factors influencing vegetation growth on the Loess Plateau, as well as their types and ranges, providing a scientific basis and guidance for improving vegetation coverage in this region. Full article

Grasslands are key components of land ecosystems, providing valuable ecosystem services and contributing to local carbon sequestration. Australian grasslands, covering approximately 70% of the continent, are vital for agriculture, pasture, and ecosystem services. Ongoing climate change introduces considerable uncertainties about the dynamic responses of different types of grasslands to changes in regional climate and its variation. This study, bringing together high-resolution meteorological data, calibrated long-term satellite NDVI data, and NPP and statistical models, investigated the spatiotemporal variability of NDVI and NPP and their predominant drivers (temperature and soil water content) across Australia’s grassland zones from 1992 to 2021. Results showed a slight, non-significant NDVI increase, primarily driven by improved vegetation in northern savannah grasslands (SGs). Areal average annual NPP values fluctuated annually but with a levelled trend over time, illustrating grassland resilience. NDVI and NPP measures aligned spatially, with values decreasing from the coastal to the inland regions and north to south. Most of the SGs experienced an increase in NDVI and NPP, boosted by abundant soil moisture and warm weather, which promoted vegetation growth and sustained a stable growing biomass in this zone. The increased NDVI and NPP in northern open grasslands (OGs) were linked to wetter conditions, while their decreases in western desert grasslands (DGs) were ascribed to warming and drier weather. Soil water availability was the dominant driver of grassland growth, with NDVI being positively correlated with soil water content but being negatively correlated with temperature across most grasslands. Projections under the SSP126 and SSP370 scenarios using ACCESS-ESM1.5 showed slight NPP increases by 2050 under warmer and wetter conditions, though western and southern grasslands may see declines in vegetation coverage and carbon storage. This study provides insights into the responses of Australian grasslands to climate variability. The results will help to underpin the design of sustainable grassland management strategies and practices under a changing climate for Australia. Full article

Although both prefectural governmental green investment (GGI_city) and provincial governmental green investment (GGI_prov) have potentially diverse impacts on prefectural cities’ carbon emission reduction (CER), previous studies have rarely examined the effects of governmental green investment (GGI) on different indicators of CER such as total carbon dioxide emissions (CE), carbon emissions intensity (CEI) and per capita carbon emissions (PCE) in the context of prefectural cities nested in provinces in China. In our research, six hierarchical linear models are established to investigate the impact of GGI_city and GGI_prov, as well as their interaction, on CER. These models consider eight control factors, including fractional vegetation coverage, nighttime light index (NTL), the proportion of built-up land (P_built), and so on. Furthermore, heterogeneous impacts across different groups based on provincial area, terrain, and economic development level are considered. Our findings reveal the following: (1) The three indicators of CER and GGI exhibit significant spatial and temporal variations. The coefficient of variation for CEI and PCE shows a fluctuating upward characteristic. (2) Both lnGGI_city and lnGGI_prov have promoted CER, but the impact strength of lnGGI_prov on lnCE and lnPCE is more pronounced than that of lnGGI_city. GGI_prov can strengthen the effect of GGI_city significantly for lnCE. Diverse control variables have exerted significant impacts on the three indicators of CER, albeit with considerable variation in their effects. (3) The effect of GGI on CER is significantly heterogeneous upon conducting grouped analysis by provincial area size, terrain complexity, and economic development level. The interaction term lnGGI_city:lnGGI_prov is stronger in the small provincial area group and simple terrain group. Among the control variables, economic Development Level (GDPpc), the logarithm of gross fixed assets investment (lnFAI), NTL, and P_built exhibit particularly pronounced differences across different groups. This study provides a robust understanding of the heterogeneous and interactive effects of GGI on CER, aiding in the promotion of sustainable development. Full article

Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content (LNC) and canopy spectral reflectance characteristics throughout the growth stages of processed tomatoes at the Laolong River Tomato Base in Changji City, Xinjiang. The experimental design incorporated nine treatments, each with three replicates. LNC data were obtained using a dedicated leaf nitrogen content analyzer, while drones were utilized to capture multispectral images for the extraction of vegetation indices. Through Pearson correlation analysis, the optimal spectral variables were identified, and the relationships between LNC and spectral variables were established using models based on backpropagation (BP), multiple linear regression (MLR), and random forests (RFs). The findings revealed that the manually measured LNC data exhibited two peak values, which occurred during the onset of flowering and fruit setting stages, displaying a bimodal pattern. Among the twelve selected vegetation indices, ten demonstrated spectral sensitivity, passing the highly significant 0.01 threshold, with the Normalized Difference Chlorophyll Index (NDCI) showing the highest correlation during the full bloom stage. The combination of the NDCI and RF model achieved a prediction accuracy exceeding 0.8 during the full bloom stage; similarly, models incorporating multiple vegetation indices, such as RF, MLR, and BP, also reached prediction accuracies exceeding 0.8. Consequently, during the seedling establishment and initial flowering stages (vegetation coverage of <60%), the RF model with multiple vegetation indices was suitable for monitoring LNC; during the full bloom stage (vegetation coverage of 60–80%), both the RF model with the NDCI and the MLR model with multiple indices proved effective; and during the fruit setting and maturation stages (vegetation coverage of >80%), the BP model was more appropriate. This research provides a scientific basis for the cultivation management of processed tomatoes and the optimization of nitrogen fertilization within precision agriculture. It advances the application of precision agriculture technologies, contributing to improved agricultural efficiency and resource utilization. Full article

The dynamics of vegetation changes and phenology serve as key indicators of interannual changes in vegetation productivity. Monitoring the changes in the Nanling grassland ecosystem using the remote sensing vegetation index is crucial for the rational development, utilization, and protection of these grassland resources. Grasslands in the hilly areas of southern China’s middle and low mountains have a high restoration efficiency due to the favorable combination of water and temperature conditions. However, the dynamic adaptation process of grassland restoration under the combined effects of climate change and human activities remains unclear. The aim of this study was to conduct continuous phenological monitoring of the Nanling grassland ecosystem, and evaluate its seasonal characteristics, trends, and the thresholds for grassland changes. The Normalized Difference Phenology Index (NDPI) values of Nanling Mountains’ grasslands from 2000 to 2021 was calculated using MOD09A1 images from the Google Earth Engine (GEE) platform. The Savitzky–Golay filter and Mann–Kendall test were applied for time series smoothing and trend analysis, and growing seasons were extracted annually using Seasonal Trend Decomposition and LOESS. A segmented regression method was then employed to detect the thresholds for grassland ecosystem restoration based on phenology and grassland cover percentage. The results showed that (1) the NDPI values increased significantly (p < 0.01) across all grassland patches, particularly in the southeast, with a notable rise from 2010 to 2014, and following an eastern to western to central trend mutation sequence. (2) the annual lower and upper NDPI thresholds of the grasslands were 0.005~0.167 and 0.572~0.727, which mainly occurred in January–March and June–September, respectively. (3) Most of the time series in the same periods showed increasing trends, with the growing season length varying from 188 to 247 days. (4) The overall potential productivity of the Nanling grassland improved. (5) The restoration of the mountain grasslands was significantly associated with the grassland coverage and mean NDPI values, with a key threshold identified at a mean NDPI value of 0.5 for 2.1% grassland coverage. This study indicates that to ensure the sustainable development and conservation of grassland ecosystems, targeted management strategies should be implemented, particularly in regions where human factors significantly influence grassland productivity fluctuations. Full article

Sinkholes are significant geohazards in karst regions that pose risks to landscapes and infrastructure by disrupting geological stability. Usually, sinkholes are mapped by field surveys, which is very cost-intensive with regard to vast coverages. One possible solution to derive sinkholes without entering the area is the use of high-resolution digital terrain models, which are also expensive with respect to remote areas. Therefore, this study focusses on the mapping of sinkholes in arid regions from open-access remote sensing data. The case study involves data from the Sentinel missions over the Mangystau region in Kazakhstan provided by the European Space Agency free of cost. The core of the technique is a multi-scale curvature filter bank that highlights sinkholes (and takyrs) by their very special illumination pattern in Sentinel-2 images. Marginal confusions with vegetation shadows are excluded by consulting the newly developed Combined Vegetation Doline Index based on Sentinel-1 and Sentinel-2. The geospatial analysis reveals distinct spatial correlations among sinkholes, takyrs, vegetation, and possible surface discharge. The generic and, therefore, transferable approach reached an accuracy of 92%. However, extensive reference data or comparable methods are not currently available. Full article

(1) Background: Global warming has intensified dry heatwaves, threatening urban tree health and ecosystem services. Crown damage in trees is a key indicator of heat stress, linked to physiological changes and urban habitat characteristics, but the specific mechanisms remain to be explored. (2) Methods: This study investigated the heatwave-induced crown damage of Wuhan’s urban tree species, focusing on the influence of physiological responses and urban habitats. Crown damage was visually scored, and physiological responses were measured via stomatal conductance (Gs) and transpiration rate (Tr). (3) Results: Significant interspecific differences in crown damage were identified, with Prunus × yedoensis showing the highest degree of crown damage, while Pittosporum tobira displayed the lowest. A strong correlation was observed between crown damage and Gs and Tr, albeit with species-specific variations. The Degree of Building Enclosure (DegBE) emerged as the most prominent habitat factor, with a mitigating effect on crown damage, followed by the Percentage of Canopy Coverage (PerCC), in contrast with the Percentage of Impermeable Surface (PerIS) that showed a significant positive correlation. (4) Conclusions: The above findings suggest that species traits and habitat configurations interact in complex ways to shape tree resilience under heatwave stress, informing strategies for urban vegetation protection against heat stress in Central Chinese cities. Full article

Introducing vegetation is an effective strategy for improving air quality and mitigating the heat island effect. Green modules, which consist of modules that support substrates and various plant species, integrate these elements. This study analyzes the acoustic absorption properties of a specific green wall module using an impedance gun and the Scan and Paint method for laboratory and on-site measurements. The impedance gun method is effective for in situ analysis, offering advantages over standardized techniques for inhomogeneous samples. The sound absorption coefficient of the substrate and the effects of different plant species were measured. Key findings reveal that the substrate primarily influences sound absorption, with its coefficient increasing with frequency, similar to porous materials. Vegetation enhances the acoustic absorption of the substrate, depending on coverage and thickness, with 80–90% of absorption attributed to the substrate and 4–20% to vegetation. However, not all dense plant species improve absorption; some configurations may decrease it. Improvement correlates with substrate coverage and vegetation layer thickness, while the impact of plant morphology remains unclear. These findings confirm vegetation’s potential as an acoustic absorption tool in urban settings. Additionally, green walls can enhance acoustic comfort in indoor environments such as offices and schools by reducing reverberation. They also improve air quality and provide aesthetic appeal, making them a multifunctional solution for modern architecture. Full article

The spatial transportation of pesticide spray droplets and their deposition and retention on plant leaf surfaces are critical factors contributing to pesticide loss. Adding adjuvants to pesticide solutions to improve their wettability and deposition behavior can enhance the targeted deposition efficiency of pesticides sprayed by unmanned aerial vehicle (UAV) sprayers. In this study, Maifei(MF), a prevalent vegetable oil adjuvant, was selected to analyze its effects on the physicochemical properties of water and 10% difenoconazole water-dispersible granules (D) and the wetting performance of droplets on litchi leaves. The changes in the drift and deposition of the spray solutions with or without MF were tested using a UAV sprayer, DJI T40. The results indicated that the addition of MF to water or D significantly decreased the surface tension (by 58.33% and 23.10%, respectively), wetting time (by 97.81% and 90.95%, respectively), and contact angle (by 40.95% to 70.75% for the adaxial and abaxial surfaces of litchi leaves), achieving the best effects at a 1% MF addition. Moreover, during the drift test, the addition of 1% MF to the solutions significantly reduced the cumulative drift rate (CDR) (by 48.10%). Finally, owing to the weakened spray drift risk and improved wettability of the droplets on litchi leaves with a 1% MF addition, the droplet deposition and penetration in the litchi canopy significantly improved, demonstrating an increased droplet density of 38.17% for the middle layers of the litchi and 15.75% for the lower layers, corresponding to increased coverage by 59.49% and 12.78%, respectively. Hence, MF can improve the interfacial properties of the spray solution on litchi leaves, reduce the drift risk, and promote deposition, thereby facilitating the efficient transfer and deposition of pesticide droplets from UAV sprayers. Full article

As the largest mountain range in Southern China, the natural vegetation of Nanling plays an irreplaceable role in maintaining the stability of the ecosystem and exerting its functions. The forested area of the Nanling Corridor encompasses 168,633 km², with a forest coverage rate exceeding 60% of all cities together. Long-term analysis of the temporal and spatial evolution of this forest and the disturbance factors in this region is of great importance for realizing the “dual carbon” goals, sustainable forest management, and protecting biodiversity. In this study, remote sensing images from a Landsat time series with a resolution of 30 m were obtained from the GEE (Google Earth Engine) cloud processing platform, and forest disturbance data were obtained using the LandTrendr algorithm. Using a machine learning random forest algorithm, the forest disturbance status and disturbance factors were explored from 2001 to 2020. The results show that the estimated disturbed forest area from 2001 to 2020 was 11,904.3 km², accounting for 7.06% of the total area of the 11 cities in the Nanling Corridor, and the average annual disturbed area was 595.22 km². From 2001 to 2016, the overall disturbed area increased, reaching a peak value of 1553.36 km² in 2008, with a low value of 37.71 km² in 2002. After 2016, the disturbed area showed a downward trend. In this study, an attribution analysis of forest disturbance factors was carried out. The results showed that the overall accuracy of forest disturbance factor attribution was as high as 82.48%, and the Kappa coefficient was 0.70. Among the disturbance factors, deforestation factors accounted for 58.45% of the total area of forest disturbance, followed by fire factors (28.69%) and building or road factors (12.85%). The regional distribution of each factor also had significant characteristics, and the Cutdown factors were mostly distributed in the lower elevations of the mountain margin, with most of them distributed in sheets. The fire factors were spatially distributed in the center of the mountains, and their distribution was loose. Building or road factors were mostly distributed in clusters or lines. These research results are expected to provide technical and data support for the study of the large-scale spatiotemporal evolution of forests and its driving mechanisms. Full article

This study explores the dynamics of urban green spaces in five major Indonesian cities—Central Jakarta, Bandung, Yogyakarta, Surabaya, and Semarang—using Sentinel-2 satellite imagery and vegetation indices, such as NDVI and EVI. As major urban areas expand and become more densely populated, development activities have significantly altered urban green spaces, necessitating comprehensive mapping through remote sensing technologies. The findings reveal significant variability in green space coverage among the cities over three periods (2019–2020, 2021–2022, 2023–2024), ensuring that the findings are comprehensive and up to date. This study demonstrates the utility of remote sensing for detailed urban analysis, emphasizing its effectiveness in identifying, quantifying, and monitoring changes in green spaces. Integrating advanced techniques, such as NDVI and EVI, offers a nuanced understanding of urban vegetation dynamics and their implications for sustainable urban planning. Utilizing Sentinel-2 data within the Google Earth Engine (GEE) framework represents a contemporary and innovative approach to urban studies, particularly in rapidly urbanizing environments. The novelty of this research lies in its method of preserving and enhancing green infrastructure while supporting the development of effective strategies for sustainable urban growth. Full article

In order to improve the current standard of analysis-ready Synthetic Aperture Radar (SAR) backscatter data, we introduce a machine learning-based approach to estimate the slope of the backscatter–incidence angle relationship from several backscatter statistics. The method requires information from radiometric terrain-corrected gamma nought time series and overcomes the constraints of a limited orbital coverage, as exemplified with the Sentinel-1 constellation. The derived slope estimates contain valuable information on scattering characteristics of different land cover types, allowing for the correction of strong forward-scattering effects over water bodies and wetlands, as well as moderate surface scattering effects over bare soil and sparsely vegetated areas. Comparison of the estimated and computed slope values in areas with adequate orbital coverage shows good overall agreement, with an average RMSE value of 0.1 dB/° and an MAE of 0.05 dB/°. The discrepancy between RMSE and MAE indicates the presence of outliers in the computed slope, which are attributed to speckle and backscatter fluctuations over time. In contrast, the estimated slope excels with a smooth spatial appearance. After correcting backscatter values by normalising them to a certain reference incidence angle, orbital artefacts are significantly reduced. This becomes evident with differences up to 5 dB when aggregating the normalised backscatter measurements over certain time periods to create spatially seamless radar backscatter composites. Without being impacted by systematic differences in the illumination and physical properties of the terrain, these composites constitute a valuable foundation for land cover and land use mapping, as well as bio-geophysical parameter retrieval. Full article

Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments. Full article

Vegetation coverage in the upper reaches of the Yangtze River is very important to the ecological balance in this area, and it also has an impact on the inflow runoff and sediment transport processes of the Three Gorges Reservoir. Based on the normalized vegetation index data (NDVI) with 250 m resolution in the upper reaches of the Yangtze River, annual runoff, sediment transport, land use, meteorology, and other data—and by using the methods of Sen + Mann–Kendall trend analysis, partial correlation analysis, and Hurst index—this paper analyzes the temporal and spatial variation characteristics, driving factors, and the influence on the water and sediment inflow processes of the Three Gorges Reservoir in each sub-basin in the upper reaches of the Yangtze River. The results show that (1) NDVI in the upper Yangtze River showed a fluctuating upward trend from 2001 to 2022, and the overall vegetation cover continued to increase, showing a spatial pattern of low in the west and high in the east. At the same time, the runoff volume of the upper reaches of the Yangtze River did not show a significant upward trend from 2006 to 2022, while the sand transport decreased significantly; (2) Among the NDVI-influencing factors in the upper reaches of the Yangtze River, the area driven by the land use factor accounts for about 43% of the whole study area, followed by precipitation; (3) Precipitation significantly affected runoff, and NDVI was negatively correlated with sand transport in most of the watersheds, suggesting that improved vegetation could help reduce sediment loss. In addition, the future trend of vegetation change was predicted to be dominated by improvement (Hurst > 0.5) based on the Hurst index, which will provide a reference for the NDVI change in the upper Yangtze River and the prediction of sediment inflow to the Three Gorges Reservoir. Full article

Biomass is a vital index used to evaluate the vegetation rebuilding effect of mining slopes after restoration. It is essential to establish models for estimating the biomass and carbon storage of the vegetation community on mining slopes. Therefore, this paper establishes models for the biomass and carbon storage of such vegetation, taking an abandoned quarry after ecological restoration in Yixing City, Jiangsu Province, as the research object. Firstly, the variables of the biomass estimation models were determined based on the correlation analysis results; the vegetation biomass model was comprehensively selected, and the accuracy of the optimal models was verified. Meanwhile, the carbon storage calculation model was established in combination with the carbon content and the growth pattern of vegetation. The results showed that (1) the optimal models were the cubic and linear functions, respectively, for the shrubs and herbs, while the relevant variables of the shrub and the herb plants were the average height multiplied by the diameter of each shrub plant (DH) and the average height multiplied by the coverage rate (CH), respectively, with the verification results of R² > 0.814, R_S > 2.8%, and R_MA > 6%; and (2) in the restored mining slopes, the vegetation biomass was 120.264 t, including 10.586 t of herbs and 109.678 t of shrubs, and the vegetation carbon storage was 50.585 t, including 3.705 t of herbs and 46.880 t of shrubs. The proposed models have good prediction accuracy and reliability after quantitative evaluation and can be applied to the biomass estimation and carbon storage calculation of restored mining slopes, providing a reference for the environmental sustainability of post-mining areas and other ecologically restored slopes. Full article