Search Results (4,947)

Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving forest management. This study investigates how multi-source remote sensing data can be used to provide accurate estimations of diameter at breast height (DBH) at the plot level, enhancing biomass estimations across 39.41 × 10⁴ km². The study is focused on Yunnan Province, China, which is characterized by complex terrain and diverse vegetation. Using ground-based survey data from hundreds of plots for model calibration and validation, the methodology combines multi-source remote sensing data, machine learning algorithms, and statistical analysis to develop models for estimating DBH distribution at regional scales. Decision tree showed the best overall performance. The model effectiveness improved when stratified by climatic zones, highlighting the importance of environmental context. Traditional methods based on the kNDVI index had a mean squared error (MSE) of 2575 t/ha and an R² value of 0.69. In contrast, combining model-estimated DBH values with remote sensing data resulted in a substantially lower MSE of 212 t/ha and a significantly improved R² value of 0.97. The results demonstrate that incorporating DBH not only reduced prediction errors but also improved the model’s ability to explain biomass variability. In addition, climatic region classification further increased model accuracy, suggesting that future efforts should consider environmental zoning. Our analyses indicate that water availability during cool and dry periods in this monsoon-influenced region was especially critical in influencing DBH across different subtropical zones. In summary, the study integrates DBH and high-resolution remote sensing data with advanced algorithms for accurate biomass estimation. The findings suggest that this approach can support regional forest management and contribute to research on carbon balance and ecosystem assessment. Full article

Climate change is driving the restructuring of global biological communities. As a species sensitive to climate change, studying the response of small rodents to climate change is helpful to indirectly understand the changes in ecology and biodiversity in a certain region. Here, we use the MaxEnt (maximum entropy) model to predict the distribution patterns, main influencing factors, and range changes of various small rodents in the Ordos desert steppe in China under different climate change scenarios in the future (2050s: average for 2041–2060). The results show that when the parameters are FC = LQHPT, and RM = 4, the MaxEnt model is optimal and AUC = 0.833. We found that NDVI (normalized difference vegetation index), Bio 12 (annual precipitation), and TOC (total organic carbon) are important driving factors affecting the suitability of the small rodent habitat distribution in the region. At the same time, the main influencing factors were also different for different rodent species. We selected 4 dominant species for analysis and found that, under the situation of future climate warming, the high-suitability habitat area of Allactaga sibirica and Phodopus roborovskii will decrease, while that of Meriones meridianus and Meriones unguiculatus will increase. Our research results suggest that local governments should take early preventive measures, strengthen species protection, and respond to ecological challenges brought about by climate change promptly. Full article

Soil moisture content is typically measured in situ using various instruments; however, due to the heterogeneous nature of soil, these measurements are only suitable at a very local scale. To overcome this limitation, earth observation satellite remote sensing data, particularly through the inversion of the closure phases of interferometric synthetic aperture radar (InSAR) observations, enables the determination of soil moisture content at regional to global scales. Here, we present, for the first time, a regional-scale study of soil moisture determined from remote sensing observations of Malawi, specifically, two areas of interest capturing arable and national parklands in Kasungu and Liwonde. We invert the closure phases of InSAR acquisitions from Sentinel-1 between 1 January 2023 and 31 May 2024 to measure the soil moisture content in the same time range. We show that soil moisture content is heavily influenced by local precipitation and highlight common trends in soil moisture in both regions. We suggest the difference in soil moisture observed inside and outside the national parks is a result of different overlying vegetation and conservation agriculture practices during the maize crop cycle in Malawi. Our results show the effectiveness and suitability of remote sensing techniques to monitor soil moisture at a regional scale. The upcoming additions to ESA’s fleet of earth observation satellites, in particular Sentinel-1C, will allow for higher-time-resolution soil moisture measurements. Full article

In the current global change scenario, valuable tools for improving soils and increasing both agricultural productivity and food security, together with effective actions to mitigate the impacts of ongoing climate change trends, are priority issues. Soil Organic Carbon (SOC) acts on these two topics, as C is a core element of soil organic matter, an essential driver of soil fertility, and becomes problematic when disposed of in the atmosphere in its gaseous form. Laboratory methods to measure SOC are expensive and time-consuming. This Systematic Literature Review (SLR) aims to identify techniques and alternative ways to estimate SOC using Remote-Sensing (RS) spectral data and computer tools to process this database. This SLR was conducted using Systematic Review and Meta-Analysis (PRISMA) methodology, highlighting the use of Deep Learning (DL), traditional neural networks, and other machine-learning models, and the input data were used to estimate SOC. The SLR concludes that Sentinel satellites, particularly Sentinel-2, were frequently used. Despite limited datasets, DL models demonstrated robust performance as assessed by R2 and RMSE. Key input data, such as vegetation indices (e.g., NDVI, SAVI, EVI) and digital elevation models, were consistently correlated with SOC predictions. These findings underscore the potential of combining RS and advanced artificial-intelligence techniques for efficient and scalable SOC monitoring. Full article

Interdisciplinary research has significantly advanced our understanding, benefits, and measurements of Urban Green Space (UGS). Further, the rapid expansion of research on this topic has resulted in a diverse array of definitions, which can rely on implicit assumptions without a formal definition. This variability highlights the need for a carefully structured framework to refine and combine these definitions. This narrative review examines constructs underlying UGS, particularly focusing on the spatial aspects of how we spatially measure UGS, the measurements of UGS, and how we define exposure; the latter focuses on two methods: viewsheds and image segmentation. Our findings reveal a shift in UGS measurement focus, moving beyond simple quantification of how much green space exists, to incorporate visibility, accessibility, and availability dimensions. Furthermore, advancements in computational tools, including artificial intelligence-driven methods, now enable high-resolution visibility measurements on a city-wide scale, supporting epidemiological research and urban development. These insights aim to guide researchers and practitioners in selecting suitable methodologies and datasets, as well as explicitly defining UGS in their work through a construct-based approach. Full article

Widespread dieback of Fagus sylvatica has been observed in several areas of Sicily (Italy) in recent decades, often associated with Biscogniauxia nummularia infections. However, the primary drivers of this decline remain debated, with climate change increasingly recognized as a key factor not only in exacerbating tree physiological stress but also in enhancing susceptibility to pathogens. This study addresses this gap by quantifying the impact of climate change on beech decline in the Nebrodi Regional Park using an integrated approach that combines climate reanalysis data (ERA5-Land) and remote sensing (Landsat imagery). Analysis of climatic trends between two climate normals (1961–1990 and 1991–2020) revealed significant increases in temperature, evapotranspiration, and solar radiation, coupled with a decline in relative humidity. NDVI trends indicate a progressive loss of beech vigor since 2009, strongly correlated with decreasing soil moisture and precipitation. Although forest cover has expanded, this does not necessarily indicate improved forest health, as persistent climate stress may compromise tree vitality and increase vulnerability to secondary pathogens such as B. nummularia. These findings highlight the need for adaptive forest management strategies, including selective thinning and species diversification, to enhance resilience against climate change. Future research should prioritize high-resolution satellite imagery (e.g., Sentinel-2) and in situ physiological measurements (e.g., leaf water potential and sap flow) to refine early detection of climate-induced stress and improve conservation strategies for Mediterranean beech forests. Full article

Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms^–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms^–1. Increased sensing speeds (0.4–0.5 ms^–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms^–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring. Full article

Heritage is the commonwealth of all humankind. In the context of the rise in global tourism and the continuous deepening of cultural and tourism integration, tourism has emerged as an effective vehicle for the preservation and development of heritage sites. However, it also imposes adverse effects on the local ecological environment and heritage sites, exerting significant pressure on regional sustainable development. In this study, three cities along Jianmen Shu Road were selected as the study area. A comprehensive evaluation index system was developed for tourism ecological security (TES) based on the Driver–Pressure–State–Impact–Response model, and an in-depth analysis of its spatial–temporal evolution characteristics, spatial–temporal migration trends, and influencing factors was performed. The results show that (1) from 2012 to 2022, the average TES in the study area decreased annually, while it increased in Jiange County, Anzhou District, and Santai County. The TES indices were generally higher in areas with a high density of heritage sites or developed economies. Additionally, the districts and counties along the Jianmen Shu Road route never exhibited a deteriorated state. (2) From 2012 to 2022, TES in the study area exhibited an obvious “northeast–southwest” directional pattern, and its center of gravity followed a “V”-shaped trajectory. Overall, the spatial patterns showed minimal variation and exhibited agglomeration characteristics. (3) From 2012 to 2022, the main factors influencing TES included the density of Jianmen Shu Road heritage sites (S6), the number of 3A and above scenic areas (S5), the proportion of cultural tourism and sports in total expenditure (R3), the Normalized Difference Vegetation Index (NDVI) (S4), and other tourism and environmental factors. Moreover, TES systems are becoming increasingly complex and diverse. Finally, based on the results, a comprehensive conceptual framework of the driving mechanism was developed. Additionally, four targeted and scientifically grounded policy recommendations were formulated for restoring, protecting, and managing the TES in the Jianmen Shu Road Heritage Area. This study provides significant reference value for ecological environment preservation and the high-quality development of cultural tourism integration in heritage areas. Full article

This study analyzed the spatiotemporal changes in grassland NDVI from 2000 to 2020 in the eastern route of the China–Mongolia–Russia Economic Corridor, a region with frequent ecological–economic interactions, and explained the main driving factors, influencing patterns, and degrees of grassland NDVI changes in different regions. Based on MODIS NDVI data, the study employs emerging spatiotemporal hotspot analysis, Maximum Relevance Minimum Redundancy (mRMR) feature selection, and Gaussian Process Regression (GPR) to reveal the spatiotemporal variation characteristics of grassland NDVI, while identifying long-term stable trends, and to select the most relevant and non-redundant factors to analyze the main driving factors of grassland NDVI change. Partial dependence plots were used to visualize the response and sensitivity of grassland NDVI to various factors. The results show the following: (1) From 2000 to 2020, the NDVI of grassland in the study area showed an overall upward trend, from 0.61 to 0.65, with significant improvement observed in northeastern China and northeastern Russia. (2) Spatiotemporal hotspot analysis indicates that 51% of the area is classified as persistent hotspots for grassland NDVI, mainly distributed in Russia, whereas 12% of the area is identified as persistent cold spots, predominantly located in Mongolia. (3) The analysis of key drivers reveals that precipitation and land surface temperature are the dominant climatic factors shaping grassland NDVI trends, while the effects of soil conditions and human activity vary regionally. In China, NDVI is primarily driven by land surface temperature (LST), GDP, and population density; in Mongolia, precipitation, LST, and GDP exert the strongest influence; whereas in Russia, livestock density and soil organic carbon play the most significant roles. (4) For the whole study area, in persistent cold spot areas of grassland NDVI, the negative effects of rising land surface temperature were most pronounced, reducing NDVI by 36% in the 25–40 °C range. The positive effects of precipitation on NDVI were most evident under low to moderate precipitation conditions, with the effects diminishing as precipitation increased. Soil moisture and soil pH have stronger effects in persistent hotspot areas. Regarding human activity factors, the livestock factor in Mongolia shows an inverted U-shaped relationship with NDVI, and increasing population density contributed to grassland degradation in persistent cold spots. Proper grazing intensity regulation strategy is crucial in these areas with inappropriate grazing intensity, while social and economic activities promoted vegetation cover improvement in persistent hotspots in China and Russia. These findings provide practical insights to guide grassland ecosystem restoration and ensure sustainable development along the eastern route of the China–Mongolia–Russia Economic Corridor. China should prioritize ecological compensation policies. Mongolia needs to integrate traditional nomadic grazing with modern practices. Russia should focus on strengthening regulatory frameworks to prevent the over-exploitation of grasslands. Especially for persistent cold spot areas of grassland NDVI in Mongolia and Russia that are prone to grassland degradation, attention should be paid to the significant negative impact of livestock on grassland. Full article

Field studies and satellite imagery were conducted on an oat cultivation field located on sandy soil with significant spatial heterogeneity in southwestern Poland. Observations and field measurements were carried out during the BBCH growth stages 12, 31, 49, 77, and 99 at 40 points each. Satellite images were acquired at specific intervals, and selected remote sensing indices (NDVI, GNDVI, SAVI, EVI, NDMI, MCARI) were calculated to investigate possibility of early detection of nitrogen demand at the early stage of oat development. The results of this study confirmed that sandy soils, characterized by limited water and nutrient capacity, require a specialized approach to resource management. The selected remote sensing indices provided an effective method for monitoring oat canopy variability in real time. At BBCH 12 growing stage, the highest correlations with plant density were shown by NDVI, SAVI, GNDVI, and EVI. The correlation coefficients ranged from 0.38 to 0.56, with a significance level of ≤0.01, which indicates their usefulness for monitoring crop emergency and early development. At early growing stage (BBCH 31–34), GNDVI was significantly correlated with the final nitrogen uptake (r = 0.44, p < 0.01) and biomass yield of oat (r = 0.39, p = 0.01). This suggests that the GNDVI index is particularly useful for predicting the final nitrogen uptake and biomass yield of oat. It offers a reliable estimation of the plant’s nitrogen status and its potential for nitrogen absorption, allowing for fertilization management at this critical stage. Full article

This study explores the application of remote sensing-based land cover change detection techniques to identify and map areas affected by three distinct wildfire events that occurred in Mediterranean islands between 2019 and 2022, namely Sardinia (2019, Italy), Thassos (2022, Greece), and Pantelleria (2022, Italy). Applying Rao’s Q Index-based change detection approach to Sentinel-2 spectral data and derived indices, we evaluate their effectiveness and accuracy in identifying and mapping burned areas affected by wildfires. Our methodological approach implies the processing and analysis of pre- and post-fire Sentinel-2 imagery to extract relevant indices such as the Normalized Burn Ratio (NBR), Mid-infrared Burn Index (MIRBI), Normalized Difference Vegetation Index (NDVI), and Burned area Index for Sentinel-2 (BAIS2) and then use (the classic approach) or combine them (multidimensional approach) to detect and map burned areas by using a Rao’s Q Index-based change detection technique. The Copernicus Emergency Management System (CEMS) data were used to assess and validate all the results. The lowest overall accuracy (OA) in the classical mode was 52%, using the BAIS2 index, while in the multidimensional mode, it was 73%, combining NBR and NDVI. The highest result in the classical mode reached 72% with the MIRBI index, and in the multidimensional mode, 96%, combining MIRBI and NBR. The MIRBI and NBR combination consistently achieved the highest accuracy across all study areas, demonstrating its effectiveness in improving classification accuracy regardless of area characteristics. Full article

Each of the NDVI, EVI, NIRv, and kNDVI has varying strengths and weaknesses in terms of representing vegetation dynamics. Identifying the comparative advantages of these indices is crucial to objectively determine the dynamics of vegetation in dryland. In this study, Central Asia was selected as the research area, which is a typical drought-sensitive and ecologically fragile region. The Mann–Kendall trend test, coefficient of variation, and partial correlation analyses were used to compare the ability of these indices to express the spatiotemporal dynamics of vegetation, its heterogeneity, and its relationships with temperature and precipitation. Moreover, the composite vegetation index (CVI) was constructed by using the entropy weighting method and its relative advantage was identified. The results showed that the kNDVI exhibited a stronger capacity to express the relationship between the vegetation and the temperature and precipitation, compared with the other three indices. The NIRv best represented the spatiotemporal heterogeneity of vegetation in areas with a high vegetation coverage, while the kNDVI had the strongest expressive capability in areas with a low vegetation coverage. The critical value for distinguishing between areas with a high and low vegetation coverage was NDVI = 0.54 for temporal heterogeneity and NDVI = 0.50 for spatial heterogeneity. The CVI had no apparent comparative advantage over the other four indices in expressing the trends of changes in vegetation coverage and their correlations with the temperature and precipitation. However, it enjoyed a prominent advantage over these indices in terms of expressing the spatiotemporal heterogeneity of vegetation coverage in Central Asia. Full article

Surface soil moisture (SSM) plays a pivotal role various fields, including agriculture, hydrology, water environment, and meteorology. To investigate the impact of land use types and fractional vegetation cover (FVC) on the accuracy of SSM estimation, this study conducted a comprehensive analysis of SSM estimation performance across diverse land use scenarios (e.g., multiple land use combinations and cropland) and varying FVC conditions. Sentinel-2 NDVI and MOD09A1 NDVI were fused by the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) to obtain NDVI with a temporal resolution better than 8 d and a spatial resolution of 20 m, which improved the matching degree between NDVI and the Sentinel-1 backscattering coefficient (σ⁰). Based on the σ⁰, NDVI, and in situ SSM, combined with the water cloud model (WCM), the SSM estimation model is established, and the model of each land use and FVC is validated. The model has been applied in Handan. The results are as follows: (1) Compared with vertical–horizontal (VH) polarization, vertical–vertical (VV) polarization is more sensitive to soil backscattering (${\mathsf{\sigma }}_{\mathrm{soil}}^0$). In the model for multiple land use combinations (Multiple-Model) and the model for the cropland (Cropland-Model), the R² increases by 0.084 and 0.041, respectively. (2) The estimation accuracy of SSM for the Multiple-Model and Cropland-Model is satisfactory (Multiple-Model, RMSE = 0.024 cm³/cm³, MAE = 0.019 cm³/cm³, R² = 0.891; Cropland-Model, RMSE = 0.023 cm³/cm³, MAE = 0.018 cm³/cm³, R² = 0.886). (3) When the FVC > 0.75, the accuracy of SSM by the WCM is low. It is suggested the model should be applied to the cropland where the FVC ≤ 0.75. This study clarified the applicability of SSM estimation by microwave remote sensing (RS) in different land uses and FVCs, which can provide scientific reference for regional agricultural irrigation and agricultural water resources management. The findings highlight that the VV polarization-based model significantly improves SSM estimation accuracy, particularly in croplands with FVC ≤ 0.75, offering a reliable tool for optimizing irrigation schedules and enhancing water use efficiency in agriculture. These results can aid in better water resource management, especially in regions with limited water availability, by providing precise soil moisture data for informed decision-making. Full article

Accurate estimation of herbage mass is crucial for managing pastoral livestock systems. The Normalized Difference Vegetation Index (NDVI) from Unmanned Aerial Vehicle (UAV) sensors shows promise for high-resolution estimations of pasture herbage mass, but it is still unknown how this method differs among forage species, seasons, and pasture management practices. A commercial sensor was calibrated to predict herbage mass using NDVI. Additionally, the effect of different forage species, days of regrowth, and nitrogen (N) status on the relationship between NDVI and herbage mass was evaluated. Two pastures of tall wheatgrass (Thinopyrum ponticum) and tall fescue (Festuca arundinacea), divided into 30 and 72 plots, respectively, were assessed during spring and autumn regrowth over two years in Balcarce, Argentina. Doses of 0, 50, and 100 kg N ha⁻¹ were applied to tall wheatgrass, and 0, 50, 100, 200, 400, and 600 kg N ha⁻¹ were applied to tall fescue to create variability in herbage mass and N status. Exponential regression models of herbage mass (y) fitted against NDVI (x) showed an average R² of 0.83 ± 0.04 and a mean absolute error of 170 ± 60 kg DM ha⁻¹. The relationship between NDVI and herbage mass differed (p ≤ 0.05) between species, seasons, and regrowth stage, but was not influenced by N status (p > 0.05). Results suggest that accurate predictions of herbage mass using NDVI measurements by an UAV require frequent model recalibrations to account for observed differences among forage species, days of regrowth, and years. Full article

The Ili River Transboundary Basin is an important area within the Belt and Road Initiative, and its ecological security impacts China–Kazakhstan diplomatic relations and the building of the Belt and Road Initiative. Using the copula method, this study quantifies the vulnerability of vegetation to drought in the Ili River Transboundary Basin based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The vulnerability of vegetation in the Ili River Transboundary Basin is highest in June, with the proportion of highly vulnerable areas reaching 63.29% under extreme drought conditions. As the drought severity increases, the probability of vegetation loss rises, with vegetation being affected the most in June. From May to June, drought-prone areas are mainly located in Almaty Oblast and East Kazakhstan. From July to September, drought-prone areas are mainly found in the Ili River Valley and southeastern Almaty Oblast. Rainfed croplands are most susceptible to drought, while, for irrigated croplands, higher drought severity enhances the mitigating effect of irrigation measures. Vegetation areas are most affected by drought in semi-arid regions, particularly in summer. These findings offer valuable scientific support for drought management and sustainable development in the region. Full article