Search Results (36)

Reconstructing relationships between urban agglomeration and relevant ecosystems from an ecosystem services perspective and quantitatively assessing their interactive status holds significant implications for achieving coordinated development. Taking Shandong Peninsula Urban Agglomeration (SPUA) as our study area, we developed a Cities-ESV Coupling Index (I) serving as a composite metric for assessing city–ecosystem coupling dynamics through a multidimensional framework encompassing the following: (1) urban development level, (2) ecosystem service value (ESV), (3) ecosystem service physical quantity, and (4) spatial balance degree of ecosystem service, operationalized through 10 selected indicators. Our methodology integrates ESV quantification, biophysical assessment, correlation analysis modeling, and spatial autocorrelation modeling to comprehensively evaluate coupling relationships between cities and ecosystems across 16 cities and 78 counties. This study innovatively integrates ESV quantification with biophysical assessment methodologies in indicator selection, while strategically incorporating spatial agglomeration metrics. The multidimensional framework effectively addresses the prevalent oversimplification in existing ecosystem service measurement paradigms. The findings are as follows: the total ESV is 13,977.87 × 10⁸ CNY/a, which accounts for about 20% of the total GDP of SPUA. The Cities-ESV coupling index (I) of four cities, including Dongying, Linyi, Yantai, and Weifang, ranks among the top in SPUA, while that of seven counties, namely Weshan, Qixia, Yiyuan, Yishui, Mengyin, and Linqu, is at a relatively high-level. The conclusion is as follows: the total ESV in SPUA had been continuously decreasing. The coupling relationship between cities and ecosystems are significantly negatively correlated, and the Cities-ESV coupling index (I) of SPUA has obvious regional differentiation characteristics. Therefore, differentiated ecological land protection policies should be formulated to curb the trend of continuous decline in ESV. Full article

The rapid expansion of built-up areas (BUAs) requires effective spatial and temporal monitoring, being a crucial practice for urban land use planning, resource allocation, and environmental studies, and spectral indices (SIs) can provide efficiency and reliability in automating the process of BUAs extraction. This paper explores the use of nine spectral indices and sixteen thresholding methods for the automatic mapping of BUAs using Landsat 8 imagery from a semi-arid climate in Morocco during spring and summer. These indices are the Normalized Difference Built-Up Index (NDBI), the Vis-red-NIR Built-Up Index (VrNIR-BI), the Perpendicular Impervious Surface Index (PISI), the Combinational Biophysical Composition Index (CBCI), the Normalized Built-up Area Index (NBAI), the Built-Up Index (BUI), the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI) and the Built-up Land Features Extraction Index (BLFEI). Results show that BLFEI, SWIRED, and BUI maintain high separability between built-up and each of the other land cover types across both seasons, as evaluated via the Spectral Discrimination Index (SDI). The lowest SDI values for all three indices were observed for bare soil against BUAs, with BLFEI recording 1.21 in the wet season and 1.05 in the dry season, SWIRED yielding 1.22 and 1.08, and BUI showing 1.21 and 1.08, demonstrating their robustness in distinguishing BUAs from other land covers under varying phenological and soil moisture conditions. These indices reached overall accuracies of $93.97\%$, $93.39\%$ and $92.81\%$, respectively, in wet conditions, and $91.57\%$, $89.17\%$ and $89.67\%$, respectively, in dry conditions. The assessment of thresholding methods reveals that the Minimum method resulted in the highest accuracies for these indices in wet conditions, where bimodal medium peaked histograms were observed, whereas the use of Li, Huang, Shanbhag, Otsu, K-means, or IsoData was found to be the most effective under dry conditions, where more peaked histograms were observed. Full article

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works. Full article

Plant colonization patterns on deglaciated terrain give insight into the factors influencing alpine ecosystem development. Our objectives were to use a chronosequence, extending from the Little Ice Age (~1850) terminal moraine to the present glacier terminus, and biophysical predictors to characterize vegetation across Sperry Glacier’s foreland—a mid-latitude cirque glacier in Glacier National Park, Montana, USA. We measured diversity metrics (i.e., richness, evenness, and Shannon’s diversity index), percent cover, and community composition in 61 plots. Field observations characterized drainage, concavity, landform features, rock fragments, and geomorphic process domains in each plot. GIS-derived variables contextualized the plots’ aspect, terrain roughness, topographic position, solar radiation, and curvature. Overall, vegetation cover and species richness increased with terrain age, but with colonization gaps compared to other forelands, likely due to extensive bedrock and slow soil development, potentially putting this community at risk of being outpaced by climate change. Generalized linear models revealed the importance of local site factors (e.g., drainage, concavity, and process domain) in explaining species richness and Shannon’s diversity patterns. The relevance of field-measured variables over GIS-derived variables demonstrated the importance of fieldwork in understanding alpine successional patterns and the need for higher-resolution remote sensing analyses to expand these landscape-scale studies. Full article

Sugarcane crops have a long cycle with successive harvests before re-planting, and row gaps are one of the main problems associated with the yield. The objective of this study was to establish an alternative methodology for measuring the planting and regrowth of sugarcane rows using UAV (Unmanned Aerial Vehicle) images and to compare it with manual measurements. This study was conducted in a 1 ha experimental area under mechanized harvesting. The reference methodology consists of measuring the continuous distances without regrowth between two plants along a planting row, considering distances greater than 0.50 m as gaps and the following gaps classes: >0.5–1.0 m, >1.0–1.5 m, >1.5–2.0 m, >2.0–3.5 m, and >3.5 m. Images were collected from a UAV equipped with a 12-megapixel RGB camera. The number of regrowth gaps measured through imaging for the class of gaps with a length between 0.5 and 1.0 m was eight times higher than field measurement. In the class of gaps with a length between 1.0 and 1.5 m, the result is the opposite, as the field measurement was approximately three times higher than the UAV measurement, with a significant difference in both classes. In the other length classes analyzed, the number of gaps did not show significant differences. Our results suggest that regrowth gaps can be quickly estimated with the proposed methodology for gaps greater than 1.5 m. For gaps smaller than <1 m, the methodology using a UAV is not accurate. Full article

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

Being located in the middle of Southern Europe, and thus likely representing a particularly dynamic member of Mediterranean Europe, Italy has experienced a sudden increase in early desertification risk because of multiple factors of change. Long-term research initiatives have provided relatively well-known examples of the continuous assessment of the desertification risk carried out via multiple exercises from different academic and practitioner stakeholders, frequently using the Environmentally Sensitive Area Index (ESAI). This composite index based on a large number of elementary variables and individual indicators—spanning from the climate to soil quality and from vegetation cover to land-use intensity—facilitated the comprehensive, long-term monitoring of the early desertification risk at disaggregated spatial scales, being of some relevance for policy implementation. The present study summarizes the main evidence of environmental monitoring in Italy by analyzing a relatively long time series of ESAI scores using administrative boundaries for a better representation of the biophysical and socioeconomic trends of interest for early desertification monitoring. The descriptive analysis of the ESAI scores offers a refined representation of economic spaces in the country during past (1960–2010 on a decadal basis), present (2020), and future (2030, exploring four different scenarios, S1–S4) times. Taken as a proxy of the early desertification risk in advanced economies, the ESAI scores increased over time as a result of worse climate regimes (namely, drier and warmer conditions), landscape change, and rising human pressure that exacerbated related processes, such as soil erosion, salinization, compaction, sealing, water scarcity, wildfires, and overgrazing. Full article

We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R² > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R² = 0.677 and RMSE = 0.349). Full article

Background: Recurrences due to discontinuity in ablation lines are substantial after pulmonary vein isolation (PVI) with radiofrequency ablation for atrial fibrillation. Data are scarce regarding the durability predictors for very high-power short-duration (vHPSD, 90 W/4 s) ablation. Methods: A total of 20 patients were enrolled, who underwent 90 W PVI and a mandatory remapping procedure at 3 months. First-pass isolation (FPI) gaps, and acute pulmonary vein reconnection (PVR) sites were identified at the index procedure; and chronic PVR sites were identified at the repeated procedure. We analyzed parameters of ablation points (n = 1357), and evaluated their roles in predicting a composite endpoint of FPI gaps, acute and chronic PVR. Results: In total, 45 initial ablation points corresponding to gaps in the ablation lines were analyzed. Parameters associated with gaps were interlesion distance (ILD), baseline generator impedance, mean current, total charge, and loss of catheter–tissue contact. The optimal ILD cut-off for predicting gaps was 3.5 mm anteriorly, and 4 mm posteriorly. Conclusions: Biophysical characteristics dependent on generator impedance could affect the efficacy of vHPSD PVI. The use of smaller ILDs is required for effective and durable PVI with vHPSD compared to the consensus targets with lower power ablation, and lower ILDs for anterior applications seem necessary compared to posterior points. Full article

The serum creatinine/cystatin C ratio (CCR) and the sarcopenia index (SI) are novel indicators for sarcopenia, but their accuracy may depend on various confounders. To assess CCR and SI diagnostic accuracy, we studied the clinical and biophysical parameters associated with sarcopenia or sarcopenic obesity. A total of 79 elderly patients (65–99 yrs, 33 females) underwent clinical, anthropometric, body composition, geriatric performance, and blood chemistry evaluation. The CCR and SI accuracy were assessed to identify sarcopenia. Sarcopenia was confirmed in 40.5%, and sarcopenic obesity in 8.9% of the subjects. Sarcopenic patients showed an increased Charlson comorbidity index, cardiovascular disease (CVD) rates and frailty, and decreased physical performance than non-sarcopenic subjects. Patients with sarcopenic obesity had increased body fat and inflammatory markers compared to obese subjects without sarcopenia. Sarcopenia was associated with a decreased CCR and SI. However, when the logistic regression models were adjusted for possible confounders (i.e., age, gender, Charlson comorbidity index, presence of CVD, and frailty score), a significant OR was confirmed for the CCR (OR 0.021, 95% CI 0.00055–0.83) but not for the SI. The AUC for the CCR for sarcopenia discrimination was 0.72. A higher performance was observed in patients without chronic kidney diseases (CKD, AUC 0.83). CCR, more than the SI, is a useful, non-invasive, and cost-effective tool to predict sarcopenia, irrespective of the potential confounders, particularly in subjects without CKD. Full article

Monitoring crop growth conditions during the growing season provides information on available soil nutrients and crop health status, which are important for agricultural management practices. Crop growth frequently varies due to site-specific climate and farm management practices. These variations might arise from sub-field-scale heterogeneities in soil composition, moisture levels, sunlight, and diseases. Therefore, soil properties and crop biophysical data are useful to predict field-scale crop development. This study investigates soil data and spectral indices derived from multispectral Unmanned Aerial Vehicle (UAV) imagery to predict crop height at two winter wheat farms. The datasets were investigated using Gaussian Process Regression (GPR), Ensemble Regression (ER), Decision tree (DT), and Support Vector Machine (SVM) machine learning regression algorithms. The findings showed that GPR (R² = 0.69 to 0.74, RMSE = 15.95 to 17.91 cm) has superior accuracy in all models when using vegetation indices (VIs) to predict crop growth for both wheat farms. Furthermore, the variable importance generated using the GRP model showed that the RedEdge Normalized Difference Vegetation Index (RENDVI) had the most influence in predicting wheat crop height compared to the other predictor variables. The clay, calcium (Ca), magnesium (Mg), and potassium (K) soil properties have a moderate positive correlation with crop height. The findings from this study showed that the integration of vegetation indices and soil properties predicts crop height accurately. However, using the vegetation indices independently was more accurate at predicting crop height. The outcomes from this study are beneficial for improving agronomic management within the season based on crop height trends. Hence, farmers can focus on using cost-effective VIs for monitoring particular areas experiencing crop stress. Full article

Fire is a natural disruption that affects the structure and function of forest systems by changing the vegetation composition, climatic situation, carbon cycle, wildlife habitat, and many other major properties. The measure of the degree of these changes’ degree is known as fire severity, and it can be assessed using remote sensing data (i.e., satellite images, aerial images, etc.) and various biophysical indices (such as Normalized Burn Ratio (NBR), Char Soil Index (CSI), Burn Area Index (BAI), etc.), in addition to the measurement of Land Surface Temperature (LST). This research aims to assess the response of the NBR and LST both pre- and post-forest fires, taking a Mediterranean forest located in the northern part of Morocco, which burned in the summer of 2022, as the study area. We used seven Landsat-8 images spanning three years: three images from 2021 (i.e., pre-fire), one image from the summer of 2022 (i.e., fire period), and three images from 2023 (i.e., post-fire). The results demonstrated a negative correlation between the LST and NBR in the pre-fire period; when the temperature rises, the NBR drops. The same was found for the fire period in summer 2022, in which the LST reached its peak at 50 °C, while the NBR decreased to its lowest point at −0.2, whereas in the recovery period (i.e., 2023), the LST and NBR showed changes in fluctuation patterns; the LST variated normally according to seasons, dropping from 50 °C to 12 °C in winter and reaching 37 °C in summer, while the NBR increased over time, going from −0.2 to −0.04 in winter to 0.03 in summer, which indicates the gradual restoration of vegetation in the study area. This study concludes that in the post-fire period when a forest is recovering, the NBR is unaffected by seasonal changes in temperature and is more reflective of the vegetation it projects more than the vegetation situation in the area, unlike the LST. Thus, relying only on the LST to measure fire severity can give biased results due to changes in seasons. Full article

The biophysical composition index (BCI)-based linear spectral mixture model (LSMM) is used in this study to extract the impervious surface (IS), vegetation, and soil coverage of the main urban region (MUR) of Hefei City over the 2001–2021 period. In addition, the Soil Conservation Service-Curve Number (SCS-CN) model is first applied to simulate the surface runoff (SR) in the MUR of Hefei City over the past 21 years, then assessed for simulation accuracy using typical waterlogging points in the study area. On this basis, the spatiotemporal evolution of IS and SR and their relationships in the MUR of Hefei City are investigated and discussed in this study. The obtained results showed that (1) the root-mean-square error (RMSE), mean absolute error (MAE), and systematic error (SE) values of the BCI index-based LSMM are smaller than those of the LSMM, demonstrating a higher extraction accuracy of urban IS extraction of the BCI index-based LSMM. (2) The IS area of the MUR of Hefei City exhibits an increasing trend from 107.555 km² in 2001 to 387.660 km² in 2021. In addition, the change rate and change intensity values indicate an increasing–decreasing–increasing trend. The highest change rate and change intensity values are 24.839 km²/year and 23.094%, respectively, and were observed in the 2001–2005 period. (3) The simulated SR (165–195 mm) in the MUR of Hefei City demonstrates an increasing trend in the 2001–2021 period at a rainfall intensity value of 200 mm/d. In addition, the simulated SR amount in the central area exhibits slight changes, while that in the surrounding areas shows substantial variations. (4) The distribution of IS and SR in the MUR of Hefei City reveals strong directional variations, which are all affected by geographical conditions. The IS coverage and SR show high positive correlation coefficients in different years. (5) The present study provides primary data for effective urban planning, water resources management and regulation, and disaster prevention and mitigation in Hefei City, as well as a scientific reference for future studies on urban IS, SR, and their quantitative relationships in other regions. Full article

The natural landscape is fast turning into impervious surfaces with the increase in urban density and the spatial extent of urbanized areas. Remote sensing data are crucial for mapping impervious surface area (ISA), and several methods for ISA extraction have been developed and implemented successfully. However, the heterogeneity of the ISA spectra and the high similarity of the ISA spectra to those of bare soil in dry climates were not adequately addressed. The objective of this study is to determine which spectral impervious surface index best represents impervious surfaces in arid climates using two seasonal Landsat-8 images. We attempted to compare the performance of various impervious surface spectral Index for ISA extraction in dry climates using two seasonal Landsat-8 data. Specifically, nine indices, i.e., band ratio for the built-up area (BRBA), built-up area extraction method (BAEM), visible red near infrared built-up index (VrNIR-BI), normalized ratio urban index (NRUI), enhanced normalized difference impervious surfaces index (ENDISI), dry built-up index (DBI), built-up land features extraction index (BLFEI), perpendicular impervious surface index (PISI), combinational biophysical composition index (CBCI), and two impervious surface binary methods (manual method and ISODATA unsupervised classification). According to the results, PISI and CBCI combined with the manual method had the best accuracy with 88.5% and 88.5% overall accuracy (OA) and 0.76 and 0.81 kappa coefficients, respectively, while DBI combined with the manual method had the lowest accuracy with 75.37% OA and 0.56 kappa coefficients. PISI is comparatively more stable than the other approaches in terms of seasonal sensitivity. The ability of PISI to discriminate ISA from soil and vegetation accounts for much of its good performance. In addition, spring is the ideal time of the year for mapping ISA from Landsat-8 images because the impervious surface is generally less likely to be confused with bare soil and sand at this time of year. Therefore, this study can be used to determine spectral indices for studying ISA extraction in drylands in conjunction with binary approaches and seasonal effects. Full article

Accurately mapping and monitoring the urban impervious surface area (ISA) is crucial for understanding the impact of urbanization on heat islands and sustainable development. However, less is known about ISA spectra heterogeneity and their similarity to bare land, wetland, and high-rise-building shadows. This study proposes a feature-based approach using decision tree classification (FDTC) to map ISAs and their spatio-temporal changes in a coastal city in southeast China using Landsat 5 TM, Landsat 8 OLI/TIRS, and Sentinel-2 images from 2009 to 2021. Atmospheric correction using simplified dark object subtraction (DOS) was applied to Landsat imagery, which enabled faster computation. FDTC’s performance was evaluated with three sensors with different spectral and spatial resolutions, with parameter thresholds held constant across remote-sensing images. FDTC produces a high average overall accuracy (OA) of 94.53%, a kappa coefficient (KC) of 0.855, and a map-level image classification efficacy (MICE) of 0.851 for ISA mapping over the studied period. In comparison with other indices such as BCI (biophysical composition index), PISI (automated built-up extraction index), and ABEI (perpendicular impervious surface index), the FDTC demonstrated higher accuracy and separability for extracting ISA and bare land as well as wetland and high-rise buildings. The results of FDTC were also consistent with those of two open-source ISA products and other remote sensing indices. The study found that the ISA in Xiamen City increased from 16.33% to 26.17% over the past 13 years due to vegetation occupation, encroachment onto bare land, and reclamation of coastal areas. While the expansion significantly reduced urban vegetation in rapidly urbanizing areas of Xiamen, ambitious park greening programs and massive redevelopment of urban villages resulted in a modest but continuous increase in urban green space. Full article