Search Results (5,651)

Northeast China has undergone large-scale cultivation of agricultural land, accompanied by internal restructuring of paddy fields and rain-fed farmland. Such a land change process has an obvious impact on the ecosystem. However, the quantitative effects of long-term cultivation of land/internal structure on the eco-environment are still lacking in the Heilongjiang region, China’s ecological barrier and grain base. To address this academic issue, the integrated method of land update technology, dynamic tracking, remote sensing classification, and improved ecosystem services were applied using satellite imagery and land products. Through satellite monitoring, the area of cultivated land changed from 127,221.71 to 173,665.12 km², with an increment of 46,443.41 km², expanding the central–northern parts and the eastern part over the past half-century. In different regions, all cities have undergone varying degrees of reclamation rate expansion ranging 0.71–29.62%. Regarding the structure, a quarter of the study area was covered by rain-fed farmland (25.29%), but the cultivation level of paddy fields (2.83%) was very low in 1970; after that, only a 13.08% increment in rain-fed farmland but a high increase of 246.14% in paddy fields was monitored from 1970 to 2020. Meanwhile, the source area of cultivated land was 59,271.48 km², with 60.41% from forest and grassland of the agricultural-forestry ecotone. Its destination area was 12,827.11 km², and 78.49% of the total was converted to construction land, forest, and grassland. From 1970 to 2020, the evaluated ecosystem service changed from 15,575.87 to 12,495.72 × 10⁸ yuan, showing a total loss of 3080.15 × 10⁸ yuan and an annual turnover rate of 0.40%. An important calculation indicated that the expansion and shrinkage of cultivated land led to a 2303.46 × 10⁸ yuan loss, which means that three-quarters (i.e., 74.78%) of ecosystem service loss was caused by cultivated land changes. Another key finding was that a large transformation of wetland into paddy fields brought about the huge loss of 847.85 × 10⁸ yuan; by contrast, the process of extensive rain-fed farmland turning into paddy fields was only a small change of 3.38 × 10⁸ yuan. Considering the ecological loss caused by cultivated land, the projects of returning farmland to forests and wetland protection should be implemented. This study provided important references for land system monitoring and environmental impact assessment in high-latitude regions around the world. Full article

Since 2007, green tides have recurrently occurred in the Yellow Sea during spring and summer, with a massive outbreak recorded in 2021. Given the critical significance of green tide monitoring and prediction for marine ecological security and sustainable development, this study developed a satellite remote sensing-validated coupled simulation system for green tide drift and growth, by integrating multi-source satellite remote sensing data and oceanographic reanalysis datasets. Leveraging this system, we systematically analyzed the spatiotemporal evolution characteristics and underlying driving mechanisms of both routine green tide processes in 2014–2015 and the extreme 2021 event. Satellite images with low cloud cover and extensive green tide distribution were screened to confirm the accuracy of green tide drift trajectories and distribution ranges for validating the model’s reliability, and the results demonstrated the spatial consistency between simulation results and satellite observations. The validated model was used to track the drift and growth–decline processes of green tides and investigate the underlying cause of high-biomass appearance in 2021. Combined with environmental parameters, our analyses revealed that variations in attachment substrates alter wind resistance coefficients, thereby potentially accelerating the northward drift velocity of green tides. Furthermore, substrate properties may exert a significant regulatory effect on the attachment, germination, and biomass accumulation of Ulva prolifera spores, which could be a leading factor driving the massive green tide outbreak. Full article

Canopy fractional vegetation cover (FVCc) is a critical indicator for evaluating the effectiveness of ecological restoration, and its accurate estimation provides valuable data for regional ecological management. In this study, Sentinel-2 and MODIS data were integrated to develop an angular relationship model for MODIS reflectance, which was then used to estimate Sentinel-2 reflectance at a 45° viewing angle. Background reflectance at a 10 m spatial resolution was derived using a four-scale model, and total and shrub-herb fractional vegetation cover were estimated using a pixel dichotomy model. Finally, an empirical model tailored to the characteristics of the study area was developed to retrieve FVCc. Cross-validation results demonstrated that the multi-angle retrieval method proposed in this study achieved higher accuracy than the single-angle approach. The spatial distribution of FVCc in Changting County is characterized by higher values in peripheral areas and lower values in the central region. Temporal transitions among fractional vegetation cover classes were predominantly upward, indicating an overall trend of continuous improvement. These findings provide important technical support and a scientific basis for estimating and monitoring dynamic changes in forest canopy fractional vegetation cover. Full article

Mountain forests on the western edge of the Sichuan Basin are challenging to monitor at high resolution because rugged topography, cloud cover, and Landsat-7 SLC-off artifacts create data gaps, while the 2008 Wenchuan earthquake and subsequent restoration further alter vegetation dynamics. We fused Landsat 5/7/8/9 surface reflectance with MODIS MOD13Q1 using an index-then-fusion STARFM framework to reconstruct a continuous 30 m NDVI record for 2000–2024 and quantified forest fraction dynamics using annual forest/non-forest maps, transition analysis, and K-means clustering of pixel-wise NDVI trajectories. To identify dominant controls, we applied a multi-output random forest with spatial block cross-validation and SHAP attribution. The fused NDVI agrees well with MODIS across 100,000 samples (R² = 0.953; RMSE = 0.032), and the regional mean NDVI increased from 0.711 (2000) to 0.774 (2024), showing a post-2008 decline–stagnation–recovery pattern. Forest fraction rose from 48.2% to 72.9%, with accelerated gains after 2010 (+21.4%), and improving trajectories dominated (70.95%), concentrating near the Longmenshan fault zone. The driver model generalized well (micro-mean R² = 0.875), and SHAP ranked elevation (32.6%) and initial forest fraction (32.3%) above temperature and precipitation. These results provide high-resolution evidence of mountain forest change and its primary controls to support terrain-informed ecological management. Full article

Drifting fish aggregating devices (DFADs) are central to tropical tuna purse-seine fisheries, yet their hydrodynamic performance under realistic seas has not been adequately addressed, particularly for emerging eco-friendly designs. A three-dimensional framework based on computational fluid dynamics is developed to assess the motion response and mooring loads of full-scale DFADs comprising raft buoys, biodegradable cotton rope, and iron sinkers, using four buoy layouts (Models A to D). Unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are performed with a realizable k–ε closure, volume of fluid (VOF) free-surface capturing, the Euler overlay method, dynamic overset meshes, and catenary mooring coupling. Regular waves representative of operational conditions (T = 1.40 to 2.40 s, H = 0.10 to 0.40 m) are imposed via a VOF wave-forcing technique, and mesh/time-step sensitivity analyses demonstrate the accurate reproduction of the first-order wave elevation (error < 0.8%). Surge drift per cycle and heave response amplitude operators, with the relative mooring force, are evaluated as functions of the relative wavelength (λ/L_a) and wave steepness (H/λ). The results reveal that the buoy layout exerts first-order control on DFAD dynamics, whereas short, steep waves dominate motion and line loads. The intermediate end-point sinker mass achieves a favorable balance between motion suppression and mooring load control, whereas distributing a fixed total sinker mass along the rope reduces heave response and mooring force by improving the tension redistribution and overall stability. Across all sea states, Models A and D reduced motion envelopes and mooring forces, indicating their suitability as robust, low-impact configurations. The proposed framework and design recommendations provide quantitative guidance for optimizing eco-DFAD geometry and deployment strategies, supporting safer and more sustainable DFAD-based tuna fisheries. Full article

Excessive accumulation of copper ions (Cu²⁺) in the environment and biological systems poses severe risks to ecological balance and human health, necessitating accurate detection and monitoring of Cu²⁺. Schiff base derivatives with favorable optical properties provide an efficient strategy for copper ion recognition. In this paper, fluorescent probe L (5-methyl-2-hydroxybenzaldehyde-(7-diethylaminocoumarin-3-formyl) hydrazone) was synthesized through a three-step reaction using 4-diethylaminosalicylaldehyde and diethyl malonate as starting materials. The structure of probe L was confirmed by melting point analysis, infrared spectroscopy, and nuclear magnetic resonance. Single-crystal X-ray analysis revealed that probe L crystallized into a triclinic lattice with space group $P_1^{-}$. Optical investigations, including UV–Vis spectroscopy, fluorescence spectroscopy, and aggregation-induced emission studies, demonstrated highly sensitive and selective fluorescence “turn-off” behavior of probe L towards Cu²⁺ ions in DMSO, with negligible interference from other metal ions. Job’s plot and crystallographic analysis revealed a 1:1 binding stoichiometry between probe L and Cu²⁺, forming the complex [Cu(L)]. Fluorescence titration experiments revealed a binding constant (K_b) of 5.2 × 10⁶ L/mol and a detection limit of 7.8 × 10⁻⁷ mol/L, indicating excellent sensitivity. These results suggest that probe L has considerable promise for Cu²⁺ detection in aqueous environments, with potential applications in environmental monitoring and public health protection. Full article

Fine-grained spatio-temporal action detection in continuous, unconstrained field videos remains a formidable challenge due to severe background clutter, high inter-class similarity, and the scarcity of domain-specific benchmarks. To address these limitations, we first introduce a large-scale Wintering-Crane Benchmark, providing dense, individual-level bounding box annotations for six complex behaviors across diverse habitat scenes. Leveraging this data, we propose AviaTAD-LGH, a real-time multi-task framework that incorporates auxiliary motion supervision into a dual-pathway 3D backbone to enhance feature discriminability. A critical bottleneck in such multi-task settings is the negative transfer caused by conflicting optimization objectives. To resolve this, we present Lightweight Gradient Harmonization (LGH), a plug-and-play optimization strategy that dynamically modulates task weights based on the cosine similarity of gradient directions. This mechanism effectively aligns optimization trajectories without introducing inference latency. Extensive experiments demonstrate that AviaTAD-LGH achieves a state-of-the-art mAP of 68.60%, surpassing strong public baselines by 7.44% and improving upon the single-task baseline by 2.80%, with significant gains observed on ambiguous dynamic classes. The proposed pipeline enables efficient, scalable ecological monitoring suitable for edge deployment. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Flexible conductive hydrogels hold great promise in wearable electronics and biomonitoring applications, yet their practical use is constrained by issues such as poor low-temperature tolerance, susceptibility to dehydration, and limited multifunctional sensing capabilities. This study successfully synthesized a dual-conductive lithium-ion and calcium-ion hydrogel based on acrylamide/gelatin via a simplified low-temperature one-pot polymerization method. At 60 °C, mixing acrylamide, gelatin, lithium chloride, and calcium chloride within 40 min constructed a network structure featuring covalent bonds, ionic bonds, and hydrogen bonds. The resulting material exhibited exceptional extensibility with a break elongation of 1408.5% and tensile strength of 134.2 kPa while maintaining strong adhesion to nine different substrates. It retained flexibility at −20 °C and demonstrated minimal mass loss (3% of initial value) after 10 days of aging. As a sensor, this hydrogel reliably responds to pressure, temperature, large-amplitude body movements, and subtle physiological signals like pulse and vocal cord vibrations. In animal simulation monitoring, its electrical resistance signals increased linearly with model body weight and remained stable between −20 °C and 20 °C. These results demonstrate the hydrogel’s broad application potential in wearable sensing, ecological monitoring, and smart agriculture. Full article

Against the backdrop of climate change, forest fires increasingly undermine ecosystem stability and reshape species distributions in Heilongjiang Province. Therefore, quantifying the drivers of fire occurrence and conducting long-term fire risk forecasting holds critical value for regional ecological security. Centered on the forested regions of Heilongjiang Province, this study systematically assessed the relative contributions of multi-source factors—including topography, vegetation, and meteorological conditions—to fire occurrence and compared the predictive performance of three models: Deep Neural Network with Residual Connections (ResDNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Modeling results based on historical fire records indicated that the ResDNN model achieved the highest accuracy (85.6%). Owing to its robust nonlinear mapping capability, it performed better in capturing complex feature interactions than ANN and SVM. These results demonstrate its strong applicability to forest fire prediction in Heilongjiang Province. Building on these findings, the study employed the best-performing ResDNN model in conjunction with CMIP6 multi-model climate projections to simulate and map the spatiotemporal probability of forest fire occurrence from 2030 to 2070. The results provide an intuitive representation of long-term fire-risk trajectories under future climate scenarios and offer scientific support for regional fire prevention, monitoring, early-warning systems, and forest management under climate change. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

Global climate change and increasingly complex geological conditions have led to more frequent landslides in the red-bed sedimentary layers of western Sichuan, China, posing severe threats to human safety and hindering progress toward regional Sustainable Development Goals (SDGs), particularly those related to disaster risk reduction and ecological protection. To address this challenge and advance sustainable disaster management, this study proposes a lightweight hybrid model, termed Transformer–Attention–LSTM, which integrates the global attention mechanism of Transformers with the local time-series modeling capabilities of Long Short-Term Memory networks. Focusing on the Kuyaogou landslide, the model achieves an optimal balance between parameter scale, sequence length, and prediction accuracy. The mean Coefficient of Determination (R²) values for the test samples in the X, Y, and Z directions reached 0.948, representing enhancements of 9.9%, 4.2%, and 2.3%, respectively, compared to the suboptimal Attention–LSTM model. Concurrently, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced to 9.23 mm and 7.17 mm, respectively. Based on these displacement predictions, the landslide evolution stage was determined by calculating the tangent angle, indicating that the Kuyaogou landslide will remain in a stable creep phase over the ensuing ten-day period with low overall risk of rapid movement, though localized instability requires continued monitoring. This research provides a ‘small, fast, and accurate’ paradigm for red-bed landslide displacement prediction, offering scientific support for disaster prevention and emergency decision-making. The framework demonstrates potential for broader application in monitoring other geological hazards, thereby contributing to the implementation of sustainable development strategies in geohazard-prone regions. Full article

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment. Full article

Monarch butterflies have declined in both eastern and western populations. Conservation initiatives that support this imperiled species are being implemented in lands managed by the energy and transportation sectors. Vegetation management strategies that encourage the presence of milkweed (Asclepias spp.), the larval host of monarch butterflies (Danaus plexippus), or floral resources to support pollinators are being practiced across North America; however, survey methods to evaluate the success of these strategies vary in accuracy and scalability. In this study, we compared five methods to quantify milkweed stem density and land cover estimates: (1) Site al, (2) Transect plot, (3) Square plot, (4) Large transect (informed by the Monarch CCAA methodology), and (5) Machine learning of images collected by UAVs. These methods encompass full coverage ground counts, partial ground counts, and aerial imagery using object-based image analysis. Sites included distribution, transmission, and gas line ROWs, solar arrays, and transportation easements. We found that Site al and Machine learning most consistently quantified milkweed stem density across sites. Partial ground count methods were likely to over or underestimate milkweed populations. Habitat characteristics (woody, broadleaf, grass, and bare ground) estimates were inconsistent across method and site. The intent of this study was to provide land managers with insight as to the most accurate, efficient, and economical approach to quantify milkweed populations and habitat characteristics. Full article