Search Results (227)

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Flood risk maps play a critical role in land-use regulation, infrastructure planning, and community preparedness, which are key components of sustainable and climate-resilient urban development. Their production, however, remains costly, labor-intensive, and time-demanding as it relies on simulation-driven workflows that combine hydrodynamic modeling with expert interpretation and extensive validation. To address this issue from a sustainability perspective, we develop a novel, practical, and near-real-time large language model (LLM)-based framework to support property-level flood risk assessment. This framework, which synthesizes geospatial, hydrological, infrastructural, and historical flood information, extends existing research and explores novel risk estimation methods for use in planning practice. Using Brisbane, Australia, as a case study, we develop Flood-LLM, a multi-agent system that transforms multi-source urban datasets into structured textual representations, models diverse neighborhood conditions, and fine-tunes a reasoning model using expert-assessed risk classifications. The results show that Flood-LLM can reproduce official flood risk labels for creek, river, storm tide, and overland-flow hazards with reasonable accuracy, outperforming classical machine learning, deep learning, and untuned LLM baselines. Visual and quantitative analyses indicate that the framework demonstrates a qualitatively nuanced capability to capture salient spatial patterns present in the official maps, while generating a textual chain-of-thought providing a transparent audit trail for its labeling decisions. These findings suggest that such LLM-based approaches can produce potential complementary tools to expert-reviewed planning classifications and support more sustainable, adaptive flood risk management by enabling timely map production and updates that facilitate informed decision-making in rapidly changing environmental conditions. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), is capable of acquiring large-scale terrain and forest structural information through photon-counting LiDAR. However, photon point clouds exhibit significant noise variability due to diurnal changes and variations in beam intensity, which undermines the accuracy and stability of terrain and canopy height retrievals in forested regions. To address the limited adaptability of existing methods under daytime/nighttime and strong/weak beam conditions, this study proposes a multi-stage processing framework integrating photon denoising, classification, and quasi-full-waveform reconstruction. First, local statistical features combined with adaptive parameter optimization were employed, applying Gaussian and exponential fitting to denoise daytime strong and weak beams and enhance the signal-to-noise ratio (SNR). Subsequently, an improved random sample consensus (RANSAC) algorithm was introduced to remove residual noise and classify photons under both diurnal and beam-intensity variations. Finally, a radial basis function (RBF) interpolation was used to reconstruct quasi-full-waveform curves for terrain and canopy heights. Compared with the ATL08 product (terrain root mean square error (RMSE): 2.65 m for daytime strong beams and 5.77 m for daytime weak beams), the proposed method reduced RMSE by 0.53 m and 1.30 m, respectively, demonstrating enhanced stability and robustness under low-SNR conditions. For canopy height estimation, all beam types showed high consistency with airborne LiDAR measurements, with the highest correlation achieved for nighttime strong beams (R = 0.90), accompanied by the lowest RMSE (4.82 m) and mean absolute error (MAE = 2.97 m). In comparison, ATL08 canopy height errors for nighttime strong beams were higher (RMSE = 5.67 m; MAE = 4.16 m). Notably, significant improvements were observed for weak beams relative to ATL08. These results indicate that the proposed framework effectively denoises and classifies photon point clouds under diverse daytime/nighttime and strong/weak beam conditions, providing a robust methodological reference for high-precision terrain and forest canopy height estimation in forested regions. Full article

Land-use and land-cover (LULC) change is one of the most pervasive drivers of socioenvironmental transformation worldwide. Given its impacts on ecosystems and climate, the systematic analysis of LULC dynamics remains a central objective of land-change science. Despite major advances in Earth observation capabilities, robust, flexible, and scalable algorithms for long-term monitoring remain unevenly adopted, particularly in remote, forested tropical regions. This study introduces the Multivariate Random Forest Land-Use and Land-Cover (MuRaF-LULC) framework, a supervised and generalizable framework that produces annual, multi-class LULC maps from Landsat time series, with interannual change derived through year-to-year comparisons. A key methodological component of the framework is its predictor-selection strategy, in which variable-importance rankings are used to identify an optimized subset of predictors prior to final model training. MuRaF-LULC was implemented in Google Earth Engine (GEE) and evaluated in Guatemala’s Maya Biosphere Reserve (MBR) for the 2018–2024 period using probability-based sampling and uncertainty-aware accuracy assessment and area estimation. Results show that MuRaF-LULC generates robust annual LULC classifications across multiple years (overall accuracy = 0.90–0.92) and reliable estimates of agropecuario expansion (the dominant transition in the study area) when change is assessed over longer temporal windows where transitions signals stabilize and for which the framework is best suited (producer’s accuracy = 0.97 ± 0.03; user’s accuracy = 0.69 ± 0.05). By prioritizing consistent annual, multiclass LULC trajectories, MuRaF-LULC complements breakpoint- and disturbance-oriented approaches commonly used in land-change studies. Implemented in publicly available, well-documented GEE scripts, MuRaF-LULC facilitates policy-relevant LULC assessment by remote sensing practitioners in governmental and private organizations, where reproducibility, clarity, and ease of deployment are as important as methodological sophistication. Full article

In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion methodologies are employed, allowing data from multiple sensors to be synthesized into robust and more conclusive classification outcomes. This study employs fully polarimetric Synthetic Aperture Radar (PolSAR) imagery and leverages the strengths of three decomposition methods, namely Pauli’s, Krogager’s, and Cloude’s, by extracting their respective components for improved detection. From each decomposition method, three scattering components are derived, enabling the extraction of informative features that describe the scattering behavior associated with various land cover types. The extracted scattering features, treated as independent sensors, were used to train three neural network classifiers. The resulting outputs were then considered as local decisions for each land cover type and subsequently fused through a decision fusion rule to generate more complete and accurate classification results. Experimental results demonstrate that the proposed Multi-Class Bahadur–Lazarsfeld Expansion (MC-BLE) fusion significantly enhances classification performance, achieving an overall accuracy (OA) of 95.78% and a Kappa coefficient of 0.94. Compared to individual classification methods, the fusion notably improved per-class accuracy, particularly for complex land cover boundaries. The core innovation of this work is the transformation of the Bahadur–Lazarsfeld Expansion (BLE), originally designed for binary decision fusion into a multi-class framework capable of addressing multiple land cover types, resulting in a more effective and reliable decision fusion strategy. Full article

This study proposes a lightweight framework for transferring pretrained land cover classification architectures without additional training. The system utilizes French IGN imagery and Korean UAV and aerial imagery. It employs FLAIR U-Net models with ResNet34 and MiTB5 backbones, along with the AI-HUB U-Net. The implementation consists of four sequential stages. First, we perform class mapping between heterogeneous schemes and unify coordinate systems. Second, a quadratic polynomial regression equation is constructed. This formula uses multispectral band statistics as hyperparameters and class-wise IoU as the dependent variable. Third, optimal parameters are identified using the stationary point condition of Response Surface Methodology (RSM). Fourth, the final land cover map is generated by fusing class-wise optimal results at the pixel level. Experimental results show that optimization is typically completed within 60 inferences. This procedure achieves IoU improvements of up to 67.86 percentage points compared to the baseline. For automated application, these optimized values from a source domain are successfully transferred to target areas. This includes transfers between high-altitude mountainous and low-lying coastal territories via proportional mapping. This capability demonstrates cross-regional and cross-platform generalization between ResNet34 and MiTB5. Statistical validation confirmed that the performance surface followed a systematic quadratic response. Adjusted R² values ranged from 0.706 to 0.999, with all p-values below 0.001. Consequently, the performance function is universally applicable across diverse geographic zones, spectral distributions, spatial resolutions, sensors, neural networks, and land cover classes. This approach achieves more than a 4000-fold reduction in computational resources compared to full model training, using only 32 to 150 tiles. Furthermore, the proposed technique demonstrates 10–74× superior resource efficiency (resource consumption per unit error reduction) over prior transfer learning schemes. Finally, this study presents a practical solution for inference and performance optimization of land cover semantic segmentation on standard commodity CPUs, while maintaining equivalent or superior IoU. Full article

Oats (Avena sativa L.) are a staple grain and forage crop with substantial market demand. In China, they are the second most-imported forage grass, only after alfalfa (Medicago sativa L.). Enhancing the salt tolerance of oats to facilitate their cultivation in saline areas can thereby increase forage yield and promote the utilization of saline land, which constitutes an important reserve land resource in China. This study aimed to identify the bacterial strain Bacillus sp. LrM2 (hereafter referred to as strain LrM2) to determine its precise species-level classification and evaluate its effects on oat photosynthesis and growth under salt stress through indoor pot experiments. The results indicated that strain LrM2, capable of urease production and citrate utilization, was identified as Bacillus mojavensis. The strain LrM2 had a positive effect on shoot and root growth of oats under 100 mM NaCl stress conditions. Strain LrM2 inoculation modulated osmotic stress in oats under 100 mM NaCl stress by significantly increasing soluble sugar and decreasing proline content in leaves. It inhibited Na⁺ uptake and promoted K⁺ absorption in the roots, thereby reducing Na⁺ translocation to the leaves and mitigating ionic toxicity. Inoculation with strain LrM2 significantly increased photosynthetic pigment content (chlorophyll a, carotenoids), improved gas exchange parameters (stomatal conductance, transpiration rate, net rate of photosynthesis), enhanced PSII photochemical efficiency (maximum quantum yield, coefficient of photochemical quenching, actual photosynthetic efficiency of PSII, electron transfer rate), and reduced the quantum yield of non-regulated energy dissipation. These improvements, coupled with increased relative water content and instantaneous water use efficiency, thereby collectively enhanced the overall photosynthetic performance. In conclusion, strain LrM2 represents a promising bio-resource for mitigating salt stress and promoting growth in oats, with direct applications for developing novel biofertilizers and sustainable agricultural strategies. Full article

Accurate identification of cultivated land in plateau and mountainous regions remains challenging due to complex terrain and the fragmented, small-scale distribution of farmland. This study develops a high-precision cropland identification model tailored to such environments, aiming to advance precision agriculture and support the scientific planning and refined management of agricultural resources. Taking Xundian County, Yunnan Province, as a case study, multispectral, synthetic aperture radar (SAR), topographic, texture, and time-series features were integrated to construct a comprehensive multi-source feature space. A baseline land use map was generated by fusing datasets from the European Space Agency (ESA), the Environmental Systems Research Institute (ESRI), and the China Resource and Environment Data Cloud (CRLC). Using 4000 randomly selected sample points, five machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), Tabular Multiple Prediction (TABM), XGBoost, and the NSGA-II optimized XGBoost (NSGA-II-XGBoost)—were compared for cropland identification. Results show that the NSGA-II-XGBoost model consistently achieved superior performance in classification accuracy, stability, and adaptability, reaching an overall accuracy of 95.75%, a Kappa coefficient of 0.91, a recall of 0.96, and an F1-score of 0.96. These findings demonstrate the strong capability of the NSGA-II-XGBoost model for cropland mapping under complex topographic conditions, providing a robust technical framework and methodological reference for farmland protection and natural resource classification in other mountainous regions. Full article

Land surface temperature (LST) is an important indicator of ecosystem sustainability and climate change resilience, particularly in highland watersheds characterized by fast land use and land cover (LULC) changes. In this research, the LST dynamics of the Laut Tawar Sub-watershed, Central Aceh, Indonesia, were investigated, based on Landsat 9 OLI/TIRS 2024 imagery. Supervised classification identified eight land cover categories, and their thermal contrasts were evident: built-up and plantation zones exhibited the highest LST values (25–32 °C), while water bodies and forests acted as natural coolers (9.5–17 °C), with elevation further modulating these patterns by creating cooler microclimates at higher altitudes (>2000 m), highlighting the impact of topography in generating microclimatic diversity. Intermediate values were shown for the moderate and sparse forest areas, which thus worked as transition zones with low cooling capabilities. Natural land covers contributed to thermal regulation, whereas built-up and agricultural expansion exacerbated surface heat and possible urban heat island (UHI) effects. This research highlights the importance of protecting forests and water bodies, controlling land conversion, and applying targeted green infrastructure informed by the thermal disparities and land cover dynamics observed. Full article

Sabina vulgaris is a keystone shrub species endemic to arid northwestern China, renowned for its exceptional drought tolerance, sand fixation capabilities, and critical role in desert ecosystem stability. This study investigates the impact of coal mining activities on the spatiotemporal dynamics of S. vulgaris shrublands in the ecologically fragile Mu Us Sandy Land, focusing on the Longde Coal Mine adjacent to the Shenmu S. vulgaris Nature Reserve. Utilizing seven periods (2013–2025) of 2 m resolution Gaofen-1 (GF-1) satellite imagery spanning 12 years of mining operations, we implemented a deep learning approach combining UAV-derived hyperspectral ground truth data and the SegU-Net semantic segmentation model to map shrub distribution via GF-1 data with high precision. Classification accuracy was rigorously validated through confusion matrix analysis (incorporating the Kappa coefficient and overall accuracy metrics). Results reveal contrasting trends: while the S. vulgaris Protection Area exhibited substantial expansion (e.g., Southern Section coverage grew from 2.6 km² in 2013 to 7.88 km² in 2025), mining panels experienced significant degradation. Within Panel 202, coverage declined by 15.4% (58.4 km² to 49.5 km²), and Panel 203 showed a 18.5% decrease (3.16 km² to 2.57 km²) over the study period. These losses correlate spatially and temporally with mining-induced groundwater depletion and land subsidence, disrupting the shrub’s shallow-root water access strategy. The study demonstrates that coal mining drives fragmentation and coverage reduction in S. vulgaris communities through mechanisms including (1) direct vegetation destruction, (2) aquifer disruption impairing drought adaptation, and (3) habitat fragmentation. These findings underscore the necessity for targeted ecological restoration strategies integrating groundwater management and progressive reclamation in mining-affected arid regions. Full article

The Sanjiangyuan Region (SJYR), located in the core of the Qinghai–Tibet Plateau, is a key ecological barrier where grasslands, the dominant land cover, are undergoing continuous degradation due to climate change and human activities. Accurate characterization of grassland is essential for ecological monitoring, yet existing land-cover products show substantial discrepancies in alpine environments. This study systematically evaluated the spatial consistency and accuracy of six publicly medium resolution land cover products: GLC_FCS30, GlobeLand30, FROM_GLC10, ESA WorldCover (ESA), ESRI Land Cover (ESRI), and Dynamic World. We evaluated these products by comparing them with the Third National Land Survey data, performing Jaccard similarity and spatial consistency analyses, and validating their accuracy using five metrics: Overall Accuracy (OA), Producer’s Accuracy (PA), User’s Accuracy (UA), F1-score, and Matthews Correlation Coefficient (MCC). Results show large variations in estimated grassland area, ranging from 91,105 km² (Dynamic World) to 325,669 km² (GLC_FCS30). Pixel-level comparison revealed significant spatial heterogeneity, with only 54.3% of the region showing the desired high consistency. Accuracy validation indicated that ESA achieved the best classification results (OA = 74.24%, MCC = 0.80), while Dynamic World performed the worst (OA = 57.45%, F1 = 0.28). These products showed lower consistency in high-altitude western areas, and classification accuracy for most products varied with elevation and slope, indicating that topographic factors significantly influence remote sensing classification capabilities. These results provide a quantitative basis for product selection in the SJYR and highlight the need for improved calibration, data fusion, and classification approaches that better account for sparse vegetation and complex topography. Full article

Protected areas play a fundamental role in the implementation of international environmental strategies in order to ensure effective management systems that support the conservation of biodiversity and the provision of ecosystem services. However, the actual capacity of national parks to generate a specific “park effect” remains an open question. This study aims to assess whether the transformations observed in Italian national parks between 1960 and 2018 can be attributed to a specific park effect or are instead the result of other territorial dynamics. We analyzed long-term changes in land use and land cover (LUMCs) and variations in ecosystem services (ES), both inside and outside park boundaries, taking into account the SNAI classification. The results show a significant expansion of forest areas (+52%) and sparse vegetation (+56%), alongside a marked decline in arable land (−60%) and permanent crops (−26%). At the same time, the overall value of ES remains stable at around EUR 4 billion per year, with regulating services—accounting for 80% of the total—increasing by 20% between 1960 and 2018 and provisioning services declining by 41%. Italy’s national parks represent strategic socioecological laboratories capable of generating benefits both locally and globally. To fully realize this potential, more integrated management is needed, enabling their transformation from mere conservation areas to drivers of territorial resilience and social cohesion. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

Ground-based LiDAR sensing offers a promising approach for delivering short-range landing feedback to aerial vehicles operating near vertiports and in GNSS-degraded environments. This work introduces a detection system capable of classifying aerial vehicles and estimating their 3D positions with sub-meter accuracy. Using a simulated Gazebo environment, multiple LiDAR sensors and five vehicle classes, ranging from hobbyist drones to air taxis, were modeled to evaluate detection performance. RGB-encoded point clouds were processed using a modified YOLOv6 neural network with Slicing-Aided Hyper Inference (SAHI) to preserve high-resolution object features. Classification accuracy and position error were analyzed using mean Average Precision (mAP) and Mean Absolute Error (MAE) across varied sensor parameters, vehicle sizes, and distances. Within 40 m, the system consistently achieved over 95% classification accuracy and average position errors below 0.5 m. Results support the viability of high-density LiDAR as a complementary method for precision landing guidance in advanced air mobility applications. Full article

Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in conventional K-Means algorithms. This study presents a comprehensive comparative analysis of eight parallelized variants of the K-Means algorithm, designed to enhance clustering efficiency and reduce computational burden for large-scale satellite image analysis. The proposed parallelized implementations incorporate optimized centroid initialization for better starting point selection using a dynamic K-Means sharp method to detect the outlier to improve cluster robustness, and a Nearest-Neighbor Iteration Calculation Reduction method to minimize redundant computations. These enhancements were applied to a test set of 114 global land cover data cubes, each comprising high-dimensional satellite images of size 3712 × 3712 × 16 and executed on multi-core CPU architecture to leverage extensive parallel processing capabilities. Performance was evaluated across three criteria: convergence speed (iterations), computational efficiency (execution time), and clustering accuracy (RMSE). The Parallelized Enhanced K-Means (PEKM) method achieved the fastest convergence at 234 iterations and the lowest execution time of 4230 h, while maintaining consistent RMSE values (0.0136) across all algorithm variants. These results demonstrate that targeted algorithmic optimizations, combined with effective parallelization strategies, can improve the practicality of K-Means clustering for high-dimensional-satellites image analysis. This work underscores the potential of improving K-Means clustering frameworks beyond hardware acceleration alone, offering scalable solutions good for large-scale unsupervised image classification tasks. Full article