Search Results (303)

The accurate positioning and state estimation of surface vessels are prerequisites to autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the autonomy of both land and aerial vehicles, which has attracted the interest of researchers in the maritime community. However, in traditional maritime surface multi-scenario applications, LiDAR scan matching has low point cloud scanning and matching efficiency and insufficient positional accuracy when dealing with large-scale point clouds, so it has difficulty meeting the real-time demand of low-computing-power platforms. In this paper, we use ICP-SVD for point cloud alignment in the Stanford dataset and outdoor dock scenarios and propose an optimization scheme (iVox + ICP-SVD) that incorporates the voxel structure iVox. Experiments show that the average search time of iVox is 72.23% and 96.8% higher than that of ikd-tree and kd-tree, respectively. Executed on an NVIDIA Jetson Nano (four ARM Cortex-A57 cores @ 1.43 GHz) the algorithm processes 18 k downsampled points in 56 ms on average and 65 ms in the worst case—i.e., ≤15 Hz—so every scan is completed before the next 10–20 Hz LiDAR sweep arrives. During a 73 min continuous harbor trial the CPU temperature stabilized at 68 °C without thermal throttling, confirming that the reported latency is a sustainable, field-proven upper bound rather than a laboratory best case. This dramatically improves the retrieval efficiency while effectively maintaining the matching accuracy. As a result, the overall alignment process is significantly accelerated, providing an efficient and reliable solution for real-time point cloud processing. Full article

The urban thermal environment poses a significant challenge to public health and sustainable urban development. Conventional pre-defined classification schemes, such as the Local Climate Zone (LCZ) system, often fail to capture the highly heterogeneous structure of complex urban areas, thus limiting their applicability. This study introduces a novel framework for urban thermal environment analysis, leveraging multi-source data and eXplainable Artificial Intelligence to investigate the driving mechanisms of Land Surface Temperature (LST) across various urban form types. Focusing on the area within Beijing’s 5th Ring Road, this study employs a K-Means clustering algorithm to classify urban blocks into nine distinct types based on their building morphology. Subsequently, an eXtreme Gradient Boosting (XGBoost) model, coupled with the SHapley Additive exPlanations (SHAP) method, is utilized to analyze the non-linear impacts of ten selected driving factors on LST. The findings reveal that: (1) The Compact Mid-rise type exhibits the highest annual average LST at 296.59 K, with a substantial difference of 11.29 K observed between the hottest and coldest block types. (2) SHAP analysis identifies the Normalized Difference Built-up Index (NDBI) as the most significant warming factor across all types, while the Sky View Factor (SVF) plays a crucial cooling role in high-rise areas. Conversely, road density (RD) shows a negative correlation with LST in Open Low-rise areas. (3) The influence of urban form is twofold: increased building height (BH) can induce warming by trapping heat while simultaneously providing a cooling effect through shading. (4) The impact of land use functional zones on LST is significantly modulated by urban form, with temperature differences of up to 2 K observed between different functional zones within compact block types. The analytical framework proposed herein holds significant theoretical and practical implications for achieving fine-grained thermal environment governance and fostering sustainable development in the context of global urbanization. Full article

Climate change substantially affects water resources and agriculture, highlighting the critical importance of assessing land suitability for surface irrigation. This study was initiated with the objective of assessing the present and future land suitability for surface irrigation in the Gardulla Zone of Southern Ethiopia, utilizing meteorological, topography, soil, land cover, and proximity data. The analytic hierarchy process and weighted overlay analysis were employed to assign factor weights, while future climate projections were downscaled via a statistical downscaling model (SDSM4.2) under the shared socio-economic pathways (i.e., SSP2-4.5 and SSP5-8.5) scenarios. Irrigation suitability mapping was performed via inverse distance-weighted interpolation. The results revealed that 8% of the area is highly suitable, 54.3% is moderately suitable, 30% is marginally suitable, and 2.3% is unsuitable under current climate conditions. In the future periods, under both SSP scenarios, highly suitable land increases (up to 9.7% and 10.3% by 2050s and 10.8% and 13.5% by the 2080s under SSP2-4.5 and SSP5-8.5, respectively), whereas unsuitable land decreases (down to 0.6% by 2080s under SSP5.8.5). In terms of area, highly to moderately suitable land expanded by 1357.6–6867.7 ha, depending on the scenario and timeframe. The study concludes that climate change is expected to affect the suitability of land for surface irrigation potential in the study area and similar hydroclimatic settings, highlighting the need for forward-looking policies and adaptation options. Therefore, it is recommended to promote climate-smart irrigation systems by integrating site-specific suitability mapping into regional land-use planning and prioritizing investment in small-scale, community-managed surface irrigation schemes that reduce water losses and ensure long-term agricultural sustainability. Full article

Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations. Full article

This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water land can vary depending on the region and flood conditions. Therefore, the flood detection performance of the dual-pol parameters was evaluated across three datasets with different geographic, climatic, and land cover conditions. The results demonstrated that accurate and stable performance in the detection of inundated areas under different surface conditions can be achieved by combining water body information from dual-pol channels in a disjunctive way. It also suggests that synergy in flood detection can be expected when using polarization observation data by considering each polarimetric channel as an independent information source and combining them rather than deriving the most relevant polarization parameter. Furthermore, combining common information from two dual-pol channels in a conjunctive way could provide the most reliable SAR flood detection results with minimum false alarms from the user’s perspective. Based on these experimental results, a two-class flood classification scheme was proposed for improving the applicability of SAR remote sensing in identifying flooded areas. Full article

Remote sensing approaches can be cost-effective for estimating forest structural attributes. This study aims to use airborne LiDAR data to assess the robustness of multispectral satellite imagery and topographic attributes derived from DEMs to predict the density of three vegetation layers in a wet eucalypt forest in Tasmania, Australia. We compared the predictive capacity of medium-resolution Landsat-8 Operational Land Imager (OLI) surface reflectance and three pixel sizes from high-resolution WorldView-3 satellite imagery. These datasets were combined with topographic attributes extracted from resampled LiDAR-derived DEMs and a geology layer and validated with vegetation density layers extracted from high-density LiDAR. Using spectral bands, indices, texture features, a geology layer, and topographic attributes as predictor variables, we evaluated the predictive power of 13 data schemes at three different pixel sizes (1.6 m, 7.5 m, and 30 m). The schemes of the 30 m Landsat-8 (OLI) dataset provided better model accuracy than the WorldView-3 dataset across all three pixel sizes (R² values from 0.15 to 0.65) and all three vegetation layers. The model accuracies increased with an increase in the number of predictor variables. For predicting the density of the overstorey vegetation, spectral indices (R² = 0.48) and texture features (R² = 0.47) were useful, and when both were combined, they produced higher model accuracy (R² = 0.56) than either dataset alone. Model prediction improved further when all five data sources were included (R² = 0.65). The best models for mid-storey (R² = 0.46) and understorey (R² = 0.44) vegetation had lower predictive capacity than for the overstorey. The models validated using an independent dataset confirmed the robustness. The spectral indices and texture features derived from the Landsat data products integrated with the low-density LiDAR data can provide valuable information on the forest structure of larger geographical areas for sustainable management and monitoring of the forest landscape. Full article

The impact of urbanization on the spatial distribution of extreme precipitation has become a major topic in the field of urban hydrology. This study used an urban canopy model (UCM) coupled with a Weather Research and Forecasting model (WRF) to analyze two extreme precipitation events experienced by the Pearl River Delta on 12–13 June (monsoon rainstorm) and 16–17 September (typhoon rainstorm) in 2018. The results showed that both experiments, considering UCM and not considering UCM, can effectively simulate the spatial distribution of two precipitation events in Pearl River Delta urban agglomeration. The root mean square errors of simulation and observation data of the two precipitation events by the UCM scheme were 14.6 mm and 16.7 mm, respectively, indicating relatively high simulation accuracy. The simulated precipitation amounts for the two rainfall events were increased by 2.3 mm and 3.0 mm, respectively. The simulation results of the two precipitation events showed that compared to agricultural land, urban and built-up land have experienced temperature increases of approximately 0.7 °C and 1 °C, respectively. The air-specific humidity of the two precipitation events increased by approximately 0.5 g/kg and 1.2 g/kg, respectively. These differences between UCM and NON simulations confirm that the increase in near-surface air humidity and temperature significantly enhances the intensity of extreme precipitation in the Pearl River Delta urban agglomeration. Full article

Western Kazakhstan is susceptible to desertification, with surface water resource scarcity constraining agricultural development. Groundwater has substantial potential as a reliable and secure alternative to other water resources, particularly for irrigation, which is required to ensure food security. Eight aquifer segments with an exploitable potential of 0.24 km³/year have been identified for the integrated assessment of groundwater’s suitability for irrigation. The assessment criteria included hydro-chemical groundwater characteristics and irrigated land soil-reclamation conditions. The primary objectives of this study were to assess the groundwater quality for irrigation and to develop a practical operation scheme for rational groundwater use in water-saving irrigation technologies and optimize agricultural crop cultivation. Approximately 90% of the groundwater in these aquifer segments was found to be suitable for irrigation, with a total amount of 6520 thousand m³/day and a salinity of up to 1 g/L, and an additional 12,971 thousand m³/day had a water salinity of up to 3 g/L. Only approximately 10% had TDS values above 3 g/L and up to 6.5 g/L, categorized as conditionally suitable for restricted customized agricultural crop irrigation. Irrigated land development by complex soil desalination agro-reclamation operations enabled the use of brackish water for irrigation. The integrated analysis allowed the development of drip irrigation and sprinkling system irrigation schemes that gradually replaced wasteful surface irrigation. The irrigated land prospective area recommended for groundwater irrigation development is 653 km², with the further restructuring of cultivated areas, reducing the number of annual grasses and grain crops and increasing the number of vegetables, potatoes, and perennial grasses. Full article

The effectiveness of snow data assimilation is closely related to the satellite data quality control that affects snow cover data used for assimilation and meteorological forcings that drive land surface model to estimate snow depth, especially over headwater regions where in situ measurements are sparse and land surface simulations are challenging. This study proposes a joint quality control scheme based on precipitation constraints and cloud thresholds, uses the Ensemble Square Root Filter to assimilate the controlled data to improve snow depth estimation from the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), and explores the impacts of different forcing data on the assimilation. The correlation between the assimilated monthly snow depth data and the in situ measurements averaged over 21 stations during November–February of 2000–2015 is 0.93, and the root mean square error is 0.22 cm. Compared with CSSPv2 model simulation, the correlation increased by 5.6%, and the error decreased by 18.5%. The joint quality control scheme has led to an average accuracy improvement of 47%, while the high-quality forcing data have resulted in an average enhancement of 58%. This study suggests that satellite data quality control and meteorological forcings are important for increasing correlation and decreasing error for snow depth assimilation, respectively. Full article

Accurate identification of dust emission sources is crucial for simulating dust aerosols in atmospheric chemical models. Therefore, a dynamically updated dust source function (DSF) was developed within the dust emission scheme of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to simulate an East Asian dust storm event from 13 to 16 March 2021. Utilizing satellite-derived input of vegetation cover, snow cover, soil texture, and land use, the DSF was updated to better identify dust source areas over bare soils and sparsely vegetated regions in western China and central-western Mongolia. With the updated DSF, simulated dust emissions increase significantly over western China and Mongolia. The dust aerosol simulations demonstrate substantial improvements in near-surface PM₁₀ concentrations, a better agreement with remotely sensed dust aerosol optical depth (DOD), and a more accurate representation of the vertical distribution of dust extinction coefficients compared to observations. This study highlights the importance of integrating real-time data to accurately characterize dust emission sources, thereby improving atmospheric environment simulations. Full article

Surface fluxes are vital to understanding land–atmosphere interactions, with similarity theory forming the basis for their parameterization. However, this theory has limitations, particularly due to large eddy effects, which have not been widely considered in Earth system models. A novel scheme was proposed to address this, considering large eddy effects under unstable atmospheric conditions. This study systematically evaluates the proposed scheme using the CoLM2014 model, FLUXNET2015 data, and ERA5 data. Based on the analysis of flux parameterization mechanisms, it proposes specific improvements aimed at enhancing the scheme’s performance. Our findings indicate that the proposed and classical schemes yield similar results, partly because they employ the same dimensionless wind speed gradient under near-neutral conditions. Furthermore, the results revealed that friction velocity responded more strongly to large eddies than did heat flux, as friction velocity influenced atmospheric stability and thereby mitigates the large eddy effects on heat flux. Additionally, our analysis reveals that bare soil exhibits the most pronounced changes in surface fluxes and energy partitioning, while grassland-type and forest-type sites display more complex responses. These findings indicate that different land cover types respond distinctly to the influence of large eddies. Overall, this research deepens our understanding of large eddy impacts and improves Earth system modeling by enhancing land–atmosphere interaction parameterization. Full article

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R² = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R² = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R² ranging from 0.79 to 0.95. Full article

The accurate modeling of complex freeze–thaw processes and hydrothermal dynamics within the active layer is challenging. Due to the uncertainty in hydrothermal simulation, it is necessary to thoroughly investigate the parameterization schemes in land surface models. The Noah-MP was utilized in this study to conduct 23,040 ensemble experiments based on 11 physical processes, which were aimed at improving the understanding of parameterization schemes and reducing model uncertainty. Next, the impacts of uncertainty of physical processes on land surface modeling were evaluated via Natural Selection and Tukey’s test. Finally, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to identify the optimal combination of parameterization schemes for improving hydrothermal simulation. The results of Tukey’s test agreed well with those of Natural Selection for most soil layers. More importantly, Tukey’s test identified more parameterization schemes with consistent model performance for both soil temperature and moisture. Results from TOPSIS showed that the determination of optimal schemes was consistent for the simulation of soil temperature and moisture in each physical process except for frozen soil permeability (INF). Further analysis showed that scheme 2 of INF yielded better simulation results than scheme 1. The improvement of the optimal scheme combination during the frozen period was more significant than that during the thawed period. Full article

Due to significant political and environmental decisions regarding clean energy, rapid adoption of solar photovoltaic (PV), wind power, and hydropower is taking place. In China, policy environmental impact assessment (EIA) plays an important role in pollution prevention, while its practice is relatively limited due to insufficient methodologies and weak legislative enforcement. Taking the clean energy action plan (CEAP) in the Qinghai region as a case study, this study explored the application of life cycle assessment (LCA) to evaluate the potential environmental impacts imposed by the installment capability of 70,000 MW solar PV in pristine areas. It was found that the CO₂ emissions of solar PV are less than 3% of that of clean coal-fired power, while the emissions of pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter only account for about 18~27% of coal-fired power. Meanwhile, from the full life cycle perspective, 4.61 million tons of solar PV panel waste will be generated, and 4172 square kilometers of land surface area will be occupied. Herein, implications for policy are proposed, including (1) advance planning of local waste disposal capacity and processing facilities, (2) the integration of clean energy planning with legal ecological environment protection schemes, and (3) rational planning of upstream and downstream solar PV industries. Full article

Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R² = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R² = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research. Full article