Earth Science Informatics最新文献

Cargo security is one of the most critical issues in modern logistics. For high-value theft-targeted (HVTT) cargo the driving phase of transportation takes up a major part of thefts. Dozen fleet management solutions based on GNSS positioning were introduced in recent years. Existing tracking solutions barely meet the requirements of TAPA 2020. Map-matching algorithms present valuable ideas on handling GNSS inaccuracy, however, universal map-matching methods are overcomplicated. Commercial map data providers require additional fees for the use of real-time map-matching functionality. In addition, at the map-matching stage, information on the actual distance from which the raw data was captured is lost. In HVTT security, the distance between the raw GNSS position and map-matched position can be used as a quantitative security factor. The goal of this research was to provide empirical data for TAPA TSR 2020 Level 1 certification in terms of tracking vehicles during typical operating conditions (cargo loading, routing, transportation, stopover, unloading) as well as detecting any geofencing violations. The Dynamic Geofencing Algorithm (DGA) presented in this article was developed for this specific purpose and this is the first known pulication to examine TAPA Standarization in terms of cargo positioning and fleet monitoring. The DGA is adaptive geometric-based matching (alternately curve-to-curve, point-to-curve, point-to-point). The idea behind the algorithm is to detect and eliminate the atypical matching circumstances—namely if the raw position is registered at one of the exceptions described in the paper. The problem of dynamic/adaptive cartographic projection is also addressed so that the robus Euclidean calculactions could be used in global scale.

Complex changes in coastlines are increasing with climate, sea level, and human impacts. Remote Sensing (RS) and Geographic Information Systems (GIS) provide critical information to rapidly and precisely monitor environmental changes in coastal areas and to understand and respond to environmental, economic, and social impacts. This study aimed to determine the temporal changes in the coastline of the Seyhan Basin, Türkiye, using Landsat satellite images from 1985 to 2023 on the Google Earth Engine (GEE) platform. The approximately 50 km of coastline was divided into three regions and analyzed using various statistical techniques with the Digital Shoreline Analysis System (DSAS) tool. In Zone 1, the maximum coastal accretion was 1382.39 m (Net Shoreline Movement, NSM) and 1430.63 m (Shoreline Change Envelope, SCE), while the maximum retreat was -76.43 m (NSM). Zone 2 showed low retreat and accretion rates, with maximum retreat at -2.39 m/year (End Point Rate, EPR) and -2.45 m/year (Linear Regression Rate, LRR), and maximum accretion at 0.99 m/year (EPR) and 0.89 m/year (LRR). Significant changes were observed at the mouth of the Seyhan delta in Zone 3. According to the NSM method, the maximum accretion was 1337.72 m, and maximum retreat was 1301.4 m; the SCE method showed a maximum retreat of 1453.65 m. EPR and LRR methods also indicated high retreat and accretion rates. Statistical differences between the methods were assessed using the Kruskal–Wallis H test and ANOVA test. Generally, NSM and EPR methods provided similar results, while other methods varied by region. Additionally, the Kalman filtering model was used to predict the coastline for 2033 and 2043, identifying areas vulnerable to future changes. Comparisons were made to determine the performance of Kalman filtering. In the 10-year and 20-year future forecasts for determining the coastline for the years 2033 and 2043 with the Kalman filtering model, it was determined that the excessive prediction time negatively affected the performance in determining the coastal boundary changes.

Detecting the influence of temperature on urban vegetation is useful for planning urban biodiversity conservation efforts, since temperature affects several ecosystem processes. In this study, the relationships between land surface temperature (LST) and vegetation phenology events (start of growing season, SOS; end of growing season, EOS; peak phenology) was examined in native savannah woodland and grass parcels of a hot climate town. For comparison, similar woodland and grass parcels on the town’s periphery, and a wetland, were used. The vegetation parcel LST values (°C) in one calendar year (2023) were obtained from Landsat-8 (L8) and Landsat-9 (L9) thermal imagery, whose combination yielded an 8-day image frequency. Phenology changes relative to seasonal air temperature and LST were determined using vegetation index (VI) values computed from accompanying 30 m resolution L8-L9 non-thermal bands: the Normalised Difference Vegetation Index (NDVI) and one improved VI, the Soil Adjusted Vegetation Index (SAVI). Higher imaging frequency, 250 m resolution NDVI and Enhanced Vegetation Index (EVI) MOD13Q1 layers supplemented the L8-L9 VIs. LST correlated highly with air temperature (p < 0.001). On nearly all L8-L9 image dates, the urban vegetation parcel’s mean LST was higher (p < 0.001) than that at its peri-urban equivalent. Improved VIs (SAVI, EVI) detected some phenology events to have occurred slightly earlier than detected by the NDVI. Associated with the higher LST, the SOS was earlier in the urban than in the peri-urban woodland. This association has scarcely been demonstrated in savannah vegetation, necessitating proactive efforts to reduce potential biodiversity effects.

Accurate digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) data are crucial for terrain analysis applications. As established in the literature, higher point density improves terrain representation but requires greater data storage and processing capacities. Therefore, point cloud sampling is necessary to reduce densities while preserving DEM accuracy as much as possible. However, there has been a limited examination directly comparing the effects of various sampling algorithms on DEM accuracy. This study aimed to help fill this gap by evaluating and comparing the performance of three common point cloud sampling methods octree, spatial, and random sampling methods in high terrain. DEMs were then generated from the sampled point clouds using three different interpolation algorithms: inverse distance weighting (IDW), natural neighbor (NN), and ordinary kriging (OK). The results showed that octree sampling consistently produced the most accurate DEMs across all metrics and terrain slopes compared to other methods. Spatial sampling also produced more accurate DEMs than random sampling but was less accurate than octree sampling. The results can be attributed to differences in how the sampling methods represent terrain geometry and retain microtopographic detail. Octree sampling recursively subdivides the point cloud based on density distributions, closely conforming to complex microtopography. In contrast, random sampling disregards underlying densities, reducing accuracy in rough terrain. The findings guide optimal sampling and interpolation methods of airborne lidar point clouds for generating DEMs for similar complex mountainous terrains.

Lake temperature forecasting is crucial for understanding and mitigating climate change impacts on aquatic ecosystems. The meteorological time series data and their relationship have a high degree of complexity and uncertainty, making it difficult to predict lake temperatures. In this study, we propose a novel approach, Probabilistic Quantile Multiple Fourier Feature Network (QMFFNet), for accurate lake temperature prediction in Qinghai Lake. Utilizing only time series data, our model offers practical and efficient forecasting without the need for additional variables. Our approach integrates quantile loss instead of L2-Norm, enabling probabilistic temperature forecasts as probability distributions. This unique feature quantifies uncertainty, aiding decision-making and risk assessment. Extensive experiments demonstrate the method’s superiority over conventional models, enhancing predictive accuracy and providing reliable uncertainty estimates. This makes our approach a powerful tool for climate research and ecological management in lake temperature forecasting. Innovations in probabilistic forecasting and uncertainty estimation contribute to better climate impact understanding and adaptation in Qinghai Lake and global aquatic systems.

This review article examines the critical role of remote sensing techniques in analysing land cover and its implications for copper deposit exploration. The study aims to provide a comprehensive review of current research and technical advancements in using remote sensing to characterise land cover in copper-rich areas. It draws attention to the complex relationships that exist between subsurface copper mineralisation, surface vegetation, and soil types by combining case studies and modern literature. Integrating satellite imagery, geospatial data, and advanced analytical methods, this review demonstrates how remote sensing can effectively identify and map areas with high potential for copper deposits. Furthermore, it discusses the challenges and opportunities associated with remote sensing applications in geological studies and offers insights into future research directions to enhance mineral exploration and environmental management practices.

Building height information is essential for determining urban morphology, urban planning studies, and manage sustainable growth. This study aims to use machine learning algorithms to estimate building heights from airborne LiDAR and spaceborne ICESat-2/ATLAS data. The performance of different machine learning algorithms was investigated when analyzing ICESat-2/ATLAS and airborne LiDAR data. The accuracy of building height information was compared with field measurements. Machine learning algorithms such as K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANNs), and Random Sample and Consensus (RANSAC) were used to classify spaceborne and airborne LiDAR data. Among all the algorithms applied to ICESat-2/ATLAS, the RF algorithm provided the best results for the strong and weak beams with 0.9683 and 0.9614, respectively. The K-NN yielded the best result for the airborne LiDAR dataset with 0.9999. Statistical analyzes were applied to both LiDAR datasets. The results of statistical analyzes for the pair of field measurement and ICESat-2 were R² = 0.9894, RMSE = 0.4131, MSE = 0.1706, MAE = 0.3184, and ME = 0.0003; for the pair of field measurement and airborne LiDAR: R² = 0.8368, RMSE = 1.9646, MSE = 3.8597, MAE = 1.0586, and ME = -0.3450; and for the pair of airborne LiDAR and ICESat-2: R² = 0.8275, RMSE = 1.6664, MSE = 2.7770, MAE = 0.9040, and ME = 0.4598. As a result of the analysis, it was seen that the data obtained from the ICESat-2 system was successful in estimating building height and provided reliable data.

Monitoring and assessing spatiotemporal changes and driving factors of ecological environment quality in the middle Yellow River Basin (MYRB) is significant for ecological environment protection, management, and high-quality development. We reconstructed data from 1986‒2023 Landsat series images using the harmonic analysis of time series (HANTS) algorithm on the Google Earth Engine platform to optimize the remote-sensing ecological index (RSEI) calculation process, and analyzed the trends and sustainability of ecological environment quality changes. The HANTS algorithm reduced dispersion and anomalies, filled in missing images, and enhanced the Landsat series image quality. The RSEI accurately reflected the ecological environment quality from 1986‒2023 in the MYRB, reducing the "pseudo-variation" conclusion of multi-year evaluations, and enhancing the stability of regional ecological environment quality assessments. Ecological environment quality in the MYRB generally showed an improving trend from 1986‒2023, with significant improvement covering 71.6% of the area; however, the change in ecological environment quality showed weak sustainability. The results reflected the positive effects of ecological restoration and the negative impact of urban construction. The optimized RSEI effectively reflected the ecological environment quality of the MYRB, improved the long-term RSEI stability, and satisfied the requirements of large-scale and long-term ecological environment quality monitoring.

This paper selects three typical observation sites in Hengshui city, Hengshui Lake wetland, and youth woodland along the river, and uses non-contact infrared temperature measurement equipment to carry out high-temperature continuous observation of four kinds of underlay surfaces, namely, asphalt, grassland, woodland, and wetland, to compare the temporal characteristics of the surface temperature of each kind of underlay surface and its relationship with meteorological factors, and to establish the multivariate linear regression equations for the four kinds of maximum surface temperatures of underlay surfaces based on a variety of meteorological factors. Regression equations were established, and the main results were as follows: ①The daily maximum temperature, daily average temperature, and daily minimum temperature change curves of asphalt underlay were significantly higher than those of other underlay, and the change trends of grassland, woodland, and wetland were the same, and the curves were close to each other. ②The maximum and minimum temperatures of the four types of underlayment were ranked as asphalt > wetland > forestland > grassland. ③The maximum surface temperatures of the four types of underlayment were positively correlated with the daily maximum air temperature and solar radiation, with correlation coefficients around 0.9, and negatively correlated with the daily total cloudiness and the daily maximum relative humidity, with correlation coefficients above 0.5. ④The four types of sub surface maximum temperature forecasts are well fitted to the observed values, with correlation coefficients of 0.70 or more, and the error results are within the acceptable range, which can meet the needs of high-temperature forecasting, among which the grassy subsurface has the best fit, with a correlation coefficient of 0.90.The results have certain reference significance for knowing thermal environment of different urban underlying surfaces, while. providing scientific evidence for the development of refined urban meteorological forecasting services.

A drought is a complex event characterized by low rainfall and has negative implications for agricultural and hydrological systems, as well as for community life. A common meteorological drought index used for drought monitoring and water resource management is the Standardized Precipitation Evapotranspiration Index (SPEI). Using SPEI can assist in predicting drought onset and estimating drought severity. The objective of this research is to assess the accuracy of machine learning models in estimating the SPEI-1 (one-month) index in semi-arid climates. To achieve this goal, the data will be analyzed using remote sensing parameters, a worldwide database, and meteorological station information. SPEI-1 was predicted in Tabriz, Iran, between 1990 and 2022 using multilayer perceptron (MLP) and random forest (RF) techniques combined with genetic algorithm (GA) methods. The parameters used are average air temperature, average relative humidity, monthly precipitation, wind speed, sunny hours, as well as the one-month standard precipitation index (SPI-1) (from ground data), daily precipitation products from satellites named PERSIANN (PRC-PR) (from remote sensing), and SPEIbase data (from global databases). The results suggest that the use of satellite remote sensing characteristics and global databases has significantly enhanced the precision and efficiency of prediction models. Based on the GA-RF model with an R² of 0.992 and an RMSE of 0.124, it exhibits the best performance among all models in Scenario 1. By combining remote sensing parameters, this study presents an innovative approach to predicting the SPEI index and demonstrates their capabilities in drought management and mitigation.