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In July 2021, a flooding event, which attracted the attention of the whole country and even the world, broke out in Henan, resulting in dramatic losses across multiple fields (e.g., economic and agricultural). The basin at the junction of Hebi, Xinxiang, and Anyang was the most affected region, as the spread of water from the Wei river submerged surrounding agricultural land (e.g., corn-dominated). To comprehensively evaluate the flooding impacts, we proposed a framework to detect the flooding area and evaluated the degree of loss using satellite time series data. First, we proposed a double-Gaussian model to adaptively determine the threshold for flooding extraction using Synthetic Aperture Radar (SAR) data. Then, we evaluated the disaster levels of flooding with field survey samples and optical satellite images. Finally, given that crops vary in their resilience to flooding, we measured the vegetation index change before and after the flooding event using satellite time series data. We found the proposed double-Gaussian model could accurately extract the flooding area, showing great potential to support in-time flooding evaluation. We also showed that the multispectral satellite images could potentially support the classification of disaster levels (i.e., normal, slight, moderate, and severe), with an overall accuracy of 88%. Although these crops were temporarily affected by this flooding event, most recovered soon, especially for the slightly and moderately affected regions. Overall, the distribution of resilience of these affected crops was basically in line with the results of classified disaster levels. The proposed framework provides a comprehensive aspect to the retrospective study of the flooding process on crops with diverse disaster levels and resilience. It can provide rapid and timely flood damage assessment and support emergency management and disaster verification work. [ABSTRACT FROM AUTHOR]

A novel method called knowledge-guided spatio-temporal consistent correlation analysis (KSTCCA) was developed to discover reliable deformation features induced by multiple factors based on multimode landslide monitoring data. Compared to conventional approaches, KSTCCA integrates both temporal and spatial correlation analysis to improve the consistency of deformation patterns and capture the spatio-temporal heterogeneities in multimode monitoring data. KSTCCA considers both the landslide deformation mechanisms and the relationships between different influential factors as knowledge. Moreover, the method extracts the morphological structures of monitoring curves based on a seven-point approach and identifies knowledge rules using the k-means clustering method. Under the guidance of prior knowledge, a spatial correlation analysis is conducted based on support vector regression, and a temporal correlation analysis of the time lag is carried out based on the morphological structure features. Finally, three kinds of typical monitoring data, including deformation, rainfall, and reservoir water level data collected in the Baishuihe landslide area, China, are used for experimental analysis to verify the validity of the proposed method. [ABSTRACT FROM PUBLISHER]

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization. [ABSTRACT FROM AUTHOR]

Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to identify corn lodging accurately and efficiently. Three groups of image features and five machine-learning approaches are used for classifying non-lodged, moderately lodged, and severely lodged areas. Our results reveal that (1) the combination of spectral bands, optimized vegetation indexes, and texture features classify corn lodging with an overall accuracy of 93.81% and a Kappa coefficient of 0.91. (2) The random forest is an efficient, robust, and easy classifier to identify corn lodging with the F1-score of 0.95, 0.92, and 0.95 for non-lodged, moderately lodged, and severely lodged areas, respectively. (3) The GF-1 PMS image has great potential for identifying corn lodging on a regional scale. [ABSTRACT FROM AUTHOR]

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

Mapping the corn dynamics at a large scale and multiple years is essential for global food security. Traditional mapping approaches by collecting training samples from field surveys are labor-intensive, challenging large-scale mapping of corn dynamics over the long term. This study developed an efficient approach to map large-scale corn dynamics in the main corn districts of the United States (US) using adaptive strategies for collecting high-quality training samples. First, this study proposed an automatic approach to collect stable and representative corn samples from the crop data layers (CDL) product. Then, this study improved the mapping performance of corn at a large scale and in earlier years by using adaptive strategies to collect limited (less than 500) but high-quality training samples. Finally, this study assessed and discussed the model performance across spaces and years using multi-source datasets from 2001 to 2020. Our results indicate the proposed approach with adaptive strategies can generate robust classification models with good performance in mapping large-scale corn dynamics. In our study area, the mean overall accuracy (OA) of corn is about 88% if using the CDL data as a reference. Besides, the R2 of corn areas at the county scale between our result and the surveyed acreage statistics is above 0.9, suggesting the proposed strategy is helpful for mapping corn dynamics across different years. The proposed approach demonstrated that limited but representative samples could map corn dynamics at a large scale with good performance, showing significant improvement compared to the traditional approach. It is feasible to map crop dynamics with multiple types by combining existing crop products with collected field samples, particularly in China, where crop products at the national scale are still lacking. [ABSTRACT FROM AUTHOR]

Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss assessment. However, most flooded crop monitoring methods based on synthetic aperture radar (SAR) imagery were developed for rice, which is probably inappropriate for crops with complex canopy structures that strongly attenuate SAR signals. Additionally, these methods often rely on empirical thresholds and region-specific reference samples, limiting their reliability and applicability on a larger spatial scale. To address these issues, we developed a novel flooded crop mapping approach at a regional scale using Sentinel-1 time-series data and an unsupervised Gaussian Mixture Model (GMM). Our approach leverages a Flood Separability Index (FSI) derived from the fitted probability density function of flooded and non-flooded crop areas in a GMM. This allows us to overcome the limitations of manual input selection in previous studies. The multi-temporal GMM was constructed using the time-series images with optimal polarization to estimate the flooded crop extents on a regional scale. We also investigated the scattering mechanisms of three typical crop disaster structures within an agricultural landscape area. Our results indicate that the proposed multi-temporal GMM is robust in crop planting areas with complex canopy structures. The performance of both single-temporal and multi-temporal GMMs surpasses that of baseline methods such as Otsu and K-means. Compared with VV polarization, VH polarization exhibits greater potential for accurately mapping flooded crops in complex agricultural regions. Our approach does not require labeled samples or many predefined parameters, making it fast and feasible for mapping flooded crops with complex canopy structures in large spatial areas. • We developed an automatic approach to map flooded crop dynamics with GMM. • We analyzed the crop scattering mechanisms under complicated flooding situations. • We presented a novel Flooding Separability Index (FSI) based on GMM. • The VH polarization is potential to identify the flooded crops. [ABSTRACT FROM AUTHOR]

The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios. [ABSTRACT FROM AUTHOR]

Currently, the rapid development of cities and the rapid increase in urban populations have led to a sharp increase in urban components, making precise urban component management, query efficiency, and operational visualization urgent problems to be solved. In this paper, an in-depth study is carried out, pointing out that the current two-dimensional map or component management method based on a real-life three-dimensional city has defects, including query difficulty, fuzzy management, and inefficiency, and it is impossible to accurately and efficiently manage urban components. Then, this paper uses a combination of GIS technology and BIM technology as the starting point. On one hand, this combined technology is based on the high efficiency of the underlying data organization of the GeoSOT grid code and the accuracy of real geographic location expression; on the other hand, based on the integrity of the building information representation and the accuracy of the relative position of internal components of BIM, a precise urban component management method based on GeoSOT grid code and BIM is proposed. Finally, based on this method, a real-time 3D Earth visualization platform is established by using the Cesium platform. Taking the fire hydrant component management of the commercial Guanlan Street in Baiyin City, Gansu Province, China as an example, the precise management of the components in this area is realized, which proves that the method can achieve precise urban component management. [ABSTRACT FROM AUTHOR]

The cellular automata (CA) based models have been extensively used in urban sprawl modeling to support sustainable urban planning. However, in most existing urban CA models, only abrupt conversion (i.e., from non-urban to urban) was considered, whereas the difference in urbanization levels among different grids, as well as the nature of continuous urban evolution process with gradually increasing urban densities, were commonly ignored. Here, we proposed an impervious surface area (ISA) based urban CA model that can simulate the urban fractional change within each grid, using annual urban extent time series data from satellite observations. We implemented the developed ISA-based urban CA model in Beijing (China) and evaluated its performance through model comparison and scenario analyses. We found the ISA-based urban CA model can well capture the dynamics of urban sprawl with improved performance compared to the traditional urban CA model. The spatial pattern of modeled ISA is generally consistent with observed satellite data, with the root mean square error between the modeled and referred results of 0.14 across all changed pixels. Besides, results from our developed model agree well with the binary urban CA model, with notably reduced overestimation errors. The urban fractional change within each grid revealed in our model can provide more details than the traditional binary urban CA models, showing great potential in supporting sustainable urban development. Furthermore, the developed ISA-based urban CA model can be used for regional, even global scale urban sprawl modeling with urban fractional information in each grid, to support decision-making on the sustainable management and conservation of natural land resources. • We developed an urban growth model to simulate urban fractional changes at the grid scale. • Long-term urban extent time series data were used to characterize the urban growth pattern. • We proposed a sigmoid growth model to characterize the urban fractional change. • The proposed model can promote the efficiency of urban land use modeling at a large (e.g., regional and global) scale. [ABSTRACT FROM AUTHOR]

Although abundant spatiotemporal data are collected before and after landslides, the volume, variety, intercorrelation, and heterogeneity of multimodal data complicates disaster assessments, so it is challenging to select information from multimodal spatiotemporal data that is advantageous for credible and comprehensive disaster assessment. In disaster scenarios, multimodal data exhibit intrinsic relationships, and their interactions can greatly influence selection results. Previous data retrieval methods have mainly focused on candidate ranking while ignoring the generation and evaluation of candidate subsets. In this paper, a semantic-constrained data selection approach is proposed. First, multitype relationships are defined and reasoned through the heterogeneous information network. Then, relevance, redundancy, and complementarity are redefined to evaluate data sets in terms of semantic proximity and similarity. Finally, the approach is tested using Mao County (China) landslide data. The proposed method can automatically and effectively generate suitable datasets for certain tasks rather than simply ranking by similarity, and the selection results are compared with manual results to verify their effectiveness. [ABSTRACT FROM AUTHOR]