China’s first optical stereo mapping satellite with a sub-meter resolution, GaoFen-7 (GF-7), launched in November 2019, shows significant potential for providing high-resolution topographic and geomorphic data for quantitative research on active tectonics. However, no studies have evaluated the capability of the GF-7-generated digital elevation model (DEM) for quantitatively studying active tectonics. This study aimed to validate the accuracy of the DEMs extracted from GF-7 stereo imagery, with or without ground control points (GCPs), and evaluated the potential of applying GF-7 DEMs to active tectonics. First, GF-7 stereo images were processed to obtain DEMs with a spatial resolution of 2 m, utilizing three different methods, including block adjustment without GCPs, block adjustment with the aid of Google Earth images and SRTM DEM, and block adjustment with GCPs derived from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data. These three generated DEMs were called GF-7 DEM_Method1, GF-7 DEM_Method2, and GF-7 DEM_Method3, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements’ accuracies. The results indicated that the accuracy of GF-7 DEM_Method1 was the worst and that of GF-7 DEM_Method3 was superior to that of GF-7 DEM_Method2. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEM_Method1, GF-7 DEM_Method2, and GF-7 DEM_Method3 were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets.

One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.

Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil–environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The void-filling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIM-FilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIM-FilterNA with the SoLIM combined with the void-filling scheme, the original SoLIM with the location-skipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIM-FilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIM-FilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme.

Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods.

In March 1966, M_s 6.8 and 7.2 earthquakes occurred in Xingtai, North China, resulting in widespread soil liquefaction that caused severe infrastructure damage and economic losses. Using Keyhole satellite imagery combined with aerial images and fieldwork records, we interpreted and identified 66,442 liquefaction points and analyzed the coseismic liquefaction distribution characteristics and possible factors that influenced the Xingtai earthquakes. The interpreted coseismic liquefaction was mainly concentrated above the IX-degree zone, accounting for 80% of all liquefaction points. High-density liquefaction zones (point density > 75 pieces/km²) accounted for 22% of the total liquefaction points. Most of the interpreted liquefaction points were located at the region with a peak ground acceleration (PGA) of >0.46 g. The liquefaction area on 22 March was significantly larger than that on 8 March. The region of liquefaction was mainly limited by sandy soil conditions, water system conditions, and seismic geological conditions and distributed in areas with loose fine sand and silt deposits, a high water table (groundwater level increases before both mainshocks corresponding to the liquefaction intensive regions), rivers, and ancient river channels. Liquefaction exhibited a repeating characteristic in the same region. Further understanding of the liquefaction characteristics of Xingtai can provide a reference for the prevention of liquefaction in northern China.