Search

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method. [ABSTRACT FROM AUTHOR]

The eastern Hengduan Mountains are located in the transition zone between the Qinghai-Tibet Plateau and the Sichuan Basin and are important for global biodiversity and water conservation in China. However, their landscape pattern vulnerability index (LVI) and its influencing factors have not been systematically studied. Therefore, the spatial distribution patterns, LVI, and the landscape artificial disturbance intensity (LHAI) of Ganzi Prefecture were analyzed using ArcGIS software based on landscape data and Digital Elevation Model (DEM) digital elevation data. Then, the LVI response to LHAI and elevation was discussed. The results showed that Ganzi Prefecture was dominated by low- and middle-LVI areas, together accounting for 56.45% of the total area. LVI values were highest in the northern regions, followed by the southern and eastern regions. Batang and Derong counties had the highest LVI values. Most areas in Ganzi Prefecture had very low- or low-LHAI values, accounting for 81.48% of the total area, whereas high-LHAI areas accounted for 2.32% of the total area. Both the LVI and LHAI of Ganzi Prefecture had clustered distributions. Spearman analysis indicated that when elevation exceeded 4500 m, it was the most important factor affecting LVI and LHAI. In the range of 4500–5400 m, the relationship between elevation and LVI shifted from a weak positive correlation to a negative correlation, whereas LHAI was positively correlated with elevation. In addition, LVI also responded significantly to LHAI. However, the relationship kept changing as elevation increased. Hence, the ecological vulnerability of high elevation areas above 4500 m deserves greater attention. In addition, pasture areas in the upstream reaches of the Yalong River in the northern region, the coastal area in the downstream reaches of the Jinsha River in the southern region, and the eastern mining area, should be prioritized for protection and restoration. This research provides a basis for appropriate environmental planning mechanisms and policy protections at the landscape level. [ABSTRACT FROM AUTHOR]

This study presents an enhanced variant of the priority-flood based algorithm proposed by Wang and Liu for treating depressions in digital elevation models (DEMs). The enhanced variant redefines spill elevation, the key concept of the original algorithm, as the lowest elevation that a pixel needs to have to ensure a non-ascending path toward the border of the DEM, plus the larger of a small number (~0.001) and the difference between the unaltered elevation values of the focal pixel and its immediate downhill neighbor. This redefinition is adopted to obtain an intermediate elevation surface to direct flow and ultimately to carve the original DEM. Each carving starts from a depression bottom and propagates downstream until a downhill cell is guaranteed in the original DEM. Tests of these algorithms on a complex terrain of the 260,000 km2 Sichuan structural basin in China shows that the enhanced algorithm maximally preserves the original flow directions and extracts realistic drainage networks. Retaining the relative heights, and therefore flow directions, of cells within depressions allows the new algorithm to offer a depressionless DEM with small modification of its origin for further hydrologic applications. The enhanced depression treatment algorithm is provided as the freely available tool BNUSinkRemv. [ABSTRACT FROM AUTHOR]