Analysis of the spatial association of geographical detector-based landslides and environmental factors in the southeastern Tibetan Plateau, China.

Steep canyons surrounded by high mountains resulting from large-scale landslides characterize the study area located in the southeastern part of the Tibetan Plateau. A total of 1766 large landslides were identified based on integrated remote sensing interpretations utilizing multisource satellite images and topographic data that were dominated by 3 major regional categories, namely, rockslides, rock falls, and flow-like landslides. The geographical detector method was applied to quantitatively unveil the spatial association between the landslides and 12 environmental factors through computation of the q values based on spatially stratified heterogeneity. Meanwhile, a certainty factor (CF) model was used for comparison. The results indicate that the q values of the 12 influencing factors vary obviously, and the dominant factors are also different for the 3 types of landslides, with annual mean precipitation (AMP) being the dominant factor for rockslide distribution, elevation being the dominant factor for rock fall distribution and lithology being the dominant factor for flow-like distribution. Integrating the results of the factor detector and ecological detector, the AMP, annual mean temperature (AMT), elevation, river density, fault distance and lithology have a stronger influence on the spatial distribution of landslides than other factors. Furthermore, the factor interactions can significantly enhance their interpretability of landslides, and the top 3 dominant interactions were revealed. Based on statistics of landslide discrepancies with respect to diverse stratification of each factor, the high-risk zones were identified for 3 types of landslides, and the results were contrasted with the CF model. In conclusion, our method provides an objective framework for landslide prevention and mitigation through quantitative, spatial and statistical analyses in regions with complex terrain.

[Spectral curve shape feature-based hyperspectral remote sensing image retrieval].

With the rapid development of technology of sensors and data transmission, using all kinds of airplane sensors and satellite sensors, the authors can get different voluminous remote sensing image data of earth. Those voluminous remote sensing image data bring problems of data storage and management. It is becoming increasingly necessary to retrieve some information the authors need from those voluminous image data. Image retrieval was proposed by CHANG firstly in 1980 and can be regarded as expansion of traditional information retrieval. Oriented to the demands of efficient retrieval for voluminous remote sensing image, and considering that there are many bands in hyperspectral remote sensing image, the authors first analyzed image distance function and similarity measure in image retrieval. The most crucial issues in retrieval are spectral features extraction and similarity measure. In the present paper, the authors used classical Douglas-Peucker algorithm (hereinafter referred to DP algorithm) for curve simplification to extract shape features of spectral curve, in order to speed up hyperspectral remote sensing image retrieval. And the authors proposed a new method of spectral curve and remote sensing image retrieval, called Douglas-Peucker Spectral Retrieval algorithm (hereinafter referred to DPSR algorithm). Spectral shape features were used in image retrieval. DPSR used features of spectral curve, reduced the computation amount, realized match and retrieval efficiently, and is suitable for spectral curve retrieval in hyperspectral remote sensing image. The authors selected four ground features (grass, apple garden, grape garden and pond) in OMISI hyperspectral remote sensing image to compute similarity measure results, in order to test the effect of DPSR algorithm. Compared with traditional analysis method such as spectral angle match (SAM) and spectral information divergence (SID), DPSR can maintain high precision of results with less amount of computation, and then a new efficient image spectral retrieval method is provided. Besides some additional work the authors need to do in the future, for example, the relationship between threshold, retrieval precision rate and retrieval speed.