基于多特征面向对象区域滑坡现象识别

高空间分辨率卫星遥感数据的发展为滑坡灾害数据获取和更新提供了新的途径。以西北黄土高原为研究区,提出了一种基于多特征面向对象区域滑坡现象的识别方法,基于单期高空间分辨率遥感数据,利用集合和特征组合进行区域滑坡现象识别,实验结果表明:该方法是识别滑坡现象有效的技术方法之一,对开展滑坡监测、影像理解和地学分析具有重要的研究意义。     

基于光谱、空间和形态特征的面向对象滑坡识别

地震经常诱发数量众多,覆盖范围广的滑坡并造成极大危害,因此,需要对大范围的滑坡灾害进行快速评估。随着遥感影像分辨率的提高,面向对象分类方法在这方面的应用比传统的目视解译和基于像素的方法更具优势。但是,目前基于面向对象方法的滑坡识别研究还相对较少,而且通常针对小范围的研究区。基于SPOT5 2.5m多光谱影像,提出一种综合光谱、空间、地形和形态特征的面向对象滑坡自动识别方法,并应用于较大范围研究区。结果表明:面向对象滑坡自动识别方法能将研究区内95%的滑坡识别出来,综合考虑滑坡的过度提取与遗漏提取情况,滑坡提取质量为74.04%,效果较好,能够快速、有效地识别大范围的滑坡。该方法可以应用于对地震或强降雨引起的大范围滑坡灾害进行快速评估,为灾后应急救援和恢复重建工作提供参考。     

基于信息熵的DEM最佳分辨率确定方法研究

分辨率是DEM的基本属性,它贯穿于DEM数据处理的每一个过程.利用合适的分辨率既可真实的反映地面特征,又可避免造成数据的冗余.本文尝试从信息论的角度出发,引入信息熵的概念来度量不同分辨率的DEM包含的地形信息量,得到地形信息熵随分辨率的变化曲线图.典型山地地区实验表明,利用1∶5万等高线插值生成DEM,当分辨率为20m时,DEM的地形信息熵基本趋于稳定,这说明DEM所包含的地形信息量基本达到饱和,由此,可认为此地区1∶5万DEM最佳分辨率为20m.     

DEM分辨率对AVSWAT2000径流模拟的敏感性分析

研究水文过程模拟时DEM分辨率对流域特征参数和产流的影响,以汉江上游马道流域为研究区,采用60m×60m、120m×120m、250m×250m、500m×500m和1000m×1000m五种不同分辨率的DEM对1981~1985年的流域径流进行模拟,结果表明:不同分辨率DEM流域地形分析计算机自动提取得到的最长河道相差较小,但河道总长、坡度等相差较大,进而影响了分布式水文模型径流模拟的效率。流域中选取4个参考研究区,用归一化的径流-河道总长比指数(NRLI)描述径流模拟与地形参数相关性,分析不同DEM分辨率对各个研究区的NRLI影响可知:低分辨率DEM的NRLI较为平缓,高分辨率DEM的NRLI在较小的研究区波动偏大,但NRLI整体趋于1,由此可以粗略推测流域内局部地区产流量,这一发现可以有效地应用于对流域水资源空间分布快速评估实践中。     

基于NDVI变化检测的滑坡遥感精细识别

随着遥感技术的发展,高分辨率的卫星影像数据逐渐丰富,滑坡灾害的信息提取被进一步推进,当前滑坡灾害应急调查主要以目视解译和野外调查为主,费时费力,难以满足灾后救援的迫切需求。面向像元和面向对象的单时相滑坡遥感信息提取方法等存在着滑坡过识别、误识别的问题。因此,在此提出以滑坡前后多时相遥感影像为数据源的变化检测滑坡识别方法,首先根据归一化植被指数（NDVI）进行基于像元的变化检测确定滑坡预选区,再结合面向对象的几何规则完成滑坡的精细识别,这种基于变化检测和几何规则相结合的方法能有效排除道路、建筑、裸地等光谱特征与滑坡相似的非滑坡部分。以九寨沟滑坡为例,采用高分一号8 m分辨率多光谱相机2015年8月1日的影像（滑前）以及2017年8月16日的影像（滑后）作为数据源,进行滑坡识别实验。结果表明,和面向对象的单时相方法相比,基于变化检测和几何规则相结合的多时相方法滑坡提取的精度较高,制图精度高达88.80%,用户精度高达81.19%,都大幅超过面向对象单时相法的精度,漏分误差及错分误差分别下降23.22%和11.72%,可为有效组织滑坡灾后救援与重建工作提供可靠依据。     

基于数字高程模型的古滑坡区提取

针对古滑坡的滑前影像无法获得,植被、纹理信息都已恢复,无法通过对比分析滑坡滑动前后的植被、纹理等信息的变化来提取滑坡区域的问题,提出了一种新的基于数字高程模型的滑坡区域范围提取方法。该方法基于简化的滑坡体模型及特征分析,对滑坡区进行水流方向、坡度、山脊山谷线提取,通过流域分析获取滑坡区域范围;利用坡度图实现滑坡壁与滑坡体提取。实验利用全球30m分辨率ASTER GDEM数据,提取了四川理县3个古滑坡体区域范围,验证了该方法的有效性。     

基于数字高程模型的古滑坡区提取

针对古滑坡的滑前影像无法获得,植被、纹理信息都已恢复,无法通过对比分析滑坡滑动前后的植被、纹理等信息的变化来提取滑坡区域的问题,提出了一种新的基于数字高程模型的滑坡区域范围提取方法。该方法基于简化的滑坡体模型及特征分析,对滑坡区进行水流方向、坡度、山脊山谷线提取,通过流域分析获取滑坡区域范围;利用坡度图实现滑坡壁与滑坡体提取。实验利用全球30m分辨率ASTER GDEM数据,提取了四川理县3个古滑坡体区域范围,验证了该方法的有效性。     

基于地基雷达点云数据的冰川表面DEM建模

冰川监测是气候变化研究的重要组成部分，为了从三维立体角度研究冰川的运动变化情况，以梅里雪山西坡的共森隆巴冰川为研究对象，基于地基雷达点云数据分别利用了不规则三角网（Triangulated Irregular Network, TIN）和反距离加权（Inverse Distance Weight, IDW）两种插值算法进行了DEM建模，以交叉验证方法分析了两种算法的关键参数对冰川DEM建模结果的影响。结果表明：(1)格网分辨率对TIN插值结果影响较大。格网分辨率较低时，会造成DEM较多的空洞，格网分辨率较高时，会导致DEM表面平滑。当格网分辨率大小选择0.75 m时，冰川表面DEM建模结果精度最高，此时R2为0.997 0,RMSE为0.457 1 m。(2)插值半径对冰川表面DEM建模效果有较大影响，插值半径较小时，空洞部分较多且较大，随着插值半径的增大，空洞部分不断减少。而格网分辨率是影响IDW插值算法精度的较大因素。虽然不同格网大小和不同插值半径下冰川DEM建模的R2和RMSE精度较高，但是DEM建模效果不理想，存在较多空洞部分，难以利用IDW算法实现完整的冰川表面建模。(3)通...     

地理本体驱动的面向对象滑坡识别

高空间分辨率遥感影像的出现促进了面向对象影像分析技术的发展,面向对象影像分析技术也由此应用到滑坡信息提取,但现阶段因为该技术对专家知识利用不够,导致其仍不能脱离传统的数据驱动的模式识别范畴,专家分析不同带来对象分类结果不一致,自动化程度较低。针对上述问题,引入知识工程中本体概念,对滑坡地理本体进行表达、建模,实现滑坡信息的自动提取。以甘肃黑方台地区的高分遥感影像数据为例,以地理本体驱动的解译框架进行了滑坡识别实验。实验表明,基于本体概念进行面向滑坡地理对象的信息提取是一种较好的思路,有利于进一步提高滑坡信息计算机提取的自动化程度和精度。     

基于纹理分水岭的斜坡单元提取算法

斜坡单元在以滑坡为主的地质灾害预防和评价中有着广泛的应用，其提取和划分是滑坡灾害风险评估的首要工作和重要基础。针对传统地理信息系统（GIS）方法提取的斜坡单元存在平行边界和误分割问题，提出了基于纹理分水岭的斜坡单元提取方法，通过分割地形图像划分斜坡单元。首先通过预处理地形数据得到数字高程模型（DEM）图像，利用灰度共生矩阵提取DEM纹理特征；然后计算融合灰度和纹理特征的梯度图像，对梯度图像进行基于标记的分水岭分割，使其能够准确获取山体和流域边界；最后，结合正负地形，对山体对象进行分水岭分割以实现斜坡单元的提取。实验结果表明，所提方法对不同地貌类型和分辨率的DEM图像都有良好的划分效果；相较于传统的GIS方法，该方法能够正确分割水平面和倾斜面，有效避免因洼地填平处理而产生的平行边界问题。     

Object-oriented Detection of Landslides based on the Spectral，Spatial and Morphometric Properties of Landslides

Earthquakes in mountain area often induce hundreds of thousands of landslides resulting in destructive casualties and economic damage.It is urgent needed to rapidly detect the extent areas of the landslides.With the advent of very high resolution satellite remote sensing,the application of the object\|oriented classification method in this area have significant advantage comparing to those of visual interpretation and pixel\|based methods.However,the study of object\|oriented landslide detection is relatively few,and the study usually has a small study area.The method of object\|oriented rapid identification of landslides based on the spectral,spatial and morphometric properties of landslides and a 2.5m SPOT5 multi\|spectral image is proposed in this paper and is applied in a relatively large study area.The normalized difference vegetation index (NDVI) threshold was set to remove vegetation objects and obtain landslide candidates.Then,the spectral characteristics,texture,terrain features and context of the image were used to build indicators to gradually separate the landslide from false positives.The small scale chessboard segmentation was conducted to further eliminate vegetation objects and get the landslide objects.The object\|oriented detection results show that the adopted method can recognize about 95% of the landslides in the study area.When considering the landslide excessive detection and omissions,the landslide detection quality percentage of the proposed method is 74.04%.Hence,the method proposed in the article can help to rapid assess landslide disasters caused by earthquakes or heavy rainfalls,providing a reference for post\|disaster emergency relief and reconstruction work.     

Semi-automatic approach for identifying locations of shallow debris slides/flows based on lidar-derived morphological features

Identification of landslides at the regional scale has always been a challenging problem. Various automatic landslide identification methods, mainly relying on spectral information from aerial photographs or satellite imagery, have been developed. This paper proposes a semi-automatic approach to identify locations of small-sized shallow debris slides and flows using airborne lidar (light detection and ranging) data. Cells related to landslide components were first extracted by using a new method based on local Moran’s I (LMI). Subsequently, cell clusters representing landslide components and other terrain objects were discriminated through geometric and contextual analysis at cluster level. The approach was tested in a study area in Hong Kong and the identification result was verified by a landslide inventory. Locations of 93.5% of recent landslides and 23.8% of old landslides were identified by the proposed approach. The result indicates that the proposed approach is able to identify both recent and old landslides with distinct morphological features. However, the proposed approach also identified a large number of locations (77.6% of all locations) unrelated to landslides. These locations may correspond to terrain objects with similar morphology to debris slides or flows, and indicate rough terrain in the study area. In addition, the effects of DEM (digital elevation model) resolution on landslide identification were analysed by applying the LMI-based method to digital elevation models (DEMs) at different resolutions. The results indicate that the smoothing effect caused by lowering DEM resolution led to extraction of fewer landslide components.     

A Object-oriented Landslide Extraction Method based on Morphological Opening Operation

Landslide is one of the most common geological disasters,and it is of great significance to quickly and accurately obtain the hazard degree and distribution of landslide.By introducing the morphological opening operation and the regional level set algorithm to construct the object\|oriented landslide extraction method,and using the multi\|spectral image of GF\|2 satellite as the data source,the landslide extraction experiment is carried out by using the constructed method and the landslide extraction method based on the pixel in the study area of the Bagmati area in Nepal,and the results of the landslide extraction are analyzed.The experimental results show that the object\|oriented landslide extraction method is more accurate than the extraction method based on the pixel,and the anti\|interference ability of the clouds and snows is stronger than that of the pixel\|based landslide extraction method.     

Identification of Rubber Plantations in Tropical Mountainous Region based on High Spatio-temporal Resolution Visible Remote Sensing Data

Since 1990s,rubber plantations has been growing rapidly in Xishuangbanna,it is vitally important to the evaluation of the relationship between the rubber plantation and the change of ecological environment in the region.Aiming at the problem of similarity of vegetation's spectral feature and complexity of topography and climatic conditions,combined with the phenological characteristics of rubber plantation fallen leaf in winter in the region,ESTARFM algorithm was used and we selected ETM+,OLI and Sentinel\|2A data to fuse with high temporal resolution MODIS data respectively to establish a high spatial and temporal resolution visible remote sensing data setand analyze the difference of the recognition accuracy of the rubber plantations in tropical mountainous environments by different fusion data sources.The results show that：(1) During the key phenological period of the rubber plantation from January to March,the phenological features extracted using remote sensing data based on high spatial and temporal resolution can remarkably improve the identification accuracy of rubber plantations,the recognition accuracy more than 89% and Kappa higher than 0.83;(2) in the vegetation classification of fragmented mountainous area,10 m resolution Sentinel\|2A data used classification will obtain higher classification accuracy than Landsat data,which indicates that the Sentinel\|2A data is more promising in the high spatial\|temporal data fusion and the tropical vegetation remote sensing application.     

地形对蚀变信息光谱影响研究与应用

通过加入地形因子进行相关光谱信息分区来提取蚀变信息,首先利用ASTER影像立体像对提取DEM,进行地形(坡向)划分,即分为阳坡、半阳坡、半阴坡、阴坡4类型,将相对应的区域统计其波谱信息,分为不同的波谱区,然后分别用主成分分析法提取蚀变信息。结果表明:波谱分区蚀变信息提取方法能够更加有效地识别出各个波谱区的蚀变异常信息,有效减小地形因素对蚀变信息提取结果的影响,并且在研究区得到很好的应用。     

遥感技术在我国滑坡研究中的应用综述

中国滑坡遥感研究与应用已有30多年的发展历史,作为滑坡调查的主要手段在大型工程建设中的滑坡灾害调查及危险性评价中发挥了重要作用。从4方面阐述了遥感技术在我国滑坡研究中的应用：① 区域滑坡灾害遥感调查;② 大型单体滑坡遥感调查;③ 滑坡灾害遥感监测;④ 遥感应用于滑坡风险评估。随着遥感技术理论的逐步完善和遥感图像空间分辨率、时间分辨率与波谱分辨率的不断提高,遥感技术已成为滑坡灾害调查、动态监测与预警、灾情实时调查与损失评估等工作中不可缺少的重要手段之一。     

基于半监督鉴别分析的高分辨率SAR影像建筑区提取

针对建筑物在城市化发展规划、地理国情信息系统更新、数字化城市以及军事侦察等方面的迫切要求,提出将半监督鉴别分析(Semi-supervised Discriminant Analysis,SDA)算法应用于高分辨率SAR影像的建筑区提取中,实现快速提取建筑区信息以及提高城市地物目标识别能力。以Radarsat-2影像和TerraSAR-X影像为实验数据,基于灰度共生矩阵计算影像的各种纹理特征;结合SDA算法进行特征提取,并以新特征作为大津法(Otsu)的输入提取建筑区;最后对分类结果进行后处理。实验结果与线性鉴别分析(Linear Discriminant Analysis,LDA)算法和局部保持投影(Local Preserving Projection,LPP)算法进行比较,结果表明:SDA算法具有较强的泛化能力,在先验类别信息较少时,适用于高分辨率SAR影像的特征提取,可以快速有效地提取建筑区信息。     

基于变化检测的滑坡灾害自动识别

在分析当前滑坡灾害信息识别方法的基础上,指出这些方法的适用性和不足之处,并针对这些不足之处,以滑坡的地学原理为依据,提出了以多时相遥感影像为数据源,结合纹理分析的变化检测自动识别滑坡灾害信息的方法。最后,以地震前(2006-5-14)后(2008-6-3)的北川县城及附近区域的福卫2号多光谱遥感影像作为数据源,以结合比率变换和纹理分析的变化检测进行滑坡灾害信息识别试验。结果表明,结合纹理分析的变化检测方法能够突破影像光谱特征的局限,对于滑坡灾害信息识别具有显著的适用性。