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。     

林地山区滑坡遥感的最优融合方法及应用

滑坡作为造成巨大经济损失和人员伤亡的地质灾害之一,越来越引起社会的高度重视。为精确识别林地山区中的滑坡灾害,以2020年7月6日发生在湖南省常德市石门县南北镇潘坪村的雷家山滑坡为研究对象,使用不同的融合方法进行Sentinel-1A C波段干涉宽幅的地距多视产品和Sentinel-2A多光谱2A级光学影像融合,得到主成分分析融合方法对分贝化处理后的S1A VV极化影像与S2A影像融合效果最优,采用支持向量机方法分别对最优融合影像和原始S2A影像进行滑坡识别,最后使用S2A影像滑坡目视解译结果为检验标准对支持向量机滑坡识别结果进行精度评价,同时以2020年7月21日发生在湖北恩施屯堡乡马者村的沙子坝滑坡作为案例检验该方案的可推广性。结果表明：与单独使用光学影像进行研究区滑坡识别相比,使用最优融合影像滑坡识别的准确率由95.24%提升到了96.65%,滑坡提取质量也由87.18%提升到了91.84%,滑坡的漏识别和过度识别均有所降低,说明光学影像和合成孔径雷达影像融合的研究方案具有可推广性,能提高林地山区滑坡识别的准确率,可以更好地为滑坡风险评估、灾后应急调查以及灾后恢复重建等提供有价值的信息。     

汶川地震大型单体滑坡遥感调查与危险性评价

＂5.12＂汶川特大地震诱发了大量次生地质灾害,山体大面积滑坡、崩塌,给人民的生命财产造成了重大损失。对这些次生性灾害进行详细的调查与监测,评价其近期的稳定性,对于防灾减灾,指导灾后重建具有现实意义。本文探讨了利用遥感影像对大型滑坡进行识别与解译,并运用AHP层次分析法对汶川地震形成的文家坝大型滑坡进行危险性评价的方法。在野外调查的基础上,对2008年5月16日获取的SPOT5影像进行解译,实现了对滑坡的规模、结构、类型、物质组成、稳定性等特征的遥感调查;选取与该滑坡发生有密切关联的地层岩性、坡度、区域降雨量、地震和水文条件5个因素作为滑坡危险性评价的指标,采用比例标度法构建判别矩阵,建立了滑坡危险度判别模型。经计算,文家坝滑坡危险性指数为0.684,危险度较高,有必要进行工程治理。     

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。     

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。     

尼泊尔震后Rasuwa区山地灾害遥感调查

针对尼泊尔4·25Gorkha地震诱发山地灾害的现象,基于多源光学遥感数据,使用目视解译方法分析了震前和震后地物特征的变化,调查了Rasuwa区影响抗震救灾的公路沿线滑坡、堰塞湖的空间分布。结果表明:本次地震新形成的大型滑坡较少,大部分为中小型滑坡、浅层滑坡及碎屑流、崩塌和落石等灾害,并有堰塞湖。而在影像上能清晰识别的灾害点有15处,其中3处危害较大,分别为Langtang滑坡、Resuo滑坡和1处堰塞湖。Langtang滑坡造成了严重的破坏。     

滑坡稳定性与土地利用关联规则挖掘及应用 ——以三峡库区秭归到巴东段为例

三峡库区滑坡灾害广泛发育,其稳定性受土地利用变化等人类工程活动的影响。采用数据挖掘技术研究库区土地利用变化对滑坡稳定性的影响及其规律,利用三个时相的遥感影像得到实验区滑坡面上两个时段间的土地利用变化监测图,用Apriori算法挖掘出滑坡稳定性与土地利用变化类型之间的强规则,用马尔可夫链模型预测滑坡面上土地利用的变化趋势,将预测结果用于对滑坡稳定性发展的分析评估。通过实验分析,所采用的方法可用于预测滑坡稳定性变化趋势,为滑坡灾害的监测预警提供决策支持。     

基于高分辨率卫星遥感影像滑坡提取方法研究现状

滑坡具有强大的爆发力和破坏性，是世界上发生频率较高的自然灾害之一，给人们的生命财产造成了严重的损害。灾后准确快速的提取滑坡，获取滑坡的分布范围，对滑坡灾害调查及危险性评估极为重要。围绕基于高分辨率卫星遥感影像监测滑坡的方法进行了调研，首先介绍了滑坡在高分辨率卫星遥感影像上的解译特征，而后论述了滑坡提取方法和精度评价分析方法的研究进展，最后总结了当前方法的优势与不足，以及未来研究的发展方向。结果表明：深度学习方法具有较大的潜力，未来应加强深度学习与其他自动化解译方法的结合在滑坡监测中的应用，解决样本规模对模型结果的影响，实现模型的可迁移性，提高其自动化程度。     

滑坡防治初级专家系统

作者运用专家系统知识和Turbo—Prolog语言,在微机上建立滑坡灾害防治方案的初级专家系统,为防治滑坡灾害及时提供专家防治方案.     

资源三号卫星在汶川震区滑坡快速识别中的应用方法研究

针对当前采用野外人工调查和遥感目视解译获取地震灾区滑坡信息存在主观性强、耗时费力等缺点。以汶川震区为实验区,应用资源三号(ZY-3)卫星影像,采用多尺度最优分割方法构建多层次识别对象,融合光谱、纹理、几何等影像特征和地形特征建立多维滑坡识别规则集合,以及基于高分辨率影像认知模式与场景理解过程提出滑坡分层识别模型,从而实现滑坡空间分布及其滑源区、滑移区和堆积区的快速识别。通过实验结果发现最低识别精度为82.97%,而滑坡的堆积区最容易识别。研究结果验证了该方法及资源三号卫星数据快速识别地震滑坡信息的有效性,可为震区滑坡调查提供技术支持,并可促进国产卫星的推广应用。     

基于面向对象的多源卫星遥感影像玉米倒伏面积提取

风灾引起的玉米倒伏可能导致玉米大量减产,利用遥感技术准确监测玉米倒伏面积与空间分布信息对灾情的评估非常重要。利用Planet和Sentinel-2影像分别结合面向对象与基于像元方法提取研究区玉米倒伏,同时评估了不同影像特征（光谱特征、植被指数和纹理特征）与不同分类方法（支持向量机法SVM、随机森林法RF和最大似然法MLC）对玉米倒伏提取精度的影响。结果表明：①使用高空间分辨率的Planet影像进行玉米倒伏提取的精度普遍高于Sentinel-2影像;②从分类精度和面积精度来看,Planet影像的光谱特征+植被指数+均值特征结合面向对象RF分类,总体精度和Kappa系数分别为93.77%和0.87,面积的平均误差最低为4.76%;③采用Planet和Sentinel-2影像结合面向对象分类提取玉米倒伏精度高于基于像元分类。研究不仅分析了面向对象方法的优势,还评估了使用不用影像数据结合面向对象方法的适用性,可以为遥感提取作物倒伏相关研究提供一定的借鉴。     

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.     

应用高分辨率遥感影像提取作物种植面积

利用中低分辨率遥感影像提取作物分类种植面积的精度,往往难以满足农业遥感估产的需要。随着新型传感器的不断出现,应用高分辨率遥感影像高精度地提取作物分类面积日益成为发展趋势。由于高分辨率遥感影像提供的地物纹理、色调与形状等信息更加丰富,当前基于对象的地物识别分类方法仍不成熟,处理操作中人为干预过多,而且较为复杂,因此尝试以地面调查信息为辅助参量,采用常规基于像元的最大似然法监督分类方法,依据多尺度遥感影像信息提取的原理,分阶段地逐步提取作物种植面积,以此为农业遥感估产服务。     

Study on Extraction Method of Abandoned Farmland based on the Seasonal Variation Characteristics of Remotely Sensed Images

In southwestern China, the cultivation conditions are poor, the plots are relatively fragmented, and the types of plots are complex. Therefore, the use of low and medium resolution remote sensing data is not able to satisfy the needs of abandoned farmland extraction. This paper explored the ability of single or multi-phased high resolution remotely sensed images in detecting abandoned farmland in southwest China, using Xiuwen County, Guizhou Province, China as a case study area. Remote sensing based monitoring methods for abandoned farmland were developed, providing a reference for the statistical survey of abandoned farmland in southwest China.The extraction method of abandoned farmland was proposed based on the field survey data, considering different types of abandoned farmland. Sensitive feature sets of different types of abandoned farmland were identified from a series of features including the spectral characteristics, vegetation indices and multi-temporal difference vegetation indices. The CART decision tree classification method was applied on the selected sensitive features to extract abandoned farmland. The results showed that:(1) There was a significant difference in the recognition ability of single-phase image in extracting different types of abandoned farmland, so it was difficult to use only single-phase image to extract abandoned farmland with high accuracy; (2) The vegetation index change characteristics of different time phases had strong recognition ability for abandoned farmland, and the ratio vegetation index was better than the difference vegetation index and normalized vegetation index; (3) The spatial distribution map of abandoned farmland and the statistical analysis of abandoned farmland area were carried out in Xiuwen County, Guizhou Province. The area of abandoned farmland in Xiuwen County was about 6,460 hectares, accounting for 13% of the cultivated land area.（4）Based on multi-temporal high-resolution remote sensing data, the method of detecting abandoned farmland using seasonal variation characteristics can meet the requirements of high-precision extraction of abandoned farmland in southwest China, and the results provided technical reference for remote sensing survey and mapping of abandoned farmland in large-scale.     

面向对象多特征融合的水域岸线目标变化检测

为认真落实河长制"清四乱"等专项行动,量化水域岸线监管测评工作,以岸线码头为目标,研究一种基于面向对象思想多特征融合的水域岸线目标变化检测方法。针对多时相高分辨率遥感影像,利用面向对象多尺度分割原理将具有空间连续性的同类区域划分为目标对象,提取目标对象的光谱、纹理及几何结构组成特征矩阵,并利用高斯径向基核函数支持向量机(RBF-SVM)进行分类;计算变化矢量差值,并与人工判读数据对比分析得到目标变化检测结果。实验结果表明,该研究应用于水域岸线上目标的变化检测中效果明显,RBFSVM分类误差影响最终目标变化检测的正确率,可为实现河湖水域岸线长效管护提供技术支撑。     

基于季相变化特征的撂荒地遥感提取方法研究

在我国西南地区耕种条件差,地块比较破碎,地块类型比较复杂,中低分辨率遥感数据难以满足撂荒地提取的需要。选取贵州修文县为试验区,基于高分辨率卫星遥感数据（哨兵2号）,探索单期或多期影像在中国西南地区的撂荒地检测能力,构建撂荒地遥感监测方法,为今后我国西南地区撂荒地统计调查提供参考。结合野外调查数据,在划分不同撂荒地类型基础上,综合遥感影像的光谱特征、植被指数特征以及多时相植被指数变化特征分析,优选不同类别撂荒地遥感提取敏感特征集,利用CART决策树分类方法,提取不同类型的撂荒地。结果表明：①单个时相对不同类型的撂荒地识别能力差异显著,基于单时相影像,难以开展撂荒地高精度遥感监测提取;②不同时相的植被指数变化特征对撂荒地的识别能力较强,其中比值植被指数优于差值植被指数和归一化植被指数;③以贵州修文县为例,开展了撂荒地空间分布制图及撂荒面积统计分析,修文县撂荒地面积约为6 460 hm²,占修文县耕地面积的13%;④基于多时相高分辨遥感数据,通过季相变化特征构建的撂荒地检测方法,能够满足我国西南地区撂荒地高精度遥感监测提取,为大范围撂荒地遥感调查和制图提供技术参考。     

The area extraction of winter wheat in mixed planting area based on Sentinel-2 a remote sensing satellite images

ABSTRACT

The traditional area extraction method mainly depends on manual field survey methods, it is workload, slow and high cost. While remote sensing technology has the advantages of accuracy, rapidity, macroscopic and dynamic, which has become an effective means to extract crop growing area. In this paper, we took Kaifeng City in Henan Province as the study area. Firstly, we explored the advantages of Sentinel-2A RENDVI in crop identification. Then used the supervised classification SVM, object-oriented classification method and assisted with field measured data to extract the winter wheat planting area, the characteristics of the two methods were compared and analysed. Finally, we combined the above two classification methods and proposed a new classification method V2OAE to remove unnecessary influencing factors. The experiment results showed that RENDVI has better recognition ability than the NDVI (Normalized Difference Vegetation Index) in distinguishing vegetation with similar spectrum, the classification effect of object-oriented classification is better than supervised classification SVM, and our classification method removes unnecessary influence factors in the results of object-oriented classification, which is further improve the monitoring accuracy.

Firstly, we have preprocessed the Sentinel-2A image data, its steps are: (1) In the first step, we made radiation calibration for remote sensing images to eliminate the image distortion caused by external factors, data acquisition and transmission systems and so on; (2) In the second step, we made atmospheric correction to eliminate changes in the spectral feature of remote sensing images caused by atmospheric absorption or scattering; (3) In the third step, we made band resampling to unify the resolution of remote sensing images and facilitate the mathematical combination operation of vegetation index; (4) In the fourth step, we made mosaic and cutting to get preprocessed remote sensing images of Kaifeng City. Secondly, we analysed the spectral features of each object and established the interpretation mark with the field measured data. then we explored the ability to identify the ground objects based on NDVI(Normalized Difference Vegetation Index) and RENDVI. Third, we used the rule-based object-oriented classification method and SVM classification to extract the planting area of the study area, the input definition of SVM is spectral feature images of ground objects and the output definition of SVM is the recognition result of ground objects in the process of data training. Then the advantages and disadvantages of the two methods in classification results were analysed. Finally, In order to extract winter wheat information more accurately, we combined the above two classification methods and proposed a new classification method V2OAE (Vector Object Oriented Area Extraction) to remove unnecessary influencing factors, then the winter wheat planting area in Kaifeng City was statistically obtained.     

Research on Wenchuan Earthquake-induced LandslidesRapid Recognition from ZY-3 Imagery

Presently,regional earthquake-induced landslides is mainly obtained by field survey and visual interpretation from remote sensing images; but these methods are objective,and time-consuming.In this study,with a main data source of domestic high-resolution remote sensing images from ZY-3 satellite as well as the study area of the Wenchuan earthquake region,objects of multilevel landslides were established using the multi-scale optimum partition method based on in-depth analysis of landslide features.A recognition rule set of multi-dimensional landslides was also built through the combination of topographic features and image features,such as spectrum,texture,and geometry.Additionally,recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes.Through all of the aforementioned efforts,the spatial distribution of the seismic landslide as well as the sliding source area,transport area,and depositional area can be identified intelligently.The analysis results of the experimental area showed a minimum recognition accuracy of 82.97%,with the depositional zone of landslides being the easiest zone to recognize,and the effectiveness of the proposed method as well as ZY-3 data.These findings may provide technical support for regional earthquake-induced landslides investigations and further promote geological hazard application of domestic high-resolution satellites. 