A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey

Over the last few decades, many researchers have produced landslide susceptibility maps using different techniques including the probability method (frequency ratio), the analytical hierarchy process (AHP), bivariate, multivariate, logistics regression, fuzzy logic and artificial neural network In addition, a number of parameters such as lithology, slope, aspect, land cover, elevation, distance to stream, drainage density, distance to lineament, seismicity, and distance to road are recommended to analyze the mechanism of landslides. The data quality is a very important issue in landslide studies, and more accurate results will be achieved if the data is adequate, appropriate and drawn from a wide range of parameters. The aim of this study was to evaluate the susceptibility of the occurrence of landslides in Trabzon province, situated in north east Turkey. This was achieved using the following five methods the frequency ratio model, AHP, the statistical index (Wi), weighting factor (Wf) methods, and the logistics regression model, incorporating a Geographical Information System (GIS) and remote sensing techniques. In Trabzon province there has been an increasing occurrence of landslides triggered by rainfall. These landslides have resulted in death, significant injury, damage to property and local infrastructure and threat of further landslides continues. In order to reduce the effects of this phenomenon, it is necessary to scientifically assess the area susceptible to landslide. To achieve this, landslide susceptible areas were mapped the landslide occurrence parameters were analyzed using five different methods. The results of the five analyses were confirmed using the landslide activity map containing 50 active landslide zones. Then the methods giving more accurate results were determined. The validation process showed that the Wf method is better in prediction than the frequency ratio model, AHP, the statistical index (Wi), and logistics regression model.     

基于GIS和数据驱动双变量分析模型的三峡库区滑坡灾害易发性制图

A detailed landslide-susceptibility map was produced using a data-driven objective bivariate analysis method with datasets developed for a geographic information system (GIS). Known as one of the most landslide-prone areas in China, the Zhongxian-Shizhu Segment in the Three Gorges Reservoir region of China was selected as a suitable case because of the frequency and distribution of landslides. The site covered an area of 260.93 km² with a landslide area of 5.32 km². Four data domains were used in this study, including remote sensing products, thematic maps, geological maps, and topographical maps, all with 25 m × 25 m pixels. Statistical relationships for landslide susceptibility were developed using landslide and landslide causative factor databases. All continuous variables were converted to categorical variables according to the percentile divisions of seed cells, and the corresponding class weight values were calculated and summed to create the susceptibility map. According to the map, 3.6% of the study area was identified as high-susceptibility. Extremely low-, very low-, low-, and medium-susceptibility zones covered 19.66%, 31.69%, 27.95%, and 17.1% of the area, respectively. The high- and medium-hazardous zones are along both sides of the Yangtze River, being in agreement with the actual distribution of landslides.     

基于IOE和SVM模型的府谷镇滑坡易发性分区

将陕西省府谷县府谷镇作为研究区,通过野外实地调查,圈定了47个滑坡点,制作了研究区滑坡编录图。以GIS软件和统计分析模型为基础,开展研究区滑坡易发性分区研究。首先通过GIS软件将滑坡点随机分成训练样本（70%）和测试样本（30%）两组。然后选择坡度、坡向、高程、距断层的距离、距道路的距离、距河流的距离、岩性、土地利用、NDVI、降雨量作为影响因子,提取因子图层。分别应用熵权模型（IOE）和支持向量机模型（SVM）计算滑坡易发性指数,利用自然间断点法将研究区划分为低易发区、中易发区、高易发区和极高易发区。最后利用ROC敏感度曲线下的面积（AUC）分别检验两种模型所得到的分区结果,结果表明,成功率曲线和预测度曲线的AUC值均在0.70～0.90,表明两种模型所得到的分区结果具有较高的精度,都可以为研究区的滑坡防治提供参考。在训练样本和测试样本中SVM模型的AUC值均最高,说明SVM模型比IOE模型适合在研究区开展滑坡预测研究。     

比较二元逻辑回归和层次分析法评估罗马尼亚Dobrovat河盆地滑坡敏感性

A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression (BLR) and analytical hierarchy process (AHP), for the assessment of landslide susceptibility over a 130-km² area in the Moldavian Plateau (eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides (covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors (altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.     

A Comparative Analysis of Binary Logistic Regression and Analytical Hierarchy Process for Landslide Susceptibility Assessment in the Dobrovǎt River Basin,Romania

A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km~2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.     

强震区滑坡活动强度的演变及敏感性评价

[目的]探讨汶川地震强震区滑坡的活动强度演变与敏感性,为滑坡、泥石流等地质灾害风险管理提供依据。[方法]选取四川省都江堰市龙池镇龙溪河12条泥石流流域为研究区,通过对研究区震后4期遥感影像的滑坡解译分析,研究震后滑坡的活动演化特征;同时利用概率综合判别法—层次分析法对滑坡物源进行多期敏感性评价。[结果]地震后该区域产生了825个强震滑坡;2009—2017年,在强震滑坡区域外新增376个滑坡,至2017年,仍活动的滑坡数量减少到368个,占滑坡总数的30.6%,同时利用曲线下面积(AUC)检验多期敏感性评价结果,准确率为75.6%～81.4%,评价效果较好。[结论]强震区震后活动滑坡数量及高敏感性区域面积整体表现降低趋势,表明震后滑坡处于逐步恢复过程,强震区的地质灾害活动但活动强度仍远远高于震前。     

Topographic controls of landslides in Rio de Janeiro: field evidence and modeling

Landslides are common features in the Serra do Mar, located along the southeastern Brazilian coast, most of them associated with intense summer storms, specially on the soil-mantled steep hillslopes around Rio de Janeiro city, where the favelas (slums) proliferated during the last few decades. On February 1996, hundreds of landslides took place in city of Rio de Janeiro triggered by intense rainstorms. Since then, many studies have been carried out in two experimental river basins in order to investigate the role played by the topographic attributes in controlling the spatial distribution of landslides inside them. Landslide scars and vegetation cover were mapped using aerial photographs and field observations. A detailed digital terrain model (4 m² resolution) of the basins was generated from which the main topographic attributes were analyzed, producing maps for slope, hillslope form, contributing area and hillslope orientation. By comparing these maps with the spatial distribution of the landslide scars for the 1996 event, a landslide potential index (LPI) for the many classes of the different topographic attributes was defined. At the same time, field experiments with the Guelph permeameter were carried out and a variety of scenarios were simulated with the SHALSTAB model, a process-based mathematical model for the topographic control on shallow landslides. The results suggest that most of the landslides triggered in the studied basins were strongly influenced by topography, while vegetation cover did affect landslide distribution. Between the topographic attributes, hillslope form and contributing area played a major role in controlling the spatial distribution of landslides. Therefore, any procedure to be used in this environment towards the definition of landslide hazards need to incorporate these topographic attributes.     

基于GIS的华宁县滑坡灾害影响因子分析及易发性评价

为了明析区域滑坡灾害影响因子及其易发性,为滑坡地质灾害的防治提供借鉴。以云南省华宁县为研究对象,利用GIS空间分析技术,对华宁县滑坡空间分布及诱发因子进行了分析研究,依据区域地质灾害详细调查资料,建立GIS灾害数据库的同时选取海拔、坡度、坡向、距水系、道路、断层距离、岩性7个诱发因子,利用统计指数对滑坡在每个因子各类别中的占比进行了权重分析,最终确定滑坡灾害易发性分区并阐述其空间分布特征。结果表明:（1）在滑坡灾害分布特征上,具有空间集中分布特征,灾害点密度以中部地区最大,受灾影响人数最大的主要分布在海拔较低的宁州街道和通红甸乡。（2）从诱发因子上看,滑坡灾害大多分布在海拔1 600~2 300 m（占76.06%）,坡度10°~30°（占71.83%）,坡向为E,NE,NW,N等方向上（占71.82%）,距离河流、断层和道路越近,发生滑坡的可能性越大。（3）在滑坡灾害易发性上,高易发区主要分布在宁州街道、青龙镇、华溪镇;中易发区主要分布在青龙镇东北部、宁州街道西南部、通红甸彝族苗族乡中部和盘溪东部;低易发区主要分布在通红甸彝族苗族乡、盘溪镇。华宁县滑坡灾害呈现出东少西多,南少北多的特征,未来华宁县应重点关注中部和西部区域的滑坡灾害预防。     

汶川地震前后崩塌和滑坡分布特征与敏感性对比分析

以汶川地震活动断裂龙门山断裂带穿过的12个县(市)为研究区,分析了研究区汶川地震前后崩塌和滑坡空间分布特征,并使用统计学方法Logistic回归模型分别对震前震后的崩塌和滑坡敏感性进行评价,结合崩塌和滑坡敏感性变化矩阵,分析了汶川地震后崩塌和滑坡敏感性的空间变化特征。震前崩塌和滑坡高敏感区主要沿河谷分布,而震后崩塌和滑坡高敏感区主要沿龙门山断裂带分布。震前的极高敏感区震后仍表现为极高敏感区,而靠近断裂带的区域,震前的低敏感区转换为高敏感和极高敏感区。     

香港暴雨中心迁移与滑坡位置关系分析

香港地区每年均有一定数量的滑坡灾害发生,诱发滑坡灾害的主要因子是强降水,即暴雨.暴雨过程中,降水强度的时空分布存在一定差异性,这种降水强度的时空与滑坡灾害的时空分布存在一定的联系.以香港地区1992年5月8日暴雨为例,以30 min为时间间隔,分析了暴雨中心时空变动与滑坡事件时空分布及出现频率之间的关系,发现空间上滑坡均出现在暴雨中心或暴雨中心的边沿地区;时间上滑坡事件出现在暴雨中心出现或上一阶段时间内;并且降水的强度与滑坡的出现频率也有一定关系.     

基于因子相关性-聚类分析的巴谢河流域滑坡危险性评价

巴谢河流域是甘肃省东部滑坡高易发区域,滑坡活动频繁。基于遥感解译与野外调查,分析了地形地貌因子、地层岩性、植被覆盖、人类工程活动等因子对流域内滑坡的相关性。结果表明:滑坡分布与地形地貌关系密切,高程2 000~2 200 m、坡度15°~40°、阳坡坡向及沟谷密度1~2.5 km/km²的范围内,滑坡尤为发育;滑坡与地层岩性、植被、人类活动等也有较好的对应关系,其中岩性为马兰黄土及泥岩、NDVI在0.2~0.3与距离道路600 m的范围内滑坡易于发生。依据各因子与滑坡的相关性对其进行聚类分析,将整个研究区域分4个危险区:高、中、低和极低危险区,所占研究区面积比例分别为30.9%,21.7%,30.3%,17.1%。     

基于多目标优化方法的滑坡易发性评价

[目的] 在滑坡易发性评价中,滑坡预测模型的选取和优化对运算过程的高效性和预测结果的准确性至关重要。针对现有单目标遗传优化算法(genetic algorithm,GA)易陷入早熟、局部搜索能力差、全局优化速度慢等问题,拟提出一种新的优化算法框架,将多目标遗传算法中的经典算法—带精英选择策略的非支配排序算法(the nondominated sorting genetic algorithm with an elite strategy,NSGA-Ⅱ)与常用机器学习模型[随机森林(random forest,RF)、支持向量机(support vector machine,SVM)]相结合,进行滑坡易发性预测。与单目标优化不同的是,NSGA-Ⅱ算法可同时进行特征选择和超参数优化,并使预测模型同时实现最优准确度、召回率、精密度和AUC(area under curve,AUC)。[方法] 以三峡库区重庆段为研究区,从模型精度评价、滑坡灾害易发性分区图、分区统计3个方面对4种优化模型(RF-GA、SVM-GA、RF-NSGA-II、SVM-NSGA-II)进行对比分析。[结果] NSGA-II较GA优化效果更明显,在模型评价和滑坡易发性分区方面,RF-NSGA-II模型具有更高的预测性能,4项评价值分别为80.91%,81.89%,80.07%,88.60%,证明NSGA-II优化算法的有效性;极低至极高危险区面积占比依次为23.06%,22.46%,22.96%,19.99%,11.53%,验证了RF-NSGA-II模型的可靠性。由RF-NSGA-II模型预测得到的易发性图表明,高和极高易发性区集中在研究区北部,且由东向西呈带状分布。[结论] 研究采取的基于多目标选择的RF-NSGA-II模型,为滑坡易发性评价中机器学习模型调优提供新思路。     

基于GIS和多目标评价方法的果树适宜性评价

漳州地区是福建省乃至全国有名的水果之乡,地貌复杂,自然资源与生态环境差异显著。为了科学合理地利用自然资源,对该地区进行果树适宜性综合评价,分析其种植现状与利用潜力,提供科学决策依据。首先建立研究区域内土壤、气候与地形等数据库,并利用地形对气候分布状况进行校正,同时通过LandsatTM遥感影像的分析解译得到研究区域内的土地利用现状分布图和主要果树种植分布图,在此基础上采用GIS和多目标评价(MCE)方法对漳州地区三种主要果树(香蕉、荔枝和龙眼)进行适宜性评价,最后综合分析这些果树适宜分布现状与利用潜力。研究结果表明,漳州地区大部分区域都非常适宜种植这三种水果,发展水果生产潜力较大。     

降雨入渗作用下秭归向斜核部南段斜坡稳定性评价

[目的]开展降雨条件下湖北省秭归县向斜核部斜坡稳定性评价研究,为政府部门减灾防灾工程提供科学支持,为滑坡灾害的预测和管理提供科学依据。[方法]以耦合了地下水动力学的TRIGRS无限斜坡稳定性计算模型为基础,详细介绍了斜坡稳定性评价的数据处理过程以及参数选取方法。[结果]发生斜坡失稳的区域多位于松散土体中等厚—较厚,地形坡度中等的区域,尤其是土层厚度在7—10m,地形坡度在20°~30°范围内为斜坡失稳高发区。[结论]在土层厚度和地形地貌的双重控制下,短历时强降雨入渗作用导致孔隙水压力增大,这些区域的斜坡土体极易发生滑动,为滑坡危险性较高的多发区域。斜坡稳定性评价结果和滑坡实际分布吻合程度较高,在一定程度上反映出降雨诱发滑坡空间分布关系和分布规律。     

甘肃东部滑坡遥感调查分析评价

以TM数据为基础资料,结合SPOT图像及航片、地形图、地质图,建立了滑坡遥感解译标志,对甘肃东部滑坡进行了遥感调查。分析了滑坡分布特征,将滑坡分为2个大区8个小区。根据滑坡的密度、规模、危害程度,对以上地区进行了危险度评价并划分出三个等级:Ⅰ级极危险区有兰州—定西滑坡带、临夏—渭源滑坡带、通渭—秦安—清水滑坡带、舟曲—武都白龙江滑坡带;Ⅱ级危险滑坡带有武山—天水滑坡带、礼县—成县嘉陵江上游滑坡带;Ⅲ级危险区有华亭—崇信滑坡带、环县—庆阳滑坡带。     

Evaluation and mapping of soil erosion susceptibility: an example from Kenya

Abstract. The erosion susceptibility of the Erosion Research Farm at Kabete Campus was mapped using a qualitative parametric method. A grid soil survey of the 4 ha farm was combined with a map of slope gradients, slope segments being delineated by breaks in slope. Rainfall erosivity and soil erodibility were also measured. Areas with the greatest erosion susceptibility according to this method were those occupying convex slope positions and slopes of more than 30%. Field observations and soil loss measurements generally supported the erosion susceptibility rating map produced by this method. The soil and erosion susceptibility maps were useful for planning erosion control measures and for selecting suitable sites for runoff plot experiments.     

汶川地震震中地区崩滑体的植被恢复与长期效应

[目的]评估汶川地震震中地区崩滑体自然植被恢复过程,旨在为该区震后地质环境恢复、灾害防治和重建提供依据。[方法]以四川省汶川县映秀镇为研究区,以多期空间分辨率为30 m×30 m的Landsat遥感影像为数据源,分析震后崩滑体上的植被动态恢复变化,结合地形因子分析地震9 a后植被恢复的空间分布特征。[结果]研究区震后至2011年,经历植被恢复程度较差,差等水平以下占比68%,期间暴雨泥石流活动频率高,至2013年后就达到一个较好的恢复水平,差等水平以下占比32%,之后就处于一个缓慢的恢复过程,至2017年植被恢复差等以下的崩滑体仍主要处于30°～50°的坡度区间,1 500～2 100 m高程区间、东南坡向。[结论]四川省汶川县映秀镇总体上经过近9 a的恢复过程,植被覆盖度恢复到0.74,与震前相比差值为0.08,根据拟合模型预计2022年植被覆盖度能恢复到震前水平,但仍主要以草本和灌木为主,植被种群结构与震前差异较大。     

基于MaxEnt模型的滑坡易发性评价--以攀枝花市为例

为了客观评价滑坡影响因子的贡献度和构建滑坡预测模型,以滑坡灾害发生较多的攀枝花市为研究区,通过筛选后选取高程、坡度、坡向、土地利用类型、归一化植被指数(NDVI)和人口密度6项因子作为滑坡易发性的评价指标;基于最大熵(maxEnt)模型和ArcGIS空间分析模块对研究区滑坡易发性进行了定量预测和分析研究。结果表明:maxEnt模型在研究区滑坡易发性研究方面的适用性等级为优秀(AUC=0.96),Kappa系数为0.86;随机选取75%的数据集用于训练模型,其余25%用于验证模型,得到的AUC值最稳定且精度最高,模型预测可信度最高;研究区高易发生和极易发生区分别占总面积的2.57%,0.80%,主要分布在人口比较密集的东部和西部地区,部分沿着金沙江、雅砻江、巴关河、安宁河和主要道路两侧发育;植被覆盖度和坡度是决定研究区滑坡易发性空间分布格局最重要的环境影响因子。     

晋西黄土区极端降雨后浅层滑坡调查及影响因素分析

极端降雨引发的浅层滑坡,造成了严重的环境破坏与社会经济损失。为探究极端降雨条件下土地利用、植被类型和地形因素对浅层滑坡的影响,通过对山西省吉县蔡家川流域2021年10月3—6日极端降雨引发的浅层滑坡进行实地调查,分析了极端降雨的特征与过程,不同土地利用和植被状况条件下、不同地形条件下浅层滑坡的数量与破损面积。结果表明：(1)极端降雨降雨量为年平均降雨量的31.2%,达到了161.3 mm,降雨历时84 h,峰值降雨强度7 mm/h,平均降雨强度2.1 mm/h,发生该种程度降雨的频率为0.16%,为625年一遇的极端降雨。(2)蔡家川流域内的农地小流域、人工林小流域和次生林小流域共计发生浅层滑坡479处,破损面积达183 881 m²,90%的浅层滑坡面积小于885 m²。(3)单位面积上浅层滑坡的数量与破损面积均表现为农地小流域(214个/km²,109 241 m²/km²)>人工林小流域(163个/km²,48 779 m²     

1985-2020年天水市黄土区滑坡灾损土地利用时空变化特征

为探索甘肃省天水市滑坡土地利用特征及时空变化规律,选取秦州区和麦积区作为典型黄土滑坡区的代表,基于12.5 m分辨率的ALOS DEM数据和1985—2020年全球30 m的精细地表覆盖动态监测产品,利用GIS空间分析、土地利用净变化量指数、土地利用转移矩阵和土地利用贡献率,分析滑坡体的特征参数、土地利用/覆被类型、土地利用转移特征及驱动因素。结果表明:(1)研究区共识别出469个滑坡样本,平均高程集中于1 200~1 400 m,平均坡度10°~15°,平均坡向为西向坡,前后缘相对高差100~150 m,滑坡面积1×10⁴~10×10⁴ m²,滑坡长度200~400 m。(2)1985—2020年滑坡区旱地最多,草原次之;期间土地利用类型呈现动态变化特征,分为1985—2000年持续变化阶段和2000—2020年微弱调整阶段;其中1995—2000年变化最剧烈,主要表现为旱地向草原和林地转化。(3)35年间旱地面积转化最多,累计8.74 km²,贡献给草原6.58 km²、封闭落叶阔叶林1.94 km²、不透水表面0.20 km²,其余的土地利用类型占比小,转化微弱;旱地、草原、封闭落叶阔叶林和不透水表面的土地利用净变化量最大。(4)单个滑坡体的利用方式逐年多样化,草原和封闭落叶阔叶林的增加提升区域的植被覆盖度,降低再次发生滑坡的可能性。天水市区滑坡土地利用类型和时空变化规律为区域灾损土地的开发再利用及生态修复提供科学依据。