DEM误差对滑坡危险性评价模型的影响

基于数字高程模型(DEM)计算得到的坡度、坡向等地形属性是滑坡危险性评价模型的重要输入数据, DEM误差会导致地形属性计算结果不确定性, 进而影响滑坡危险性评价模型的结果。本文选择基于专家知识的滑坡危险性评价模型和逻辑斯第回归模型, 采用蒙特卡洛模拟方法, 研究DEM误差所导致的滑坡危险性评价模型结果不确定性。研究区位于长江中上游的重庆开县, 采用5 m分辨率的DEM, 以序贯高斯模拟方法模拟了不同大小(误差标准差为1 m、7.5 m、15 m)和空间自相关性(变程为0 m、30 m、60 m、120 m)的12 类DEM误差场参与滑坡危险性评价。每次模拟包括100 个实现, 通过对每次模拟分别计算滑坡危险性评价结果的标准差图层和分类一致性百分比图层, 用以评价结果不确定性。评价结果表明, 在不同的DEM精度下, 两个滑坡危险性评价模型所得结果的总体不确定性随空间自相关程度的变化趋势并不相同。当DEM空间自相关性程度不同时, 基于专家知识的滑坡危险性评价模型的评价结果总体不确定随着DEM误差增加而呈现不同的变化趋势, 而逻辑斯第回归模型的评价结果总体不确定性随着DEM误差大小增加而单调增加。从评价结果总体不确定性角度而言, 总体上逻辑斯第回归模型比基于专家知识的滑坡危险性评价模型更加依赖于DEM数据质量。     

Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM)

In the field of digital terrain analysis (DTA), the principle and method of uncertainty in surface area calculation (SAC) have not been deeply developed and need to be further studied. This paper considers the uncertainty of data sources from the digital elevation model (DEM) and SAC in DTA to perform the following investigations: (a) truncation error (TE) modeling and analysis, (b) modeling and analysis of SAC propagation error (PE) by using Monte-Carlo simulation techniques and spatial autocorrelation error to simulate DEM uncertainty. The simulation experiments show that (a) without the introduction of the DEM error, higher DEM resolution and lower terrain complexity lead to smaller TE and absolute error (AE); (b) with the introduction of the DEM error, the DEM resolution and terrain complexity influence the AE and standard deviation (SD) of the SAC, but the trends by which the two values change may be not consistent; and (c) the spatial distribution of the introduced random error determines the size and degree of the deviation between the calculated result and the true value of the surface area. This study provides insights regarding the principle and method of uncertainty in SACs in geographic information science (GIScience) and provides guidance to quantify SAC uncertainty.     

Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

A geomorphological study focussing on slope instability and landslide susceptibility modelling was performed on a 278 km² area in the Nalón River Basin (Central Coalfield, NW Spain). The methodology of the study includes: 1) geomorphological mapping at both 1:5000 and 1:25,000 scales based on air-photo interpretation and field work; 2) Digital Terrain Model (DTM) creation and overlay of geomorphological and DTM layers in a Geographical Information System (GIS); and 3) statistical treatment of variables using SPSS and development of a logistic regression model. A total of 603 mass movements including earth flow and debris flow were inventoried and were classified into two groups according to their size. This study focuses on the first group with small mass movements (10⁰ to 10¹ m in size), which often cause damage to infrastructures and even victims. The detected conditioning factors of these landslides are lithology (soils and colluviums), vegetation (pasture) and topography. DTM analyses show that high instabilities are linked to slopes with NE and SW orientations, curvature values between − 6 and − 0.7, and slope values from 16° to 30°. Bedrock lithology (Carboniferous sandstone and siltstone), presence of Quaternary soils and sediments, vegetation, and the topographical factors were used to develop a landslide susceptibility model using the logistic regression method. Application of “zoom method” allows us to accurately detect small mass movements using a 5-m grid cell data even if geomorphological mapping is done at a 1:25,000 scale.     

Sensitivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model

We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one‐at‐a‐time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random following a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, than to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non‐linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as ±2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs that have distinctive characteristics from that of numerical outputs.     

Mapping illegal domestic waste disposal potential to support waste management efforts in Queensland,Australia

Illegal disposal of waste is a significant management issue for contemporary governments because of the hazards posed to both human and ecosystem health. Understanding the complex distribution pattern of illegal waste and the range of economic, environmental and social factors influencing this distribution is valuable for improving the effectiveness and efficiency of waste management efforts. This article examines the applicability of mapping illegal waste disposal in the Sunshine Coast (Queensland, Australia) through the identification and integration of predictive spatial data in a geographic information system. A statistical model of illegal waste disposal was developed using a binary logistic regression analysis to identify explanatory variables suitable for predicting the distribution of illegal waste. Five statistically significant explanatory variables were identified through this analysis: population density, primary land use, distance to the nearest road, waste facility and roadside amenity. The generated statistical model had a predictive success of 86.1% with all indicators suggesting good model fit (χ² = 474.3, P = 0 with df = 22) across the study area. Standardised spatial data on each explanatory variable were combined using a weighted linear combination analysis and the results were classified into five categories from very low to very high illegal waste disposal potentials using the equal interval method. The resultant mapping identified 6.9% of the study area as having very high illegal waste disposal potential, and subsequent validation indicated that 32.9% of known illegal waste disposal sites were located within these areas.     

Comparison of spatial and aspatial logistic regression models for landmine risk mapping

Landmines continue to affect the lives of millions of people living in war-torn countries. One major challenge in humanitarian mine action (HMA) is finding new and integrated approaches to land release, which remains a slow and costly process. The use of geographic information systems (GIS) in HMA can improve the land release process by efficient mapping and prioritizing of landmine risk areas. This study explores the usage of aspatial and spatial regression techniques to construct a predictive geo-statistical model for landmine risk mapping in a small 160 km² municipality in Bosnia and Herzegovina (BiH) and a large 4500 km² region in Colombia. The first application of logistic geographically weighted regression to landmine risk mapping is presented. The results show that in the BiH study area, the effect of local parameters that influence the distribution of landmine risk varies significantly across the study area. Conversely, in the Colombia case study the effect of explanatory variables remains more homogeneous over the study area. We produced two landmine risk maps for each study area, based on aspatial and spatial regression models. Risk maps are classified into five classes, i.e. very low, low, medium, high, and very high risk. The landmine risk maps created through the usage of these innovative methodologies improve the assessment of risk and prioritization of the land release process in mine-contaminated areas, compared to existing approaches.     

Spatial distribution and morphologic characteristics of gullies in the Black Soil Region of Northeast China: Hebei watershed

Gully erosion in the Black Soil Region of China has posed a threat to food security. This study aimed to determine the spatial distribution and morphologic characteristics of gullies in the region and their topographic thresholds. A 28 km² watershed was surveyed and 117 gullies measured. The results showed that: (1) Gullies were distributed equally on both hillslope and valley floor positions, with a total gully density of .66 km/km². (2) The mean depth, width, and cross-sectional area of gullies were .74 m, 2.39 m, and 2.43 m², respectively. These characteristics varied among gullies according to their topographic positions and slope gradients. Individual gully volume (V) was well predicted from gully length (L) by V = 2.08L^0.96 (r² = .66). Total gully volume (V) of each sub-watershed was predicted from mean slope gradient (S) and drainage area (A) as V = 275800S ? 8600A (r² = .73). (3) Gully erosion was more serious in steeper sub-watersheds and steeper hillslope positions. Gullies were wider in regions with relatively larger drainage areas, except for those developed in the main valley. The topographic threshold for gully initiation was S = .10A^?0.34, which indicated gully erosion was dominated by surface runoff. (4) Human activities, such as road construction, played a significant role in gully erosion.     

Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events

The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km² was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919–2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region.The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of rainfalls as well as by improving the accuracy of the DEM and the locations of shallow landslide initiation.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

Evaluation of the SNOWPACK model’s metamorphism and microstructure in Canada: a case study

The snow thermodynamic multi-layer model SNOWPACK was developed to address the risk of avalanches by simulating the vertical properties of snow. Risk and stability assessments are based on the simulation of the vertical variability of snow microstructure, as well as on snow cohesion parameters. Previous research has shown systematic error in grain size simulations (equivalent optical grain size) over several areas in northern Canada. To quantify the simulated errors in snow grain size and uncertainties in stability, the snow specific surface area (SSA) was measured with a laser-based instrument. Optical grain size was retrieved to validate the optical equivalent grain radius from SNOWPACK. The two study plots are located in Glacier National Park, BC, and Jasper National Park, AB, Canada. Profiles for density and stratigraphic analysis were obtained as well as grain size profiles, combined with snow micropenetrometer (SMP) measurements. Density analysis showed good agreement with the simulated values (R² = 0.76). Optical grain size analysis showed systematic overestimation of the modeled values, in agreement with the current literature. The error in SSA evolution for a rounding environment was mostly constant, whereas error for conditions driven by a temperature gradient was linked to the size of the facetted grains.     

The 1786 earthquake-triggered landslide dam and subsequent dam-break flood on the Dadu River, southwestern China

Chinese historic documents recorded that on June 1, 1786, a strong M=7.75 earthquake occurred in the Kangding-Luding area, Sichuan, southwestern China, resulting in a large landslide that fell into the Dadu River. As a result, a landslide dam blocked the river. Ten days later, the sudden breaching of the dam resulted in catastrophic downstream flooding. Historic records document over 100,000 deaths by the flood. This may be the most disastrous event ever caused by landslide dam failures in the world. Although a lot of work has been carried out to determine the location, magnitude and intensity of the 1786 earthquake, relatively little is known about the occurrence and nature of the landslide dam. In this paper, the dam was reconstructed using historic documents and geomorphic evidence. It was found that the landslide dam was about 70 m high, and it created a lake with a water volume of about 50×10⁶ m³ and an area of about 1.7 km². The landslide dam breached suddenly due to a major aftershock on June 10, 1786. The peak discharge at the dam breach was estimated using regression equations and a physically based predictive equation. The possibility of a future failure of the landslide seems high, particularly due to inherent seismic risk, and detailed geotechnical investigations are strongly recommended for evaluating the current stability of the landslide.     

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.     

Computation of the third‐order partial derivatives from a digital elevation model

Loci of extreme curvature of the topographic surface may be defined by the derivation function (T) depending on the first‐, second‐, and third‐order partial derivatives of elevation. The loci may partially describe ridge and thalweg lines. The first‐ and second‐order partial derivatives are commonly calculated from a digital elevation model (DEM) by fitting the second‐order polynomial to a 3×3 window. This approach cannot be used to compute the third‐order partial derivatives and T. We deduced formulae to estimate the first‐, second‐, and third‐order partial derivatives from a DEM fitting the third‐order polynomial to a 5×5 window. The polynomial is approximated to elevation values of the window. This leads to a local denoising that may enhance calculations. Under the same grid size of a DEM and root mean square error (RMSE) of elevation, calculation of the second‐order partial derivatives by the method developed results in significantly lower RMSE of the derivatives than that using the second‐order polynomial and the 3×3 window. An RMSE expression for the derivation function is deduced. The method proposed can be applied to derive any local topographic variable, such as slope gradient, aspect, curvatures, and T. Treatment of a DEM by the method developed demonstrated that T mapping may not substitute regional logistic algorithms to detect ridge/thalweg networks. However, the third‐order partial derivatives of elevation can be used in digital terrain analysis, particularly, in landform classifications.     

Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district,Bartın,northern Turkey

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.     

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

Landslide susceptibility mapping using GIS and the weight-of-evidence model

The weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide susceptibility using GIS. Using landslide location and a spatial database containing information such as topography, soil, forest, geology, land cover and lineament, the weights-of-evidence model was applied to calculate each relevant factor's rating for the Boun area in Korea, which had suffered substantial landslide damage following heavy rain in 1998. In the topographic database, the factors were slope, aspect and curvature; in the soil database, they were soil texture, soil material, soil drainage, soil effective thickness and topographic type; in the forest map, they were forest type, timber diameter, timber age and forest density; lithology was derived from the geological database; land-use information came from Landsat TM satellite imagery; and lineament data from IRS satellite imagery. Tests of conditional independence were performed for the selection of factors, allowing 43 combinations of factors to be analysed. For the analysis of mapping landslide susceptibility, the contrast values, W ⁺ and W ^-, of each factor's rating were overlaid spatially. The results of the analysis were validated using the previous landslide locations. The combination of slope, curvature, topography, timber diameter, geology and lineament showed the best results. The results can be used for hazard prevention and land-use planning.     

Spatially and temporally distributed modeling of landslide susceptibility

Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km² Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.     

The occurrence of alpine permafrost in the Front Range of Colorado

Permafrost distribution, or ground that remains frozen for at least 2 years, has been modeled using a combination of Geographic Information System (GIS) techniques, Digital Elevation Model (DEM) variables, and land cover in alpine regions of the world. In the Front Range, however, no such empirical models have been developed, and field data are restricted in spatial extent, but rock glaciers are in abundance. Here, I present a probabilistic logistic regression model that is based on topoclimatic information (elevation and aspect) for rock glaciers derived from U.S. Geological Survey (USGS) 10-m DEMs. Classes of land cover, obtained from an Enhanced Thematic Mapper Plus (ETM+) image classification, were assigned weights and were then multiplied by the regression results to refine estimates. The effectiveness of the model was evaluated by comparing mean probability scores with rock glacier activity categories, Mean Annual Air Temperature (MAAT) from climatic stations on Niwot Ridge, and Bottom Temperature of winter Snow (BTS) measurements, while a Monte Carlo simulation was used to detect uncertainty associated with the original DEM. Permafrost scores >50% covered about 8.9% (242 km²) of the study area (2722 km²) with the highest scores clustered around Longs and Rowe Peaks. Permafrost locations showed a strong correlation with rock glacier activity classes, the −1.0 °C MAAT isotherm, and BTS measurements less than −3.0 °C. The uncertainty analysis revealed that slight global differences exist between the original and error prone DEM; however, local variations in aspect caused the most uncertainty. These results indicate that the model accurately represents regional distribution of permafrost. Therefore, topoclimatic information from rock glaciers and land cover, when combined with an uncertainty analysis, can effectively be used to map the occurrence of Front Range permafrost, providing an imperative tool for cartographers, planners, and geocryologists.     

LiDAR-derived DEM evaluation of deep-seated landslides in a steep and rocky region of Japan

In steep and rocky terrains, their rough surfaces make it difficult to create landslide inventories even with detailed maps/images produced from airborne LiDAR data. To provide objective clues in locating deep-seated landslides, the surface textures of a 5 km² steepland area in Japan was investigated using the eigenvalue ratio and slope filters calculated from a very high resolution LiDAR-derived DEM. The range of filter values was determined for each of a number of surface features mapped in the field and these included: cracked bedrock outcrops, coarse colluvial deposits, gently undulating surfaces, and smooth surfaces. Recently active slides commonly contained patches of ground in which deposition and erosion occurred together near the erosion front, or where cracked bedrock outcrops and coarse colluvial deposits coexisted under a gently undulating surface. The characteristic eigenvalue and slope filter values representing this sliding process were applied to maps of the DEM derived filter values to extract potential sites of recent landslide activity. In addition, the relationships between the filter values of deep-seated landslides at various stages of evolution within the field mapped area were extended to the entire study area, to assess the contribution that landslide evolution makes to change in the landscape as a whole. While landslide components made up the steepest as well as the gentlest parts of the landscape depending on their evolutionary stage, landslides were constantly coarsened and steepened by progressive erosion, probably initiated by river bank erosion at the foot of slopes.