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1.
《地理与地理信息科学》2015,(4)
为探究DEM不确定性对地形简化的作用规律,对不同简化方法的抗差性能进行研究。利用蒙特卡洛方法模拟生成一系列具有不确定性特征的模拟DEM;基于不同方法对初始DEM与带有误差的DEM进行地形简化;分别从高程变化程度、骨架吻合程度及地形等高线差异三部分对简化表面间的地形差异进行对比分析,对不同方法的抗差性能进行评估。研究表明:当地形简化在小尺度上演变时,基于特征点的简化模型能够一定程度地抑制初始地形误差的扰动,当简化的演变尺度较大时,特征点方法对误差十分敏感,此时,顾及地形全局特征的骨架约束法能够更加稳定地抑制DEM不确定性对简化的影响。 相似文献
2.
以浙江省仙居县为实验样区,通过气温空间分布的地形调节统计模型,使用10个气象站(哨)气温资料和4种不同空间分辨率的DEM(5 m,源于1∶1万数字化地形图;30 m,来源于Aster GDEM v2;90 m,来源于SRTMv4.1;900 m,源于GTOPO30’)模拟不同空间尺度年均气温空间分布,比较其误差大小及随宏观地形(海拔高度)和微观地形(坡度和坡向)的分布差异。结果表明:基于4种不同空间分辨率DEM模拟气温呈较大空间分布差异性;随着DEM空间分辨率减小,误差逐渐增加,空间差异性降低。微观地形因子(坡度和坡向)随空间分辨率的变化产生显著变化,明显影响气温空间分布,不同坡度和坡向间年均气温差最高可达到10～12.5℃,最小仅为1.9～2.6℃。 相似文献
3.
数字高程模型地形描述误差的量化模拟--以黄土丘陵沟壑区的实验为例 总被引：4，自引：0，他引：4
本研究以陕北黄土高原丘陵沟壑区为试验样区 ,综合运用统计分析及比较分析的原理与方法 ,通过数学模拟、对比分析、误差可视化分析 ,研究不同空间尺度下DEM地形描述误差的数学转换模型。实验结果证明 ,目前适用于欧洲地区的地形描述误差的模拟方程EtD=q·a·(EtD′) b,经过参数的修正 ,在我国黄土高原丘陵沟壑区具有较高的模拟精度。该模型的建立可以实现应用低空间分辨率的DEM数据在栅格水平模拟高空间分辨率DEM所提取的地形描述误差 ,从而 ,在一定精度范围内大大提高Et的计算速度和计算效率。并且 ,对我国其它地貌类型区类似模型的建立 ,都具有重要的参考意义 相似文献
4.
基于SINMAP模型的区域滑坡危险性定量评估及模型验证 总被引：1，自引：0，他引：1
武利 《地理与地理信息科学》2012,28(2):35-39,113
利用稳定态水文学理论与无限斜坡稳定性模型,构建分布式斜坡稳定性定量评估模型SINMAP,以坡体滑塌十分发育的陕西省略阳县为试验区,利用Grid DEM提取坡度、流向、地形湿度指数和有效汇水面积等流域地形水文数据,将GIS专题图、遥感数据等作为模型输入数据,获得地表斜坡稳定性分级专题图,实现滑坡危险性定量评估;将模型模拟结果与目前国内最具有权威性的中国县(市)地质灾害调查结果进行对比分析,发现两者在稳定性分级标准划分、滑坡点定性评价、滑坡危险性分区等方面都具有很好的相似性和可比性,说明模型的模拟结果能够客观反映研究区地表滑坡危险性,对可能出现的滑坡具有一定的预测精度。因此,该模型的研究有望为定量分析区域滑坡与环境因子的关系、区域滑坡预测等工作奠定基础。 相似文献
5.
以浙江省仙居县为实验区,通过气温空间分布的地形调节统计模型,并使用了10个气象站(哨)的气温资料和不同空间分辨率的DEM(均来源于1:1万的数字化地形图),模拟了不同空间尺度的年平均气温空间分布,比较了它们的误差大小以及随宏观地形(海拔)和微观地形(坡度和坡向)的分布差异.结果表明:基于不同空间分辨率DEM模拟的平均气温呈现较大的空间分布差异性;随着DEM空间分辨率的减小,误差逐渐增加(最大绝对误差为2.04℃,相对误差为15.10％),且空间差异性降低.而且微观地形因子(坡度和坡向)随着空间分辨率的变化产生显著变化,进而明显影响气温的空间分布,不同坡度之间的年平均气温差最大为9.5℃,最小为1.8℃.不同坡向之间的年平均气温差最大为12.2℃,最小为2.4℃. 相似文献
6.
7.
DEM不确定性影响评价中的填洼分析 总被引：4，自引：0，他引：4
洼地广泛存在于DEM实现中,洼地的处理会影响DEM不确定性评价结果。该文利用蒙特卡罗方法模拟DEM不确定性,用偏差指标评价DEM不确定性对坡度和地形指数的影响,将填洼与不填洼情况下的偏差指标相减来量化填洼对DEM不确定性评价的影响。研究发现,洼地对不同参数DEM不确定性影响评价作用不同,随着DEM不确定性的增大,洼地的影响也增大。 相似文献
8.
流域输沙过程是地貌学和地表动力学的重要研究内容,但传统的输沙过程监测方法仅能得到某个区域的总输沙率,无法推算其空间分布。论文以黄土高原绥德县窑家湾小流域为例,利用无人机摄影测量技术得到其2006年和2019年2期数字高程模型(DEM)并计算地形变化量;然后,根据质量守恒原理和多流向算法建立泥沙在空间上的输送模型,进而计算小流域输沙率的空间分布。实验结果表明,该模型能有效模拟泥沙在空间上的输送情况,输沙率出现质量不守恒的区域面积占比小于4%,且不守恒区域多为人类活动影响区。同时,论文讨论了DEM的选择和不同地形变化检测水平对模型结果的影响。当使用第一期DEM进行泥沙搬运路径推算时,质量不守恒区域的面积显著降低。使用误差空间分布图进行地形变化检测得到的输沙率结果鲁棒性更强。使用中误差进行地形检测得到的结果在不同置信度下变化较大。基于无人机地形变化检测的空间输沙模型能方便、快捷地提供详尽的输沙率空间分布,为地表过程研究带来了新的机遇。 相似文献
9.
复杂地形任意天气情形下太阳直接辐射量模拟 总被引：2，自引：1，他引：1
以太阳辐射传输参数化模型为基础,结合MODIS影像两次白天的云产品和水汽产品及DEM,构建了复杂地形任意天气情形下每日太阳直接辐射量模型。选取代表不同气候类型与地形起伏状况的3个典型站点（拉萨、北京、额济纳旗）,以2007年每日实测值对模拟结果进行了验证,其相关系数分别为0.77、0.77和0.85。研究表明:有云天气下,云是影响地表太阳直接辐射数量和空间分布的主要因子;模型对时间步长不敏感。引起误差的原因主要是MODIS云产品的时空分辨率较低以及云的3D效应导致模拟的困难,对地形起伏较大地区,小比例尺的DEM也会导致较大的误差,同时实测值与模拟值的空间尺度不匹配也引起了一定误差。 相似文献
10.
该文以提升滑坡危险性评价精度为核心目标,对深度神经网络在滑坡危险性评价中的可行性和适用性进行研究,以期充分发挥深度神经网络强大的非线性学习和拟合能力,取得更加合理的滑坡危险性评价结果。选取滑坡灾害多发的深圳市作为实例,基于深圳市815条历史滑坡数据,开展了深度神经网络建模训练;通过与广义线性模型及分类与回归树模型训练效果的对比,对深度神经网络的建模效果进行了评价,深度神经网络、广义线性模型和分类与回归树模型的AUC值依次是0.908、0.861和0.857。将训练所得的模型应用于深圳市全区,对3种模型输出的滑坡危险性评价成果的合理性和可靠性进行了对比分析,结果表明:深度神经网络建模精度良好,优于常见的广义线性模型和分类与回归树模型,输出的滑坡危险性评价成果具有合理性,适用于滑坡危险性评价工作。 相似文献
11.
Cheng-Zhi Qin Li-Li Bao Rong-Xun Wang Xue-Mei Hu 《International journal of geographical information science》2013,27(7):1364-1380
Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression. 相似文献
12.
Zhi Huang § Shawn W. Laffan 《International journal of geographical information science》2013,27(11):1433-1452
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. 相似文献
13.
GIS支持下三峡库区秭归县滑坡灾害空间预测 总被引：3，自引：1，他引：2
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%。 相似文献
14.
Chang Li Sisi Zhao Qing Wang Wenzhong Shi 《International journal of geographical information science》2018,32(9):1837-1859
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. 相似文献
15.
Dr Helen Mounsey 《International journal of geographical information science》2013,27(3):259-261
Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail. 相似文献
16.
地形元素（如山脊、沟谷等）是地表形态类型基本单元,通过地形元素的不同空间组合可形成更高级别的地貌类型。现有的地形元素提取方法大多依靠地形属性计算,难以克服地形元素的空间相关性表达与局部地形属性计算存在不对应的矛盾,Jasiewicz和Stepinski提出的Geomorphons方法——基于高程相对差异信息进行地形元素分类,可避免这一问题,但Geomorphons方法本质上是在单一分析尺度上选择地形特征点用于判别,易受局部地形起伏的影响而造成误分类。针对这一问题,设计出一种多分析尺度下综合判别的地形元素分类方法。应用结果表明：相比Geomorphons方法,利用该方法得到的地形元素的分类结果更为合理。 相似文献
17.
Pece V. Gorsevski Paul E. Gessler Jan Boll William J. Elliot Randy B. Foltz 《Geomorphology》2006,80(3-4):178-198
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. 相似文献
18.
Harry West Michael Horswell Nevil Quinn 《International journal of geographical information science》2018,32(6):1172-1193
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. 相似文献
19.
GIS and ANN model for landslide susceptibility mapping 总被引：1，自引：0，他引：1
XU Zeng-wang 《地理学报》2001,11(3):374-381
Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards. 相似文献
20.
GIS and ANN model for landslide susceptibility mapping 总被引：4，自引：0，他引：4
XU Zeng-wang 《地理学报(英文版)》2001,11(3):374-381
1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a… 相似文献