基于TerraSAR-X影像的格陵兰岛海岸水边线提取

海岸线是海洋与陆地的交界线,全球气候变化将引起海岸线的变迁,极地区域拥有最大的冰川,冰川的消融将导致全球海岸线的迅速变迁,因此研究极地海岸线具有重要的科学意义。由于潮汐等影响,卫星影像提取的海洋与陆地的交界线只是海岸水边线,本文以格陵兰岛海岸为实验对象,采用Gamma滤波方法对合成孔径雷达(SAR)数据的相干斑进行预处理,提出了应用支持向量机(SVM)和Canny边缘检测算子的海岸水边线提取方法,基于Terra SARX影像的实验结果表明,本文提取的格陵兰海岸水边线准确率都达60%以上。     

基于正射影像匹配的地形变化检测与更新算法

利用新时期的航空影像和旧时期的DEM,基于正射影像匹配的相关理论,提出了地形变化检测和数据更新的自动化算法,该算法能够利用两个时期的未变化信息进行控制,避免了传统的定向过程,极大减少了人工编辑,能够同时得到变化检测的结果、更新后的DEM和DOM。实验结果证明了该算法的实用性和高效性。     

Landform classification from a digital elevation model and satellite imagery

The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.     

DEM格网与畸变遥感影像的一体化显示研究

遥感影像可以极大地增强DEM的表达效果,然而由于各种因素的影响,通常需对其进行预处理,传统方法是通过同名控制点进行校正。该文提出一种新的自动匹配算法,即提取DEM和遥感影像对应的特征线,利用Douglas-Peucker算法提取对应的特征点,以DEM特征点为离散点进行Delaunay三角剖分,并基于TIN完成纹理映射。实验结果表明,该算法显示效果较好,可有效改善畸变图像引起的错误显示。     

Glacier mapping based on Chinese high-resolution remote sensing GF-1 satellite and topographic data

The precise glacier boundary is a fundamental requirement for glacier inventory,the assessment of climate change and water management in remote mountain areas.However,some glaciers in mountain areas are covered by debris.The high spatial resolution images bring opportunities in mapping debris-covered glaciers.To discuss the capability of Chinese GaoFen-1 satellite lacking the short wave infrared band and thermal infrared band in mapping glaciers,this study distinguished supraglacial terrain from surrounding debris by combining GaoFen-1(GF-1)wide-field-view(WFV)images,the ratio of the thermal infrared imagery and morphometric parameters(DEM and slope)with 30 m resolution.The overall accuracy of 90.94%indicated that this method was effective for mapping supraglacial terrain in mountain areas.Comparing this result with the combination of GF-1 WFV and low-resolution morphometric parameters shows that a high-quality DEM and the thermal infrared band enhanced the accuracy of glacier mapping especially debris-covered ice in steep terrain.The user's and producer's accuracies of glacier area were also improved from 89.67%and 85.95%to 92.83%and 90.34%,respectively.GF data is recommended for mapping heavily debris-covered glaciers and will be combined with SAR data for future studies.     

Uncertainty due to DEM error in landslide susceptibility mapping

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.     

Bidirectional DEM relief shading method for extraction of gully shoulder line in loess tableland area

The gully shoulder line, one of the most important topographical features, reveals the erosional process and reflects the geomorphological evolution of a loess area. Existing shoulder-line extraction methods are based on local window-filter or image-edge detection, which are sensitive to image noises and algorithm parameters, causing unsatisfactory accuracy and efficiency. This paper proposes a bidirectional relief-shading (BRS) method for loess shoulder-line extraction based on a 5-m Digital Elevation Model (DEM). First, two hill-shaded images are simulated with appropriate altitude and symmetric direction of the light. Second, the grey value difference between inter-gully and inner-gully area can be easily identified by fusing the two images with the mean method. Finally, the shoulder line can be derived by image segmentation using a threshold determined by an empirical equation. Experiments in three areas of loess tableland in Shaanxi Province validated the method, which has the advantages of being a simple operation with relatively high accuracy, 89.7% compared with manual digitalization, and high efficiency. We discuss three parameters in this method: zenith angle, azimuth angle, and segment threshold. Results suggest that the method is applicable for broad-scale gully shoulder line extraction in loess tablelands.     

‘WHY FAMILIES MOVE’: A RE-EXAMINATION

Abstract

This study examines the role of surface geomorphic features in tree establishment at the alpine treeline in Glacier National Park, Montana, through the presentation of a multiscale, conceptual model of biogeomorphic relationships at alpine treeline. Empirical observations gathered through a multiscale field methodology over three summers serve as a base for the model. The model highlights the importance of surface geomorphic features, specifically boulders and terrace risers, in creating favorable local site conditions, largely by protecting seedlings from wind. The sheltering effect of surface features enables initial seedling establishment, and in some cases survival, above current treeline locations, thereby initiating a positive feedback effect that encourages subsequent tree establishment. Geomorphic features are therefore important in linking scales of pattern and process at the alpine treeline ecotone.     

Modelling topographic potential for erosion and deposition using GIS

Abstract

Modelling of erosion and deposition in complex terrain within a geographical information system (GIS) requires a high resolution digital elevation model (DEM), reliable estimation of topographic parameters, and formulation of erosion models adequate for digital representation of spatially distributed parameters. Regularized spline with tension was integrated within a GIS for computation of DEMs and topographic parameters from digitized contours or other point elevation data. For construction of flow lines and computation of upslope contributing areas an algorithm based on vector-grid approach was developed. The spatial distribution of areas with topographic potential for erosion or deposition was then modelled using the approach based on the unit stream power and directional derivatives of surface representing the sediment transport capacity. The methods presented are illustrated on study areas in central Illinois and the Yakima Ridge, Washington.     

基于G0分布的SAR图像水边线提取方法

为了更加精准地从合成孔径雷达（SAR）图像中提取水边线,提出基于G0分布的SAR图像水边线提取方法。首先,将SAR图像域划分为大小相等的子块,并假设每个子块内像素强度服从独立同一的G0分布;利用矩估计方法得到对应每个子块的粗糙度和散射性参数;根据设定的参数阈值,可划分出粗略的水域,并确定该区域的几何中心;以该几何中心为圆心,向四周做射线,保留经过陆地区域的射线;利用似然函数确定每条射线上的水-陆分界点,依次连接分界点,进而实现SAR图像水边线提取。采用本文方法,分别对模拟和真实SAR图像进行水边线提取实验。定性和定量结果表明：采用基于G0分布的SAR图像水边线提取方法能够有效地克服斑点噪声的影响。     

Editorial

Abstract

With the increase in volume of spatial data now available, more effective ways must be found of storing and processing these data. This paper presents a compacted version of the linear quadtree and a spatially-referenced index method that can significantly reduce the storage requirements of a set of images and the time taken to process spatial queries. The index acts as a high-level summary of a regular-sized portion of the underlying image and so can be used to avoid examining areas of the image where none of the required features is present. Some example results are given. A method for the optimization of spatial searches is presented which takes into account the area and distribution of features within an image. Finally, a method for directly associating the edges of features with the individual nodes of a quadtree is reported. This is important since the edges of objects are no longer explicitly present in linear quadtrees and so must be recalculated when they are required for part of a query. Recalculation of object edges or boundaries is expensive; it is best, therefore, to perform the operation once only, and then save the results.     

Automated terrain feature identification from remote sensing imagery: a deep learning approach

ABSTRACT

Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science.     

A geospatial approach to assessing land change in the built-up landscape of Wa Municipality of Ghana

ABSTRACT

Urban landscapes are changing in response to changes in socio-economic conditions. Land change scientists seek to understand these land dynamics in the coupled human-environment system of urban landscapes. This study assessed land change in the built-up area of Wa Municipality between 1986 and 2016 using Landsat images. We used the Support Vector Machine algorithm for classifying the images. We recorded image classification accuracies of 97%, 95%, 92% and 96% for the 1986, 1996, 2006 and 2016 classified images, respectively. Our study finds that over the 1986–2016 period, agricultural land and bare land transitioned to build-up land by 9.23% and 3.79%, respectively, as compared to 2.79% for vegetation and 0.05% for water. Our in-municipal level analysis thus shows that urban landscapes could expand more sustainably by targeting other dominant land categories instead of the vegetation cover. The findings in this paper could serve as a spatial model for planning and reducing the unintended socio-ecological impacts of expansion in the built-up area.     

基于小流域尺度的土地石漠化信息遥感提取

为增强石漠化信息提取的客观性,文章提出了基于小流域的图斑确定方法,并以贵州西部为研究区,基于2013-06-16的Landsat遥感影像和DEM（30 m）数据,分析小流域图斑分割在石漠化信息提取中的应用;并以2013-06-16的Landsat遥感影像为基础提取土地利用边界,以其为地理单元提取石漠化信息。在此基础上构建石漠化稳定指数（DSI）,分析不同地理分割单元对石漠化信息提取的影响。结果表明,研究区2种地理单元的中度及以下石漠化面积比率＞80%,而无明显、轻度、中度、重度和强度石漠化提取结果差异分别为66.5%、26.8%、72.4%、75.4%和55.9%,石漠化稳定指数仅0.15,不同地理分割单元对石漠化信息提取有显著影响。基于小流域图斑分割的方法,具有明确的地理意义和良好的稳定性,是石漠化信息提取中理想的分割方法。     

基于语义和边缘特征融合的高分辨率遥感影像水体提取方法

利用卷积神经网络从遥感影像中提取水体时,水体对象边缘像素的特征与内部像素的特征之间往往存在较大差异,导致提取结果中边界模糊、内部像素与边缘像素的提取精度差异较大,影响了整体精度的提高。针对如何从高分辨率遥感影像中进行水体高精度、自动化提取的问题,文章首先以高分辨率遥感图像为基础,利用边缘提取算法生成边缘图像,然后以高分辨率遥感图像和边缘图像作为输入,建立了语义特征和边缘特征融合的高分辨率遥感图像水体提取模型（Semantic Feature and Edge Feature Fusion Network, SEF-Net）,用于从高分辨率遥感图像中提取水体对象。实验结果表明,SEF-Net模型在3个数据集中的召回率（91.97%、92.07%、93.97%）,精确率（91.12%、98.37%、97.88%）,准确率（89.56%、95.07%、94.06%）和F1分数（91.54%、95.12%、95.88%）均优于对比模型,说明SEF-Net模型从高分辨率遥感图像中提取水体时,具有更高的精度和泛化能力。     

面向地貌学本源的DEM增值理论框架与构建方法

数字高程模型（DEM）在表达地貌形态、认知地表过程、揭示地学机理等研究中发挥着基础性的作用,是重要的地理空间数据模型,广泛地应用于地学分析与建模中。但是,传统DEM具有属性单一的天然缺陷,难以支撑面向地学过程与机理挖掘的地球系统科学研究。亟待在传统DEM的基础上实现其数据模型的增值,服务于新地貌学研究范式和新对地观测技术背景下的数字地形建模与分析。立足于以上问题,本文构建了DEM增值的理论框架,主要包括DEM增值的概念、内涵、内容、类别、不同增值类别之间的相互关系,以及此理论框架的研究意义和应用范畴。提出了DEM增值的构建方法,包含：① 强调地上地下一体化、时间空间相耦合的DEM空间维度和时间维度增值方法;② 重视地下、地表和地上物质构成,形态属性耦合的物质属性和形态属性增值方法;③ 顾及自然过程、人工作用的地物对象、地貌形态的地物要素和形体要素增值方法。最后,分别以数字阶地模型、数字坡地模型和数字流域模型为例,阐释DEM在面向地貌学本源问题时的不同增值方法及应用场景。期望通过对DEM进行维度、属性和要素3个层面的增值,实现现代对地观测技术背景下数字高程模型表达方法的突破,并支撑知识驱动的数字地貌问题分析。     

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.     

Development of a cloud detection method from whole-sky color images

A method is proposed for detecting clouds from whole-sky color images obtained with an all-sky camera (ASC) system. In polar regions, cloud detection using whole-sky images usually suffers from large uncertainties in fractional cloud cover retrievals because of large solar zenith angles (SZAs) and high surface albedo, which cause “whitening” in the images. These problems are addressed by using differences between real images and virtual clear-sky images for a particular observation time with the same SZA. The method is applied to ASC images obtained at Ny-Ålesund, Svalbard in May of 2005–2007, and the results are compared with Micro-Pulse Lidar (MPL) measurements. When no clouds were detected by MPL, the false cloud detection rate from ASC classification was 2.1% in total hours. Conversely, when clouds were detected by MPL, the ASC classification underestimated the clouds by 11.6%. In most cases, this occurred when MPL detected very optically thin clouds. Furthermore, the variability of cloud fractions estimated by MPL and ASC was roughly constant regardless of the SZA. Thus, it is confirmed that the method developed in this study is valid for cloud detection from whole-sky color images.     

基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例

土地利用分类精度直接决定土地利用/土地覆被变化相关研究的准确性,而基于决策树的遥感影像分类是近年来提高土地利用分类精度的重要方法。QUEST决策树在影像解译和空间表达方面,运算速度和分类精度均优于普通CART等决策树方法。本文以云南丽江地区为例,应用QUEST决策树分类方法,对该地区的Landsat TM 5影像图进行分类,同时将地形因素、植被指数作为地学辅助数据的因子添加到分类波段中,进行不同特征融合,来处理目标类别间的非线性关系,该方法在处理图像理解知识方面具有更大的灵活性;同时与普通决策树分类法的遥感影像分类的结果相比较,Kappa系数值从原来的0.789提高到0.849.在地形复杂的山地地区,针对TM影像数据,选择基于QUEST决策树分类能够有效提高土地利用分类结果精度。