The technique of distance decayed visibility for forest landscape visualization

The paper discusses an approach known as ‘distance‐decayed visibility’, implemented using GIS, to the composition of 3D scenes for static forest landscape visualization. The distance‐decay concept is employed to model a spatial interaction in terms of visibility between any observation points and vegetations visible from those points. The vegetation visibility is measured in level of detail where high visibility uses high level of detail and vice versa. The interaction is modelled mathematically using an exponential decay function. The point distribution of projected models on a 3D scene is calculated in GIS to assign an appropriate value of ‘visibility decay constant’ to relate the distance from the observation point to the vegetations with the level of detail of the vegetations in the function. The advantages of the technique as measured against the quantitative benchmarks of rendering time and the number of polygons computed are addressed. Although the forest landscape of the study area consists of a large number of vegetation models, the technique helps to model the relation of vegetation visibility and viewing distance so realistically that the technique yields the visual quality of the result 3D scene visually and statistically comparable to the finest resolution scene.     

High rates of forest loss and turnover obscured by classical landscape measures

Loss of native forest is a key conservation concern globally, for reasons of biodiversity, climate change and ecosystem services. Landscape measures are used widely to characterize forest loss and associated landscape structure, but often without regard for structure imposed by the data used, and associated assumptions. Notably, forest loss is often expressed simply as net change in forest cover over time, but this approach does not account for turnover (i.e. the gross losses and gains of forest). It also ignores forest age (e.g. young regrowth forest or mature forest), which is significant in conservation terms. We investigate the effects of removing common data constraints on landscape characterization, as typically used in landscape dynamic analyses. We produced fine-resolution (0.0225 ha) classified maps from satellite imagery of the temperate Araucanía Region of Chile, for 1986, 1999 and 2008. We calculated areas of land-use classes and associated landscape indices. Landscape measures and trends through time varied markedly around the region, with forest loss and fragmentation confined to areas not designated as protected. Net (‘headline’) figures masked very large turnover through time, with about 30% of unprotected land switching land use each decade. Accounting for this, in unprotected areas the loss of established native forest was 2.4% and 3.5% per year in the two time periods, much higher than equivalent ‘standard’ figures. Using finer-resolution data increased estimates of native forest loss and reversed temporal trends in patch density and mean patch size, compared with the commonly-used National Vegetation Classification (6.25 and 4.5 ha resolution). Interestingly, mean patch size of native forest actually switched, from a decreasing trend to an increasing one, with continued deforestation. We conclude that landscape characterization can lead to effective conservation practices, but it is necessary to use appropriate data resolution, define the data domain carefully and examine change through time, including the degree of dynamism (turnover) within the landscape: our results suggest a strong need to consider continuity of forest cover as well as overall totals.     

土地利用分类对景观格局指数的影响

基于景观格局指数的空间格局分析是当前景观生态学研究的重要基础内容,不仅数据源准确度、尺度效应显著影响景观格局指数,土地利用类型划分也对景观格局指数具有显著影响,但我们对这种影响的总体理解尚很缺乏。本研究选取24种常用景观格局指数,以深圳市宝安区为试验区,探讨景观格局指数随土地利用分类系统变化的基本规律。研究结果表明,土地利用分类对景观格局指数的确具有显著影响。而根据景观格局指数对土地利用类型数目变化响应的可预测性,可将其分为三类：① 第一类指数随土地利用分类系统变化的可预测性强,能用简单函数关系 (对数函数关系、S形曲线、反比曲线关系) 来表达,包括斑块数目、斑块密度、边界密度、平均斑块面积、景观形状指数、平均斑块形状指数、周长面积比分维数、平均斑块分维数、聚合度、Shannon多样性指数、Simpson多样性指数和修改Simpson多样性指数;② 第二类指数随土地利用分类系统变化可预测性较差,表现为典型的分段 (或阶梯形) 变化,存在多种可能 (S形曲线、直线、反比曲线与复合曲线关系),包括斑块面积标准差、斑块面积变异系数、最大斑块指数、面积加权平均斑块形状指数、面积加权平均斑块分维数、分离度和斑块结合度;③ 第三类指数由于在度量相关空间格局特征时考虑了土地利用类型数多少的影响,随土地利用分类系统变化呈无规律变化,难以用一种简单函数或分段函数来预测其变化行为,包括蔓延度、Shannon均匀度指数、Simpson均匀度指数、修改Simpson均匀度指数和优势度指数。这些指数随土地利用分类系统变化的变化规律,使试验区不同时期、不同土地利用分类系统下的空间格局比较成为可能。     

基于地理加权回归的漫湾库区景观破碎化及影响因子分析

应用地理加权回归模型分析漫湾库区景观破碎化指数——有效筛网大小与相关因子之间的空间关系。选取的解释变量分别是距道路的距离、距乡村的距离、距河流的距离、坡度。结果表明：大坝修建后4种解释变量与有效筛网大小呈现较显著的正相关性。与线性回归模型相比,地理加权回归模型的拟合效果显著提高。1974~1988年,有效筛网大小对各影响因子最敏感的区域面积呈现显著的时空变化这为确定水电站建设及其他因素对景观破碎化影响的大小,并进一步改善库区景观破碎化的现状提供了依据。     

旅游地景观生态分类方法探讨——以福州市青云山风景区为例

综合分析了影响旅游地景观的主要因子,以人为干扰为基础,以地貌为基本线索,以植被为标志,以土壤为参照,结合实地考察和景观生态制图技术,建立研究区的三级景观生态分类体系.结论认为:旅游地景观类型的划分应强调整体的综合,而不是对个别组分进行分类;多要素综合的分级分类体系,既能反映控制景观形成过程的主要因子,又可体现景观的综合性,能较好地反映客观实际;旅游地景观生态类型的划分,应根据景观异质性、图形比例尺和图斑的需要,明确分类单元的等级,确定分类的基础单元.     

长沙市土地利用格局变化的空间粒度效应

基于2003年、2006年、2009年及2013年的遥感图像对长沙市土地利用进行分类,在ArcGIS 10.0、ENVI 4.8、Fragstats 4.0和Excel 2007软件的支持下,通过上推尺度像元的比例占优法改变空间粒度,从类型水平和景观水平上分析了土地利用格局变化的粒度效应。得出以下结论：① 类型水平上,随着粒度的增大,各土地类型的指数呈现出3种变化趋势:增大、减小及无明显规律;随着时间的变化,空间粒度越小,对斑块密度及斑块形状指数变化的影响越明显。空间粒度越大,对斑块结合度和斑块集聚度变化的影响越明显。② 景观水平上,随着粒度的增大,蔓延度指数先减小后基本不变;香农多样性指数和香农均匀度指数缓慢减小;随着时间的变化,蔓延度指数呈减小的趋势,香农多样性指数和均匀度指数呈增大的趋势。90 m是本研究中分析空间粒度的最佳值。     

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.     

Quantifying landscape structure of the Heihe River Basin, north-west China using FRAGSTATS

We have compiled landscape map of the Heihe River Basin based on the land-use map using geographic information systems. A total of six ecoregions was delineated. Various landscape metrics of each zone, including area, patch density and size, edge, shape, nearest-neighbor, diversity, and contagion and interspersion metrics were calculated using the landscape structure analysis program FRAGSTATS. The results indicated that the landscape pattern of the Heihe River Basin is mainly controlled by the distribution of water resources. The landscape structure in the mountainous area of the Heihe River Basin shows a high continuity and complex patch shapes. The landscape structure of the oasis area shows the most complex landscape structure, the richest patch types, and the highest diversity. The oases are distributed along rivers and artificial channels, and are heterogeneous mosaics embedded in the desert landscape matrix. The landscape structures of oases in different zones vary greatly. In the oasis zone in the Hexi Corridor, the artificial oasis, dominated by irrigated farmland, is one of the most important landscape elements. Conversely, in the Ruoshui River Delta zone, the patches are fragmented and the landscape is easily disturbed. In the transitional belt between oasis and desert, the patch types are meadow, salinized meadow, salt desert, and marsh, which are fragmented and distributed in clusters. These patch types reflect the biodiversity in the arid region and play an important role in resisting the desertification in the area, suggesting they must be protected carefully. In the desert area, such as the northern Alxa High Plain, the bare gobi is a landscape matrix with absolute dominance and very high contagion, while other landscape types in the zone are heterogeneous mosaics embedded in the matrix with very small percentage.     

不确定性视角下的中国省际人口迁移机制分析

人口迁移过程具有内在的不确定性。贝叶斯模型平均方法（BMA）为不确定性问题提供了行之有效的解决方案。然而,当前该方法多用于线性回归模型在变量选择时出现的模型不确定性问题,很少用于空间建模。本文以2010—2015年中国省际人口迁移流为例,将BMA方法应用于空间OD模型,在考虑网络空间结构的基础上选取迁出地和迁入地各7个解释变量及距离因素,利用马尔可夫链—蒙特卡罗模型综合方法（MC³）进行模型抽样,以后验模型概率为权重计算相应变量的迁出地、迁入地和网络效应等,定量分析不确定性背景下省际人口迁移影响因素和空间机制。结果表明：① BMA模型估计结果更为稳健可靠。与单一模型相比,BMA中变量效应估计的90%可信区间明显缩小,不确定性程度显著降低,结果更为精确;② 区域经济社会发展对省际迁移至关重要。经模型空间抽样后,迁出地人口规模和GDP、迁入地教育水平和迁移存量等的变量后验包含概率大于90%;③ 网络效应在省际迁移过程中不可忽视。所有变量的网络效应占总体效应的40%以上,其中工资、城镇化率、教育和迁移存量等的网络效应（绝对值）大于各自的迁出地和迁入地效应;④ 若不考虑迁移建模中的不确定性,绝大多数区域经济社会变量对省际迁移的影响会被高估。     

Vague cognitive regions in geography and geographic information science

Cognitive regions are regions in the mind, reflecting informal ways individuals and cultural groups organize their understanding of earth landscapes. Cognitive region boundaries are typically substantially vague and their membership functions are substantially variable – the transition from outside to inside the region is imprecise or vague, and different places within the region are not equally strong or clear as exemplars of the region. Methods for assessing and cartographically depicting cognitive regions, as with other vague geographic regions, have traditionally implied an inappropriate level of boundary sharpness and membership uniformity, such as when boundaries are mapped as precise lines. Research in recent decades has explored methods for assessing and depicting boundary vagueness and membership variability, either within or across individuals, but has still assumed homogeneity and regularity in the vagueness and variability. In this article, we present two studies that assess the cognitive regions of ‘Northern’ and ‘Southern’ California, and, for comparison, ‘Northern’ and ‘Southern’ Alberta. The first study uses a standard boundary-drawing task; the second uses a novel task in which participants rate cells of a high-resolution grid laid over an outline map. This technique allows us to assess and depict vagueness and nonuniformity that is heterogeneous and irregular across different areas. Differences in the conceptualization of ‘Northern’ and ‘Southern’ regions in California, as compared to those in Alberta, point to thematic influences on cognitive regions in California but not in Alberta. As is often true with cognitive regions, Northern and Southern California are about attitude, not just latitude.     

中国土地覆盖时空变化未来情景分析

在对HLZ (Holdridge life zone) 分类系统与土地覆盖类型分类系统之间的差异性进行对比分析的基础上,根据土地覆盖类型与HLZ生态系统类型的最大对应概率,构建了基于栅格的土地覆盖边际转换模型。采用基于HadCM3 A1FI、A2a、B2a等三种未来气候变化情景数据模拟获得的中国HLZ生态系统时空变化的系列栅格数据,运行模型后获得相应时段中国未来土地覆盖时空变化情景系列数据。引入土地覆盖类型景观指数及平均中心系列模型和构造平均中心偏移距离及偏移方向的计算模型,对中国未来土地覆盖的景观指数变化及其平均中心的偏移距离、偏移方向及偏移趋势进行综合分析。基于HadCM3 A1FI、A2a、B2a三种情景模拟结果分析表明：在2000~2099年间耕地、草地、湿地、水域、冰川雪被等土地覆盖类型面积逐渐减少,林地、建设用地、荒漠等土地覆盖类型面积逐渐增加,沙漠面积有所减少。其中,林地增加速度最快 (平均每10年增加2.34%),裸露岩石减少速度最快 (平均每10年减少2.38%)。     

德国农业低地流域土地利用格局的空间解释变量的定量识别分析（英文）

Land use patterns arise from interactive processes between the physical environment and anthropogenic activities. While land use patterns and the associated explanatory variables have often been analyzed on the large scale, this study aims to determine the most important variables for explaining land use patterns in the 50 km~2 catchment of the Kielstau, Germany, which is dominated by agricultural land use. A set of spatially distributed variables including topography, soil properties, socioeconomic variables, and landscape indices are exploited to set up logistic regression models for the land use map of 2017 with detailed agricultural classes. Spatial validation indicates a reasonable performance as the relative operating characteristic(ROC) ranges between 0.73 and 0.97 for all land use classes except for corn(ROC = 0.68). The robustness of the models in time is confirmed by the temporal validation for which the ROC values are on the same level(maximum deviation 0.1). Non-agricultural land use is generally better explained than agricultural land use. The most important variables are the share of drained area, distance to protected areas, population density, and patch fractal dimension. These variables can either be linked to agriculture or the river course of the Kielstau.     

形态学图像处理方法在扎龙湿地景观格局分析中的应用

以数学形态学图像处理技术为主要方法,对扎龙湿地景观格局进行分类,形成核心、斑块、边缘和孔隙4种格局类型,并在此基础上与土地覆盖类型图叠加处理,分析湿地不同景观格局与土地覆盖类型的关系。结果表明,基于形态学图像处理技术的景观格局分类保持了像元水平的精度,优于传统的分类方式。1979-2008年,扎龙湿地的核心湿地减少,斑块、边缘和孔隙都相应增加。作为湿地中的优势类型,含地表水的湿地逐渐转移到非核心的斑块、边缘和孔隙湿地中,原有的核心湿地逐渐变得破碎。     

Representing and reducing error in natural‐resource classification using model combination

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster–Shafer's Theory of Evidence have long claimed to be more error‐tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine‐learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster–Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a ‘majority voting system’, a simple average, Dempster–Shafer's Theory of Evidence, and fuzzy‐set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second‐stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second‐stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction.     

城市景观对住宅价格的影响——以杭州市为例

优美的城市景观是人们日常生活休闲、娱乐的场所, 给人们带来心理上的愉悦和舒适感, 对城市的品质内涵也有重要影响。由于大多数景观效益是隐性的, 其舒适性价值难以用货币价格直接测量, 国内外学者广泛采用特征价格法进行实证研究。本文以杭州市为例, 从建筑、邻里、区位、景观四个维度选择25个解释变量构建特征价格模型, 定量评估了城市内部各类景观对住宅价格的影响。实证结果表明:住宅价格与到西湖和公园的距离呈负相关关系, 而与公园的面积呈正相关关系:其中, 到西湖和最近公园的距离每增加1%,住宅价格将分别下降0.240%和0.036%;公园的面积每增大1%,附近的住宅价格则提高0.012%;广场、山景、钱塘江等景观也对周边一定范围内房价具有显着的提升作用。     

Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth

The neighborhood definition, which determines the influence on a cell from its nearby cells within a localized region, plays a critical role in the performance of a cellular automaton (CA) model. Raster CA models use a cellular grid to represent geographic space, and are sensitive to the cell size and neighborhood configuration. However, the sensitivity of vector-based CAs, an alternative to the raster-based counterpart, to neighborhood type and size remains uninvestigated. The present article reports the results of a detailed sensitivity analysis of an irregular CA model of urban land use dynamics. The model uses parcel data at the cadastral scale to represent geographic space, and was implemented to simulate urban growth in Central Texas, USA. Thirty neighborhood configurations defined by types and sizes were considered in order to examine the variability in the model outcome. Results from accuracy assessments and landscape metrics confirmed the model’s sensitivity to neighborhood configurations. Furthermore, the centroid intercepted neighborhood with a buffer of 120 m produced the most accurate simulation result. This neighborhood produced scattered development while the centroid extent-wide neighborhood resulted in a clustered development predominantly near the city center.     

Does I = PAT Work in Local Places?

An aspect of global change currently not well understood is how processes operating on spatial scales finer than those used in recent global circulation models (GCMs) contribute to changes in atmospheric composition and the subsequent changes in climate. We use the ‘IPAT’ formulation as a framework to test relationships among social driving forces and user group greenhouse gas (GHG) emissions in northwestern North Carolina. Using regression, correlation, and bivariate mapping to examine relationships between a suite of socioeconomic variables and GHG emissions for the residential, commercial/industrial, and agricultural end‐user categories, we find that various measures of population and affluence serve equally well as explanatory variables.     

A review of assessment methods for cellular automata models of land-use change and urban growth

ABSTRACT

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors.     

Novel shape indices for vector landscape pattern analysis

The formation of an anisotropic landscape is influenced by natural and/or human processes, which can then be inferred on the basis of geometric indices. In this study, two minimal bounding rectangles in consideration of the principles of mechanics (i.e. minimal width bounding (MWB) box and moment bounding (MB) box) were introduced. Based on these boxes, four novel shape indices, namely MBLW (the length-to-width ratio of MB box), PAMBA (area ratio between patch and MB box), PPMBP (perimeter ratio between patch and MB box) and ODI (orientation difference index between MB and MWB boxes), were introduced to capture multiple aspects of landscape features including patch elongation, patch compactness, patch roughness and patch symmetry. Landscape pattern was, thus, quantified by considering both patch directionality and patch shape simultaneously, which is especially suitable for anisotropic landscape analysis. The effectiveness of the new indices were tested with real landscape data consisting of three kinds of saline soil patches (i.e. the elongated shaped slightly saline soil class, the circular or half-moon shaped moderately saline soil, and the large and complex severely saline soil patches). The resulting classification was found to be more accurate and robust than that based on traditional shape complexity indices.