A method to analyse neighbourhood characteristics of land use patterns

Neighbourhood interactions between land use types are often included in the spatially explicit analysis of land use change. Especially in the context of urban growth, neighbourhood interactions are often addressed both in theories for urban development and in dynamic models of (urban) land use change. Neighbourhood interactions are one of the main driving factors in a large group of land use change models based on cellular automata (CA).This paper introduces a method to analyse the neighbourhood characteristics of land use. For every location in a rectangular grid the enrichment of the neighbourhood by specific land use types is studied. An application of the method for the Netherlands indicates that different land use types have clearly distinct neighbourhood characteristics. Land use conversions can be explained, for a large part, by the occurrence of land uses in the neighbourhood.The neighbourhood characterization introduced in this paper can help to further unravel the processes of land use change allocation and assist in the definition of transition rules for cellular automata and other land use change models.     

Comparison of simulation models in terms of quantity and allocation of land change

Our article illustrates how to compare the outputs from models that simulate transitions among categories through time. We illustrate the concepts by comparing two land change models: Land Change Modeler and Cellular Automata Markov. We show how the modeling options influence the quantity and allocation of simulated transitions, and how to compare output maps from pairs of model runs with respect to a reference map of transitions during the validation interval. We recommend that the first step is to assess the quantity of each transition and to determine the cause of the variation in quantity among model runs. The second step is to assess the allocation of transitions and to determine the cause of the variation in allocation among model runs. The separation of quantity and allocation of the transitions is a helpful approach to communicate how models work and to describe pattern validation.     

Characterizing the scale sensitivity of the cellular automata simulated urban growth: A case study of the Seberang Perai Region, Penang State, Malaysia

In modeling the complex behaviour of urban systems using a cellular automata approach, scale is an important concept in representing space since the result obtained at specific scale shall not be expected to be valid at another scale. The selection of scale, however, has often been taken for granted on the basis of information available to be mapped. Although it is desirable to run a model with fine spatial scale datasets, such datasets may well be costly to collect and add computational expenses in processing. This article investigates the effect of changing scale on the performance of a GIS-based cellular automata (CA) model developed to simulate the spatial pattern of urban growth. The sensitivity of scale of the input data used to parameterize the model was undertaken by evaluating the resulting prediction accuracy of the model and the morphology of urban areas. It seemed that the model managed to perform well and produce realistic urban form only up to specific range of scale. Thus, the selection of scale to represent urban system under consideration has to be undertaken with care, in order to ensure that the output produced maintains the overall accuracy of the model and morphology of urban areas.     

基于GIS的城市土地利用分析与建模框架研究

本文针对城市土地利用数据的时空特性,依托地理信息系统(GIS)丰富的空间分析工具以及对海量空间数据的高性能计算优势,围绕城市土地利用研究有关数据的处理、分析、建模等方面问题设计了一个基于GIS的城市土地利用分析与建模框架;框架主体结构中有关城市土地利用变化的驱动力机制建模方法选取逻辑回归模型,对地理数据的空间自相关性处理则根据Getis自相关系数构建滤波模型;具体应用则结合深圳市国土资源局的"城市土地利用虚拟政策实验室"项目,取得良好效果     

Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area

Urbanization process is a major factor of change in the Mediterranean region where pre-urban cities and new urban settlements have raised over the past decades. Several cities rapidly became regional centres or international nodes according to economic and political pressures. Urbanization (and informal settlement) causes land cover changes which can lead to deeper social, economic and environmental changes. The main objective of this paper is to provide time-series information to define and locate the evolution trends of the Tunis Metropolitan Area. In a first step, satellite imagery has been used (1986-1996 SPOT XS) to extract the land cover, identify the urbanization processes and estimate the changes. One of the main aspects is to locate informal settlement areas that grow significantly along the roadway networks in the Tunisian capital. Results show a global progression of the built-up areas of 13% in 10 years. In a second step, the urban growth evolution has been approached by using a potential model that provides general trends of feasible urban expansion, taking into account protection laws of natural and agricultural land. This type of model has not been tested on developing cities and as such it corresponds to a new planning contribution for planners who have no concept of spatially how their urban areas have changed over time and where the growth is occurring. In this case, it has been calibrated over the period of 1986-1996, and then applied to predict the location of the built-up growth over the next 10 years (1996-2006). These results can provide local authorities and other stakeholders with information towards decision support documents for future planning and monitoring plans. Moreover, they can be updated systematically through the integration of remote sensing data.     

Urban Expansion and Driving Force Analysis of Ulaanbaatar in Mongolia in Recent Thirty Years

Since the reform of the regime in the 1990s,Mongolian urban experienced a rapid development.Understanding the characteristics of urbanization and development in Mongolia is much of significance to China’s implementation of the “Belt and Road” strategy and “China-Mongolia-Russia Economic Corridor”.This study was based on theLandsat TM/OLI remote sensing image,using object-oriented classification method,and obtained 1990,2001,2010,2017 land cover data set,the overall classification accuracy were 86%,89%,91.6%,94.80% respectively,Kappa coefficient were 0.83,0.869,0,898,0,935.based on the transfer matrix,the information of land cover change from 1990 to 2017 in Ulaanbaatar was mined,the results showed that：① the areas of built area,barren,and water showed an increasing trend,and the built area was increased most.On the contrary,the area of forest,cropland,and grassland showed a tendency to decrease,and the forest area decreased most.② the transfer between grassland and forest,grassland and built area,forest and grassland played a major role in land cover change of Ulaanbaatar.The change area from 1990 to 2001,2001 to 2010 and 2010 to 2017 accounted for nearly 71%,74%,and 79% of the total change area.③ the expansion trend of built area was significant,the area has increased from 99.87 km2to 216.16 km2,the growth rate has reached 216%,the expansion rate was 8.01 km2/a,which belonged to the rapid expansion mode.The middle-north with the type of summer house,northeast with the types of traditional houses based on the structure of home-household-yard,mixture of Mongolian yurts and low buildings,west with the types of industrial land and residential land.The urbanization in Ulaanbaatar caused by the interaction of external social economic development and national policies,among of which,land privatization and market economic were the main policy driving forces of urbanization     

基于OPI方法的模式城市识别偏差对地表辐射收支的影响分析

城市化影响着局地与区域气候变化。区域气候模式是探讨城市与环境问题演变机制与城市化气候效应的重要研究手段,然而模式地表参数化方法处理地表参数时难以全面反映下垫面状况,可能影响模拟结果的合理性。因此以卫星数据源为基础,分析了OPI方法处理下垫面参数导致的偏差,结合其导致的地表辐射收支差异说明不同空间尺度偏差的辐射收支效应,进而通过与CO²排放关联定量分析OPI方法对城市化气候效应的影响。结果发现随着空间尺度的变大,OPI方法导致的城市面积提取误差也在变大,导致城市下垫面转变成了其周边的土地覆盖类型,增强了城市群区域的反照率,降低了城市区域的辐射收支,弱化了城市气候效应。研究发现城市地表单位面积反照率增加0.01相当于减少了2.82 kg的CO²排放,京津冀城市群2009年案例分析表明其弱化程度相当于减少了CO²排放2.28×10⁵ t,扩展到中国区域尺度上则相当于减少了1.81×10⁶ t CO²排放。     

Modelling the impacts of land system dynamics on human well-being: Using an agent-based approach to cope with data limitations in Koper,Slovenia

To cope with data limitations and to provide insight into the dynamics of LUCC for local stakeholders in the Municipality of Koper, Slovenia, we constructed an ABM (loosely defined) that integrates utility theory, logistic regression, and cellular automaton-like rules to represent the decision-making strategies of different agents. The model is used to evaluate the impact of LUCC on human well-being, as represented by the provision of highly productive agricultural soil, the extent of noise pollution, and quality-of-life measurements. Results for the Municipality of Koper show that, under a range of model assumptions, (1) high quality agricultural soils are disproportionately affected by urban growth, (2) aggregate resident quality of life increases non-linearly with a change in development density, (3) some drivers of residential settlement produce non-linear preference responses, and (4) clustering industrial development had a beneficial impact on human well-being. Additional novel contributions include the incorporation of noise pollution feedbacks and an approach to empirically inform agent preferences using a conjoint analysis of social survey data.     

The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian financial statements

In this paper a brute force logistic regression (LR) modeling approach is proposed and used to develop predictive credit scoring model for corporate entities. The modeling is based on 5 years of data from end-of-year financial statements of Serbian corporate entities, as well as, default event data. To the best of our knowledge, so far no relevant research about predictive power of financial ratios derived from Serbian financial statements has been published. This is also the first paper that generated 350 financial ratios to represent independent variables for 7590 corporate entities default predictions’. Many of derived financial ratios are new and were not discussed in literature before. Weight of evidence (WOE) method has been applied to transform and prepare financial ratios for brute force LR fitting simulations. Clustering method has been utilized to reduce long list of variables and to remove highly correlated financial ratios from partitioned training and validation datasets. The clustering results have revealed that number of variables can be reduced to short list of 24 financial ratios which are then analyzed in terms of default event predictive power. In this paper we propose the most predictive financial ratios from financial statements of Serbian corporate entities. The obtained short list of financial ratios has been used as a main input for brute force LR model simulations. According to literature, common practice to select variables in final model is to run stepwise, forward or backward LR. However, this research has been conducted in a way that the brute force LR simulations have to obtain all possible combinations of models that comprise of 5–14 independent variables from the short list of 24 financial ratios. The total number of simulated resulting LR models is around 14 million. Each model has been fitted through extensive and time consuming brute force LR simulations using SAS® code written by the authors. The total number of 342,016 simulated models (“well-founded” models) has satisfied the established credit scoring model validity conditions. The well-founded models have been ranked according to GINI performance on validation dataset. After all well-founded models have been ranked, the model with highest predictive power and consisting of 8 financial ratios has been selected and analyzed in terms of receiver-operating characteristic curve (ROC), GINI, AIC, SC, LR fitting statistics and correlation coefficients. The financial ratio constituents of that model have been discussed and benchmarked with several models from relevant literature.     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

遥感技术在全球变化研究中的应用

首先通过全球卫星遥感监测系统的介绍,阐述了遥感技术在全球环境数据获取中的重要性。然后概述了遥感技术在土地覆盖、痕量气体,湿地以及地表参数反演等全球变化研究热点问题中的应用及进展情况,遥感技术已渗入到全球变化研究诸多领域,成为全球变化研究计划的重要组成部分,从辅到主要手段乃至某些问题研究的唯一手段,遥感技术发挥着越来越重要的作用。     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

基于Landsat TM数据的江苏海岸带土地利用/覆被变化检测方法比较研究

土地利用/覆被变化是全球环境变化研究的核心问题之一,选择适当的变化检测方法在江苏海岸带土地利用/覆被变化研究工作中具有重要意义。本研究选取江苏海岸带具有典型代表性的大丰市作为变化检测方法比较研究的试验区,采用1992年和2002年两期Landsat TM影像,分别运用主成分分析法、混合变化检测法和变化矢量分析法进行变化检测,提取土地利用/覆被变化信息,并对每种变化检测方法进行定量分析和综合比较。研究表明,主成分分析法对于试验区的土地利用/覆被变化研究具有检测速度快、检测精度高等优点,适合试验区以及整个江苏海岸带的土地利用/覆被变化的检测。最终,综合运用主成分分析法和分类后比较法获得了5期近30年江苏海岸带土地覆被分类图,证明了主成分分析法可有效应用于江苏海岸带的变化检测。     

Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts

Accurate land cover change estimates are among the headline indicators set by the Convention on Biological Diversity to evaluate the progress toward its 2010 target concerning habitat conservation. Tropical deforestation is of prime interest since it threatens the terrestrial biomes hosting the highest levels of biodiversity. Local forest change dynamics, detected over very large extents, are necessary to derive regional and national figures for multilateral environmental agreements and sustainable forest management. Current deforestation estimates in Central Africa are derived either from coarse to medium resolution imagery or from wall-to-wall coverage of limited areas. Whereas the first approach cannot detect small forest changes widely spread across a landscape, operational costs limit the mapping extent in the second approach. This research developed and implemented a new cost-effective approach to derive area estimates of land cover change by combining a systematic regional sampling scheme based on high spatial resolution imagery with object-based unsupervised classification techniques. A multi-date segmentation is obtained by grouping pixels with similar land cover change trajectories which are then classified by unsupervised procedures. The interactive part of the processing chain is therefore limited to land cover class labelling of object clusters. The combination of automated image processing and interactive labelling renders this method cost-efficient. The approach was operationally applied to the entire Congo River basin to accurately estimate deforestation at regional, national and landscape levels. The survey was composed of 10 × 10 km sampling sites systematically-distributed every 0.5° over the whole forest domain of Central Africa, corresponding to a sampling rate of 3.3%. For each of the 571 sites, subsets were extracted from both Landsat TM and ETM+ imagery acquired in 1990 and 2000 respectively. Approximately 60% of the 390 cloud-free samples do not show any forest cover change. For the other 165 sites, the results are depicted by a change matrix for every sample site describing four land cover change processes: deforestation, reforestation, forest degradation and forest recovery. This unique exercise estimates the deforestation rate at 0.21% per year, while the forest degradation rate is close to 0.15% per year. However, these figures are less reliable for the coastal region where there is a lack of cloud-free imagery. The results also show that the Landscapes designated after 2000 as high priority conservation zones by the Congo Basin Forest Partnership had undergone significantly less deforestation and forest degradation between 1990 and 2000 than the rest of the Central African forest.     

视觉选择性注意模型化计算中的特征整合权值估计与图像显著性区域提取

针对视觉选择性注意模型化计算过程中不同特征在整合阶段的权值判定,提出一种基于特征图分布的权值估计方法,并在静态图像显著性区域提取中取得了令人满意的应用效果。首先提取原始图像的颜色、方向和强度特征图像,然后计算各个特征图的广义高斯分布参数与方差,进而给出一种特征图权值估计算法,最后通过对特征图的加权整合与归一化实现对原始图像的显著性区域提取。实验结果表明,通过此方法计算的权值对特征进行加权调制所提取的显著性区域的效果更加符合人眼的观测结果。     

Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

Global warming has obtained more and more attention because the global mean surface temperature has increased since the late 19th century. As more than 50% of the human population lives in cities, urbanization has become an important contributor for global warming. Pearl River Delta (PRD) in Guangdong Province, southern China, is one of the regions experiencing rapid urbanization that has resulted in remarkable Urban Heat Island (UHI) effect, which will be sure to influence the regional climate, environment, and socio-economic development. In this study, Landsat TM and ETM+ images from 1990 to 2000 in the PRD were selected to retrieve the brightness temperatures and land use/cover types. A new index, Normalized Difference Bareness Index (NDBaI), was proposed to extract bare land from the satellite images. Additionally, Shenzhen, which has experienced the fastest urbanization in Guangdong Province, was taken as an example to analyze the temperature distribution and changes within a large city as its size expanded in the past decade. Results show that the UHI effect has become more prominent in areas of rapid urbanization in the PRD region. The spatial distribution of heat islands has been changed from a mixed pattern, where bare land, semi-bare land and land under development were warmer than other surface types, to extensive UHI. Our analysis showed that higher temperature in the UHI was located with a scattered pattern, which was related to certain land-cover types. In order to analyze the relationship between UHI and land-cover changes, this study attempted to employ a quantitative approach in exploring the relationship between temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBaI and temperature are negative when NDVI is limited in range, but positive correlation is shown between NDBI and temperature.     

Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia

In Queensland, Australia, forest areas are discriminated from non-forest by applying a threshold (∼ 12%) to Landsat-derived Foliage Projected Cover (FPC) layers (equating to ∼ 20% canopy cover), which are produced routinely for the State. However, separation of woody regrowth following agricultural clearing cannot be undertaken with confidence, and is therefore not mapped routinely by State Agencies. Using fully polarimetric C-, L- and P-band NASA AIRSAR and Landsat FPC data for forests and agricultural land near Injune, central Queensland, we corroborate that woody regrowth dominated by Brigalow (Acacia harpophylla) cannot be discriminated using either FPC or indeed C-band data alone, because the rapid attainment of a canopy cover leads to similarities in both reflectance and backscatter with remnant forest. We also show that regrowth cannot be discriminated from non-forest areas using either L-band or P-band data alone. However, mapping can be achieved by thresholding and intersecting these layers, as regrowth is unique in supporting both a high FPC (> ∼ 12%) and C-band SAR backscatter (> ~ − 18 dB at HV polarisation) and low L-band and P-band SAR backscatter (e.g. < =∼ 14 dB at L-band HH polarisation). To provide a theoretical explanation, a wave scattering model based on that of Durden et al. [Durden, S.L., Van Zyl, J.J. & Zebker, H.A. (1989). Modelling and observation of radar polarization signature of forested areas. IEEE Trans. Geoscience and Remote Sensing, 27, 290-301.] was used to demonstrate that volume scattering from leaves and small branches in the upper canopy leads to increases in C-band backscattering (particularly HV polarisations) from regrowth, which increases proportionally with FPC. By contrast, low L-band and P-band backscatter occurs because of the lack of double bounce interactions at co-polarisations (particularly HH) and volume scattering at HV polarisation from the stems and branches, respectively, when their dimensions are smaller than the wavelength. Regrowth maps generated by applying simple thresholds to both FPC and AIRSAR L-band data showed a very close correspondence with those mapped using same-date 2.5 m Hymap data and an average 73.7% overlap with those mapped through time-series comparison of Landsat-derived land cover classifications. Regrowth mapped using Landsat-derived FPC from 1995 and JER-1 SAR data from 1994-1995 also corresponded with areas identified within the time-series classification and true colour stereo photographs for the same period. The integration of Landsat FPC and L-band SAR data is therefore expected to facilitate regrowth mapping across Queensland and other regions of Australia, particularly as Japan's Advanced Land Observing System (ALOS) Phase Arrayed L-band SAR (PALSAR), to be launched in 2006, will observe at both L-band HH and HV polarisations.     

Bayesian Approach for Estimating the Distribution of Magnitudes,Interevent Times and Distances of Earthquake Sequences

Abstract

A Bayesian approach has been applied to estimate the distribution of magnitudes, interevent distances and times of earthquakes occurred in 2017 in central Italy by using a small amount of random samples drawn from the distribution of the same seismic parameters for the earthquakes occurred in 2014-2016. We applied the method to the whole and aftershock-depleted seismicity by using the exponential and the normal model to fit the distributions of the seismic parameters. Our findings indicate that the exponential model fits the distributions of the seismic parameters much better than the normal model. Furthermore, in the whole seismicity case, the method requires at least 2100 to 2300 random samples to estimate the distributions of the seismic parameters of earthquakes occurred in 2017 with an estimation error less than 0.01; while in the aftershock-depleted case, a minimum number of random samples varying between 360 and 1470 occurred in 2014-2017 is required to estimate the distributions of the seismic parameters of earthquakes occurred in 2017 with an estimation error less than 0.01.     

近70年内蒙古呼和浩特城市演化的形态特征及影响因素剖析

城市演化的形态特征及其影响因素分析对于城市生态环境治理及国土空间管控具有重要的应用价值。然而,目前对城市形态特征演化过程的长期识别,尤其对北疆地区及少数民族地区的研究仍显不足。以呼和浩特市为例,基于遥感影像、土地利用/覆盖数据集和城市不透水面与绿地空间组分数据集等信息,利用地理统计方法,分析了1949～2018年城市扩展时空特征以及21世纪以来的城市土地覆盖变化,揭示了城市演化的影响因素,进一步探讨了城市规划在城市演化过程中的作用。研究表明：70年间,呼和浩特市区城市土地面积以“减速—保持—加速”的扩展特征增长了67.62倍,在形态上呈现“填充式—圈层式—单轴线外延—双轴线外延”模式。2000年以来,呼和浩特市区城市不透水面比例呈“先上升后下降”的趋势,城市绿地空间比例则呈现“波动上升”的趋势。国家西部大开发战略、城市总体规划等相关规划政策,以及经济因素是影响呼和浩特市城市演化的重要因素。本研究可为呼和浩特市以及北疆少数民族地区的城市可持续发展提供重要的参考。     

基于一致度、覆盖度和包含度的感兴趣区域不一致性决策算法

医学影像感兴趣区域(ROI)的噪声和疾病误判是一个典型的不一致性决策问题,同时也是困扰临床诊断的一个难题。针对这个问题,基于宏观与微观结合、全局与局部相结合的思想,提出了基于一致度、覆盖度和包含度的磁共振成像(MRI)前列腺肿瘤ROI不一致决策算法(ItoC-CIC)。首先提取MRI前列腺肿瘤ROI的高维特征,得到完备不一致决策信息表;然后通过计算不一致度找到不一致样本所在的等价类;再计算不一致等价类的覆盖度和包含度得到Score值,利用Score值筛选不一致样本,实现不一致性决策向一致性决策的转换;最后通过典型算例、UCI数据集和实验提取的前列腺肿瘤ROI特征构成的不一致决策信息表等进行验证。实验结果表明,所提算法能有效地找到并筛选掉不一致性样本。