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.     

Beyond the urban-rural dichotomy: Towards a more nuanced analysis of changes in built-up land

Urban land and rural land are typically represented as homogenous and mutually exclusive classes in land change analyses. As a result, differences in urban land use intensity, as well as mosaic landscapes combining urban and rural land uses are not represented. In this study we explore the distribution of urban land and urban land use intensity in Europe and the changes therein. Specifically, we analyze the distribution of built-up land within pixels of 1 km². At that resolution we find that most built-up land is distributed over predominantly non-built-up pixels. Consistently, we find that most urban land use changes between 2000 and 2014 come in small incremental changes, rather than sudden large-scale conversions from rural to urban land. Using urban population densities, we find that urban land use intensity varies strongly across 1 km² pixels in Europe, as illustrated by a coefficient of variation of 85%. We found a similarly high variation between urban population densities for most individual countries and within areas with the same share of built-up land. Population changes have led to different combinations of urban land expansion and urban intensity changes in different study periods (1975–1990, 1990–2000, and 2000–2015) and countries. These findings suggest that land use change models could be improved by more nuanced representations of urban land, including mosaic classes and different urban land use intensities.     

Calibration of cellular automata urban growth models from urban genesis onwards - a novel application of Markov chain Monte Carlo approximate Bayesian computation

Cellular Automata (CA) models are widely used to study spatial dynamics of urban growth and evolving patterns of land use. One complication across CA approaches is the relatively short period of data available for calibration, providing sparse information on patterns of change and presenting problematic signal-to-noise ratios. To overcome the problem of short-term calibration, this study investigates a novel approach in which the model is calibrated based on the urban morphological patterns that emerge from a simulation starting from urban genesis, i.e., a land cover map completely void of urban land. The application of the model uses the calibrated parameters to simulate urban growth forward in time from a known urban configuration.This approach to calibration is embedded in a new framework for the calibration and validation of a Constrained Cellular Automata (CCA) model of urban growth. The investigated model uses just four parameters to reflect processes of spatial agglomeration and preservation of scarce non-urban land at multiple spatial scales and makes no use of ancillary layers such as zoning, accessibility, and physical suitability. As there are no anchor points that guide urban growth to specific locations, the parameter estimation uses a goodness-of-fit (GOF) measure that compares the built density distribution inspired by the literature on fractal urban form. The model calibration is a novel application of Markov Chain Monte Carlo Approximate Bayesian Computation (MCMC-ABC). This method provides an empirical distribution of parameter values that reflects model uncertainty. The validation uses multiple samples from the estimated parameters to quantify the propagation of model uncertainty to the validation measures.The framework is applied to two UK towns (Oxford and Swindon). The results, including cross-application of parameters, show that the models effectively capture the different urban growth patterns of both towns. For Oxford, the CCA correctly produces the pattern of scattered growth in the periphery, and for Swindon, the pattern of compact, concentric growth. The ability to identify different modes of growth has both a theoretical and practical significance. Existing land use patterns can be an important indicator of future trajectories. Planners can be provided with insight in alternative future trajectories, available decision space, and the cumulative effect of parcel-by-parcel planning decisions.     

SINS/CNS组合导航系统陀螺在线标定技术

陀螺仪长时间工作时,由于环境变化等因素,会产生陀螺漂移、标度因数误差和安装轴不正交误差,而且由积分得到的姿态参数信息误差也会相应变大,因此需要对陀螺进行在线标定。借助CNS提供的高精度姿态信息,基于四元数误差建立陀螺在线标定模型,采用卡尔曼滤波器对SINS/CNS组合导航系统陀螺在线标定技术进行仿真研究。仿真结果验证了该算法的有效性,能够估计误差模型中的所有参数,并且满足标定精度要求,具有工程应用价值。     

1990~2015年吉林省西部耕地变化与“旱改水”时空特征研究

以Landsat遥感影像为数据源，利用面向对象和决策树方法获得多期土地覆被数据；以此为基础，分析了1990~2015年吉林省西部耕地变化与旱田水田转化特征及驱动因素。结果表明：1990~2015年期间，吉林省西部耕地面积增加了2159.33 km²，增速逐渐变缓。旱田面积在1990~2000和2000~2010年期间有小幅增加，但在2010~2015年期间呈减少趋势。水田面积持续扩张，25年间增加了1139.39 km²（51.7%），旱田净转化为水田的面积不断增加，1990~2000年为69.13 km²，2000~2010年为156.19 km²，2010~2015年为288.27 km²。人口和经济的增长是导致耕地面积迅速增长的主要原因，影响水田面积扩张和旱田向水田转化的驱动因素有：科技进步、水利设施建设、政策倾向和利益驱动。最后提出了吉林省西部地区耕地保护的建议，为区域农业生产和生态建设提供科学借鉴。     

1990~2015年吉林省西部耕地变化与“旱改水”时空特征研究

以Landsat遥感影像为数据源,利用面向对象和决策树方法获得多期土地覆被数据;以此为基础,分析了1990~2015年吉林省西部耕地变化与旱田水田转化特征及驱动因素。结果表明：1990~2015年期间,吉林省西部耕地面积增加了2159.33 km²,增速逐渐变缓。旱田面积在1990~2000和2000~2010年期间有小幅增加,但在2010~2015年期间呈减少趋势。水田面积持续扩张,25年间增加了1139.39 km²（51.7%）,旱田净转化为水田的面积不断增加,1990~2000年为69.13 km²,2000~2010年为156.19 km²,2010~2015年为288.27 km²。人口和经济的增长是导致耕地面积迅速增长的主要原因,影响水田面积扩张和旱田向水田转化的驱动因素有：科技进步、水利设施建设、政策倾向和利益驱动。最后提出了吉林省西部地区耕地保护的建议,为区域农业生产和生态建设提供科学借鉴。     

Applicability and calibration of an irregular cellular automata model for land use change

Cellular automata (CA) models of spatial change have been developed and applied in the context of large regional or metropolitan areas and usually use regular cells, with spatial interactions and transition rules operating within fixed-size neighbourhoods. Model calibration has also been an area of intensive research with many models still using expert-based input to ensure visual calibration of modelled land use maps. In this paper, we present an innovative CA model where irregular cells and variable neighbourhoods are used to better represent space and spatial interaction. Calibration is based on an optimisation procedure that uses particle swarm (PS) to determine the optimal set of parameters of the CA model. Hypothetical test instances are used to assess the CA model and its calibration to small urban areas. Our conclusion was that the use of PS ensures calibration results for the CA model that compare very well with results obtained through other approaches reported in the literature.     

A comparative analysis of cellular automata models for simulation of small urban areas in Galicia,NW Spain

Urban growth models developed in the second half of the 20th century have allowed for a better understanding of the dynamics of urban growth. Among these models, cellular automata (CA) have become particularly relevant because of their ability to reproduce complex spatial and temporal dynamics at a global scale using local and simple rules. In the last three decades, many urban CA models that proved useful in the simulation of urban growth in large cities have been implemented. This paper analyzes the ability of some of the main urban CA models to simulate growth in a study area with different characteristics from those in which these models have been commonly applied, such as slow and low urban growth. The comparison of simulation results has allowed us to analyze the strengths and weaknesses of each model and to identify the models that are best suited to the characteristics of the study area. Results suggest that models which simulate several land uses can capture better land use dynamics in the study area but need more objective and reliable calibration methods.     

A method of exploiting spatial information for improving classification rules: Application to the construction of polygon-based land cover maps

This article proposes a method of exploiting spatial information to improve classification rules constructed by automated methods such as k-nearest neighbour or linear discriminant analysis. The method is intended for polygonbased, land cover type mapping using remote sensing information in a GIS. Our approach differs from contextual allocation methods used in lattice- or pixel-based mapping because it does not rely on spatial dependence models. Instead, the method uses a Bayes-type formula to modify the estimated posterior probabilities of group membership produced by automated classifiers. The method is found to substantially improve classification accuracy estimates in areas where there is a moderate or greater degree of physiographic variation across the map extent.     

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA-AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift-corrected NOAA-AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA-AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.     

基于遥感与GIS的黄土丘陵半干旱区土地利用定量化研究-以中国定西县为例

研究区域为典型的黄土丘陵沟壑半干旱区,以中国定西县为例。从1990年和2000年的Landsat TM影像图中提取两个时段的土地利用图,包括9种土地利用类型。从1∶10万的等高线数据中提取坡度数据。参考景观生态指标,我们选择或修改了10个指标实现定量化土地利用特征,动态监测土地利用变化,并分析地形与土地利用和土地利用变化的关系及土地利用变化与政府政策之间的关系。结果表明:①所选择的指标可以量化中国典型的黄土丘陵半干旱区土地利用及其变化特征,当转移矩阵采用不同的基数,可以体现不同的转换的量的特征;②土地利用与土地利用变化在不同坡度的空间分布上有很好的规律性;③政府政策是土地利用变化的主要驱动力因素,包括梯田改造、集雨工程和退耕还林还草。     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.     

基于遥感和陆表信息集成的广东省土地覆盖分类方法研究

土地覆盖信息是估算地-气间的生物物理过程和能量交换的关键参数,也是区域和全球尺度气候和生态系统过程模型所需要的重要参量。如何高效地利用遥感数据提取土地覆盖信息是当前研究迫切需要解决的问题。面向对象的分类方法不但充分利用了遥感数据的光谱信息,同时也利用了影像的纹理结构信息和更多的地物分布信息关系,在遥感分类中具有较大的潜力。研究基于2010年多时相的环境卫星数据、TM数据以及DEM数据,并结合地表采集的4000多个样点数据,采用面向对象的分类方法对广东省土地覆盖进行分类。经采样验证,广东省土地覆盖平均精度为85%,分类结果精度远高于常规的分类算法,说明结合陆表信息的面向对象分类方法比常规的分类算法更具有优势,可以实现高精度的土地覆盖分类。     

Detecting systemic change in a land use system by Bayesian data assimilation

A spatially explicit land use change model is typically based on the assumption that the relationship between land use change and its explanatory processes is stationary. This means that model structure and parameterization are usually kept constant over the model runtime, ignoring potential systemic changes in this relationship resulting from societal changes. We have developed a methodology to test for systemic changes and demonstrate it by assessing whether or not a land use change model with a constant model structure is an adequate representation of the land use system given a time series of observations of past land use. This was done by assimilating observations of real land use into a land use change model, using a Bayesian data assimilation technique, the particle filter. The particle filter was used to update the prior knowledge about the model structure, i.e. the selection and relative importance of the explanatory processes for land use change allocation, and about the parameters. For each point in time for which observations were available the optimal model structure and parameterization were determined. In a case study of sugar cane expansion in Brazil, it was found that the assumption of a constant model structure was not fully adequate, indicating systemic change in the modelling period (2003–2012). The systemic change appeared to be indirect: a factor has an effect on the demand for sugar cane, an input variable, in such a way that the transition rules and parameters have to change as well. Although an inventory was made of societal changes in the study area during the studied period, none of them could be directly related to the onset of the observed systemic change in the land use system. Our method which allows for systemic changes in the model structure resulted in an average increase in the 95% confidence interval of the projected sugar cane fractions of a factor of two compared to the assumption of a stationary system. This shows the importance of taking into account systemic changes in projections of land use change in order not to underestimate the uncertainty of future projections.     

Scene selection and the use of NASA's global orthorectified Landsat dataset for land cover and land use change monitoring

This study examines the utility of NASA's circa 1990 and circa 2000 global orthorectified Landsat dataset for land cover and land use change mapping and monitoring across Africa. This is achieved by comparing the temporal and spatial variation of NDVI, measured independently by the NOAA‐AVHRR at the time of Landsat scene acquisition, against the seasonal mean for each Landsat scene extent. Decadal sequences of drift‐corrected NOAA‐AVHRR imagery were used to calculate NDVI means and standard deviations for the periods covered by the scenes composing the c.1990 and c.2000 Landsat datasets. The specific NOAA‐AVHRR NDVI values at the acquisition date of each individual Landsat scene were also calculated and the differences, both from the mean and scaled by standard deviation, were mapped for the Landsat scene footprints in the c.1990 and c.2000 datasets. The resulting maps show the temporal position of each Landsat scene within the seasonal NDVI cycle, and provide a valuable guide to assist in quantifying uncertainty and interpreting land cover and land use changes inferred from these Landsat data.     

Participatory land use modelling,pathways to an integrated approach

The increasing adoption of land use models in planning and policy development highlights the need for an integrated approach that combines analytical modelling techniques with discursive ‘soft-science’ methodologies. Recent scientific contributions to the discipline have tended to focus on analytical problems such as statistical assessment of model goodness of fit through map comparison techniques, while the problem of integrating stakeholder information into land use models has received little attention. Using the example of a land use model developed for the Guadiamar basin in South West Spain, location of the emblematic Doñana natural area, an integrated methodology for participatory calibration and evaluation of model results is presented which combines information from key stakeholders across a range of sectors with analytical model calibration techniques. Both discursive and analytical techniques are presented side by side to demonstrate that including participatory approaches is likely to improve both calibration results and model applicability. Integration of participatory methods into land use models is more likely to be successful if stakeholders are selected carefully so as to make best possible use of their time and knowledge, and are involved in the modelling process from the beginning of the project cycle.     

Simultaneous particle tracking in multi-action motion models with synthesized paths

This paper proposes human motion models of multiple actions for 3D pose tracking. A training pose sequence of each action, such as walking and jogging, is separately recorded by a motion capture system and modeled independently. This independent modeling of action-specific motions allows us 1) to optimize each model in accordance with only its respective motion and 2) to improve the scalability of the models. Unlike existing approaches with similar motion models (e.g. switching dynamical models), our pose tracking method uses the multiple models simultaneously for coping with ambiguous motions. For robust tracking with the multiple models, particle filtering is employed so that particles are distributed simultaneously in the models. Efficient use of the particles can be achieved by locating many particles in the model corresponding to an action that is currently observed. For transferring the particles among the models in quick response to changes in the action, transition paths are synthesized between the different models in order to virtually prepare inter-action motions. Experimental results demonstrate that the proposed models improve accuracy in pose tracking.     

Modelling multi-regional urban growth with multilevel logistic cellular automata

Simulation models based on cellular automata (CA) are useful for revealing the complex mechanisms and processes involved in urban growth and have become supplementary tools for urban land use planning and management. Although the urban growth mechanism is characterized by multilevel and spatiotemporal heterogeneity, most existing studies focus only on simulating the urban growth of singular regions without considering the heterogeneity of the urban growth process and the multilevel factors driving urban growth within regions that consist of multiple subregions. Thus, urban growth models have limited performance when simulating the urban growth of multi-regional areas. To address this issue, we propose a multilevel logistic CA model (MLCA) by incorporating a multilevel logistic regression model into the traditional logistic CA model (LCA). In the MLCA, multilevel driving factors are considered, and the multilevel logistic model allows the transition rules to not only vary in space, but also change when the subregional level factors change. To verify the MLCA's validity, it was applied to simulate the urban growth of Tongshan County, located in China's Xuzhou Prefecture. The results were compared with three comparative models, LCA1, which only considered grid cell-level factors; LCA2, which considered both grid cell- and subregional-level factors; and artificial neural network CA. Urban growth data for the periods 2000–2009 and 2009–2017 were used. The results show that the MLCA performs better on both visual comparison and indicators for accuracy verification. The Kappa of the results increased by <5%, but the improvement was significant, while increases for the accuracy of urban land and figure of merit were much higher than 5%. In addition, the results of MLCA had the smallest mean absolute percentage error when allocating new urban land areas to the various subregions. The results reveal that higher-level (e.g., town level) factors either strengthened or weakened the effects of grid cell-level factors on urban growth, which indirectly affected the spatial allocation of new urban land. The MLCA model is an effective step towards simulating nonstationary urban growth of multi-regional areas, using the comprehensive effects of multilevel driving factors.     

基于随机森林的遥感土地利用分类及景观格局分析

2009年福建平潭综合实验区设立,作为闽台合作及国家对外开放的窗口,其土地利用变化主要受社会经济因素的影响和自然地理环境的制约,也与未来的土地利用规划密切相关.本文利用1990、2000、2010和2017年4期Landsat遥感影像数据,定量分析近27年的土地利用变化对景观格局的影响.结果表明:(1)在选择合适训练样本的情况下,利用随机森林方法可获得较高的遥感土地利用分类精度(4期遥感影像分类的总体精度均在87%以上,Kappa系数均在0.84以上);(2)1990~2017年,水域面积急剧减少31.04 km2,流失的水域主要转化为建设用地和林地;建设用地增加40.98 km2,年平均增长1.52 km2.近十年呈快速增长趋势,年平均增长3.87 km2;(3)在斑块类型级别上,逐年增加的建设用地导致最大斑块占景观面积比例(LPI)、聚合度(AI)和边缘密度(ED)呈上升趋势,其中LPI受到建设用地增加的影响最显著.在景观类型级别上,多样性(SHDI)和景观形状(LSI)呈下降趋势.