Calibrating cellular automata based on landscape metrics by using genetic algorithms

Landscape metrics have been widely used to characterize geographical patterns which are important for many geographical and ecological analyses. Cellular automata (CA) are attractive for simulating settlement development, landscape evolution, urban dynamics, and land-use changes. Although various methods have been developed to calibrate CA, landscape metrics have not been explicitly used to ensure the simulated pattern best fitted to the actual one. This article presents a pattern-calibrated method which is based on a number of landscape metrics for implementing CA by using genetic algorithms (GAs). A Pattern-calibrated GA–CA is proposed by incorporating percentage of landscape (PLAND), patch metric (LPI), and landscape division (D) into the fitness function of GA. The sensitivity analysis can allow the users to explore various combinations of weights and examine their effects. The comparison between Logistic- CA, Cell-calibrated GA–CA, and Pattern-calibrated GA–CA indicates that the last method can yield the best results for calibrating CA, according to both the training and validation data. For example, Logistic-CA has the average simulation error of 27.7%, but Pattern-calibrated GA–CA (the proposed method) can reduce this error to only 7.2% by using the training data set in 2003. The validation is further carried out by using new validation data in 2008 and additional landscape metrics (e.g., Landscape shape index, edge density, and aggregation index) which have not been incorporated for calibrating CA models. The comparison shows that this pattern-calibrated CA has better performance than the other two conventional models.     

ASSESSING THE ONSET OF SPRING: A CLIMATOLOGICAL PERSPECTIVE

This study is part of a program to examine spring plant-climate interactions in the major agricultural regions of eastern North America. The project's objectives were to: (1) document the yearly onset of the spring green wave and associated phenomena from 1908 to 1987 using phenological models, and (2) analyze regional dynamics and recent changes. Examining past variations in the green wave provides a context within which present and future variations can be assessed. Spring index dates, last -2.2°C (28°F) frost dates, and the difference between these two dates (termed the damage index) were generated for selected United States Daily Historical Climatology Network (HCN) stations between 1908 and 1987. The results show considerable geographic and temporal variations over the study period, and suggest that the threat of late spring frost damage may have decreased slightly from about 1960 to 1987. In future research, an improved version of the spring index (and associated surface phenological data) will be compared with satellite-derived vegetation condition data, in order to facilitate many kinds of atmosphere-biosphere interaction studies. [Key words: phenology, spring, “green wave” climatology.]     

Forest transition and biogeographic meaning of the current laurel forest landscape in Canary Islands,Spain

This study provides a geographical synthesis of the current natural landscape dynamics of the Canarian laurel forest based on the results of a biogeographic study of its structure, composition, and dynamics. Historical socioeconomic changes and recent environmental conservation strategies have led to natural forest dynamics that make it necessary to change our current scientific conception of the Canarian laurel forest landscape. According to the forest transition theory, in the protected areas of Anaga and Teno Rural Parks (Tenerife) and Garajonay National Park (La Gomera), the forest reaction to the gradual abandonment of economic activity reveals how strongly human activity has shaped the features of the laurel forest, which traditionally has been considered to be relatively untouched by humankind. How natural disturbances have affected this forest over the past few years has also contributed to a better understanding of its dynamics. This study explains the key patterns of spontaneous dynamics in the Canarian laurel forest to enrich our understanding of this forest and its landscape, while highlighting the importance of human activity as the catalyst for its current vigorous dynamics.     

Influence of landscape evolution on the distribution of floristic patterns in northern Amazonia revealed by δ13C data

The Amazonian lowlands include large patches of open vegetation which contrast sharply with the rainforest, and the origin of these patches has been debated. This study focuses on a large area of open vegetation in northern Brazil, where δ¹³C and, in some instances, C/N analyses of the organic matter preserved in late Quaternary sediments were used to achieve floristic reconstructions over time. The main goal was to determine when the modern open vegetation started to develop in this area. The variability in δ¹³C data derived from nine cores ranges from ?32.2 to ?19.6‰, but with nearly 60% of data above ?26.5‰. The most enriched values were detected only in ecotone and open vegetated areas. The development of open vegetation communities was asynchronous, varying between estimated ages of 6400 and 3000 cal a BP. This suggests that the origin of the studied patches of open vegetation might be linked to sedimentary dynamics of a late Quaternary megafan system. As sedimentation ended, this vegetation type became established over the megafan surface. In addition, the data presented here show that the presence of C₄ plants must be used carefully as a proxy to interpret dry paleoclimatic episodes in Amazonian areas. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Relative pollen productivities of the major plant taxa of subtropical evergreen–deciduous mixed woodland in China

Quantifying the relationship between pollen and vegetation is an essential step in the pollen-based quantitative reconstruction of past vegetation cover. In this study, we use the Extended R-Value (ERV) model and a modern dataset of pollen (collected from moss polsters) and related vegetation from 50 sites in the Daba Mountains (subtropical China) to (i) estimate the relevant source area of pollen (RSAP) of the moss samples and the relative pollen productivities (RPPs) of nine major plant taxa-characteristic of the region, and (ii) evaluate the obtained RPPs. The RSAP estimates of moss polsters vary between 225 and 610 m depending on the ERV submodels and models of pollen dispersal and deposition used. The RPP estimates are different from values published in previous studies from temperate and subtropical China. This may be explained by differences in methodology, climate and vegetation (species composition and spatial distribution), of which vegetation is probably the most important factor. The ranking of the RPP estimates for the nine taxa is Pinus > Juglandaceae > D − Quercus (deciduous Quercus) > Poaceae > Rosaceae > Cyperaceae > Anacardiaceae > Castanea > Fabaceae. We use a ‘leave-one-out’ cross-validation strategy and the Landscape Reconstruction Algorithm (LRA) for pollen-based reconstruction of regional and local plant cover to evaluate the ERV model-based RPP estimates. Both the REVEALS (Regional Estimates of VEgetation Abundance from Large Sites)-based and the LOVE (LOcal Vegetation Estimates)-based plant cover using the RPP estimates are closer to the modern vegetation composition than pollen percentages, thus confirming the applicability of the ERV model and the LRA approach in subtropical China.     

古村落景观基因图谱的平台系统设计

我国古代先进的社会经济文化孕育了为数众多的古村落。本文分析了近些年我国古村落研究的基本特点,并以"文化景观基因学说"分析了古村落景观基因信息图谱平台构建的内容与意义,以及其关键技术与解决方案。同时,讨论了该程序的基本框架和主要功能、需求分析、功能模块划分与实现思路。最后,以湖南江永女书为例,对古村落景观基因信息进行了提取实验。     

北京一号小卫星数据的城市景观格局监测分析——以徐州市城区为例

为了推进北京一号小卫星遥感数据在城市景观生态研究中的应用,针对其全色影像4m分辨率、多光谱影像32m分辨率的特点,试验分析北京一号小卫星影像在城市景观格局变化中的应用效果和特点。通过对不同分类器的比较,选择支持向量机分类器对多光谱影像、全色影像、全色与多光谱融合影像三个数据集进行景观组分分类,结果表明,全色与多光谱融合影像的分类精度最高。利用多时相、多光谱遥感数据统计分析了城市景观组分与格局变化,表明32m空间分辨率的多光谱影像可以用于城市景观格局变化和土地覆盖变化分析。本文全面试验和分析评价了北京一号多分辨率数据在城市景观格局研究中的应用效果。通过对同一年份全色影像和多光谱影像计算的景观格局指标的分析表明,全色数据能更有效地描述景观详细信息,多光谱数据可展现城市景观的整体格局,而融合后对景观格局分析能够获得优于单一数据的效果。试验和分析表明,北京一号小卫星4m全色高分辨率影像和32m多光谱数据的波段组合,能从不同尺度揭示城市景观格局和变化过程。     

近30年天津滨海新区湿地景观格局遥感监测分析

在GIS和FRAGSTATS软件支持下,结合景观格局数量分析方法,利用1979-2008年间共6期TM遥感影像与非遥感数据,对天津滨海新区近30年的湿地景观空间格局进行了动态分析。结果表明,滨海新区湿地(以人工湿地为主),1979-2004年滨海新区湿地总面积变化不大,但各类型间转换较大,主要是自然湿地转变为人工湿地,沼泽湿地几近消失。2006-2008年,由于城市建设大量滨海滩涂湿地被围填占用,到2009年5月自然海岸线的90%以上被占用。景观破碎度增加,湿地斑块数量从137个增加到704个,同时造成平均斑块面积下降。驱动力分析表明,降雨量减少的同时,年平均气温上升是主要的自然驱动因素。和自然因素相比,人类干扰为主要人文驱动因素,表现在水产养殖为主的农业经济活动、城市建设用地占用和围海造地等方面。目前,滨海新区几乎没有自然湿地生态系统,破碎化和人工化是该区域湿地的主要特征。     

基于景观格局的汶上县城市空间布局评价

在县域城市快速发展的背景下,以汶上县为研究对象,利用景观生态学的分析方法,选取斑块数、平均斑块面积、最大斑块指数、景观形状指数、平均斑块分维数、平均形状指数、平均邻近指数等指标,对城区不同土地利用类型的现状景观格局进行评价,分析整体及局部布局特征并指出存在的布局问题,对城市未来的发展提升提出建议。