Modeling spatial and temporal change of soil erosion based on multi-temporal remotely sensed data

In order to monitor the pattern, distribution, and trend of land use/cover change (LUCC) and its impacts on soil erosion, it is highly appropriate to adopt Remote Sensing (RS) data and Geographic Information System (GIS) to analyze, assess, simulate, and predict the spatial and temporal evolution dynamics. In this paper, multi-temporal Landsat TM/ETM+ remotely sensed data are used to generate land cover maps by image classification, and the Cellular Automata Markov (CA_Markov) model is employed to simulate the evolution and trend of landscape pattern change. Furthermore, the Revised Universal Soil Loss Equation (RUSLE) is used to evaluate the situation of soil erosion in the case study mining area. The trend of soil erosion is analyzed according to total/average amount of soil erosion, and the rainfall (R), cover management (C), and support practice (P) factors in RUSLE relevant to soil erosion are determined. The change trends of soil erosion and the relationship between land cover types and soil erosion amount are analyzed. The results demonstrate that the CA_Markov model is suitable to simulate and predict LUCC trends with good efficiency and accuracy, and RUSLE can calculate the total soil erosion effectively. In the study area, there was minimal erosion grade and this is expected to continue to decline in the next few years, according to our prediction results.     

Using neural networks and cellular automata for modelling intra‐urban land‐use dynamics

Empirical models designed to simulate and predict urban land‐use change in real situations are generally based on the utilization of statistical techniques to compute the land‐use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non‐parametric approaches. This work introduces a simulation experiment on intra‐urban land‐use change in which a supervised back‐propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land‐use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium‐sized town in the Midwest of São Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985–1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures.     

Spatiotemporal analysis of rural–urban land conversion

Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns.     

LUCC驱动力模型研究综述

驱动力研究是土地利用变化研究中的核心问题。土地利用变化驱动力模型是分析土地利用变化原因和结果的有力工具，模型通过情景分析可为土地利用规划与决策提供依据。基于不同理论的驱动力研究方法很多，论文选取了几种国内外应用较多的LUCC驱动力模型进行综述，分析了每个模型的优缺点及适用范围，最后得出结论：1) 基于过程的动态模型更适于研究复杂的土地利用系统。2) 基于经验的统计模型能弥补基于过程的动态模型的不足。3) 基于不同学科背景的模型进一步集成将是LUCC驱动力模型未来的发展趋势。     

A comparative approach to modelling multiple urban land use changes using tree-based methods and cellular automata: the case of Greater Tokyo Area

Urban multiple land use change (LUC) modelling enables the realistic simulation of LUC processes in complex urban systems; however, such modelling suffers from technical challenges posed by complicated transition rules and high spatial heterogeneity when predicting the LUC of a highly developed area. Tree-based methods are powerful tools for addressing this task, but their predictive capabilities need further examination. This study integrates tree-based methods and cellular automata to simulate multiple LUC processes in the Greater Tokyo Area. We examine the predictive capability of 4 tree-based models – bagged trees, random forests, extremely randomised trees (ERT) and bagged gradient boosting decision trees (bagged GBDT) – on transition probability prediction for 18 land use transitions derived from 8 land use types. We compare the predictive power of a tree-based model with multi-layer perceptron (MLP) and among themselves. The results show that tree-based models generally perform better than MLP, and ERT significantly outperforms the three other tree-based models. The outstanding predictive performance of ERT demonstrates the advantages of introducing bagging ensemble and a high degree of randomisation into transition probability modelling. In addition, through variable importance evaluation, we found the strongest explanatory powers of neighbourhood characteristics for all land use transitions; however, the size of the impacts depends on the neighbourhood land use type and the neighbourhood size. Furthermore, socio-economic and policy factors play important roles in transitions ending with high-rise buildings and transitions related to industrial areas.     

京津冀地区1985–2000期间林地变化的时空格局及其影响因素（英文）

林地是维护生态安全,实现区域可持续发展的根本基础资源。林地变化可能导致一些生态环境问题,包括土壤侵蚀,水资源短缺,干旱加剧以及生物多样性的丧失。本文以景观生态学和逻辑回归模型为基础,探讨了京津冀地区1985-2000期间林地变化的时空格局及其影响因素。格局分析结果表明,林地景观破碎化正在下降和林地形状变得越来越规则。通过建立Logistic回归模型,这项研究旨在探讨这一区域1985-2000期间林地变化的重要变量。对于京津冀地区1985-2000期间林地变化而言,土壤有机质含量,坡度(5°),到最近村庄的距离以及人均国内生产总值是最重要的解释变量。研究表明,空间异质性会影响到林地变化的逻辑回归模型的可预测性。     

威海市土地利用分形特征动态变化

基于威海市三期土地利用遥感解泽数据,应用土地利用空间形态分形模型,获得了1985年、1995年和2000年三个时期威海市各主要地类的分形维数和稳定性指数;通过叠加分析等方法,计算出了1985-1995年、1995-2000年和1985-2000年三个时间段各主要地类动态度.在此基础上.对威海市土地利用分形特征时空格局变化特点、动态度变化特点进行了研究.结果显示:1985年、1995年和2000年分维数较大的主要地类均是未利用土地、城镇用地和水域等,分维数较小的地类均是农村居民点和其他建设用地.从1985年到2000年,各主要地类的分维数平均值旱现出逐渐减少的趋势,总的来看,土地利用类型的形态向着简单、规则的方向发展.在不同时段,各主要地类的分维数和稳定性指数变化特点存在较大差异.农村居民点、其他建设用地和未利用土地在三个时期的空间结构较稳定,水域、城镇用地、草地和耕地在三个时期的空间结构较不稳定.1985-1995年,威海市城镇用地年增长率最大,林地年减少率最大;1995-2000年,城镇用地年增长率最大,水域年减少率最大;1985-2000年,城镇用地年增长率最大,未利用土地年减少率最大;威海市城镇扩展迅速,城镇化趋势显著.社会经济快速发展、政府规划、城镇化等因素是威海市主要地类分形维数、稳定性指数和动态度变化的主要原因.     

南京市热岛效应变化时空特征及其与土地利用变化的关系

利用遥感数据和气象统计数据分析自1985年以来南京市热岛效应变化的时空特征及其与土地利用变化的关系。结果表明:自1985年以来,南京市热岛效应强度和范围在增长。南京市的年均温逐年上升,与郊区六合县的温差呈上升趋势;南京市建成区内4级热岛效应面积增加了107.88km²。热岛效应增长在空间上存在差异,大厂>雨花台>市辖区>栖霞>浦口>江宁。南京市土地利用变化的结果使得热岛效应空间分布更广泛,城镇居民点及工矿用地利用强度变化最大的地方即城市热岛效应增加最多的地方,两者在空间上具有很好的吻合;而和城镇居民点及工矿用地的扩展规模没有很好的空间相关性。     

基于SD和CLUE-S模型的张掖市甘州区土地利用情景分析

近年来,学者们基于不同研究需要开发了很多土地利用变化/覆盖(LUCC)模型.但迄今没有一个单独的模型可在不同时空尺度上揭示所有土地利用变化的关键过程.本文尝试开发一种集成SD模型与CLUE-S模型的建模方法,用以弥补已有LUCC模型缺陷,并将其应用于张掖市甘州区的土地利用情景分析中,得到的LUCC模拟结果可为该区土地利...     

A Comparison of Methods to Predict Soil Surface Texture in an Alluvial Basin

Surface soil texture controls many important ecological, hydrological, and geomorphic processes in arid regions and is therefore important from a land‐management perspective. Soil survey efforts have traditionally fulfilled this need, but they are constrained by the size, remoteness, and inaccessibility of many arid regions, which renders simple field measurements prohibitively expensive. This article compares several different predictive soil‐mapping techniques with a sparse data set in order to develop surficial soil texture maps. Our results suggest that data collected at the landscape scale can be used as input to predictive soil‐mapping techniques to create maps of soil texture at higher fidelity and a fraction of the cost than would be required using traditional methods.     

不同非空间模拟方法下CLUE-S模型土地利用预测——以秦淮河流域为例

基于快速城镇化背景下秦淮河流域土地利用历史状况,选择CLUE-S模型对其2020年土地利用情况进行模拟预测。分别使用线性回归、Markov模型、灰色GM（1,1）模型预测CLUE-S模型非空间模块的土地利用需求量,再嵌入CLUE-S中得到3种预测结果,对预测结果进行比较。另外设定“自然发展”情景与考虑规划政策影响的“优化格局”情景,模拟2020年不同情景下秦淮河流域土地利用格局情况,并进行景观格局分析。结果表明：线性回归模型、Markov模型、灰色GM（1,1）模型的Kappa指数分别为0.866、0.849、0.867,3种方法均满足模型精度要求;自然发展情景中2020年水域、水田、林地、城镇用地、旱地面积相对于2010年分别变化21.5%、-15.3%、-9.0%、51.5%、-28.9%,而优化格局情景下水域、水田、林地、城镇用地、旱地面积分别变化3.1%、-1.6%、10.8%、6.3%、-10.6%,相比于自然发展情景,优化情景土地利用状况更符合保护基本农田、增加生态用地连通性、提高雨水下渗能力以及缓解城市热岛效应的要求,为后期土地利用规划提供了依据。     

基于局部化转换规则的元胞自动机土地利用模型

传统土地利用元胞自动机(Cellular automata,CA)模型基于空间同质性假设,使用全局性模型建立元胞转换规则,忽略了土地利用变化驱动因素的驱动作用在空间上的变化。以美国佛罗里达州的橙县(Orange County)2003-2009年土地利用变化为例,提出了基于局部化转化规则的CA土地利用模型,其中元胞的土地利用类型适宜性由地理加权多项logit模型(Geographically weighted multinomial logit,GWML)获得。结果表明:GWML模型较传统全局性多项logit(Multinomial logit,MNL)模型有更高的数据解释能力。基于GWML模型的土地利用CA模型能反映局部土地利用变化模式,因而较基于MNL模型的CA模型具有更高的模拟精度。所得结论对未来国内地区的研究有借鉴意义。     

Modeling Spatially and Temporally Complex Land‐Cover Change: The Case of Western Honduras*

This article presents an econometric analysis of land‐cover change in western Honduras. Ground‐truthed satellite image analysis indicates that between 1987 and 1996 net reforestation occurred in the 1,015‐km² study region. While some reforestation can be attributed to a 1987 ban on logging, the area of reforestation greatly exceeds that of previously clear‐cut areas. Further, new area was also deforested between 1987 and 1996. Thus, the observed land‐cover changes represent a complex mosaic of changing land‐use patterns across time and space. The analysis contributes to the literature on land‐cover change modeling in that: (1) it compares two econometric approaches to capture complex and often bidirectional changes in land cover from 1987 to 1996 as a function of agricultural suitability and transportation costs, and (2) it addresses techniques to identify and correct for spatial autocorrelation in a categorical regression framework.     

改进的案例推理CA模型及土地覆盖变化模拟

对基于案例推理的元胞自动机模型(CBR-CA)进行改进,将各类别的宏观转移概率添加到目标函数中,体现各类别的转变特征,并增加时间权重来确定转移概率,实现时间尺度上的模拟;由于土地覆盖变化的多样性和空间结构的复杂性,利用Monte Carlo(M-C)法确定土地覆盖的最终转换类别。选择黄河源区为试验区,利用1977年、1985年土地覆盖数据建立原始案例库,模拟了该区域1995年、2000年和2006年的土地覆盖变化,模拟的各类别转换的数量精度与实际相吻合,各年份的总体误差分别为0.002%、0.012%和0.005%,空间位置精度总体在70%以上,并进行未来土地覆盖情景预测。该模型可用于多类别、长时间序列区域土地覆盖变化的模拟与预测。     

黄土台塬区土地利用转移流及空间集聚特征分析

提出了土地利用转移流和土地利用活跃度的概念,基于Landsat 遥感影像数据,采用土地利用变化测度模型,以5 年和25 年两种时间尺度对黄土台塬区土地利用时空动态特征进行分析,并将密度制图法应用到土地利用变化的空间集聚特征识别中.研究表明：① 黄土台塬区耕地占绝对优势,后备耕地资源严重不足,林地、草地、水域比例较低,存在较大生态风险;② 耕地与建设用地、草地、林地之间的转移关系是黄土台塬区土地利用转移的关键关系,决定着台塬区土地利用变化特征;③ 1985-2010 年耕地转建设用地流高达26668.80 hm²,占土地转移流40.75%,草地转耕地流18923.90 hm²,占28.91%,建设占用耕地情况严重,耕地占补平衡主要通过挤占草地实现;④ 25 年以来,土地利用变化对水域扰动最大,其次为森林,草地第三;⑤ 土地利用变化存在阶段性,1990-2000 年土地利用变化速率高于其他时段;⑥不同时间尺度下土地利用变化的空间集聚特征不同,25 年尺度下城镇附近土地变化幅度较高,5 年尺度下土地利用变化热点区由台塬中部向东西边缘区推移.     

基于生态服务价值的伊犁河谷土地利用变化环境影响评价

运用遥感技术、GIS和实地调查方法,对新疆伊犁河谷1985—2005年间的土地利用变化进行了分析,同时借助土地利用变化驱动下的生态服务价值变化量,探讨了研究区土地利用变化对生态环境的影响及效应。结果表明,1985—2005年,研究区耕地、水域和建设用地面积增加,林地、草地和未利用地面积减少,其中耕地和草地面积变化最大,分别为31.53万 hm2和-29.02万hm2。土地利用变化引起了研究区生态环境的变化,对生态服务价值亦产生了一定的影响,伊犁河谷生态服务功能整体提高了4.43%,其中正向变化最大的特克斯县,其生态服务功能增加了7.57%,负向变化最大的是伊宁市,其生态服务功能下降了27.33%;各单项服务功能价值中,生态系统所提供的气体调节、土壤形成与保护、生物多样性保护和原材料等4项生态服务功能有所下降,其余8项生态服务功能均有不同程度的提高。敏感度分析表明,赋予生态服务功能价值指数的大小对研究区总的生态服务价值变化影响不明显,研究结果可信。     

CLUE-S模型应用进展与改进研究

土地利用变化是景观生态学重要的研究领域,通过模型模拟土地利用变化趋势是该领域的主要研究内容之一。CLUE-S模型可以全面考虑自然和人文因子,通过迭代方法综合空间分析和非空间分析,较好地模拟小尺度范围内土地利用变化情景,具有综合性、开放性、空间性、竞争效率性等特点,已经在国内外多个地区的土地利用变化研究中得到广泛应用,形...     

转型时期广州市社会区分析

运用主成分分析和聚类分析方法对广州2000年第五次人口普查的200个反映社会空间结构的变量进行分析,提取出影响广州市社会空间结构的5个主因子,据此将广州市划分为7类社会区。与1985年的广州社会区研究对比,归纳出基于老城区发展的、基于工业和教育飞地发展的、基于农村社会区发展的社会区演变的三种模式,从转型期制度与政策变化、历史因素、政府对城市管理与调控等方面分析了社会区分异的机制,总结了转型期中国大城市的社会空间结构模型,比较了中西方社会区结构的差异,并从职业分异、家庭生命周期、外来人口等方面对广州未来社会区的演变做了分析。     

Evaluating Volumetric Glacier Change Methods Using Airborne Laser Scanning Data

Assessments of geodetic volume change are widely used in glaciology and have a long tradition dating back to the nineteenth century. Over time, the geodetic method and corresponding data storage have been developed further, but the resulting methodological heterogeneity can lead to errors that are difficult to separate from other survey uncertainties. In this study we used high‐resolution airborne laser scanning data from the Findelengletscher in the Swiss Alps to evaluate state‐of‐the‐art volumetric glacier change methods. For the first time we have been able to simulate errors arising from different geodetic methods and spatial resolutions. The evaluation showed that, although the digital elevation models were perfectly co‐registered, systematic and random method‐ and scale‐dependent errors still occurred. These errors have an impact on the resulting volume changes at lower spatial resolutions and may lead to exponentially larger uncertainties. Volume changes from contour methods provided reasonably accurate results, while volumetric change assessments from central profile lines were especially prone to biases at any scale.     

新疆耕地变化对区域碳平衡的影响

基于“Bookkeeping”模型,对1975-2005 年期间新疆耕地变化对区域碳平衡的影响进行了分析。荒漠土地开垦和耕地转移是新疆耕地变化的两种主要方式,1975-2005 年这两种耕地变化方式使新疆碳储量增加了20.6 Tg C,其中土地开垦使区域碳储量增加了51.8 Tg C,而耕地转移则向大气排放了31.2 Tg C。在1975-1985 年期间,新疆耕地大规模转移,区域碳储量的变化趋势受耕地转移的影响较大;1985 年后随新疆土地开垦规模的增加,碳储量变化趋势主要受土地开垦影响。30 年间,新疆碳储量增加主要是由草地开垦为耕地引起,而耕地转移为草地是新疆碳储量减少的主要原因。新疆地区进行合理的水土开发活动有利于区域碳固定,且长期的耕作管理活动会进一步增强耕地的碳汇功能。