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1.
Land use/cover changes (LUCC) are central to tourism because land is used in multiple ways as a resource for tourism-focused activities. Tourism is essentially a geographical phenomenon, encompassing the movement and flow of people (seen as the demand side) and spatial distribution patterns relating to land use consumption (seen as the supply side). However, the impacts of tourism on LUCC are difficult to track and monitor. Contributing factors of this problem include a lack of empirical studies, shortage of micro-level LUCC datasets, and scarce methodological frameworks which can be used for assessments. This paper aims to provide a LUCC modelling approach in order to explore the impacts of tourism development on built-up areas. We developed a Cellular automata model (CA) which integrates Markovian transition probabilities and logistic regression transition suitability maps. LUCC rules for tourism development are framed within the national land use policy guidelines for the development of new tourism accommodation establishments (TAE). This primarily takes into consideration land cover compatibility and the proposed development's proximity to the coastline.Three scenarios were established to explore the impacts of tourism development in LUCC for the year 2020 in a Portuguese coastal region: business as usual (BAU); tourism trends (TOUR); and natural restrictions (NATR). TOUR results indicate that the tourism and urban land use/cover growth is higher and focuses heavily on the coastal region (within 5,000 m) when compared to the other scenarios. The overall results for BAU and NATR show a general convergence with the land use policy guidelines in terms of tourism nucleation and new TAE distance to the coastline.  相似文献   

2.
谢花林  李波 《地理研究》2008,27(2):294-304
本文以农牧交错带的典型区域——内蒙古翁牛特旗为例,考虑土地利用变化过程的空间变量,建立了不同土地利用变化过程的logistic回归模型。结果表明:模型中转为耕地的主要解释变量是到农村居民点的距离和农业气候区;转为草地的主要解释变量是到农村居民点的距离、土壤表层有机质含量和到乡镇中心的距离;转为林地的主要解释变量是到农村居民点的距离和海拔;空间异质性和土地利用变化过程的时间变量共同影响着使用logistic回归模型来解释土地利用变化驱动力的能力;通过对草地logistic回归模型的检验,得出空间统计模型能较好地揭示不同土地利用变化过程的主要驱动力及其作用机理。  相似文献   

3.
土地利用/ 土地覆被变化(LUCC) 是当前研究全球变化的重要内容, 而区域土地利用 格局模拟是LUCC 研究的核心内容之一。以张家界市永定区为研究单元, 根据由2005 年土地 利用现状图和数字高程模型数据源得到的土地利用、地形、河流以及道路等空间数据, 对区 域土地利用类型空间格局的空间自相关性特征进行了建模研究, 并通过在传统Logistic 模型 中引入描述空间自相关性的成份, 实现了能够考虑自相关性因素的回归分析模型 (AutoLogistic 模型), 同时应用该模型对区域土地利用格局进行了模拟和分析。结果显示, 通 过与没有考虑空间自相关性的回归模型(传统Logistic 模型) 相比较, 该模型显示了更好的拟 合优度和更高的拟合准确率(耕地、林地、建设用地及未利用地的ROC 值分别从0.851、 0.913、0.877 和0.852 提高到0.893、0.940、0.907 和0.863)。研究结果说明了基于 AutoLogistic 方法的土地利用格局的相关性建模在一定意义上是合理的。同时研究结果也可以 为永定区及其相似地区的土地利用规划决策提供更为科学的依据。  相似文献   

4.
太仆寺旗土地利用变化时空格局的动态模拟   总被引：37，自引：4，他引：33  
本文以太仆寺旗为研究区 ,通过将土地利用驱动因子分解为稳定少动控制因子、年际变动影响因子与社会经济驱动因子 ,求解了太仆寺旗土地利用变化驱动因子作用系数矩阵 ,揭示了不同类型因子驱动土地利用变化的方向与强度。在此基础上 ,以CLUE S模型为框架 ,构建了太仆寺旗土地利用变化时空格局模拟模型 ,通过集成基于太仆寺旗土地利用系统动力学模型获取的土地利用变化及其社会经济驱动因子信息 ,动态模拟了太仆寺旗土地利用变化的时空模式 ,进行了参考模式、生态模式与经济模式下的情景分析。  相似文献   

5.
ABSTRACT

Geographically weighted regression (GWR) is a classic and widely used approach to model spatial non-stationarity. However, the approach makes no precise expressions of its weighting kernels and is insufficient to estimate complex geographical processes. To resolve these problems, we proposed a geographically neural network weighted regression (GNNWR) model that combines ordinary least squares (OLS) and neural networks to estimate spatial non-stationarity based on a concept similar to GWR. Specifically, we designed a spatially weighted neural network (SWNN) to represent the nonstationary weight matrix in GNNWR and developed two case studies to examine the effectiveness of GNNWR. The first case used simulated datasets, and the second case, environmental observations from the coastal areas of Zhejiang. The results showed that GNNWR achieved better fitting accuracy and more adequate prediction than OLS and GWR. In addition, GNNWR is applicable to addressing spatial non-stationarity in various domains with complex geographical processes.  相似文献   

6.
隋雪艳  吴巍  周生路  汪婧  李志 《地理科学》2015,35(6):683-689
以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。  相似文献   

7.
Nowadays,spatial simulation on land use patterns is one of the key contents of LUCC.Modeling is an important tool for simulating land use patterns due to its ability to inte-grate measurements of changes in land cover and the associated drivers.The conventional regression model can only analyze the correlation between land use types and driving factors, but cannot depict the spatial autocorrelation characteristics.Land uses in Yongding County, which is located in the typical karst mountain areas in northwes...  相似文献   

8.
区域土地利用变化模拟是LUCC研究的核心内容之一。以地处湘西北岩溶山区的张家界市永定区为研究对象,针对目前国际上广泛使用的CLUE-S土地利用变化模型,通过在传统Logistic回归模型中引入空间自相关变量,对CLUE-S模型的空间分析模块进行了改进。实验与分析结果表明,改进的空间分析模块拟合优度、拟合精度都有较大的提高。耕地、林地及居民点工矿用地的拟合优度(ROC值)分别从0.784、0.821和0.741提高到0.827、0.875和0.838。在此基础之上,采用改进的CLUE-S模型,模拟和预测了研究地区2005~2020年的土地利用时空变化。研究结果说明对CLUE-S模型空间分析模块的改进在一定意义上是合理的,同时也可以为永定区及其相似地区的土地利用规划决策提供更为科学的依据。  相似文献   

9.
Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cover and the associated drivers. The conventional regression model can only analyze the correlation between land use types and driving factors, but cannot depict the spatial autocorrelation characteristics. Land uses in Yongding County, which is located in the typical karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a conventional logistic model, we constructed a regression model (Autologistic model), and used this model to simulate and analyze the spatial land use patterns in Yongding County. According to the comparison with the conventional logistic model without considering the spatial autocorrelation, this model showed better goodness and higher accuracy of fitting. The distribution of arable land, wood land, built-up land and unused land yielded areas under the ROC curves (AUC) was improved to 0.893, 0.940, 0.907 and 0.863 respectively with the autologistic model. It is argued that the improved model based on autologistic method was reasonable to a certain extent. Meanwhile, these analysis results could provide valuable information for modeling future land use change scenarios with actual conditions of local and regional land use, and the probability maps of land use types obtained from this study could also support government decision-making on land use management for Yongding County and other similar areas.  相似文献   

10.
Understanding and analysis of drivers of land-use and -cover change (LUCC) is a requisite to reduce and manage impacts and consequences of LUCC. The aim of the present study is to analyze drivers of LUCC in Southern Mexico and to see how these are used by different conceptual and methodological approaches for generating transition potential maps and how this influences the effectiveness to produce reliable LUCC models. Spatial factors were tested for their relation to main LUCC processes, and their importance as drivers for the periods 1993–2002 and 2002–2007 was evaluated by hierarchical partitioning analysis and logistic regression models. Tested variables included environmental and biophysical variables, location measures of infrastructure and of existing land use, fragmentation, and demographic and social variables. The most important factors show a marked persistence over time: deforestation is mainly driven by the distance of existing land uses; degradation and regeneration by the distance of existing disturbed forests. Nevertheless, the overall number of important factors decreases slightly for the second period. These drivers were used to produce transition potential maps calibrated with the 1993–2002 data by two different approaches: (1) weights of evidence (WoE) to represent the probabilities of dominant change processes, namely deforestation, forest degradation, and forest regeneration for temperate and tropical forests and (2) logistic RM that show the suitability regarding the different land-use and -cover (LUC) classes. Validation of the transition potential maps with the 2002–2007 data indicates a low precision with large differences between LUCC processes and methods. Areas of change evaluated by difference in potential showed that WoE produce transition potential maps that are more accurate for predicting LUCC than those produced with RM. Relative operating characteristic (ROC) statistics show that transition potential models based on RM do usually better predict areas of no change, but the difference is rather small. The poor performance of maps based on RM could be attributed to their too general representation of suitability for certain LUC classes when the goal is modeling complex LUCC and the LUC classes participate in several transitions. The application of a multimodel approach enables to better understand the relations of drivers to LUCC and the evaluation of model calibration based on spatial explanatory factors. This improved understanding of the capacity of LUCC models to produce accurate predictions is important for making better informed policy assessments and management recommendations to reduce deforestation.  相似文献   

11.
基于局部化转换规则的元胞自动机土地利用模型   总被引：3，自引：1，他引：2  
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。  相似文献   

12.
The Qilian mountain area was examined for using the Logistic-CA-Markov coupling model combined with GIS spatial analyst technology to research the transformation of LUCC, driving force system and simulate future tendency of variation. Results show that:(1) Woodland area decreased by 12.55%, while grassland, cultivated land, and settlement areas increased by 0.22%, 7.92%, and 0.03%, respectively, from 1986 to 2014. During the period of 1986 to 2000, forest degradation in the middle section of the mountain area decreased by 1,501.69 km~2. Vegetation cover area improved, with a net increase of grassland area of 38.12 km~2 from 2000 to 2014.(2) For constructing the system driving force, the best simulation scale was 210m×210m. Based on logistic regression analysis, the contribution(weight) of composite driving forces to land use and cover change was obtained, and the weight value was more objectively compared with AHP and MCE method.(3) In the natural scenarios, it is predicted that land use and cover distribution maps of Qilian mountain area in 2028 and 2042, and the Lee-Sallee index test was adopted. Over the next 27 years(2015–2042), farmland, woodland, grassland, settlement areas show an increasing trend, especially settlements with an obvious change of 0.56%. The area of bare land will decrease by 0.89%. Without environmental degradation, tremendous structural change of LUCC will not occur, and typical characteristic of the vertical zone of the mountain would remain. Farmland and settlement areas will increase, but only in the vicinity of Qilian and Sunan counties.  相似文献   

13.
长时间序列的土地利用/ 土地覆被数据是开展全球变化、可持续发展及生态安全等各项研究的重要基础。然而,早期的土地利用/ 土地覆被数据,特别是卫星遥感数据出现之前 的土地利用/ 土地覆被信息通常很难获取。利用TM、MSS 遥感影像数据和地形图、气候、地质、地貌、土壤、植被、水文等自然环境背景图件以及数据,社会经济统计数据等多源数 据,选择大庆市杜尔伯特蒙古族自治县作为典型案例区,在GIS 技术支持下建立了土地利用/ 土地覆被数字重建模型,再现了典型研究区20 世纪30 年代和50 年代土地利用/ 土地覆被空间分布状况。通过野外调查和历史文献资料对土地利用数字重建结果进行精度评价并初步得到以下结论：① 采用逐个图斑跟踪记录的方法对研究区各个时期土地利用/ 覆被变化的敏感 性进行分析,有利于揭示区域土地利用/ 土地覆被变化的规律;② 在定量、定位分析环境背景对土地利用/ 土地覆被分布及其变化的影响基础上,综合判断各种土地利用/ 土地覆被分布概率,其结果可为土地利用数字重建提供依据;③ 对1:10 万地形图提取土地利用信息的可行性与可信度分析表明,地形图中土地利用信息完全能够达到一级土地利用分类精度,同时疏林地、灌木林、沼泽地、盐碱地、沙地等二级分类信息也能获取。  相似文献   

14.
新疆和田河流域土地利用/覆被变化及其驱动力分析   总被引：11，自引：4，他引：7  
土地利用／覆盖变化及其驱动力分析是土地利用／覆盖研究的一个主要核心内容。利用1990年、1999年和2005年和田河流域3期土地利用数据,对和田河流域1990—2005年土地利用/覆被变化进行了定量的分析研究,同时利用相关分析和主成分分析探讨了土地利用/覆被变化的驱动力。研究结果表明:在15 a尺度上研究区土地利用/覆被变化总特征是耕地、林地、水域和建设用地面积增加,草地和未利用地面积减少;土地利用程度变化量为0.56%,流域土地利用处于发展时期;优势度没有发生明显的变化,未利用地一直最高。研究区土地利用/覆被变化的主要驱动力为人口因素、经济因素、富裕程度、政策因素、技术因素和水文因素等,各驱动因素以合力形式作用于土地利用/覆被使其变化,其中,人口因素在土地利用/覆被变化中起主导作用。  相似文献   

15.
通过引入人工蜂群算法用于构建土地利用变化的驱动力模型,分析土地利用变化的驱动力机制。算法原理通过模仿蜜蜂采蜜行为,自动搜索和提取土地利用变化样本中不同土地变化类型所对应的驱动力分类规则。分类规则的构建采用“IF…THEN”形式,并选取3种不同的适应度函数分别进行模拟验证。研究案例基于UCI实验数据集和美国纽卡斯尔市真实土地利用变化数据集。由实验结果可知,采用蜂群算法模型的总体精度和Kappa系数评价优于其它算法,表明蜂群算法应用于土地利用变化建模具有可行性。  相似文献   

16.
土地利用/土地覆被变化(LUCC)是影响生态环境和气候变化的主要驱动力之一,同时又是受其影响的结果。LUCC研究对于开展生态环境变化及气候变化的研究均具有重要的意义。针对目前境外LUCC研究中土地利用/覆被分类效率低的问题,探索一种适用于大数据量而又精度较高的分类方法。以额尔齐斯河国外部分-斋桑湖流域为研究区域,以1990年及2007年的Landsat TM/ETM+夏秋季影像以及DEM作为数据源,综合利用影像光谱、纹理信息参与到决策树构造中,进而利用决策树分类方法分别提取这两个时期的土地利用/覆被空间分布信息,最后分析两个时期的土地利用时空变化状况。实验结果表明:(1)光谱与空间纹理信息参与的决策树分类方法具有较高的分类精度;(2)两个时期的土地利用变化分析发现,近20年来该区域土地利用发生了较大的变化,耕地和灌木林地大面积减少,而低覆盖度草地和未利用地却显著增加。  相似文献   

17.
远程耦合视角下的土地利用/覆被变化解释   总被引：3，自引：0，他引：3  
传统的土地利用覆被/变化驱动力研究在很大程度上并未充分考虑远距离相互作用的影响,对全球化世界中由远距离相互作用驱动的土地利用/覆被变化日益缺乏解释力。鉴于此,本文旨在将远程耦合（Telecoupling）理论框架引入到土地利用/覆被变化的动力机制研究中来。在概述土地利用/覆被变化驱动力研究的基础上,从驱动力背景的变化出发,切入远程耦合框架的介绍,并基于此给出远距离相互作用驱动土地利用/覆被变化的经验证据;认为远程连接、全球化和城市化是远距离相互作用驱动土地利用/覆被变化的3种主要形式;进而提出建立“时—空—事”三位一体的土地利用/覆被变化解析路径、土地利用/覆被变化的近远程驱动力分解和基于网络的跨系统综合研究是土地利用覆被/变化驱动力研究中应用远程耦合框架的重点内容。  相似文献   

18.
土地利用/覆被变化（Land Use and Cover Change， LUCC）模拟是LUCC研究的主要内容和重要手段。时间间隔是模拟过程中的重要参数，对模拟结果精度有何影响，有待深入研究。以新疆玛纳斯河流域典型绿洲区四道河子镇为例，基于遥感影像提取1975、1985、1995、2000、2005、2010年和2015年的土地利用数据，分别以20 a、15 a、10 a和5 a为时间间隔构建CA-Markov模型，模拟2015年土地利用结构，定量探讨时间间隔对CA-Markov模型精度的影响。结果表明：1975—2015年，四道河子镇LUCC以耕地和草地为主，期间耕地、建设用地迅速扩张，林地、草地和未利用地大幅减少，水域在1985—2000年呈现小幅增长。耕地的增加和草地及林地的减少是研究区近40 a LUCC最显著的特征。对比模拟结果与实际结果，时间间隔为20 a、15 a、10 a、5 a的TFOM分别为70.35%，69.18%，76.32%和88.00%。基于2005—2010年转移概率的模拟结果更接近于2015年实际结果，适合模拟四道河子镇未来的土地利用变化。土地利用模拟应依据区域LUCC特征确定最佳的时间间隔，提高模拟精度。  相似文献   

19.
Spatially explicit land use/cover models are indispensable for sustainable rural land use planning, particularly in southern African countries that are experiencing rapid land use/cover changes. Using Zimbabwe as an example, we simulated future land use/cover changes up to 2030 based on a Markov-cellular automata model that integrates Markovian transition probabilities computed from satellite-derived land use/cover maps and a cellular automata spatial filter. A multicriteria evaluation (MCE) procedure was used to generate transition potential maps from biophysical and socioeconomic data. Dynamic adjustments of transition probabilities and transition potential map thresholds were implemented in the Markov-cellular automata model through a multi-objective land allocation (MOLA) procedure. Using the normalised transition probabilities, the Markov-cellular automata model simulated future land use/cover changes (up to 2030) under the 2000 calibration scenario, predicting a continuing downward trend in woodland areas and an upward trend in bareland areas. Future land use/cover simulations indicated that if the current land use/cover trends continue in the study area without holistic sustainable development measures, severe land degradation will ensue.  相似文献   

20.
唐常春  李亚平 《地理研究》2020,39(11):2626-2641
多中心城市群具有交通网络发达、功能联系密切、空间多维复合等特征,其土地利用/覆被变化（LUCC）是一个相当复杂的过程。采用地学信息图谱方法,探索城市群LUCC数量结构与时空格局一体化耦合机理,研究用地变化与区域发展互动关系,有助于深入揭示多中心城市群土地利用时空演变规律,为同类城市群的国土空间规划和健康发展提供参考。本文以典型案例长株潭城市群为例,构建1995—2015年四个时期地学信息图谱,并耦合重心转移模型,在总体量化分析基础上,重点从城际与城乡视角深入解析城市群土地利用时空变化特征和一体化发展态势。研究结果表明：① 土地利用总体动态加速演变。四期建设用地扩张年动态度分别为1.06、4.10、2.21和7.39,转移强度动态增加,耕地和林地呈加速减少态势。② 城际与城乡地类图谱转移呈现多维演变。图谱“15（耕地→建设用地）、25（林地→建设用地）、45（水域→建设用地）”重心由株洲城区（Ⅰ 期）向长沙城区（Ⅱ 期和Ⅲ 期）再向湘潭城区（Ⅳ期）迁移,经历“相对均衡-长沙加速崛起-有机均衡”和“城市加速集聚-城乡相对均衡”演变过程。③ 近年来,城际转移强度差异缩小,转移结构分异增加,区域主体功能逐步凸显。同时,外围区县加速发展,城乡一体化水平明显提升。④ 城市群土地利用涨落势图谱与经济社会一体化发展高度耦合,同时耕地保护与高质量发展有待加强。  相似文献   

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