地理研究  2015 , 34 (9): 1619-1629 https://doi.org/10.11821/dlyj201509002

Orginal Article

基于Dyna-CLUE模型的滇池流域土地利用情景设计与模拟

陆文涛, 代超, 郭怀成

北京大学环境科学与工程学院,水沙科学教育部重点实验室,北京 100871

Land use scenario design and simulation based on Dyna-CLUE model in Dianchi Lake Watershed

LU Wentao, DAI Chao, GUO Huaicheng

Key Laboratory of Water and Sediment Sciences (MOE), College of Environmental Science and Engineering, Peking University, Beijing 100871, China

通讯作者:  郭怀成(1953- ),男,教授,博士生导师,研究方向为水环境学、环境规划与管理。E-mail: hcguo@pku.edu.cn

收稿日期: 2015-02-22

修回日期:  2015-06-13

网络出版日期:  2015-09-15

版权声明:  2015 《地理研究》编辑部 《地理研究》编辑部

基金资助:  国家水体污染控制与治理科技重大项目(2013ZX07102-06)

作者简介:

作者简介:陆文涛(1990- ),男,博士研究生,研究方向为环境规划与管理。E-mail: luwt@pku.edu.cn

展开

摘要

以滇池流域为研究对象,基于1999年、2002年两期TM遥感解译数据和区域自然与社会经济数据,应用Dyna-CLUE模型模拟2008年滇池土地利用空间分布。结合滇池流域土地利用变化趋势与退耕还林政策,设定了三种土地需求情景,模拟2022年区域土地利用空间分布情况。研究结果表明:① 两期模拟结果Kappa系数分别为0.6814与0.7124,具有高度的一致性,表明Dyna-CLUE模型在滇池流域有较强的适用性。② 三种情景的模拟结果显示,至2022年流域内未利用地、耕地显著减少,建设用地、林地显著增加,水域与草地相对变化较小,因加大退耕还林政策实施力度,三种情景中,位于官渡区、呈贡区、嵩明县及晋宁县的耕地与林地呈现不同的变化情况。③ 滇池流域建设用地扩张,增加了滇池非点源污染负荷。不合理的土地利用布局,将会恶化滇池水质,加剧水环境压力。研究结果可为未来滇池流域土地利用合理规划与非点源污染控制提供参考依据和决策支持。

关键词: 土地利用/土地覆盖变化 ; Dyna-CLUE模型 ; 情景分析 ; 滇池流域

Abstract

Taking Dianchi Lake Watershed as a study area, we use Dyna-CLUE to simulate the land use spatial distribution pattern of the Dianchi Lake Watershed in 2008 in combination with the interpretation data from TM image in 1999 and 2002 and local natural and socio-economic data. According to the trend of land use change in the Dianchi Lake Watershed and the policy of returning farmland to forestland, three scenarios of land use change from 2008 to 2022 were constructed, and land use spatial pattern in 2022 in the study area was stimulated under the above-mentioned scenarios by using the Dyna-CLUE model. The results show that Kappa coefficients of the two simulated results from1999 to 2008 and from 2002 to 2008 were 0.6814 and 0.7124, compared with the interpretation data of 2008. The simulated results were credible and the Dyna-CLUE model has a good applicability in simulating land use change in the Dianchi Lake Watershed. The simulation results of the three scenarios show that the unused land and farmland will be reduced significantly, construction land and woodland will increase rapidly, and water bodies and grassland will change a little. Because of the difference in the policy of returning farmland to forestland, farmland and forestland in Guandu district, Chenggong district, Songming county and Jinning county will be different under the three scenarios. The expansion of construction land in the watershed will increase the load of non-point source pollution in the Dianchi Lake. Unreasonable distribution of land use will result in further deterioration of the water quality and increase the pressure on the water environment in the Dianchi Lake. The research can provide scientific support for rational planning of land use and prevention of non-point source pollution.

Keywords: land use/land cover change ; Dyna-CLUE model ; scenario analysis ; Dianchi Lake Watershed

0

PDF (3484KB) 元数据 多维度评价 相关文章 收藏文章

本文引用格式 导出 EndNote Ris Bibtex

陆文涛, 代超, 郭怀成. 基于Dyna-CLUE模型的滇池流域土地利用情景设计与模拟[J]. , 2015, 34(9): 1619-1629 https://doi.org/10.11821/dlyj201509002

LU Wentao, DAI Chao, GUO Huaicheng. Land use scenario design and simulation based on Dyna-CLUE model in Dianchi Lake Watershed[J]. 地理研究, 2015, 34(9): 1619-1629 https://doi.org/10.11821/dlyj201509002

1 前言

土地利用/土地覆盖变化是全球环境变化的研究热点,其受区域自然资源和社会经济发展等因素约束,同时也影响着区域内各生态过程的变化[1]。定量化模拟和预测区域的土地利用变化有助于研究土地利用对区域各生态过程的影响机理及其变化的趋势,通过预测和优化区域土地利用格局,为区域生态环境保护提供决策依据。

近年来,多种模型被用于土地利用变化模拟,其中主要包括SD(system dynamics)模型[2]、Markov模型[3]、CA(cellular automaton)模型[4]、Agent-based模型[5]、CLUE(the conversion of land use and its effects modeling framework)模型[6]及其改进版本CLUE-S (the conversion of land use and its effects at small regional extent)模型[7]。本文选用的Dyna-CLUE模型是在CLUE、CLUE-S模型基础上发展而来,该模型综合了土地利用变化的宏观驱动因素与微观格局演化特征,对于多尺度的应用具有更强的适用性[8]。Dyna-CLUE模型推出后,在国外被应用于地下水脆弱性评价[9]、农业政策对农田生物多样性影响评价[10]、土地利用变化对滑坡风险的影响[11]及政策改革对土地利用的影响[12,13]等多个领域的研究之中,并取得了良好的效果;在国内被应用于三江源区生态系统固碳潜力预测[14]和上海市[15]、脱甲河流域[16]及整个中国区域[17]的土地利用空间模拟,并与SWAT模型结合进行了黑河流域土地利用与水文响应研究[18]。总体来说,Dyna-CLUE模型推出时间较短,应用较少,还未见其应用于滇池流域土地利用变化研究中。

流域内的土地利用变化直接影响流域内的水环境质量,不同的土地利用类型对流域的水环境质量有着不同的影响[19]。预测土地的未来变化趋势,优化土地布局,对流域内非点源的控制具有积极的意义。滇池流域位于云南省政治、经济的核心区域,流域内第二、第三产业发达。近年来,滇池水质日益恶化,富营养化严重,由水土流失、农田施肥、地表径流及农村生活废弃物等引起的非点源污染逐步成为滇池水污染的主要来源[20,21],而该类污染主要来源于流域内的耕地与建设用地[19]。因此,预测滇池流域土地利用变化情况,可以有效地预测滇池流域未来的污染情况,为滇池的水污染防治提供决策依据。此外,因土地变化受政策影响显著,在预测未来土地利用时,重点考虑当前正在实行的退耕还林政策对土地利用变化的影响。

分别用1999年与2002年两期TM遥感解译数据模拟2008年的土地利用分布情况,并与2008年的TM遥感解译数据进行对比,以验证模型在滇池流域的适用性。根据区域的土地变化趋势与退耕还林政策,设定2008-2022年的三种不同土地需求情景,应用Dyna-CLUE模型模拟各情景下的滇池流域土地利用变化情况,以期为滇池流域未来土地利用规划、非点源污染控制提供参考依据和决策支持。

2 研究区概况

滇池流域位于云南省昆明市(图1),经纬度为24°29′N~25°28′N、102°29′E~103°01′E,位于长江、红河、珠江三大水系分水岭地带,流域面积2920 km2。整个滇池流域为南北长、东西窄的湖盆地,地形分为山地丘陵(约69.5%)、淤积平原(约20.2%)和滇池水域(约10.3%)3个层次。流域内行政辖区包括昆明市的西山区、盘龙区、五华区、官渡区、呈贡区及晋宁县6个区县的大部分(昆阳镇、晋城镇、宝峰镇、新街乡、上蒜乡、六街乡)和嵩明县的一部分(滇源镇、阿子营乡),其总面积仅占昆明市总面积的13.6%,但却承载了昆明市大部分的人口和产值,是昆明市乃至云南省人口密度最高、经济活动最密集的地区。滇池流域属亚热带湿润季风气候,气温年较差小,流域雨期长,雨量充沛,年平均降雨量为1035 mm,约有80%集中在5-10月,年平均相对湿度达到73%~75%。

图1   滇池流域位置图

Fig. 1   Location of the Dianchi Lake Watershed

3 数据来源与研究方法

3.1 数据来源与处理

研究数据主要包括研究区1999年、2002年和2008年Landsat TM分辨率为30 m的遥感影像、滇池流域分辨率为30 m的DEM数据及相关的人口、社会经济统计数据等。利用ENVI 4.6与ArcGIS 9.3软件对3年的遥感影像进行解译,得到流域内耕地、林地、草地、水域、建设用地和未利用地6个一级地类的3期土地利用情况。本文所需的人口与社会经济统计数据均来源于各年昆明市及其各区县的社会经济统计年鉴。

Dyna-CLUE模型以栅格为单元进行计算,要求所有输入数据的栅格单元大小、数量和分布完全一致,并且限定了计算栅格的数量,因此权衡已有数据分辨率情况、滇池流域实际范围及模型对计算栅格限制等因素后,最终确定栅格数据的分辨率为120 m×120 m。

以滇池流域1999年与2002年的遥感影像为基础,分别数字化获取各期的水系、城镇与农村居住地数据。本文获得的滇池流域道路数据主要包括国道与省道数据。将得到的城镇与农村居住地、水系、道路数据分别转化为分辨率为120 m×120 m的栅格数据。利用ArcGIS 9.3工具箱中的距离函数计算流域内各栅格到城镇与农村居住地、水系及道路的最近距离,将计算结果存为分辨率为120 m×120 m的栅格数据。社会经济数据的输入单元确定为五华区、盘龙区、官渡区、西山区与呈贡区六区的各街道及嵩明县与晋宁县两县的各乡镇。根据统计年鉴的城乡人口数量与土地面积数据计算各输入单元的人口密度与城镇人口比例,但因GDP统计数据仅为区县一级无法直接进行计算,所以在计算过程中按照统计年鉴中各单元的财政收入比例对GDP进行了分配,再结合人口数据计算出各单元的人均GDP数据。利用ArcGIS 9.3软件将社会经济数据导入各乡镇所在地,利用ArcGIS 9.3工具箱中的插值工具计算人口密度、人均GDP、城镇人口比例在滇池流域的分布情况,最后利用数据转换工具将所得数据存为120 m×120 m的栅格数据。数据处理方法与CLUE-S模型数据处理方法相同,具体可参照相关研究[14,16]

3.2 研究方法

3.2.1 Dyna-CLUE模型介绍 Dyna-CLUE模型是在CLUE、CLUE-S模型的基础上发展而来的。CLUE[22]模型是基于土地利用及其驱动因子间的经验量化关系和不同土地利用类型间的竞争动态变化模型所构建的。该模型是基于国家和大陆尺度而开发,由于研究区域的尺度较大,其空间分析的分辨率往往较低,在区域尺度上往往不适用。为了满足小尺度上对于土地利用变化时空模拟的需求,研究人员在CLUE模型的基础上开发了CLUE-S[23]模型,该模型主要用以解决小尺度上的土地利用类型空间合理布局以及多种土地利用类型用地需求的协调分配。Dyna-CLUE模型是最新版本的CLUE模型,该模型综合了土地利用变化的宏观驱动因素与微观格局演化特征,对于多尺度的应用具有更强的适用性。

3.2.2 Dyna-CLUE模型原理 Dyna-CLUE模型与CLUE-S模型相同,可分为非空间需求和空间分配过程2个模块。其中,非空间模块是在时间层面输入各模拟年土地利用需求情况。而空间分配过程模块是以栅格为单元,计算每一个栅格内土地利用的变化情况,依据土地利用类型的概率分布、土地利用类型间的竞争力以及转移规则矩阵,在空间上对各模拟年的土地利用需求进行分配。

模型运行时,其输入主要分为4个部分:① 空间政策与限制部分,该部分表示由于地区政策限制或者特殊的地区因素在模拟时段内土地利用类型不允许发生改变或者改变方向相对固定,如自然保护区、基本农田等,本文设定滇池为限制区域,禁止占用滇池水域,其他区域允许土地类型的自由转换;② 土地利用类型转移部分,该部分主要包括各类土地的转移弹性系数与可转移性设置(转移矩阵);③ 土地利用需求部分,该部分主要对应模型的非空间模块,表明从模拟起始年到终止年各类土地每年的需求变化情况;④ 空间分布适宜性部分,该部分主要研究各类土地利用方式与各驱动因子之间的定量关系,一般选用空间Logistic回归(逐步向后)分析进行计算,该定量关系表示研究区域内每一栅格单元可能出现某种土地利用类型的概率,概率越大,该栅格单元的空间分布适宜性越高。Logistic回归公式如下[23]

lg(Pi1-Pi)=α0+α1X1,i+α2X2,i++αnXn,i(1)

式中:Pi表示土地类型i在该栅格中出现的概率;Xn,i为影响土地利用类型变化的生物物理性或社会经济驱动因子; αi为回归方程的回归系数,表示土地利用类型与驱动力因子的定量关系。

3.2.3 土地利用类型转移设置 该部分主要包括各类土地的转移弹性系数设置与可转移性设置。

转移弹性系数(ELAS)是0到1之间的数值,表示土地改变的难易程度。建设投资较高的土地类型一般难以被改变,ELAS也相对较高。ELAS设定时,一般将不会发生变化的地类的ELAS设定为1,极易发生变化的地类的ELAS设定为0,变化难易程度介于两者之间的地类的ELAS设定为0~1之间数值。在ELSA设定过程中,可参考该区域已有研究中ELSA值进行设定,并在计算过程中通过比较每次调整后计算结果的Kappa系数,选取模拟结果最优的ELSA值[24]。本文首先结合滇池流域土地变化转出率及该区域已有的研究结果[25]对ELSA值进行初步设定,然后分别以1999年与2002年为模拟起始年模拟2008年的土地利用布局,将模拟结果与2008年的TM遥感解译数据进行对比,计算其Kappa系数,多次调整ELSA值,最终确定1999-2008年与2002-2008年两组模拟实验的ELSA参数(表1)。

表1   ELSA参数表

Tab. 1   ELSA parameters

组别水域建设用地耕地林地草地未利用地
1999-2008年0.71.00.40.60.40.4
2002-2008年0.71.00.60.70.60.4

新窗口打开

可转移性设置指在一定情景下,各种土地利用类型之间相互转移的可能性,具体通过矩阵形式来表示各地类之间转化的可能性(表2)。表2中横向表示未来的土地利用类型,纵向表示现在的土地利用类型,其转移的可能性用0或1表示,0表示不能转化,1表示可以转化。该转移矩阵一般要结合研究区的实际情况与确定的ELSA参数进行设定。

表2   土地可转移性矩阵表

Tab. 2   Land use transition sequences

年份土地利用类型水域建设用地耕地林地草地未利用地
1999-2008年水域111111
建设用地000000
耕地111111
林地111111
草地111111
未利用地111111
2002-2008年水域111111
建设用地000000
耕地111111
林地111111
草地111111
未利用地111111

注:0表示不能转化,1表示可以转化。

新窗口打开

3.2.4 空间分布适宜性设置 土地利用方式的变化往往与某些因素高度相关,如土地特征和海拔,然而,当前区域土地管理的决策不能仅仅依赖区域的自然特征,还需综合考虑其他的社会经济因素。因此在空间分布适宜性部分,本文充分考虑了区域的自然因素与社会经济因素,结合数据的可获取性,最终选定了坡度、坡向、高程、到道路最近距离、到水系最近距离、到城镇与农村居住地最近距离、人口密度、人均GDP、城镇人口比例等9个驱动力因子,并且为求结果准确,社会经济数据均细化到街道一级。采用SPSS软件,选用Logistic回归(逐步向后)分析,计算土地利用空间布局及其驱动因子之间的定量关系。

3.2.5 Dyna-CLUE模型校准 选用Kappa系数进行Dyna-CLUE模型的校准。Kappa系数一般用于评价遥感图像分类的正确程度,由Cohen于1960年提出。将模拟结果与真实的土地利用类型进行比较,获得Kappa系数[23],Kappa系数是介于0~1之间的连续数值,其值越接近1表明拟合精度越高。通常,当0.80<Kappa≤1时,表明真实图与模拟图几乎完全一致(almost perfect),当0.61<Kappa≤0.80时,表明高度的一致性(substantial);当0.40<Kappa≤0.60时,中等的一致性(moderate);当0.20<Kappa≤0.40时,一般的一致性(fair);0.00≤Kappa≤0.20时,极低的一致性(slight)。其计算公式为[26]

Kappa=P0-Pcpp-Pc(2)

式中:P0指真实图与模拟图间的一致性比例,可以应用ArcGIS 9.3中Spatial Analyst的Map Algebra模块计算;Pc是随机情况下期望的一致性比例,由模拟图与真实图的转移矩阵求得;Pp是理想情况下一致性比例,一般取1,即真实图与模拟图完全一致。

3.2.6 土地利用情景构建 根据不同的土地利用需求情景,Dyna-CLUE模型可以模拟出不同情景下的土地利用空间分布情况。在情景设定部分,首先考虑了退耕还林政策对滇池流域的影响。自1999年退耕还林政策实施以来,截至2014年昆明市已累计完成77.8万亩退耕还林工程,尤其是在2012年以后,随着昆明市政府退耕还林投入的加大,昆明市更是以每年约20万亩的速度进行退耕还林工作,并确定了力争10年内完成流域内退耕还林工作的目标。其次,在情景设定过程中,还参考了《关于滇池流域农业产业结构调整的实施意见》、各县区土地整治规划等文件,对各类型土地需求进行了校正,以确保三种情景中各类型土地均能满足发展需求。本次模拟选定退耕还林工作完成年(2022年)为情景年,对其未来土地需求情况进行设定,具体情景如下:

情景一:趋势发展情景。该情景主要根据当前已有的1999-2008年的土地利用类型的数量数据,计算其土地变化速率,并以此递推出2022年的土地利用需求情况。该情景主要表明滇池流域土地利用的自然变化情况,对于退耕还林工作,只考虑了1999-2008年的常规执行强度。

情景二:对坡度大于25°的耕地进行退耕还林。根据《中华人民共和国水土保持法》,坡度大于25°的坡地禁止开垦农作物,因此,在该情景下对坡度大于25°的全部耕地进行退耕还林,该部分耕地约有3451.68 hm2。除耕地和林地外,其他土地利用类型仍按照情景一的需求进行设定。

情景三:对坡度大于15°的耕地进行退耕还林。扩大退耕还林的坡度范围,主要考虑到流域雨期长、雨量充沛且集中,高密度的降雨对流域的水土保持有很不利的影响,并且雨后径流易造成水体污染,为加强水土保持、流域污染控制,在情景三中对坡度大于15°的耕地进行退耕还林,该部分耕地面积约为5800.32 hm2

4 结果分析

4.1 Logistic回归结果与分析

根据前述的9个驱动力因子,采用SPSS软件,选用Logistic回归(逐步向后)分析方法探讨了各土地利用类型与驱动因子之间的关系,并对各回归结果进行了ROC(relative operating characteristics)检验,1999-2008年与2002-2008年两期回归结果如表3所示。一般认为ROC值大于0.7时,表示回归方程对该土地利用类型有很好的解释能力[27]。两组回归结果中,水域、建设用地、林地与耕地的ROC值均大于0.7,因此该4类土地的回归方程能很好的解释各驱动因子与土地利用类型的关系,而草地与未利用地的ROC值均介于0.6与0.7之间,解释能力稍差。将两组回归方程的ROC值进行比较可知,除草地外,各类型土地回归方程的ROC值均是2002-2008年优于1999-2008年。因此,总体来说两组回归方程对各土地类型均具有良好的解释能力,相比较2002-2008年的回归方程解释能力优于1999-2008年的回归方程。

表3   滇池流域驱动因子回归结果

Tab. 3   Regression results of driving factors in the Dianchi Lake Watershed

驱动因子1999-2008年各驱动因子回归系数
水域建设用地耕地林地草地未利用地
常数项4.479773.9534711.61447-11.10474-7.77952-3.09047
高程-0.00434--0.005730.004800.00241-
坡向0.00079-0.000750.00137-0.001420.00086-0.00132
坡度0.00446--0.021270.02724--0.00432
到道路最近距离0.00049-0.00016-0.000950.00095-0.00069-
到水系最近距离-0.00023--0.000140.00015-0.000090.00002
到城镇与农村居住地最近距离--0.049010.000440.00165-0.00150-0.00147
人均GDP-0.00241---0.000020.000030.00004
人口密度-0.000570.00016-0.00006-0.00044-0.00012-0.00006
城镇人口比例1.45434--1.031771.711380.512660.18524
ROC检验0.8130.9930.7780.8520.6710.646
驱动因子2002-2008年各驱动因子回归系数
水域建设用地耕地林地草地未利用地
常数项4.0157916.7838610.11585-13.06222-2.97532-4.10292
高程-0.00375--0.005010.005740.00024-
坡向0.00064-0.00108-0.00083-0.00135-0.00114
坡度0.01834--0.028860.034750.00397-0.01157
到道路最近距离0.00048--0.000950.00096-0.00062-0.00029
到水系最近距离-0.00013--0.000110.00013--
到城镇与农村居住地最近距离-0.00300-0.275630.000160.00149-0.00010-0.00034
人均GDP-0.00009-0.00004-0.00006-0.000040.00004
人口密度-0.00063--0.00015-0.00037-0.00006-
城镇人口比例1.68856--0.974531.77595-1.07686
ROC检验0.8191.0000.7820.8750.6150.669

新窗口打开

4.2 模拟结果检验

根据Logistic回归结果,结合1999年和2002年两期TM遥感解译数据与上文确定的参数,应用Dyna-CLUE模型,对2008年的土地利用空间分布进行模拟,分别计算两组模拟结果的Kappa系数,对其模拟的准确程度进行解释,土地利用解译数据及模拟结果如图2所示。1999-2008年模拟结果的Kappa系数为0.6814,2002-2008年模拟结果的Kappa系数为0.7124。两次模拟结果的Kappa系数均介于0.61与0.80之间,因此两次的模拟结果均具有高度的一致性,但2002-2008年滇池流域土地利用空间格局模拟结果的Kappa系数较高,模拟结果较好。因此,Dyna-CLUE模型适用于滇池流域土地利用空间布局的模拟。

图2   滇池流域土地利用解译数据与模拟结果

Fig. 2   Land use interpretation data and simulation results in the Dianchi Lake Watershed

4.3 三种情景模拟结果分析与讨论

应用Dyna-CLUE模型对2022年的三种情景进行预测,结果表明随着流域的开发建设,未利用地、耕地显著减少,建设用地、林地显著增加,水域与草地相对变化较小,土地利用变化情况仍按照原有趋势发展明显,昆明城区面积继续扩大,城区周边部分耕地被占用,并且滇池四周耕地面积减少,建设用地环绕滇池趋势明显。因退耕还林政策实施力度差异,情景二、情景三较情景一,耕地与林地面积发生了变化。情景二中,流域内耕地面积进一步减少,该部分退耕土地主要集中在官渡区、呈贡区、嵩明县及晋宁县,且大部分集中在滇池周边地区,且呈明显的条带状分布。情景三中,退耕范围进一步扩大,较情景二中多退耕的土地主要集中在嵩明县(图3)。

图3   滇池流域土地利用预测结果

Fig. 3   Land use predicting results of the Dianchi Lake Watershed

随着退耕还林政策的实施,耕地逐步减少,有利于流域水土流失和农业面源污染的防控,但滇池整体的污染负荷并未得到改善。因为模拟结果显示,流域内建设用地面积显著增加,且环绕滇池的趋势明显,当前已有研究表明,滇池流域入湖河流污染主要来源于建设用地上承载的城镇、工矿企业及农村居民[19],并且建设用地与水体污染浓度存在显著正相关[28,29],所以建设用地的增加势必会对滇池的水质造成不利影响。此外,滇池东、南、北三面水域仍是直接与耕地或建设用地相连,降雨后产生的地表径流携带大量的污染物进入湖体,非点源污染显著。综上,滇池流域土地利用布局不合理,将会造成滇池水质的进一步恶化,其未来发展将会加剧滇池的水环境压力。因此,应合理规划土地布局,降低农田施肥强度,加强生态用地保护,开展沿湖湿地建设,恢复沿湖生态带,湖滨带地区集约发展建设用地,以控制区域非点源污染。

因Dyna-CLUE模型对输入栅格的长宽数量有限制,所以未能采用更高的分辨率进行模拟,进而对土地利用空间布局模拟结果的准确度有一定的影响。在今后的研究中,应针对范围较小的区域可进行不同分辨率情况下的模拟准确度的研究,以确定区域模拟的最优分辨率。

由于土地利用变化受政策影响较为明显,随着滇池流域社会经济的进一步发展,土地利用的空间格局变化将会存在一定的不确定性。Dyna-CLUE模型很难模拟出政策突变对土地利用空间布局的影响,如工业园区或开发区的建设,这对于部分用地的模拟将会产生一定的误差。此外,在土地利用需求设定方面,虽然灰色线性规划[30]、马尔科夫链模型[31]、系统动力学[32]、线性内插[33]等方法已应用于该方面研究,但此类方法与相关政策的结合较弱,因此对最终的需求结果会有一定的影响。因此,在今后的研究中,如何充分考虑政策因素,合理设定未来的土地需求,将会成为土地利用空间布局模拟研究的重点之一。

5 结论

(1)应用Dyna-CLUE模型,结合滇池流域1999年、2002年两期TM遥感解译数据和区域自然、社会经济数据,模拟了2008年的滇池土地利用空间布局情况,并与2008年土地利用解译数据进行比较,经过Kappa系数检验,两次模拟结果均取得较好的效果,其中2002-2008年的模拟结果较好,表明Dyna-CLUE模型在滇池流域具有较好的模拟能力。

(2)结合滇池流域退耕还林政策与流域土地利用变化趋势,设定三种情景模拟2008-2022年区域土地利用空间分布情况。模拟结果均显示,至2022年流域内未利用地、耕地显著减少,建设用地、林地显著增加,水域与草地相对变化较小,滇池周边耕地面积明显减少,建设用地环绕滇池趋势明显。

(3)滇池流域建设用地的扩张,增加了滇池非点源污染的负荷。不合理的土地利用布局,将会进一步恶化滇池水质,加剧水环境压力。为控制区域的非点源污染,应合理规划土地布局,降低农田施肥强度,加强生态用地保护,开展沿湖湿地建设、恢复沿湖生态带、湖滨带地区集约发展建设用地。

The authors have declared that no competing interests exist.


参考文献

[1] Guo X D, Chen L D, Fu B J.

Effects of land use/land cover changes on regional ecological environment.

Advances in Environmental Science, 1999, 7(6): 66-75.

URL      [本文引用: 1]      摘要

Land use/land cove changes has been the core program of global environmental change research. Its impacts to regional ecological environment are one of the important contents of land use/land cover research. This paper analyzed the effects of land use/land cover changes on regional climate, atmospheric quality, soil properties, water quantity and quality. Land use/land cover changes influence regional climate by altering the surface albedo and the composition of green gas and trace gas in atmosphere. They affect some soil ecological processes and different compositions of land use/land cover have important effects on nutrient flow in soil. Non-point source pollution is the main methods by which land use/land cover changes influence water quality, many important Non-point pollution sources are linked with the changes. Some eco-environmental problems caused by human improper utility such as soil erosion, land degradation, water scarcity and sea approaching are also proposed.
[2] 谢正磊, 许学工, 孙强.

基于Patch Dynamics模式的土地覆被变化预测: 以北京市为例

. 北京大学学报: 自然科学版, 2008, 44(3): 452-458.

URL      [本文引用: 1]      摘要

利用北京市土地利用变化数据建立了Patch-dynamics动力模型。模拟结果表明了在不同的时间尺度上耕地所占比例都持续下降,城镇建设用地、林地所占比例继续上升。由于该模式考虑了模拟对象的变化过程以及不同土地利用类型之间的相互影响,其模拟结果比一般的简单动力模型模拟效果好。通过与实际情况的比较检验,用1996年数据对2001年的模拟结果与实际差别不大,误差在0~0.05之间,对于面积较小地类的模拟误差较小,说明这个方法可以用来对未来的土地利用变化状况进行模拟、预测。然而,这个模型只适应于十年尺度的预测,模拟时间越长,误差越大。在此基础上,提出协调土地利用矛盾,维持一定规模的城市绿色空间,实现首都城市定位目标的建议。

[Xie Zhenglei, Xu Xuegong, Sun Qiang.

Prediction of land cover change based on the Patch-Dynamics model: A case study of Beijing.

Acta Scientiarum Naturalium Universitatis Pekinensis, 2008, 44(3): 452-458.]

URL      [本文引用: 1]      摘要

利用北京市土地利用变化数据建立了Patch-dynamics动力模型。模拟结果表明了在不同的时间尺度上耕地所占比例都持续下降,城镇建设用地、林地所占比例继续上升。由于该模式考虑了模拟对象的变化过程以及不同土地利用类型之间的相互影响,其模拟结果比一般的简单动力模型模拟效果好。通过与实际情况的比较检验,用1996年数据对2001年的模拟结果与实际差别不大,误差在0~0.05之间,对于面积较小地类的模拟误差较小,说明这个方法可以用来对未来的土地利用变化状况进行模拟、预测。然而,这个模型只适应于十年尺度的预测,模拟时间越长,误差越大。在此基础上,提出协调土地利用矛盾,维持一定规模的城市绿色空间,实现首都城市定位目标的建议。
[3] 蒲英霞, 马荣华, 马晓冬, .

长江三角洲地区城市规模分布的时空演变特征

. 地理研究, 2009, 28(1): 161-172.

https://doi.org/10.11821/yj2009010018      URL      [本文引用: 1]      摘要

Urban is the engine of economic growth.With the rapid development of urbanization process across the world,the dynamics of city-size distribution has been a hot topic.The heat debates centering on the optimal city size have exerted impacts on the urbanization courses in China.From several different perspectives,this paper investigates the spatio-temporal dynamics of city-size distribution in the Yangtze River Delta during the period 1984-2002.Empirical results show that the evolution of urban system in the Yangtze River Delta has undergone primate,rank-size and primate distribution patterns.The primacy of Shanghai was the lowest in 2002,but the whole pattern of urban system in the Yangtze River Delta shifted to primate distribution pattern again,which to a large extent reflects the corresponding adjustments of urbanization guidelines in China in the 21st century.In term of the relationship between city size and city growth,the whole urban system takes the form of convergent growth,which means the initial smaller cities grow faster than larger cities.However,the difference in city growth is not significant.From the long-term tendency,the number of cities over two times of the average size will greatly decrease to about 6%,and middle-sized cities will dominate the urban systems in the future.Generally speaking,it will take about 16 years for a non-city area to develop into a city with half of the average size.On the whole,the change of city-size distribution in the Yangtze River Delta is becoming much even,but the tendency of spatial polarization and concentration is not the case.The spatial agglomeration in southern Jiangsu and Hangzhou Bay rim continues to be strengthened with the deepening of the policy opening to the outside world,which contrasts with the relative quiescence in northern Jiangsu and southern Zhejiang.

[Pu Yingxia, Ma Ronghua, Ma Xiaodong, et al.

Spatio-temporal dynamics of city-size distribution in Yangtze River Delta.

Geographical Research, 2009, 28(1): 161-172.]

https://doi.org/10.11821/yj2009010018      URL      [本文引用: 1]      摘要

Urban is the engine of economic growth.With the rapid development of urbanization process across the world,the dynamics of city-size distribution has been a hot topic.The heat debates centering on the optimal city size have exerted impacts on the urbanization courses in China.From several different perspectives,this paper investigates the spatio-temporal dynamics of city-size distribution in the Yangtze River Delta during the period 1984-2002.Empirical results show that the evolution of urban system in the Yangtze River Delta has undergone primate,rank-size and primate distribution patterns.The primacy of Shanghai was the lowest in 2002,but the whole pattern of urban system in the Yangtze River Delta shifted to primate distribution pattern again,which to a large extent reflects the corresponding adjustments of urbanization guidelines in China in the 21st century.In term of the relationship between city size and city growth,the whole urban system takes the form of convergent growth,which means the initial smaller cities grow faster than larger cities.However,the difference in city growth is not significant.From the long-term tendency,the number of cities over two times of the average size will greatly decrease to about 6%,and middle-sized cities will dominate the urban systems in the future.Generally speaking,it will take about 16 years for a non-city area to develop into a city with half of the average size.On the whole,the change of city-size distribution in the Yangtze River Delta is becoming much even,but the tendency of spatial polarization and concentration is not the case.The spatial agglomeration in southern Jiangsu and Hangzhou Bay rim continues to be strengthened with the deepening of the policy opening to the outside world,which contrasts with the relative quiescence in northern Jiangsu and southern Zhejiang.
[4] 聂婷, 肖荣波, 王国恩, .

基于Logistic回归的CA模型改进方法: 以广州市为例

. 地理研究, 2010, 29(10): 1909-1919.

URL      [本文引用: 1]     

[Nie Ting, Xiao Rongbo, Wang Guo'en, et al.

An improvement on CA model of logistic regression: A case study of Guangzhou.

Geographical Research, 2010, 29(10): 1909-1919.]

URL      [本文引用: 1]     

[5] 薛领, 翁谨, 杨开忠, .

基于自主体(agent)的单中心城市化动态模拟

. 地理研究, 2009, 28(4): 947-956.

https://doi.org/10.11821/yj2009040009      URL      [本文引用: 1]      摘要

In this paper,the UrbanSwarm,a computational environment,is developed for a better understanding of urbanization by agent-based modeling.UrbanSwarm contains multiple economic interactions between urban and rural agents that are crucial in urbanization process and mechanism because the basic force driving the urbanization is inherently microscopic.This paper gives a detailed discussion on the influencing factors such as migration policy,domestic demand,and negative effect of agglomeration on urbanization process in mono-centric scenario.(1) The segmentation policy not only limited the urban-rural migration but enlarged urban-rural income disparity as well.It simulates the household registration system(hukou) before 1978,which confined most Chinese citizens to their places of birth.(2) Raising urban-rural propensity to consumer contributes to both urban-rural income level and urbanization level.Meanwhile,it can narrow the urban-rural gap.(3) With the increase of the negative effect of agglomeration on urbanization,urbanization level drops gradually.The urbanization process loses its sustainability if the negative effect of agglomeration on urbanization exceeds some thresholds in mono-centric pattern.(4) The industrialization and urbanization level has a close relationship if the technology and preference are never changed.The process of industrialization is faster than the process of urbanization in simulation. This parallel processing agent-based approach has various advantages over existing economic approaches such as neoclassical model and equilibrium analysis.UrbanSwarm can be used for both simulation and computation purposes.MAS provides an exploratory platform to test hypotheses behind the space-time dynamics as well as to experiment with 'what-if' games within complex urbanization process,i.e.using the computer as an artificial laboratory for the study of urban and regional systems.Besides,it can be used to achieve some computational intensive tasks through the collective work of individual agents such as rural and urban households,local government,firms and so on.Future work should focus on removing the limitations of reactiv

[Xue Ling, Weng Jin, Yang Kaizhong, et al.

Dynamic simulation of impact factors on mono-centric urbanization by using agent-based modeling.

Geographical Research, 2009, 28(4): 947-956.]

https://doi.org/10.11821/yj2009040009      URL      [本文引用: 1]      摘要

In this paper,the UrbanSwarm,a computational environment,is developed for a better understanding of urbanization by agent-based modeling.UrbanSwarm contains multiple economic interactions between urban and rural agents that are crucial in urbanization process and mechanism because the basic force driving the urbanization is inherently microscopic.This paper gives a detailed discussion on the influencing factors such as migration policy,domestic demand,and negative effect of agglomeration on urbanization process in mono-centric scenario.(1) The segmentation policy not only limited the urban-rural migration but enlarged urban-rural income disparity as well.It simulates the household registration system(hukou) before 1978,which confined most Chinese citizens to their places of birth.(2) Raising urban-rural propensity to consumer contributes to both urban-rural income level and urbanization level.Meanwhile,it can narrow the urban-rural gap.(3) With the increase of the negative effect of agglomeration on urbanization,urbanization level drops gradually.The urbanization process loses its sustainability if the negative effect of agglomeration on urbanization exceeds some thresholds in mono-centric pattern.(4) The industrialization and urbanization level has a close relationship if the technology and preference are never changed.The process of industrialization is faster than the process of urbanization in simulation. This parallel processing agent-based approach has various advantages over existing economic approaches such as neoclassical model and equilibrium analysis.UrbanSwarm can be used for both simulation and computation purposes.MAS provides an exploratory platform to test hypotheses behind the space-time dynamics as well as to experiment with 'what-if' games within complex urbanization process,i.e.using the computer as an artificial laboratory for the study of urban and regional systems.Besides,it can be used to achieve some computational intensive tasks through the collective work of individual agents such as rural and urban households,local government,firms and so on.Future work should focus on removing the limitations of reactiv
[6] 郭延凤, 于秀波, 姜鲁光, .

基于CLUE模型的2030年江西省土地利用变化情景分析

. 地理研究, 2012, 31(6): 1016-1028.

https://doi.org/10.11821/yj2012060005      URL      [本文引用: 1]      摘要

Using CLUE(The Conversion of Land Use and its Effects)model based on GIS spatial analysis and statistics,this paper introduced three scenarios("Business as Usual Scenario","Planned Scenario"and"Optimal Scenario")to simulate the land use spatial change in Jiangxi Province from 2001to 2030.The paper has developed three scenarios on land use change to conduct a comparative analysis.Scenarios provide an effective tool to assess the risks of current land use patterns and the policy options,and offer more comprehensive and meaningful scientific information to policy-makers from different approaches by taking various factors into account.As a result,scenario analysis plays a critical role in this study,where nine types of land use are identified to show what might take place under different scenarios.This model is applied to simulate the future land use scenarios in the next three decades,and to validate the simulated results with the land use map in 2005.The validation suggests that the model has accurately positioned the simulated results to an appropriate spatial location.The results are shown as follows.(1)"Business as Usual Scenario".The arable lands continue to decline,and lands for construction purposes increase sharply,while forested land areas remain stable.(2)"Planned Scenario".The arable lands continue to grow,and lands for construction purposes increase slightly and remain unchanged in 2020;forested land areas show a slight change,and high-density forest areas grow;the areas of rivers and lakes decrease marginally;while the areas of marshes and peat lands grow rapidly.(3) "Optimal Scenario".The forest areas grow relatively slowly than that under"Planned Scenario";while all the areas of rivers and lakes,marshes and peat lands increase significantly.The study also suggests that the CLUE model is very powerful in predicting the future land use change,and the land use changes under different scenarios vary greatly in spatial distribution.The results are expected to provide reference for future development and revision of land use planning,as well as for sustainable land management in the study area.

[Guo Yanfeng, Yu Xiubo, Jiang Luguang, et al.

Scenarios analysis of land use change based on CLUE model in Jiangxi province by 2030.

Geographical Research, 2012, 31(6): 1016-1028.]

https://doi.org/10.11821/yj2012060005      URL      [本文引用: 1]      摘要

Using CLUE(The Conversion of Land Use and its Effects)model based on GIS spatial analysis and statistics,this paper introduced three scenarios("Business as Usual Scenario","Planned Scenario"and"Optimal Scenario")to simulate the land use spatial change in Jiangxi Province from 2001to 2030.The paper has developed three scenarios on land use change to conduct a comparative analysis.Scenarios provide an effective tool to assess the risks of current land use patterns and the policy options,and offer more comprehensive and meaningful scientific information to policy-makers from different approaches by taking various factors into account.As a result,scenario analysis plays a critical role in this study,where nine types of land use are identified to show what might take place under different scenarios.This model is applied to simulate the future land use scenarios in the next three decades,and to validate the simulated results with the land use map in 2005.The validation suggests that the model has accurately positioned the simulated results to an appropriate spatial location.The results are shown as follows.(1)"Business as Usual Scenario".The arable lands continue to decline,and lands for construction purposes increase sharply,while forested land areas remain stable.(2)"Planned Scenario".The arable lands continue to grow,and lands for construction purposes increase slightly and remain unchanged in 2020;forested land areas show a slight change,and high-density forest areas grow;the areas of rivers and lakes decrease marginally;while the areas of marshes and peat lands grow rapidly.(3) "Optimal Scenario".The forest areas grow relatively slowly than that under"Planned Scenario";while all the areas of rivers and lakes,marshes and peat lands increase significantly.The study also suggests that the CLUE model is very powerful in predicting the future land use change,and the land use changes under different scenarios vary greatly in spatial distribution.The results are expected to provide reference for future development and revision of land use planning,as well as for sustainable land management in the study area.
[7] 吴桂平, 曾永年, 冯学智, .

CLUE-S模型的改进与土地利用变化动态模拟: 以张家界市永定区为例

. 地理研究, 2010, 29(3): 460-470.

URL      [本文引用: 1]      摘要

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。
Land use/land cover change (LUCC) is an important content of geographical research on global change today, while spatial simulation on land use change is one of the key content of LUCC. In recent years, CLUE-S (The Conversion of Land Use and its Effects at Small Region Extent) model has been used widely in the world. This paper mainly aimed at CLUE-S model to improve the model in spatial analysis module by incorporating components describing the spatial autocorrelation into a classic logistic model. Meanwhile, Yongding County in Zhangjiajie city, which is one of the typical karst mountain areas in northwestern Hunan Province, was selected as the research area. By using the improved CLUE-S model, this paper simulated and analysed regional land use change in Yongding County. All driving factors such as distance to town, distance to river, distance to road, population density, altitude, slope and aspect were produced with ArcGIS spatial analysis tools. Then the weighting coefficient of every land use type was analysed with SPSS13.0. The results indicated that improved spatial analysis module of CLUE-S model showed better goodness of fitting and higher accuracy of fitting. The distribution of land use types of cultivated land, forest land and residence land areas under the ROC curves (AUC) were increased to 0.827, 0.875 and 0.838 respectively. For a better understanding of the future land use change in the region, the improved CLUE-S model is further put into application to predict spatial distribution of land use change from 2005 to 2020. It is argued that the improved CLUE-S model based on Autologistic method is reasonable to some degree. At the same time, these types of analysis can provide valuable information for government decisions on land use management in Yongding County and similar areas.

[Wu Guiping, Zeng Yongnian, Feng Xuezhi, et al.

Dynamic simulation of land use change based on the improved CLUE-S model: A case study of Yongding county, Zhangjiajie.

Geographical Research, 2010, 29(3): 460-470.]

URL      [本文引用: 1]      摘要

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。
Land use/land cover change (LUCC) is an important content of geographical research on global change today, while spatial simulation on land use change is one of the key content of LUCC. In recent years, CLUE-S (The Conversion of Land Use and its Effects at Small Region Extent) model has been used widely in the world. This paper mainly aimed at CLUE-S model to improve the model in spatial analysis module by incorporating components describing the spatial autocorrelation into a classic logistic model. Meanwhile, Yongding County in Zhangjiajie city, which is one of the typical karst mountain areas in northwestern Hunan Province, was selected as the research area. By using the improved CLUE-S model, this paper simulated and analysed regional land use change in Yongding County. All driving factors such as distance to town, distance to river, distance to road, population density, altitude, slope and aspect were produced with ArcGIS spatial analysis tools. Then the weighting coefficient of every land use type was analysed with SPSS13.0. The results indicated that improved spatial analysis module of CLUE-S model showed better goodness of fitting and higher accuracy of fitting. The distribution of land use types of cultivated land, forest land and residence land areas under the ROC curves (AUC) were increased to 0.827, 0.875 and 0.838 respectively. For a better understanding of the future land use change in the region, the improved CLUE-S model is further put into application to predict spatial distribution of land use change from 2005 to 2020. It is argued that the improved CLUE-S model based on Autologistic method is reasonable to some degree. At the same time, these types of analysis can provide valuable information for government decisions on land use management in Yongding County and similar areas.
[8] Verburg P H, Overmars K P.

Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model.

Landscape ecology, 2009, 24(9): 1167-1181.

https://doi.org/10.1007/s10980-009-9355-7      URL      [本文引用: 1]      摘要

Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamics
[9] Lima M L, Zelaya K, Massone H.

Groundwater vulnerability assessment combining the drastic and Dyna-Clue model in the Argentine pampas.

Environmental management, 2011, 47(5): 828-839.

[本文引用: 1]     

[10] Overmars K P, Helming J, van Zeijts H, et al.

A modelling approach for the assessment of the effects of Common Agricultural Policy measures on farmland biodiversity in the EU27.

Journal of Environmental Management, 2013, 126: 132-141.

URL      PMID: 23708145      [本文引用: 1]     

[11] Promper C, Puissant A, Malet J P, et al.

Analysis of land cover changes in the past and the future as contribution to landslide risk scenarios.

Applied Geography, 2014, 53: 11-19.

https://doi.org/10.1016/j.apgeog.2014.05.020      URL      [本文引用: 1]      摘要

Various factors influence the spatial and temporal pattern of landslide risk. Land cover change is one of the crucial factors influencing not only the natural process &ldquo;landslide&rdquo; and thus the hazard, but also the spatial distribution of elements at risk. Therefore the assessment of past and future landslide risk at regional scales implies the analysis of past and future land cover development. In this study, the first step in the analysis of landslide risk development over time is approached by analysing past land cover, as well as modelling potential future scenarios. The applied methods include analysis of orthophotographs and landcover scenario modelling with the Dyna-CLUE model. The timespan of the analysis covers 138 years from 1962 to 2100. The study area is located in Waidhofen/Ybbs (Austria) in the alpine foreland. A high number of landslides are recorded in the district. The predominant land cover types are grassland and forest. Buildings and residential areas are located in the valley bottoms and scattered on the hilltops. The results show clear changes in the land cover development of the past and in the future including spatial changes in the distribution of elements at risk. The trends show an increase in forest on the expense of grassland. The spatial evolution of the surfaces of arable land is rather high whereas the surfaces of residential zones increase steadily. The spatial analysis indicates also the development of new building areas and consequently potentially new landslide risk hotspots.
[12] Renwick A, Jansson T, Verburg P H, et al.

Policy reform and agricultural land abandonment in the EU.

Land Use Policy, 2013, 30(1): 446-457.

URL      [本文引用: 1]     

[13] Price B, Kienast F, Seidl I, et al.

Future landscapes of Switzerland: Risk areas for urbanization and land abandonment.

Applied Geography, 2015, 57: 32-41.

[本文引用: 1]     

[14] 杨园园.

三江源区生态系统碳储量估算及固碳潜力研究

. 北京: 首都师范大学硕士学位论文, 2012.

URL      [本文引用: 2]      摘要

生态系统碳储量变化对于全球气候变化及生态系统服务功能有重大影响,生态系统碳储量变化受诸多因素影响,土地利用格局变化是影响生态系统碳储量变化的重要因素之一。探究土地利用与覆被格局变化是研究生态系统碳储量变化的基础,土地利用格局变化对生态系统碳储量及固碳潜力影响的研究是从局部区域角度研究全球变化的切入点。本文研究三江源区碳储量变化,不仅是深入认识生态系统服务功能理论的基础,同时也有助于决策者制定合理的土地利用规划方案,不仅具有理论探索价值,也对指导区域可持续发展具有现实意义。 本研究通过分析研究区过去25年土地利用格局变化,探究土地利用格局变化的驱动因素,采用spss软件分析各驱动因素与各地类之间的相关性大小;以此为基础,采用dyna-clue模型模拟未来(2005-2035年)源区不同情景(现状推移、减速演化和加速演化)的土地利用格局;最后采用InVEST模型评估不同情景的土地利用格局对源区土壤碳储量和土壤固碳量及土壤碳储量价值和固碳量价值的影响;以评估结果为依据,提出源区未来土地利用规划建议及生态补偿方案。主要结论如下: 1)研究区未来土地利用格局:三江源区2005-2035年三种情景下其他用地面积均不断扩大,且从北向南逐渐连成一片。情景二的其他用地比情景一小,但其低覆盖度草地面积远远大于情景一;情景三的其他用地比情景一大,但其高覆盖度草地的面积远远超过情景一;同时,情景一、情景二和情景三中的用地格局变化最大的地类初始时刻大部分是低覆盖度草地。模拟结果表明,低覆盖度草地更容易发生转化,其临近哪种用地就更容易转变成那种用地。 2)土地利用格局变化对生态系统碳储量及固碳潜力的影响:2005年之后,三种情景下土壤碳储量均呈稳定上升趋势,情景二碳储量最少,情景三碳储量最多.2035年情景一和情景三碳储量基本相当;土壤碳储量总体增长速度情景三最快,情景一最慢;2025年之后情景三土壤碳储量增长速度变慢。总体来看,种情景下,源区中部均为碳汇区,每百公顷固碳量为300t;中南部碳汇能力较强,可达每百公顷1400t:东北部有少部分碳源区,排碳量达每百公顷3300t。 3)土地利用格局变化对碳储量价值和固碳价值的影响:根据三种模拟情景计算的结果表明,三江源区土壤碳库碳储量的价值在4123亿$至4160亿$之间,从各年来看,情景三碳储量价值最高,情景二碳储量价值最低。2005-2035年三种情景下土壤碳储量价值均呈上升趋势,但是情景三三碳储量价值的增长速率从2025年开始下降。总体来看,三汀源区土壤碳库不论哪种情景下,土壤碳库均为碳汇区,碳汇价值情景三最高,30年累计固碳价值达2×109$;情景二最低,30年累计固碳价值达1.2×109$;情景一固碳价值居中,达1.9×109$。因此,源区在未来发展过程中,应主要遵循情景三的土地利用模式,并切实做好对低覆盖度草地的管理和利用。

[Yang Yuanyuan.

The ecosystem carbon storage estimation and carbon sequestration potential research in river source area.

Beijing: Master Dissertation of Capital Normal University, 2012.]

URL      [本文引用: 2]      摘要

生态系统碳储量变化对于全球气候变化及生态系统服务功能有重大影响,生态系统碳储量变化受诸多因素影响,土地利用格局变化是影响生态系统碳储量变化的重要因素之一。探究土地利用与覆被格局变化是研究生态系统碳储量变化的基础,土地利用格局变化对生态系统碳储量及固碳潜力影响的研究是从局部区域角度研究全球变化的切入点。本文研究三江源区碳储量变化,不仅是深入认识生态系统服务功能理论的基础,同时也有助于决策者制定合理的土地利用规划方案,不仅具有理论探索价值,也对指导区域可持续发展具有现实意义。 本研究通过分析研究区过去25年土地利用格局变化,探究土地利用格局变化的驱动因素,采用spss软件分析各驱动因素与各地类之间的相关性大小;以此为基础,采用dyna-clue模型模拟未来(2005-2035年)源区不同情景(现状推移、减速演化和加速演化)的土地利用格局;最后采用InVEST模型评估不同情景的土地利用格局对源区土壤碳储量和土壤固碳量及土壤碳储量价值和固碳量价值的影响;以评估结果为依据,提出源区未来土地利用规划建议及生态补偿方案。主要结论如下: 1)研究区未来土地利用格局:三江源区2005-2035年三种情景下其他用地面积均不断扩大,且从北向南逐渐连成一片。情景二的其他用地比情景一小,但其低覆盖度草地面积远远大于情景一;情景三的其他用地比情景一大,但其高覆盖度草地的面积远远超过情景一;同时,情景一、情景二和情景三中的用地格局变化最大的地类初始时刻大部分是低覆盖度草地。模拟结果表明,低覆盖度草地更容易发生转化,其临近哪种用地就更容易转变成那种用地。 2)土地利用格局变化对生态系统碳储量及固碳潜力的影响:2005年之后,三种情景下土壤碳储量均呈稳定上升趋势,情景二碳储量最少,情景三碳储量最多.2035年情景一和情景三碳储量基本相当;土壤碳储量总体增长速度情景三最快,情景一最慢;2025年之后情景三土壤碳储量增长速度变慢。总体来看,种情景下,源区中部均为碳汇区,每百公顷固碳量为300t;中南部碳汇能力较强,可达每百公顷1400t:东北部有少部分碳源区,排碳量达每百公顷3300t。 3)土地利用格局变化对碳储量价值和固碳价值的影响:根据三种模拟情景计算的结果表明,三江源区土壤碳库碳储量的价值在4123亿$至4160亿$之间,从各年来看,情景三碳储量价值最高,情景二碳储量价值最低。2005-2035年三种情景下土壤碳储量价值均呈上升趋势,但是情景三三碳储量价值的增长速率从2025年开始下降。总体来看,三汀源区土壤碳库不论哪种情景下,土壤碳库均为碳汇区,碳汇价值情景三最高,30年累计固碳价值达2×109$;情景二最低,30年累计固碳价值达1.2×109$;情景一固碳价值居中,达1.9×109$。因此,源区在未来发展过程中,应主要遵循情景三的土地利用模式,并切实做好对低覆盖度草地的管理和利用。
[15] 尹昌应, 石忆邵.

规划情景约束下的城市土地利用空间格局模拟

. 地理与地理信息科学, 2014, 30(2): 66-71.

https://doi.org/10.7702/dlydlxxkx20140214      [本文引用: 1]      摘要

以上海市为例,采用遥感与GIS技术,基于遥感影像及土地利用现状和社会经济统计数据,构建Dyna-CLUE模型模拟土地资源利用规模在土地利用规划政 策情景约束下的空间格局,旨在为城市化进程中的土地利用规划实施,特别是土地利用宏观需求的空间配置提供技术思路.结果表明:遥感、GIS和Dyna- CLUE模型的集成应用,可从地理空间上定量刻画土地变化的驱动力,是模拟政策情景约束条件下土地资源空间分配问题的有效手段.

[Yin Changying, Shi Yishao.

Modeling urban land use pattern under the constraints of land planning scenarios. Geography and

Geo-Information Science. 2014, 30(2): 66-71.]

https://doi.org/10.7702/dlydlxxkx20140214      [本文引用: 1]      摘要

以上海市为例,采用遥感与GIS技术,基于遥感影像及土地利用现状和社会经济统计数据,构建Dyna-CLUE模型模拟土地资源利用规模在土地利用规划政 策情景约束下的空间格局,旨在为城市化进程中的土地利用规划实施,特别是土地利用宏观需求的空间配置提供技术思路.结果表明:遥感、GIS和Dyna- CLUE模型的集成应用,可从地理空间上定量刻画土地变化的驱动力,是模拟政策情景约束条件下土地资源空间分配问题的有效手段.
[16] 刘新亮.

脱甲河流域LUCC响应全球气候变化的动态模拟

. 长沙: 湖南农业大学硕士学位论文, 2011.

URL      [本文引用: 2]      摘要

土地利用变化模拟研究变得越来越重要,使用动态模型研究土地利用变化成为一个很有效的研究方法,本研究选取Dyna-CLUE模型动态模拟了脱甲河流域未来100年以内的土地利用变化情况,研究区面积为135km2。首先,搜集了研究区四幅土地利用现状图,分别为1933、1955、1990、2005,同时又依据原行政边界把研究区分为三个区域(金井、观佳、脱甲)。对研究区土地利用变化做了一个总体分析,结果表明1933年到2005年,景观格局大体一致,水田、林地始终占据主要面积,但同样存在着林地不断减少,水田不断增加的趋势,有12.7kmm2的林地转化为水田,占1933年林地的13%。景观指数分析表明从1933年到1990年景观破碎化逐年加重,但从1990年开始这种异质性趋势逐渐变缓。分区景观指数计算揭示出金井地区在景观优势度指数上不同于其他两个区域,表明在金井区存在明显的城镇化现象,观佳区在分形维数指数方面不同于其他两区,说明该区的发展呈无序化状态,需要制定一个总体发展规划。景观驱动力分析表明占主导优势的驱动力是地形和政府决策,人文活动在景观异质性方面也起了重要作用。通过计算与反复模拟分析,得到土地利用类型转化弹性和土地利用转化次序,茶园、居民地、水田、林地、公路、湖泊、河流的转移参数分别为0.5、0.3、0.8、0.8、0.1、0.3、0.1。把各种参数带入Dyna-CLUE中并使用1933、1955、2005年的土地利用现状图检验模型,各年份的拟合度分别为80.4%、85.6%、87.5%。最后使用建好的Dyna-CLUE模型结合研究区未来100年内温度变化动态模拟各年份的土地利用变化情况,结果证明使用Dyna-CLUE模型能很好的模拟研究区未来土地利用变 化。

[Liu Xinliang.

LUCC dynamic simulation responding to global climate change in Tuojia river catchment.

Changsha: Master Dissertation of Hunan Agricultural University, 2011]

URL      [本文引用: 2]      摘要

土地利用变化模拟研究变得越来越重要,使用动态模型研究土地利用变化成为一个很有效的研究方法,本研究选取Dyna-CLUE模型动态模拟了脱甲河流域未来100年以内的土地利用变化情况,研究区面积为135km2。首先,搜集了研究区四幅土地利用现状图,分别为1933、1955、1990、2005,同时又依据原行政边界把研究区分为三个区域(金井、观佳、脱甲)。对研究区土地利用变化做了一个总体分析,结果表明1933年到2005年,景观格局大体一致,水田、林地始终占据主要面积,但同样存在着林地不断减少,水田不断增加的趋势,有12.7kmm2的林地转化为水田,占1933年林地的13%。景观指数分析表明从1933年到1990年景观破碎化逐年加重,但从1990年开始这种异质性趋势逐渐变缓。分区景观指数计算揭示出金井地区在景观优势度指数上不同于其他两个区域,表明在金井区存在明显的城镇化现象,观佳区在分形维数指数方面不同于其他两区,说明该区的发展呈无序化状态,需要制定一个总体发展规划。景观驱动力分析表明占主导优势的驱动力是地形和政府决策,人文活动在景观异质性方面也起了重要作用。通过计算与反复模拟分析,得到土地利用类型转化弹性和土地利用转化次序,茶园、居民地、水田、林地、公路、湖泊、河流的转移参数分别为0.5、0.3、0.8、0.8、0.1、0.3、0.1。把各种参数带入Dyna-CLUE中并使用1933、1955、2005年的土地利用现状图检验模型,各年份的拟合度分别为80.4%、85.6%、87.5%。最后使用建好的Dyna-CLUE模型结合研究区未来100年内温度变化动态模拟各年份的土地利用变化情况,结果证明使用Dyna-CLUE模型能很好的模拟研究区未来土地利用变 化。
[17] 孙晓芳, 岳天祥, 范泽孟.

中国土地利用空间格局动态变化模拟: 以规划情景为例

. 生态学报, 2012, 32(20): 6440-6451.

URL      [本文引用: 1]      摘要

Land use change is a key subject in the research of sustainable development in environment.The spatial pattern of land use change closely related to earth system functioning,such as climate warming,biogeochemical circle and landscape biodiversity.In order to improve ecological environment and promote social development,a series of land use policies such as afforestation,restoration of degraded grassland and protection of cultivated land had been formulated.These policies will exert a great influence on the spatial pattern of land use in China.However,the land use policies only provide an overview of the land use changes at the national scale but can′t give insight into the changes at the regional and landscape scales.In this paper,the Dyna-CLUE model,which is a dynamic,spatially explicit land use change model had been used to simulate the spatial pattern of land use change in China in the coming decades.The planned development scenario was developed,in which the total area for each land use types in the future were defined as required by the land use policies.The Chinese level land use demands were downscaled to land use pattern at 2 km2 resolution.Six land use types were distinguished which are built-up land,arable land,grassland,forest land,water area and other land.The spatial allocation of land uses were simulated based on the location suitability and user-specified decision rules.The driving factors include climatic and economic condition,traffic situation,soil texture,topography and demography.Logistic regression was used to quantify the relation between land use patterns and these drving factors.Climatic factors,traffic and population were defined as dynamic driving factors.In the future,the HadCM3 B2 climatic scenario was adopted to provide climatic data;the spatial pattern of population was simulated by SMPD(surface modelling of population distribution),and the railway and road development plan was made by the government.Other stable driving factors such as topography,soil texture were assumed to remain unchanged in the future 15 years.The performance of the land use change model was validated,showing that this method can simulate the spatial pattern of land use change accurately.The results indicate that the area of cultivated land would keep no less than 120.33 million hectares,however,it would decrease in western region where the land is not suitable for cultivation and would increase in central south China.The forest area would increase by 14.28 million hectares,mainly in northeastern and southwestern China,where the climate is sufficiently hospitable for forest growth.The area of built-up land would increase by 5.3176 million hectares,mainly in eastern and southeastern regions of China which are characterized by high population density and advanced economy.The simulation has the potential to help decision makers and scientists identify the critical regions that need specific consideration.The high spatial resolution of the results enable the assessment of impact of land use change on a large number of environmental indicators,including climate change,carbon sequestration and landscape diversity.

[Sun Xiaofang, Yue Tianxiang, Fan Zemeng.

Simulation of the spatial pattern of land use change in China: The case of planned development scenario.

Acta Ecologica Sinica, 2012, 32(20): 6440-6451.]

URL      [本文引用: 1]      摘要

Land use change is a key subject in the research of sustainable development in environment.The spatial pattern of land use change closely related to earth system functioning,such as climate warming,biogeochemical circle and landscape biodiversity.In order to improve ecological environment and promote social development,a series of land use policies such as afforestation,restoration of degraded grassland and protection of cultivated land had been formulated.These policies will exert a great influence on the spatial pattern of land use in China.However,the land use policies only provide an overview of the land use changes at the national scale but can′t give insight into the changes at the regional and landscape scales.In this paper,the Dyna-CLUE model,which is a dynamic,spatially explicit land use change model had been used to simulate the spatial pattern of land use change in China in the coming decades.The planned development scenario was developed,in which the total area for each land use types in the future were defined as required by the land use policies.The Chinese level land use demands were downscaled to land use pattern at 2 km2 resolution.Six land use types were distinguished which are built-up land,arable land,grassland,forest land,water area and other land.The spatial allocation of land uses were simulated based on the location suitability and user-specified decision rules.The driving factors include climatic and economic condition,traffic situation,soil texture,topography and demography.Logistic regression was used to quantify the relation between land use patterns and these drving factors.Climatic factors,traffic and population were defined as dynamic driving factors.In the future,the HadCM3 B2 climatic scenario was adopted to provide climatic data;the spatial pattern of population was simulated by SMPD(surface modelling of population distribution),and the railway and road development plan was made by the government.Other stable driving factors such as topography,soil texture were assumed to remain unchanged in the future 15 years.The performance of the land use change model was validated,showing that this method can simulate the spatial pattern of land use change accurately.The results indicate that the area of cultivated land would keep no less than 120.33 million hectares,however,it would decrease in western region where the land is not suitable for cultivation and would increase in central south China.The forest area would increase by 14.28 million hectares,mainly in northeastern and southwestern China,where the climate is sufficiently hospitable for forest growth.The area of built-up land would increase by 5.3176 million hectares,mainly in eastern and southeastern regions of China which are characterized by high population density and advanced economy.The simulation has the potential to help decision makers and scientists identify the critical regions that need specific consideration.The high spatial resolution of the results enable the assessment of impact of land use change on a large number of environmental indicators,including climate change,carbon sequestration and landscape diversity.
[18] 张凌, 南卓铜, 余文君.

基于模型耦合的土地利用变化和水文响应多情景分析

. 地球信息科学学报, 2013, 15(6): 829-839.

https://doi.org/10.3724/SP.J.1047.2013.00829      URL      [本文引用: 1]      摘要

排放情景下未来气候变化和土地利用变化及其水文响应是流域管理十分关心的问题。本文通过耦合土地利用/覆被变化模型Dyna-CLUE和水文模型SWAT,选择政府间气候变化专门委员会(IPCC)发布的两个温室气体排放情景(A1B和B1),对黑河流域中上游土地利用变化及水文响应进行情景分析。模型校准和验证结果表明,Dy-na-CLUE和SWAT的模拟精度均比较满意。土地利用变化情景分析表明,不同排放情景下未来黑河流域中上游土地利用变化幅度均不大,同一情景下土地利用变化在中上游表现出各自的特点。水文响应多情景分析表明,不考虑土地利用变化,相对于参考情景即以1990-2009年历史数据模拟结果,高排放A1B情景下黑河流域上游和中游2011-2030年年平均河川径流分别呈微弱减少和明显增加的趋势,而低排放B1情景下分别呈明显减少和微弱减少的趋势。同一情景下,水文响应具有明显的区域差异性。考虑土地利用变化,高排放A1B情景下黑河流域上游和中游2011-2030年年平均河川径流分别小于和大于不考虑土地利用变化的情况,低排放B1情景下均小于不考虑土地利用变化的情况。分析表明,排放情景下气候变化和土地利用变化导致流域水文水资源的变化,而土地利用变化可能加剧或削弱气候变化导致的水文响应。

[Zhang Ling, Nan Zhuotong, Yu Wenjun.

Coupling LUCC and hydrological models to predict land use change and hydrological response under multiple scenarios

. Journal of Geo-Information Science, 2013, 15(6): 829-839.]

https://doi.org/10.3724/SP.J.1047.2013.00829      URL      [本文引用: 1]      摘要

排放情景下未来气候变化和土地利用变化及其水文响应是流域管理十分关心的问题。本文通过耦合土地利用/覆被变化模型Dyna-CLUE和水文模型SWAT,选择政府间气候变化专门委员会(IPCC)发布的两个温室气体排放情景(A1B和B1),对黑河流域中上游土地利用变化及水文响应进行情景分析。模型校准和验证结果表明,Dy-na-CLUE和SWAT的模拟精度均比较满意。土地利用变化情景分析表明,不同排放情景下未来黑河流域中上游土地利用变化幅度均不大,同一情景下土地利用变化在中上游表现出各自的特点。水文响应多情景分析表明,不考虑土地利用变化,相对于参考情景即以1990-2009年历史数据模拟结果,高排放A1B情景下黑河流域上游和中游2011-2030年年平均河川径流分别呈微弱减少和明显增加的趋势,而低排放B1情景下分别呈明显减少和微弱减少的趋势。同一情景下,水文响应具有明显的区域差异性。考虑土地利用变化,高排放A1B情景下黑河流域上游和中游2011-2030年年平均河川径流分别小于和大于不考虑土地利用变化的情况,低排放B1情景下均小于不考虑土地利用变化的情况。分析表明,排放情景下气候变化和土地利用变化导致流域水文水资源的变化,而土地利用变化可能加剧或削弱气候变化导致的水文响应。
[19] 孙金华, 曹晓峰, 黄艺.

滇池流域土地利用对入湖河流水质的影响

. 中国环境科学, 2011, 31(12): 2052-2057.

https://doi.org/10.1007/s11783-010-0264-4      URL      [本文引用: 3]      摘要

Based on data of interpreting TM orthophoto images and water quality monitoring in July 2008,the correlativity of landuse,which was categorized to urban,agriculture,forest and grassland by National Standard for Landuse Category in China,and water quality was analyzed in terms of the whole watershed and sub-watersheds.In whole watershed of Dianchi lake,the urban landuse proportion illustrated positive relations with the water pollution stand(indicating with concentration of CODMn,TP,TN and NH3-N in water),while the other three types of landuse,agriculture,forest and grass,showed negative relations.The urban landuse was the major contributor to pollutants in the watershed,which submerged the contribution of cultivated land.Furthermore,classified 21 sub-watersheds to be 3 types,urban,agriculture and forest-watershed according to different proportions of landuse in the each sub-watershed.There were constant positive relationships between the urban-watersheds and water pollution,while negative relationships were observed for the forest-watershed.Also,the impact of landscape spatial pattern on water quality was discussed.The proportion and pattern of landuse both exerted great effects on the water quality at the scales of the whole watershed and the sub-watershed.

[Sun Jinhua, Cao Xiaofeng, Huang Yi.

Effect of land use on inflow rivers water quality in lake Dianchi watershed.

China Environmental Science, 2011, 31(12): 2052-2057.]

https://doi.org/10.1007/s11783-010-0264-4      URL      [本文引用: 3]      摘要

Based on data of interpreting TM orthophoto images and water quality monitoring in July 2008,the correlativity of landuse,which was categorized to urban,agriculture,forest and grassland by National Standard for Landuse Category in China,and water quality was analyzed in terms of the whole watershed and sub-watersheds.In whole watershed of Dianchi lake,the urban landuse proportion illustrated positive relations with the water pollution stand(indicating with concentration of CODMn,TP,TN and NH3-N in water),while the other three types of landuse,agriculture,forest and grass,showed negative relations.The urban landuse was the major contributor to pollutants in the watershed,which submerged the contribution of cultivated land.Furthermore,classified 21 sub-watersheds to be 3 types,urban,agriculture and forest-watershed according to different proportions of landuse in the each sub-watershed.There were constant positive relationships between the urban-watersheds and water pollution,while negative relationships were observed for the forest-watershed.Also,the impact of landscape spatial pattern on water quality was discussed.The proportion and pattern of landuse both exerted great effects on the water quality at the scales of the whole watershed and the sub-watershed.
[20] 杨文龙, 杨树华.

滇池流域非点源污染控制区划研究

. 湖泊科学, 1998, 10(3): 55-60.

URL      [本文引用: 1]      摘要

湖泊的恢复是一个复杂的系统工程,非点源污染的控制则是其中最重要的内容。本文介绍限滇池点源污染物的来源,根据其污染特征和流域的生态特征,划分出了六片污染控制区,并提出了相应的治理措施。

[Yang Wenlong, Yang Shuhua.

Study on the divisions of non-point pollution soures in Dianchi Lake Basin.

Journal of Lake Sciences, 1998, 10(3): 55-60.]

URL      [本文引用: 1]      摘要

湖泊的恢复是一个复杂的系统工程,非点源污染的控制则是其中最重要的内容。本文介绍限滇池点源污染物的来源,根据其污染特征和流域的生态特征,划分出了六片污染控制区,并提出了相应的治理措施。
[21] 陈吉宁, 李广贺, 王洪涛.

滇池流域面源污染控制技术研究

. 中国水利, 2004, (9): 47-50.

https://doi.org/10.3969/j.issn.1000-1123.2004.09.015      URL      [本文引用: 1]      摘要

随着工业废水和城市生活污水等点源污染的有效控制,我国正处在污染构成快速转变时期,面源污染的负荷比重在上升.以清华大学为主的课题组通过对滇池流域面源污染控制技术的研究,在面源污染控制关键技术与设备、工程实施、软件开发、运行管理等方面取得了一系列重要成果,为我国未来大规模开展面源污染控制提供了有益的借鉴.

[Chen Ji'ning, Li Guanghe, Wang Hongtao.

Research on area sourse pollution controlling technology for the Dianchi Lake

. China Water Research, 2004(9): 47-50.]

https://doi.org/10.3969/j.issn.1000-1123.2004.09.015      URL      [本文引用: 1]      摘要

随着工业废水和城市生活污水等点源污染的有效控制,我国正处在污染构成快速转变时期,面源污染的负荷比重在上升.以清华大学为主的课题组通过对滇池流域面源污染控制技术的研究,在面源污染控制关键技术与设备、工程实施、软件开发、运行管理等方面取得了一系列重要成果,为我国未来大规模开展面源污染控制提供了有益的借鉴.
[22] Veldkamp A, Fresco L O.

CLUE: a conceptual model to study the conversion of land use and its effects.

Ecological modelling, 1996, 85(2): 253-270.

https://doi.org/10.1016/0304-3800(94)00151-0      URL      [本文引用: 1]      摘要

A dynamic model to simulate Conversion of Land Use and its Effects (CLUE) is presented. For an imaginary region, CLUE simulates land use conversion and change in space and time as a result of interacting biophysical and human drivers. Within CLUE regional land use changes only if biophysical and human demands cannot be met by existing land use. After a regional assessment of land use needs, the final land use decisions are made on a local grid level. Important biophysical drivers are local biophysical suitability and their fluctuations, land use history, spatial distribution of infrastructure and land use, and the occurrence of pests and diseases. Important human land use drivers in CLUE are population size and density, regional and international technology level, level of affluence, target markets for products, economical conditions, attitudes and values, and the applied land use strategy. Initial CLUE simulations suggest that the integrated land use approach of CLUE can make a more realistic contribution to predictions of future land cover than currently used biophysical equilibrium approaches.
[23] Verburg P H, Soepboer W, Veldkamp A, et al.

Modeling the spatial dynamics of regional land use: the CLUE-S model.

Environmental management, 2002, 30(3): 391-405.

https://doi.org/10.1007/s00267-002-2630-x      URL      PMID: 12148073      [本文引用: 3]      摘要

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation
[24] 刘庆凤, 刘吉平, 宋开山, .

基于CLUE-S模型的别拉洪河流域土地利用变化模拟

. 东北林业大学学报, 2010, 38(1): 64-67.

https://doi.org/10.3969/j.issn.1000-5382.2010.01.020      [本文引用: 1]      摘要

A CLUE-S model was constructed to simulate the spatial pattern of land use change in Bielahong River Basin using roads,rivers,residential area,lakes,ditches,elevation,slope gradient,slope aspects,and population density as driving factors based on land use data of 1986,2000,and 2005.The key driving factors were obtained by Logistic stepwise regression.Results indicate that population density,slope gradient,and elevation are dominant factors influencing the spatial pattern of land uses.It is proved that the CLUE-S model could well simulate land use change in the study area.The CLUE-S model was applied to simulate the spatial pattern of land use change in 2000 and 2002.At the basic grid level(300m×300m),the simulation accuracy reached 78.45% and 90.93%,and Kappa indexes were 0.74 and 0.89 respectively.Therefore,the CLUE-S model constructed in this study is an important tool to explore the temporal and spatial change of land use in Bielahong River Basin for the future,which provides a scientific basis for land use programming.

[Liu Qingfeng, Liu Jiping, Song Kaishan, et al.

Simulation on spatial pattern of land use change in Bielahong River Basin based on CLUE-S model.

Journal of Northeast Forestry University, 2010, 38(1): 64-67.]

https://doi.org/10.3969/j.issn.1000-5382.2010.01.020      [本文引用: 1]      摘要

A CLUE-S model was constructed to simulate the spatial pattern of land use change in Bielahong River Basin using roads,rivers,residential area,lakes,ditches,elevation,slope gradient,slope aspects,and population density as driving factors based on land use data of 1986,2000,and 2005.The key driving factors were obtained by Logistic stepwise regression.Results indicate that population density,slope gradient,and elevation are dominant factors influencing the spatial pattern of land uses.It is proved that the CLUE-S model could well simulate land use change in the study area.The CLUE-S model was applied to simulate the spatial pattern of land use change in 2000 and 2002.At the basic grid level(300m×300m),the simulation accuracy reached 78.45% and 90.93%,and Kappa indexes were 0.74 and 0.89 respectively.Therefore,the CLUE-S model constructed in this study is an important tool to explore the temporal and spatial change of land use in Bielahong River Basin for the future,which provides a scientific basis for land use programming.
[25] 张薇, 刘淼, 戚与珊.

基于CLUE-S模型的昆明市域土地利用预案模拟

. 生态学杂志, 2014, 33(6): 1655-1662.

URL      [本文引用: 1]      摘要

Land-use model plays an important role in land-use change analysis,simulation and prediction. Land-use maps in 1986,1996 and 2006 in Kunming were interpreted based on remote sensing images. The suitability of the CLUE-S model was estimated in Kunming with complex topography. Three scenarios were designed considering different polices and development trends for land use from 2007 to 2020. In the "historic development trend scenario",forestland area( matrix of the landscape) would decrease constantly,while build-up land and grassland area would increase. The landscape pattern would be more fragmental. In the "planning scenario",build-up land would increase rapidly,while farmland and forest land would decrease. The trend of landscape fragment would be less severe than that in the "historical development trend scenario". In the "ecology-priority scenario",forestland would increase,while farmland and grassland would decrease,and the trajectory of landscape fragment and the landscape pattern would be more optimized. The simulated results of CLUE-S model provide a scientific support for land-use planning and policy-making in Kunming.

[Zhang Wei, Liu Miao, Qi Yushan.

Land-use scenarios simulation based on the CLUE-S model in Kunming.

Chinese Journal of Ecology, 2014, 33(6): 1655-1662.]

URL      [本文引用: 1]      摘要

Land-use model plays an important role in land-use change analysis,simulation and prediction. Land-use maps in 1986,1996 and 2006 in Kunming were interpreted based on remote sensing images. The suitability of the CLUE-S model was estimated in Kunming with complex topography. Three scenarios were designed considering different polices and development trends for land use from 2007 to 2020. In the "historic development trend scenario",forestland area( matrix of the landscape) would decrease constantly,while build-up land and grassland area would increase. The landscape pattern would be more fragmental. In the "planning scenario",build-up land would increase rapidly,while farmland and forest land would decrease. The trend of landscape fragment would be less severe than that in the "historical development trend scenario". In the "ecology-priority scenario",forestland would increase,while farmland and grassland would decrease,and the trajectory of landscape fragment and the landscape pattern would be more optimized. The simulated results of CLUE-S model provide a scientific support for land-use planning and policy-making in Kunming.
[26] 布仁仓, 常禹, 胡远满, .

基于Kappa系数的景观变化测度: 以辽宁省中部城市群为例

. 生态学报, 2005, 25(4): 778-784.

[本文引用: 1]     

[Bu Rencang, Chang Yu, Hu Yuanman, et al.

Measuring spatial information changes using Kappa coefficients: A case study of the city groups in central Liaoning province.

Acta Ecologica Sinica, 2005, 25(4): 778-784.]

[本文引用: 1]     

[27] Pontius Jr R G, Schneider L C.

Land-cover change model validation by an ROC method for the Ipswich Watershed, Massachusetts, USA. Agriculture,

Ecosystems & Environment, 2001, 85(1): 239-248.

https://doi.org/10.1016/S0167-8809(01)00187-6      URL      [本文引用: 1]      摘要

Scientists need a better and larger set of tools to validate land-use change models, because it is essential to know a model's prediction accuracy. This paper describes how to use the relative operating characteristic (ROC) as a quantitative measurement to validate a land-cover change model. Typically, a crucial component of a spatially explicit simulation model of land-cover change is a map of suitability for land-cover change, for example a map of probability of deforestation. The model usually selects locations for new land-cover change at locations that have relatively high suitability. The ROC can compare a map of actual change to maps of modeled suitability for land-cover change. ROC is a summary statistic derived from several two-by-two contingency tables, where each contingency table corresponds to a different simulated scenario of future land-cover change. The categories in each contingency table are actual change and actual non-change versus simulated change and simulated non-change. This paper applies the theoretical concepts to a model of deforestation in the Ipswich watershed, USA.
[28] 刘松波.

滇池流域土地利用变化与入湖河流水质关系研究

. 环境科学导刊, 2013, 32(2): 42-44.

https://doi.org/10.3969/j.issn.1673-9655.2013.02.010      [本文引用: 1]      摘要

Taking the south-east Dianchi Lake Catchment as an example,the correlation between the land use type and lake water quality is analyzed by the stepwise regression model.It is found that the water quality pollution index and the farmland are always in a positive correlation,while it is an opposite situation for the forested land and urban land.Therefore the farmland is a major pollution source for the water quality of the inflowing rivers in the research area,also with an overriding pollution contribution.

[Liu Songbo.

Impact of land use change on inflowing water quality in Dianchi Lake Catchment.

Environmental Science Survey, 2013, 32(2): 42-44.]

https://doi.org/10.3969/j.issn.1673-9655.2013.02.010      [本文引用: 1]      摘要

Taking the south-east Dianchi Lake Catchment as an example,the correlation between the land use type and lake water quality is analyzed by the stepwise regression model.It is found that the water quality pollution index and the farmland are always in a positive correlation,while it is an opposite situation for the forested land and urban land.Therefore the farmland is a major pollution source for the water quality of the inflowing rivers in the research area,also with an overriding pollution contribution.
[29] 张洪, 雷冬梅, 黎海林, .

滇池流域建设用地景观格局与滇池水质关系分析

. 水土保持通报, 2013, 33(4): 103-108.

URL      [本文引用: 1]      摘要

运用灰色关联度分析法,研究了高原湖滨区滇池流域建设用地的景观格局指数与滇池水体的TN,TP浓度变化之间的关系.结果表明,1988-2008年,滇池草海与外海TN,TP浓度呈不断上升的趋势;滇池流域建设用地斑块的团聚程度和整合性升高,建设用地斑块间距离变小,大面积的建设用地增多,且建设用地斑块形状趋于复杂,具有不规则的特征;滇池流域建设用地对水质的影响与其景观空间格局关系密切,特别是聚合度和面积加权的平均斑块分维数. 要保证该地区迅速发展的城市化背景下的水环境安全,滇池流域土地利用调整的基本方向是在湖滨带地区,保留足够的生态用地,避免建设用地的过度整合及大面积 建设用地的形成.

[Zhang Hong, Lei Dongmei, Li Hailin, et al.

Relationship between landscape pattern of construction land in Dianchi Lake Basin and water quality in Dianchi Lake.

Bulletin of Soil and Water Conservation, 2013, 33(4): 103-108.]

URL      [本文引用: 1]      摘要

运用灰色关联度分析法,研究了高原湖滨区滇池流域建设用地的景观格局指数与滇池水体的TN,TP浓度变化之间的关系.结果表明,1988-2008年,滇池草海与外海TN,TP浓度呈不断上升的趋势;滇池流域建设用地斑块的团聚程度和整合性升高,建设用地斑块间距离变小,大面积的建设用地增多,且建设用地斑块形状趋于复杂,具有不规则的特征;滇池流域建设用地对水质的影响与其景观空间格局关系密切,特别是聚合度和面积加权的平均斑块分维数. 要保证该地区迅速发展的城市化背景下的水环境安全,滇池流域土地利用调整的基本方向是在湖滨带地区,保留足够的生态用地,避免建设用地的过度整合及大面积 建设用地的形成.
[30] 刘静怡, 蔡永立, 於家, .

基于CLUE-S和灰色线性规划的嘉兴北部土地利用优化配置研究

. 生态与农村环境学报, 2013, 29(4): 529-536.

URL      [本文引用: 1]      摘要

A new model or method for optimizing land use arrangement is invented based on CLUE-S and grey linear programming(GLP),and can be used to conduct integrated optimization of quantity structure and spatial distribution of land use,after the data of regional policy social economy,ecological targets and land use status quo are imported into the model.The area of North Jiaxing was selected as a case for study.Three different scenarios,i.e.current trend(A),programmed target(B) and programmed sustainable(C),were simulated.Results show that as Scenario A emphasized social,economic and environmental benefits,its effect of controlling rapid growth of construction land and continuous decrease in farmland was significant.Therefore,Scenario A may provide ecologically sensitive areas with dual protections,environment and food safety,and hence is an ideal optimization programme for land use.The use of CLUE-S model coupled with GLP is an effective solution to the problem of optimal arrangement of land resources,because it can be used to simulate variation of land use in quantity and spatial distribution by taking into account the current situation and development targets of a region simultaneously.

[Liu Jingyi, Cai Yongli, Yu Jia, et al.

Optimization of land use arrangement in north Jiaxing based on CLUE-S and Grey linear programming models.

Journal of Ecology and Rural Environment, 2013, 29(4): 529-536.]

URL      [本文引用: 1]      摘要

A new model or method for optimizing land use arrangement is invented based on CLUE-S and grey linear programming(GLP),and can be used to conduct integrated optimization of quantity structure and spatial distribution of land use,after the data of regional policy social economy,ecological targets and land use status quo are imported into the model.The area of North Jiaxing was selected as a case for study.Three different scenarios,i.e.current trend(A),programmed target(B) and programmed sustainable(C),were simulated.Results show that as Scenario A emphasized social,economic and environmental benefits,its effect of controlling rapid growth of construction land and continuous decrease in farmland was significant.Therefore,Scenario A may provide ecologically sensitive areas with dual protections,environment and food safety,and hence is an ideal optimization programme for land use.The use of CLUE-S model coupled with GLP is an effective solution to the problem of optimal arrangement of land resources,because it can be used to simulate variation of land use in quantity and spatial distribution by taking into account the current situation and development targets of a region simultaneously.
[31] 陆汝成, 黄贤金, 左天惠, .

基于CLUE-S和Markov复合模型的土地利用情景模拟研究: 以江苏省环太湖地区为例

. 地理科学, 2009, 29(4): 577-581.

URL      [本文引用: 1]      摘要

基于江苏省环太湖地区1990、2000年TM影像和2005年中巴卫星遥感影像,综合集成区域DEM、交通图和居民点分布图等,充分利用CLUE-S模型空间模拟特长和Markov模型数量预测优势,应用 CLUE-S和Markov复合模型及GIS分析技术分别对现有土地转移速率发展和根据规划约束对转移概率进行调整的严格保护耕地的土地利用变化情景进行时空模拟,揭示不同情景下的土地利用格局变化。通过模拟得知,江苏省环太湖地区今后确需控制建设用地总量,提高节约集约用地水平,最严格地保护水田等优质耕地。
Based on the remote sensing image in the years 1990, 2000 and 2005, combined with regional DEM, traffic map, residential area maps and so on, this paper simulated the land use change scenarios through the application of CLUE-S, Markov composite model and GIS, explored the trend of land use change in different scenarios. The results show that CLUE-S and Markov composite model can simulate spatial and temporal land use changes in Taihu Lake rim. In simulation of the future land use scenarios, "scenarioⅠ" assumes that simulation of the land use change scenarios will obey the current land transfer rate. The result shows construction land will increase sharply and paddy fields reduce immensely in 2015. However, the "scenarioⅡ" supposes that land use change scenarios will follow the policy of strictly protecting cultivated land based on the constraints of land use planning, the rate which paddy field and dry land convert construction land will be slow down 50% and 20% from 2005. The reducing rate of paddy fields will less reduce 21.16%, and increasing the rate of construction land will less increased 20.82% in 2015 than the current land transfer rate’s scenario. For future regional land use regulation and management and in order to ensure food security and scientific land use, and promote rational economic growth around the Taihu Lake Region, it is important to strengthen the protection of cultivated land and control the construction land area.

[Lu Rucheng, Huang Xianjin, Zuo Tianhui, et al.

Land use scenarios simulation based on CLUE-S and Markov composite model: A case study of Taihu Lake Rimin Jiangsu province.

Scientia Geographica Sinica, 2009, 29(4): 577-581.]

URL      [本文引用: 1]      摘要

基于江苏省环太湖地区1990、2000年TM影像和2005年中巴卫星遥感影像,综合集成区域DEM、交通图和居民点分布图等,充分利用CLUE-S模型空间模拟特长和Markov模型数量预测优势,应用 CLUE-S和Markov复合模型及GIS分析技术分别对现有土地转移速率发展和根据规划约束对转移概率进行调整的严格保护耕地的土地利用变化情景进行时空模拟,揭示不同情景下的土地利用格局变化。通过模拟得知,江苏省环太湖地区今后确需控制建设用地总量,提高节约集约用地水平,最严格地保护水田等优质耕地。
Based on the remote sensing image in the years 1990, 2000 and 2005, combined with regional DEM, traffic map, residential area maps and so on, this paper simulated the land use change scenarios through the application of CLUE-S, Markov composite model and GIS, explored the trend of land use change in different scenarios. The results show that CLUE-S and Markov composite model can simulate spatial and temporal land use changes in Taihu Lake rim. In simulation of the future land use scenarios, "scenarioⅠ" assumes that simulation of the land use change scenarios will obey the current land transfer rate. The result shows construction land will increase sharply and paddy fields reduce immensely in 2015. However, the "scenarioⅡ" supposes that land use change scenarios will follow the policy of strictly protecting cultivated land based on the constraints of land use planning, the rate which paddy field and dry land convert construction land will be slow down 50% and 20% from 2005. The reducing rate of paddy fields will less reduce 21.16%, and increasing the rate of construction land will less increased 20.82% in 2015 than the current land transfer rate’s scenario. For future regional land use regulation and management and in order to ensure food security and scientific land use, and promote rational economic growth around the Taihu Lake Region, it is important to strengthen the protection of cultivated land and control the construction land area.
[32] 梁友嘉, 徐中民, 钟方雷.

基于SD和CLUE-S模型的张掖市甘州区土地利用情景分析

. 地理研究, 2011, 30(3): 564-576.

https://doi.org/10.3724/SP.J.1011.2011.00415      URL      [本文引用: 1]      摘要

Recently,scientists have developed different models of land use/cover change(LUCC) depending on their objectives and background.However,no single model is able to capture all of key processes essential to explore land use change at different scales and make a full assessment of driving factors and impacts.In this paper,we would like to make our efforts to develop an approach in combination of SD model and CLUE-S model to deal with some shortcomings of the existing LUCC models and to properly address the processes at different scales that give rise to the land use dynamics.The approach presented in this study will be helpful to understand the complexity of land use change and provide scientific support for land use planning and managements,and also can be used as data source in scenario analysis of different hydrological processes based on different underlying surfaces of LUCC.The objectives of the study are:(1) to develop an SD model to calculate and predict demands for different land use types at the macro-scale as a whole during the period 2000~2035,(2) to improve the characterization and presentation of the land use change processes by developing a CLUE-S model that will transfer and allocate land demands from SD model to spatially explicit land use patterns at a finer spatial scale(at 500 m resolution in our study),and the Kappa value of the land use map simulation in 2000 is 0.86 and the Kappa value is 0.81 in 2005,and(3) to discuss the advantages and disadvantages of combining and integrating the current land use change models.The further objective of this study is to find the key driving factors of LUCC(e.g.,human factors,including social capital,different cultural types and so on),and these factors should be represented as different spatial maps and integrated into the model analysis to improve land use change modeling and projection.

[Liang Youjia, Xu Zhongmin, Zhong Fanglei.

Land use scenario analyses by based on system dynamic model and CLUE-S model at regional scale:A case study of Ganzhou district of Zhangye city.

Geographical Research, 2011, 30(3): 564-576.]

https://doi.org/10.3724/SP.J.1011.2011.00415      URL      [本文引用: 1]      摘要

Recently,scientists have developed different models of land use/cover change(LUCC) depending on their objectives and background.However,no single model is able to capture all of key processes essential to explore land use change at different scales and make a full assessment of driving factors and impacts.In this paper,we would like to make our efforts to develop an approach in combination of SD model and CLUE-S model to deal with some shortcomings of the existing LUCC models and to properly address the processes at different scales that give rise to the land use dynamics.The approach presented in this study will be helpful to understand the complexity of land use change and provide scientific support for land use planning and managements,and also can be used as data source in scenario analysis of different hydrological processes based on different underlying surfaces of LUCC.The objectives of the study are:(1) to develop an SD model to calculate and predict demands for different land use types at the macro-scale as a whole during the period 2000~2035,(2) to improve the characterization and presentation of the land use change processes by developing a CLUE-S model that will transfer and allocate land demands from SD model to spatially explicit land use patterns at a finer spatial scale(at 500 m resolution in our study),and the Kappa value of the land use map simulation in 2000 is 0.86 and the Kappa value is 0.81 in 2005,and(3) to discuss the advantages and disadvantages of combining and integrating the current land use change models.The further objective of this study is to find the key driving factors of LUCC(e.g.,human factors,including social capital,different cultural types and so on),and these factors should be represented as different spatial maps and integrated into the model analysis to improve land use change modeling and projection.
[33] 李娜, 张丽, 闫冬梅, .

基于CLUE-S模型的天津滨海新区土地利用变化情景模拟

. 遥感信息, 2013, 28(4): 62-68.

https://doi.org/10.3969/j.issn.1000-3177.2013.04.011      [本文引用: 1]      摘要

基于滨海新区1984年和2000年土地利用数据,通过Logistic回归分析该区土地利用变化驱动机制,应用CLUE-S模型预测滨海新区2006年 和2009年土地利用空间分布,采用2006年和2009年土地利用现状数据进行验证,预测精度达到75%以上,表明该模型在该区域有较好的适用性.在此 基础上进一步模拟2020年滨海新区在自然增长情景、经济快速发展情景和生态保护情景3种情景方案下的土地利用空间格局.结果表明经济发展情景下的模拟结 果与该区2020年的土地利用规划图的一致性最好,说明该研究区在未来仍处于快速发展时期,城市化过程加快,大量的农业用地被侵占、坑塘被填挖、生态系统 严重破坏.通过该预测模型模拟结果,可为海岸带生态脆弱区的保护以及适应新一轮规划修编中土地的可持续利用研究提供参考数据.

[Li Na, Zhang Li, Yan Dongmei, et al.

Land use change prediction for Tianjin Binhai new city.

Remote Sensing Information, 2013, 28(4): 62-68.]

https://doi.org/10.3969/j.issn.1000-3177.2013.04.011      [本文引用: 1]      摘要

基于滨海新区1984年和2000年土地利用数据,通过Logistic回归分析该区土地利用变化驱动机制,应用CLUE-S模型预测滨海新区2006年 和2009年土地利用空间分布,采用2006年和2009年土地利用现状数据进行验证,预测精度达到75%以上,表明该模型在该区域有较好的适用性.在此 基础上进一步模拟2020年滨海新区在自然增长情景、经济快速发展情景和生态保护情景3种情景方案下的土地利用空间格局.结果表明经济发展情景下的模拟结 果与该区2020年的土地利用规划图的一致性最好,说明该研究区在未来仍处于快速发展时期,城市化过程加快,大量的农业用地被侵占、坑塘被填挖、生态系统 严重破坏.通过该预测模型模拟结果,可为海岸带生态脆弱区的保护以及适应新一轮规划修编中土地的可持续利用研究提供参考数据.

/