国土资源遥感, 2018, 30(4): 82-89 doi: 10.6046/gtzyyg.2018.04.13

基于RS和GIS的山区重要生态服务功能区划定方法

李尧1, 叶成名,2, 谢强3, 梁莉1

1. 中国科学院成都山地灾害与环境研究所山地灾害与地表过程重点实验室,成都 610041

2. 成都理工大学地球探测与信息技术教育部重点实验室,成都 610059

3. 四川省环境科学保护研究院,成都 610041

Mapping the key ecological service regions of mountains based on remote sensing and GIS

LI Yao1, YE Chengming,2, XIE Qiang3, LIANG Li1

1. Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China

2. Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China

3. Sichuan Provincial Research Academy of Environmental Protection Sciences, Chengdu 610041, China

通讯作者: 叶成名(1978-),男,博士,副教授,主要研究方向为生态环境遥感和地质灾害遥感。Email:rsgis@sina.com

责任编辑: 张仙

收稿日期: 2017-05-11   修回日期: 2017-09-12   网络出版日期: 2018-12-15

基金资助: 国家重点研发计划项目“区域协同遥感监测与应急服务技术体系——多源遥感突发事件时空信息智能提取与服务”.  2016YFB0502603
四川省科技支撑计划项目“重要生态功能区县生态红线划定技术研究”共同资助.  2015SZ0198

Received: 2017-05-11   Revised: 2017-09-12   Online: 2018-12-15

作者简介 About authors

李尧(1991-),男,博士研究生,研究方向为生态遥感。Email:LY3400@sina.cn。 。

摘要

重要生态服务功能区划定与保护是社会经济可持续发展的重要基础。随着遥感(remote sensing,RS)与地理信息系统(geographic information system,GIS)技术的发展,对大区域生态环境的监测与生态服务功能快速评估技术日益成熟。提出生态保护效益计算方法,分析不同分割阈值情景下的生态保护效益系数,当得到最大生态保护效益系数时,划定重要生态服务功能区。将方法应用于典型的山地区域四川省万源市,对其进行3项生态服务功能(生物多样性保护、水土保持和水源涵养)快速评估,在归一化评估结果的基础上计算分析生态保护效益系数,最后在最优生态保护效益系数的情况下划定重要生态服务功能区。结果显示,研究区重要生态服务功能区划定面积为2 651.99 km 2,占总面积的65.4%; 其中主要集中分布在海拔高于800 m,坡度范围为[15°,35°)的区域,管理部门应加大该区域的保护力度。该方法可为山地区县级重要生态服务功能区与生态红线划定提供技术参考。

关键词: 山地区域 ; RS和GIS ; 生态保护效益系数 ; 重要生态服务功能区

Abstract

Mapping and protecting the key ecological service regions has been considered as the basis of the development of the society and economy. With the development of remote sensing (RS) and geographic information system (GIS) technology, this technique has been applied to monitoring the ecological environment and evaluating the ecological services. This paper proposes an approach to calculating the efficiency index of protection and mapping the key ecological service regions in the situation where the index is maximal. As a application, Wanyuan, the typical mountain region, was examined. The results show that the key ecological service regions cover a total area of 2 651.99 km 2, accounting for 65.4% of the study area. And it could be concluded that the key ecological service regions are distributed from steep slopes [15°,35°) where the elevation is more than 800 meters. The results suggest management to increase the efforts on protection. This paper can provide technical support for mapping the key ecological service regions of mountains.

Keywords: mountain region ; RS and GIS ; efficiency index of protection ; key ecological service regions

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本文引用格式

李尧, 叶成名, 谢强, 梁莉. 基于RS和GIS的山区重要生态服务功能区划定方法. 国土资源遥感[J], 2018, 30(4): 82-89 doi:10.6046/gtzyyg.2018.04.13

LI Yao, YE Chengming, XIE Qiang, LIANG Li. Mapping the key ecological service regions of mountains based on remote sensing and GIS. REMOTE SENSING FOR LAND & RESOURCES[J], 2018, 30(4): 82-89 doi:10.6046/gtzyyg.2018.04.13

0 引言

自党的十八大提出“生态文明建设”以来,中央及地方政府高度重视生态环境的保护,逐步开展了生态红线划定的试点工作。生态服务功能评估是生态红线划定的前提与基础,多年来一直是生态学研究领域的热点。1970年,关键环境问题研究(study of critical environmental problems,SCEP)报告中首次提出了“服务功能”的概念[1]; 后来Costanza等[2]对生态服务功能的概念和分类做了详细的介绍,主要分为供给服务、调节服务、文化服务以及维持其他类型服务所必须的支持服务4种类型。我国学者也对生态服务功能定义展开了大量研究,欧阳志云等[3]将生态服务功能定义为“生态系统与生态过程所形成及所维持的人类赖以生存的自然环境条件与效用”; 董全[4]定义生态服务功能为“自然过程所产生和维持的有关环境资源的各种条件和服务”。

近年来,在明确了生态服务功能定义的基础上,涌现了大量生态服务功能评估模型。基于遥感(remote sensing,RS)与地理信息系统(geographic information system,GIS)的评估模型发展最为迅速,RS为生态服务功能评估模型提供了水平、垂直、多光谱、多时段、多尺度的景观信息,减少了大量的野外调查工作[5],在生物多样性评估与监测中应用广泛[6,7,8]; GIS拥有强大的空间分析能力,为生态服务功能可视化和空间相关分析提供有效的解决手段,大量的学者将其应用于水土保持、水源涵养和碳存储功能等定量评估领域[9,10,11,12]。在评估模型研究日渐成熟的基础上,重要生态服务功能区划定成为了研究热点。香宝等[13]运用RS和GIS技术,采用专家打分的综合指标法对成渝区生态服务功能重要性进行分级评价,划定了重要生态服务功能区; 李月臣等[14]在单一服务功能评估结果基础上构建生态服务功能重要性指数,采用自然间断分割法确定了重要生态服务功能区。以往研究中大多只考虑生态服务功能的重要性,或者注重某斑块功能的高低,较少考虑保护的高效性和斑块在区域内的地位。国家原环境保护部2017年颁发的《生态保护红线划定技术指南》中将包含单项生态服务功能评估结果总量50%的区域划定为该项服务功能极重要区,合并叠加各项生态服务功能极重要区组成生态服务功能重要区[15]。鉴于此,本研究以四川省万源市为例,结合山区特殊性,借助RS和GIS技术,对主要生态服务功能,即生物多样性保护功能、水土保持功能和水源涵养功能等进行快速评估,在评估结果基础上计算生态保护效益系数,划定该研究区重要生态服务功能区,为生态保护方案的制定提供科学依据。

1 研究区概况及数据源

1.1 研究区概况

本文研究区万源市为四川省辖县级市,位于川东北大巴山腹心区,位置界于N30°39'~32°20',E107°28'~108°31'之间,下辖12个镇和40个乡,面积约为4 065 km2。研究区地处中国南北气候的分界线和嘉陵江、汉江分水岭地带,且位于川、陕、渝三省(市)的结合部,是进出四川的重要门户,也是连接川陕渝经济、文化、交通的重地,享有“万宝之源”的美誉(图1)。区内地形起伏大,沟壑纵横,潜在水土流失量大,多年平均降雨量为1 169 mm,降雨集中于每年7—8月份。区内物种丰富,拥有岩羊、珙桐和红豆杉等国家一、二级重点保护动植物; 同时拥有丰富的煤矿、石灰石和硒等重要资源。然而,随着国民经济的快速发展,动植物多样性保护与资源开采面临着不可避免的矛盾,因此有必要对辖区进行生态服务功能评估,以辅助资源的合理开发决策。

图1

图1   研究区范围

Fig.1   Coverage of study area


1.2 数据源

采用的遥感数据为美国地质调查局(United States Geological Survey,USGS)的Landsat8卫星数据,接收时间为2016年6月17日,云量低,质量较好。对影像做了几何纠正与基础处理后,根据欧阳志云等[16]提出的生态系统3级分类体系与研究区实况,实地调查采集区内不同生态系统的样本数据,通过监督分类得到了地表覆盖类型(图2(a))。

图2

图2   研究区遥感影像解译结果

Fig.2   Image interpretation of study area


为提高数据的准确性,利用已有的规划数据对解译结果进行了检查与调整,图2(b)—(c)示出部分区域规划调整前后的差异。降雨数据采用四川省127个气象站点的降雨量监测数据,其时间跨度为2010—2015年(分5期),含月降雨量。通过克吕金空间插值,得到全省降雨分布数据,然后计算降雨侵蚀力。数字高程模型(digital elevation model,DEM)来源于中国地理空间数据云,采样间隔为30 m。土壤数据来源于四川省土壤属性数据库,数据包含土壤中各种物质的含量,通过GIS技术转换成了栅格数据格式。

2 生态服务功能区划定方法

2.1 技术路线

中国疆域广阔,自然地理环境复杂,各种典型地貌的重要生态服务功能区划定技术方法仍处于探索研究阶段,暂无统一的技术参考。本研究针对四川省东北部山区的独特地理环境,结合RS和GIS技术优势,基于景观尺度对重要生态服务功能区进行识别研究。划定方法遵循管理的合理性与管理资源最优化分配原则,在功能区划定过程中既保证斑块的高聚集性,又保证在管理成本最小的情况下能达到最优生态效益,以便主管部门进行片区化管理监督与任务下达。总体方法流程如图3

图3

图3   重要生态服务功能区评估方法流程

Fig.3   Flowchart of methodology


2.2 生态服务功能评估模型

2.2.1 生物多样性保护功能评估方法

傅伯杰等[17]在郭中伟等[5]研究的基础上进行了大量研究,构建了适用于中国的生物多样性与生态系统服务评估指标体系。该体系以压力、状态和响应3个指标表征生态系统的生物多样性,因此研究通过栖息地及其胁迫源之间的相互作用来模拟栖息地生存环境质量,用该质量作为生物多样性定量评估指标。研究采用生态系统服务功能综合估价和权衡得失评估模型(integrated valuation of environmental services and tradeoffs,InVEST)中的生物多样性评估模块进行生物多样性保护功能的评估,该模型在国内外生态系统服务功能评估中得到了普遍应用,是主流生态评估模型,计算公式为[18]

Qxj=Hj1-DzxjDzxj+kz

式中: Qxj为生态系统类型 j中像元 x的生物多样性保护功能; Hj为生态系统类型 j的生境适宜性; Z为尺度参数,为常量; k为半饱和常数,若满足

1-DzxjDzxj+kz=0.5

则k值等于 Dxj值; Dxj为生态系统类型 jx的生境胁迫水平。Dxj计算公式为

Dxj=r=1Ry=1Yrwrr=1RwrryirxyβxSjr

式中: ry为像元 y的胁迫因子; R为胁迫因子种类数量; Yr为总像元数; wr为胁迫因子的权重,表明某一胁迫因子(如工矿交通)对所有其他生态系统的相对破坏力; βx为像元 x的可达性水平,1表示极容易达到; Sjr为生态系统类型 j对胁迫因子 r的敏感性,越接近1表示越敏感; irxyry对像元 x中生境的胁迫作用。irxy计算公式有线性和指数2种,分别为

irxy=1-dxydrmax
irxy=exp[-2.99drmaxdxy]

式中: dxyxy之间的直线距离; drmax为胁迫因子 r的最大影响距离。当胁迫因子对其他生态系统的胁迫作用受到障碍物(如山体、人工隔离带)影响较大时,常采用指数公式计算胁迫作用。

参照文献[19,20],本文将万源市胁迫因子 r属性设置如表1所示,各生态系统类型j对胁迫因子的敏感性Sjr设置如表2所示。

表1   生态胁迫因子

Tab.1  Ecological threat factor

胁迫因子最大胁迫距离/km权重衰退
居住地1.500.600
工矿交通3.000.750
耕地0.500.350

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表2   不同生态系统对胁迫因子的敏感性

Tab.2  Sensitivity of ecosystems to each threat factor

生态系
统类型
适宜生物
栖息分数
居住地工矿交通耕地
林地1.000.800.750.70
灌木丛0.800.8000.20
园地0.800.8000.20
水域1.000.9000.85
耕地0.40000.40
居住地0000
工矿交通0000
裸地0.100.200.400.10

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2.2.2 水土保持功能评估方法

水土保持功能评估模型采用改进的通用水土流失方程进行评价,计算公式为[21]

SC=SEp-SEa
SEp=R·K·LS
SEa=R·K·LS·C·P

式中: SEp为潜在土壤侵蚀模数,t/(hm2∙a); SEa为实际土壤侵蚀模数,t/(hm2∙a); SC为每年单位面积土壤保持量,t/(hm2∙a); LS为地形因子即坡长坡度因子; P为水土保持措施因子,取值范围为[0,1],参考已有的相关研究结果[22]来确定研究区内不同土地利用类型的 P值,即园地为0.1、耕地为0.01、林地为0.2、灌木丛为0.4、居住地为1、工矿交通为1、裸地为1、水域为0; R为降雨侵蚀力,MJ∙mm/(hm2∙h∙a); C为植被覆盖因子; K为土壤可蚀性因子,t∙hm2∙h/(hm2∙MJ∙mm)。

降雨侵蚀力 R计算公式为[23]

R=i=1121.735×101.5lgMi2M-0.818

式中: i为月份; Mi为月降水量; M为年降水量。当满足暴雨频发、降雨强度大的条件时,研究区的降雨侵蚀力较大,是诱发泥石流等自然灾害的主要因素。

CK的计算公式分别为[24,25]

C=[(1-NDVI)/2]1+NDVI

式中 NDVI为归一化植被指数;

K=(-0.01383+0.51575K0)×0.1317
K0={0.2+0.3exp[-0.0256ms(1-msilt/100)]}×[msilt/(mc+msilt)]0.3×{1-0.25orgC/[orgC+exp(3.72-2.95orgC)]}×{1-0.7(1-ms/100)/{(1-ms/100)+exp[-5.51+22.9(1-ms/100)]}}

式中: K0为修正前土壤可蚀性因子; ms为土壤砂粒百分含量; msilt为土壤粉粒百分含量; mc为土壤粘粒百分含量; orgC为有机碳百分含量。

坡长L与坡度α是水土流失的主控因子之一,地表起伏度变化越大的区域值越大。小尺度研究中可以通过实地测量获得,但万源市属于山区,地形复杂,是典型的大尺度复杂研究区域,因此根据数字高程确定坡长坡度因子LS的计算方法为[26]

LS=(L22.1)m×(65.41sin2α+4.56sinα+0.065)
m=0.5   α2.86°0.4   1.72°α<2.86°0.3   0.57°α<1.72°0.2   α<0.57°

2.2.3 水源涵养功能评估方法

水源涵养功能评估采用降水贮存量法,即用生态系统的蓄水效应来衡量其涵养水分的功能[28],具体为

Q=A·J·Y
J=J0·X
Y=Y0-Yg

式中: Q为与裸地相比,林地和耕地等生态系统涵养水分的单位面积增加量,mm/hm2; A为生态系统面积,hm2; J为研究区年均产流降雨量,mm; J0为研究区年均降雨总量,mm; X为研究区产流降雨量占降雨总量的比例,结合研究区的实际情况,参照文献[28],将X取值为0.6; Y为与裸地比较,生态系统减少径流的效益系数[28,29](表3); Y0为产流降雨条件下裸地降雨径流率; Yg为产流降雨条件下生态系统降雨径流率。

表3   生态系统减少径流的效益系数

Tab.3  Benefit coefficient of the ecological system to reduce runoff

Ⅱ级分类YⅡ级分类Y
林地0.39耕地0.16
灌木丛0.273居住地0.12
园地0.24工矿交通0.1
水域1裸地0.14

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2.3 生态保护效益系数

重要生态服务功能区的划定需考虑生态管理部门的工作高效性与管理工作效益最大化原则,本文提出了生态保护效益系数计算方法,根据评估结果设定合理的分割阈值,然后确定相应生态服务功能重要区的备选区域,最后将3个服务功能备选区域合并,对合并结果做小斑块的去除优化处理得到最终重要生态服务功能区。其中分割阈值通过分析生态保护效益系数曲线得到,曲线到达增长拐点时确定最佳分割阈值,生态保护效益系数计算公式为

EPi=T·IaPi·G

式中: EPi为生态保护效益系数; T为研究区总面积; Pi表示第i个重要生态服务功能区面积; G为研究区生态服务评估结果总量; Ia为重要区生态服务功能评估结果总量。

3 评估结果分析

3.1 生态服务功能评估结果

根据参数属性表与遥感解译的地表覆盖数据,得到需要的模型因子栅格数据,再将因子数据带入模型得到最终的服务功能评估(图4表4)。表4中水土保持和水源涵养量为各生态系统年总面积的量。

图4

图4   评估结果

Fig.4   Assessment results of study area


表4   水土保持、水源涵养功能统计

Tab.4  Statistics of soil conservation and water conservation

生态系统
类型
水土保持
量/万t
占水土保持总
量百分比/%
水源涵养
量/亿t
占水源涵养总
量百分比/%
林地737.2271.278.3471.82
灌木丛208.6820.172.1718.70
园地4.810.460.040.36
水域5.770.560.413.50
耕地50.164.850.373.14
居住地5.800.560.070.60
工矿交通6.110.590.040.32
裸地15.911.540.181.56

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生物多样性指数评估结果表明,在人迹罕见的地方生物多样性保护功能较高,在城镇区域生物物种结构单一,生物多样性保护功能较低。从3种功能综合分析可以看出,万源市生态服务功能较好,符合其实际情况。水源涵养功能最高值和最低值分别为342.77 mm/(hm2∙a)和83.22 mm/(hm2∙a),较低值主要分布在居住地和工矿交通等人工地表区域。水土保持能力方面,万源市土壤保持量普遍偏低,最高为98.23 t/(hm2∙a),主要分布在植被覆盖度较高的林地区域,最低为0 t/(hm2∙a),主要分布在低海拔、地势较平且不发生潜在水土流失的区域和水域。其中万源市西北部的高山区域潜在水土流失量较大,但由于近年来实施的环保政策提高了该区域植被覆盖度,改善了水土保持功能。通过野外实地调查,并用2011年全国第一次水利普查结果验证,评估结果符合实际情况。

评估结果归一化后设定不同的分割阈值从而计算生态保护效益系数,计算结果如图5所示。从图中可知,当生物多样性分割阈值为0.9时,生态保护效益系数最大,此时生物多样性保持功能重要区面积为1 509.76 km2; 水土保持分割阈值为0.4,效益系数增速变缓,得到最大生态保护效益系数,由此水土保持功能重要区面积为1 236.20 km2; 水源涵养分割阈值为0.9,得到最大生态保护效益系数,水源涵养功能重要区面积为1 669.81 km2

图5

图5   生态保护效益系数

Fig.5   Benefit coefficient of ecological protection


3.2 重要生态服务功能区空间分布特征

根据生态服务功能评估结果,计算分割阈值得到3种服务功能备选区,合并、优化后得到万源市重要生态服务功能区。将该重要区域与已有自然保护区与森林公园叠加分析,结果表明,花萼山、蜂桶山自然保护区和森林公园被纳入重要区的面积比例分别为88.28%,85.84%和97.61%,与重要生态服务功能区重合度较高(图6)。经过统计得知,重要生态服务功能区总面积为2 651.99 km2,占研究区面积65.4%; 主要分布在海拔800 m以上的区域。该海拔以上的区域植被覆盖度高,人口密度相对较小,许多区域很少有人类活动,未造成原始地表覆盖的破坏,适宜野生动植物的生存; 其次林地、灌木丛等植被根系对水土保持、水源涵养具有较强的保持功能,降低了河流下游发生滑坡、泥石流等地质灾害的风险。

图6

图6   重要生态服务功能区分布

Fig.6   Key regions of ecological service


将万源市按平坡、缓坡、斜坡、陡坡、急坡和险坡6种坡地类型统计(表5)。

表5   重要区不同坡地类型面积统计

Tab.5  Area of different slopes in key regions

坡地类型坡度范围/(°)面积/km2比例/%
平坡<589.463.37
缓坡[5,15)411.2715.51
斜坡[15,25)811.1230.59
陡坡[25,35)812.3730.63
急坡[35,45)414.9515.65
险坡≥45112.824.25

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其中重要生态服务功能区主要分布在[15°,35°)的斜坡和陡坡区域。该区域地表坡度较大,且地质结构不稳定,沿坡面向下的剪切力较大,在风侵蚀力等外力与暴雨冲刷的作用下极易发生滑坡、泥石流等地质灾害导致水土大量流失。

4 结论

1)RS和GIS技术凭借其快速宏观的优势,可使重要生态服务功能区划定工作量大幅度削减。结合地面验证和以往的研究成果,本文方法应用结果符合研究区内的实际情况,证明该方法可行。

2)通过控制因子参数和GIS缓冲区空间分析方法,可以遵循扰动区对环境破坏随距离增加而衰减的自然规律,从而得到区域内连续的生态服务功能评估值,本文提取的研究区四川省万源市高植被覆盖区域能提供80%以上生态服务功能,对维持生态平衡至关重要。

3)采用生态保护效益系数来控制重要生态服务功能区划定,可以提高生态环境保护过程中人力、财力资源配置的高效性与合理性,有效保障生态环境保护投入产出比。

本研究的重要生态服务功能区划定,基础是遥感技术对生态系统的解译与评估模型因子的参数设定。因此,如何提高因子的合理性与科学性至关重要。今后,需要结合实地生境情况和评估结果对比分析,更合理地调整遥感解译尺度和模型参数,以进一步提高重要生态服务功能区划定的精度。

志谢: 本文研究过程中参考了国家生态红线划定技术指南,评委专家及编辑老师提供了宝贵的修改意见, 在此表示衷心的感谢!

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DOI:10.11870/cjlyzyyhj201507006      URL     [本文引用: 1]

Assessment of ecosystem services are more and more applied in natural resource management. In this paper, we applied the FoPIA approch to build different land use/land cover scenarios in Poyang Lake wetlands by 2020. Then, we used the scenario parameters and land use/land cover indexes as input into the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST). InVEST has been used to assess a typical village's habitat quality of migratory birds which is the biodiversity index of species in Poyang Lake wetlands. Results showed that: under the conversation scenario, the scale of habitat quality of migratory birds will be increased and the threat of human activities will be decreased obviously, influenced by ecological engineering, all these will making the average of habitat quality of the migratory birds in the typical village of Poyang Lake wetlands reach 0.89, showing an increase of 27.0%. Under the economic developing scenarios, human activities will be expanded and some of the superior quality habitat will be occupied by agriculture lands, therefore, the average habitat quality will decrease to 0.57, showing a decline of 14.0%. Under the nature developing scenarios, human activities will not change so much, and the average habitat quality is 0.66, showing an increase of 1.5%.

Sun C Z, Zhen L, Wang C , et al.

Biodiversity simulation of Poyang Lake wetlands by invest model under different scenarios

[J]. Resources and Environment in the Yangtze Basin, 2015,24(7):1119-1125.

[本文引用: 1]

Tallis H T, Ricketts T, Guerry A D , et al.

InVEST 2.5.6 User’s Guide[Z]

Stanford:The Natural Capital Project, 2013.

[本文引用: 1]

Polasky S, Nelson E, Pennington D , et al.

The impact of land use change on ecosystem services,biodiversity and returns to landowners:A case study in the State of Minnesota

[J]. Environmental and Resource Economics, 2014,48(2):219-242.

DOI:10.1007/s10640-010-9407-0      URL     [本文引用: 1]

Land-use change has a significant impact on the world ecosystems. Changes in the extent and composition of forests, grasslands, wetlands and other ecosystems have large impacts on the provision of ecosystem services, biodiversity conservation and returns to landowners. While the change in private returns to landowners due to land-use change can often be measured, changes in the supply and value of ecosystem services and the provision of biodiversity conservation have been harder to quantify. In this paper we use a spatially explicit integrated modeling tool (InVEST) to quantify the changes in ecosystem services, habitat for biodiversity, and returns to landowners from land-use change in Minnesota from 1992 to 2001. We evaluate the impact of actual land-use change and a suite of alternative land-use change scenarios. We find a lack of concordance in the ranking of baseline and alternative land-use scenarios in terms of generation of private returns to landowners and net social benefits (private returns plus ecosystem service value). Returns to landowners are highest in a scenario with large-scale agricultural expansion. This scenario, however, generated the lowest net social benefits across all scenarios considered because of large losses in stored carbon and negative impacts on water quality. Further, this scenario resulted in the largest decline in habitat quality for general terrestrial biodiversity and forest songbirds. Our results illustrate the importance of taking ecosystem services into account in land-use and land-management decision-making and linking such decisions to incentives that accurately reflect social returns.

Abdo H, Salloum J .

Mapping the soil loss in Marqya basin:Syria using RUSLE model in GIS and RS techniques

[J]. Environmental Earth Sciences, 2017,76(3):114.

DOI:10.1007/s12665-017-6424-0      URL     [本文引用: 1]

Soil loss by water is the most important eco-environmental threat in Marqya basin without conservation measures. Mapping the soil loss is a useful and necessary in planning and maintenance works in Ma

杨冉冉, 徐涵秋, 林娜 , .

基于RUSLE的福建省长汀县河田盆地区土壤侵蚀定量研究

[J]. 生态学报, 2013,33(10):2974-2982.

DOI:10.5846/stxb201205130701      URL     [本文引用: 1]

以RS、GIS和RUSLE模型为主要技术,选取典型的土壤侵蚀区福建省长汀县河田盆地区为研究区,通过对模型因子的合理选择,估算了该地区1988年、1998年和2010年的土壤侵蚀量,实现土壤侵蚀状况的定量评价和动态监测.结果表明:在1988年至2010年期间,研究区土壤侵蚀状况得到明显改善,平均土壤侵蚀模数由4259.11 t·km-2·a-1下降为1280.09 t·km-2·a-1,年侵蚀量由252.42万t下降至75.87万t;中度及其以上侵蚀面积由176 km2减少至62.69 km2,微度侵蚀面积由225.85 km2增加至358.9 km2.研究结果说明近22年来针对长汀河田盆地区土壤侵蚀的治理所采取的措施是卓有成效的.长汀河田盆地区水土流失进一步重点治理的区域应集中在盆地中心及其西北部等地区的高程低于400 m、植被覆盖度为20%-50%的地区.

Yang R R, Xu H Q, Lin N , et al.

RUSLE-based quantitative study on the soil erosion of the Hetian Basin area in Changting County,Fujian Province,China

[J]. Acta Ecologica Sinica, 2013,33(10):2974-2982.

[本文引用: 1]

李晓松, 吴炳方, 王浩 , .

区域尺度海河流域水土流失风险评估

[J]. 遥感学报, 2011,15(2):372-387.

[本文引用: 1]

Li X S, Wu B F, Wang H , et al.

Regional soil erosion risk assessment in Hai Basin

[J]. Journal of Remote Sensing, 2011,15(2):372-387.

[本文引用: 1]

吴昌广, 李生, 任华东 , .

USLE/RUSLE模型中植被覆盖管理因子的遥感定量估算研究进展

[J]. 应用生态学报, 2012,23(6):1728-1732.

URL     [本文引用: 1]

Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor-over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.

Wu C G, Li S, Ren H D , et al.

Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data:A review

[J]. Chinese Journal of Applied Ecology, 2012,23(6):1728-1732.

[本文引用: 1]

宋春风, 陶和平, 刘斌涛 , .

长江上游地区土壤可蚀性空间分异特征

[J]. 长江流域资源与环境, 2012,21(9):1123-1130.

[本文引用: 1]

Song C F, Tao H P, Liu B T , et al.

Spatial distribution characteristics of soil erodibility K value in the upper Yangtze River

[J]. Resources and Environment in the Yangtze Basin, 2012,21(9):1123-1130.

[本文引用: 1]

张宏鸣, 杨勤科, 刘晴蕊 , .

基于GIS的区域坡度坡长因子提取算法

[J]. 计算机工程, 2010,36(9):246-248.

DOI:10.3969/j.issn.1000-3428.2010.09.087      URL     [本文引用: 1]

为提高基于地理信息系统的区域土壤侵蚀研究、水土保持环境效应评价、流域水文分析等的应用效率,设计新的坡度坡长(LS)因子算法,利用正向-反向遍历算法取代原累积坡长算法,以获取区域尺度下的LS因子.实验结果表明,在计算精度允许的范围内,新算法使计算机运行效率有较大幅度的提高.

Zhang H M, Yang Q K, Liu Q R , et al.

Regional slope length and slope steepness factor extraction algorithm based on GIS

[J]. Computer Engineering, 2010,36(9):246-248.

[本文引用: 1]

张彪, 李文华, 谢高地 , .

森林生态系统的水源涵养功能及其计量方法

[J]. 生态学杂志, 2009,28(3):529-534.

为全面认识与正确评价森林生态系统的水源涵养功能,本文探讨了其概念、表现形式及其计量方法,认为:1)森林的水源涵养功能是一个动态、综合的概念,随着人们对森林水文作用认识的不断深入,其内涵不断丰富扩大,因此森林的水源涵养功能概念存在狭义和广义之分;2)森林的水源涵养功能(广义)有多种表现形式,包括拦蓄降水、调节径流、影响降雨和净化水质等,不过其具体表现形式与研究对象、研究目的以及研究尺度有关;3)目前森林拦蓄降水功能的计量方法主要有土壤蓄水能力法、水量平衡法、年径流量法和多因子回归法等8种,不过这些方法都存在一定的局限性,实际应用中需要综合考虑。

Zhang B, Li W H, Xie G D , et al.

Water conservation function and its measurement methods of forest ecosystem

[J]. Chinese Journal of Ecology, 2009,28(3):529-534.

赵同谦, 欧阳志云, 郑华 , .

中国森林生态系统服务功能及其价值评价

[J]. 自然资源学报, 2004,19(4):480-491.

DOI:10.3321/j.issn:1000-3037.2004.04.010      URL     [本文引用: 3]

森林生态系统对维持我国自然生态系统格局、功能和过程具有特殊的生态意义.客观衡量森林生态系统的服务效能,对于森林资源保护及其科学利用具有重要意义.论文在分析森林生态系统服务功能的基础上,根据其提供服务的机制、类型和效用,把森林生态系统的服务功能划分为提供产品、调节功能、文化功能和生命支持功能四大类,建立了由林木产品、林副产品、气候调节、光合固C、涵养水源、土壤保持、净化环境、养分循环、防风固沙、文化多样性、休闲旅游、释放氧气、维持生物多样性13项功能指标构成的森林生态系统评价指标体系,并对其中的10项功能指标以2000年为评价基准年份进行了初步评价.结果表明,森林生态系统10类生态系统服务功能的总生态经济价值为14 060.05×108元/a,其中直接价值和间接价值分别为2 519.45×108元和11540.60×108元,间接价值是直接价值的4.6倍.研究认为,森林生态系统服务功能及其价值评价工作应进一步加强森林生态系统服务功能机制的基础研究和不同尺度下空间数据的耦合和应用方法研究.

Zhao T Q, Ouyang Z Y, Zheng H , et al.

Forest ecosystem services and their valuation in China

[J]. Jounal of Natural Resources, 2004,19(4):480-491.

[本文引用: 3]

何艳虎, 陈晓宏, 林凯荣 , .

东江流域近50年径流系数时空变化特征

[J]. 地理研究, 2014,33(6):1049-1058.

[本文引用: 1]

He Y H, Chen X H, Lin K R , et al.

Temporal and spatial characteristics of runoff coefficient variations in the Dongjiang Basin during 1964—2012

[J]. Geographical Research, 2014,33(6):1049-1058.

[本文引用: 1]

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