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地理学报, 2022, 77(3): 547-558 doi: 10.11821/dlxb202203004

理论与方法探索

青藏高原放牧强度空间化方法与应用

胡晓阳,1,2, 王兆锋,1,2, 张镱锂1,2,3, 宫殿清1,2

1.中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 100101

2.中国科学院大学,北京 100049

3.青藏高原地球系统与资源环境国家重点实验室,北京 100101

Spatialization method of grazing intensity and its application in Tibetan Plateau

HU Xiaoyang,1,2, WANG Zhaofeng,1,2, ZHANG Yili1,2,3, GONG Dianqing1,2

1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment, Beijing 100101, China

通讯作者: 王兆锋(1976-), 男, 山东齐河人, 博士, 副研究员, 主要从事青藏高原土地利用变化及其土壤环境效应研究。E-mail: wangzf@igsnrr.ac.cn

收稿日期: 2020-09-30   修回日期: 2022-01-24  

基金资助: 第二次青藏高原综合科学考察研究(2019QZKK0603)
国家自然科学基金项目(41861134038)
中国科学院战略性先导科技专项(XDA20040201)

Received: 2020-09-30   Revised: 2022-01-24  

Fund supported: The Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0603)
Program of National Natural Science Foundation of China(41861134038)
The Strategic Priority Research Program of Chinese Academy of Sciences(XDA20040201)

作者简介 About authors

胡晓阳(1997-), 女, 山东临沂人, 硕士生, 主要从事土地利用研究。E-mail: huxiaoyang321@163.com

摘要

人类活动强度空间化是分析人类活动区域差异及其变化过程的基础,也是准确辨识土地变化驱动因素、合理调控人类活动的科学依据。当前人类活动强度研究多以数理方法对代用指标进行空间化,缺乏对人类活动机理过程的反映,也制约了人类活动强度空间异质性的展现。本文以青藏高原放牧活动为研究对象,在综合放牧喜好和草地管理策略的基础上,从放牧行为机理的角度,提出了区分放牧区与非放牧区的方法;以放牧密度衡量草地整体放牧压力,以放牧概率刻画放牧活动的空间差异,构建了区域放牧强度空间化模型。并以高原典型牧业县—泽库县为例,基于乡镇级牧业数据和自然地理基础信息进行了实证研究。结果表明：空间化结果较好反映了泽库县放牧强度的空间特征;直接指标的选取和客观的赋值方法提高了放牧强度空间化结果的准确性;比现有研究结果更能体现出县域内放牧强度的空间差异,且放牧强度变化过程与同期NDVI值变化具有较好的耦合性。本文为人类活动空间化方法研究提供了有益参考。

关键词： 人类活动; 放牧强度; 放牧密度; 放牧概率; 青藏高原

Abstract

Spatialization of human activity intensity is the basis for examining regional differences of human activities and its change process, and it is also the scientific basis for accurately identifying the driving factors of land change and rationally regulating human activities. The current research on human activity intensity mostly uses mathematical methods to spatially assign indirect indicators, which lacks the reflection of the mechanism of human activity and also restricts the display of the spatial heterogeneity of human activity intensity. This paper took grazing activity on the Tibetan Plateau as the research object in an attempt to spatialize grazing intensity. First, based on grazing preferences and grassland management strategies, and from the perspective of grazing mechanism, we proposed a method to distinguish grazing areas and non-grazing areas; then, we used grazing density to measure the overall grazing pressure of grassland, and used grazing probability to describe the spatial difference of grazing pressure. In addition, we developed a spatial model of regional grazing intensity according to grazing density and grazing probability. As a case study, we used this model to spatialize grazing intensity of Zeku County, a typical pastoral area on the Tibetan Plateau, based on township-level animal husbandry data and basic physical geographic information. The results showed that the spatial model of grazing intensity we developed can reflect the spatial characteristics of grazing activity well, and the selection of direct indicators and objective assignment methods improve the accuracy of the spatialization results. It is also found that the results of this paper can more clearly reflected the spatial difference of grazing intensity within the county than the previous research results, and the change process of grazing intensity had a better coupling with the change of NDVI value over the same period. This paper could provide a useful reference for the research on spatialization methods of human activities.

Keywords： human activity; grazing intensity; grazing density; grazing probability; Tibetan Plateau

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本文引用格式

胡晓阳, 王兆锋, 张镱锂, 宫殿清. 青藏高原放牧强度空间化方法与应用. 地理学报, 2022, 77(3): 547-558 doi:10.11821/dlxb202203004

HU Xiaoyang, WANG Zhaofeng, ZHANG Yili, GONG Dianqing. Spatialization method of grazing intensity and its application in Tibetan Plateau. Acta Geographica Sinice, 2022, 77(3): 547-558 doi:10.11821/dlxb202203004

1 引言

人类活动强烈影响陆地表层格局,Crutzen在分析人类活动对自然环境影响的基础上,提出人类世的概念[1]。一方面,人类活动对陆地表层的影响范围不断加大,其影响的陆地区域已经占到了全球无冰区域的3/4[2];另一方面,人类活动的影响程度不断加深,人类活动改变地球表面的土地利用和土地覆盖状况[3]、生态系统的结构和功能[4]、景观生态格局和景观异质性[5]、造成陆地生态系统受损、生物多样性下降、生态环境压力加大。因此科学量化人类活动强度,是正确认识人类活动影响、合理调控人类活动的科学基础,也是深入剖析土地变化机制的前提。

人类活动强度空间化方法是近年来人类活动强度评估的重要内容之一。早期的人类活动强度空间化以行政单元为基础[6],随着遥感和GIS技术的发展,栅格数据被应用[7],人类活动强度空间化更加精细和准确。近期人类活动强度空间化方法多选取间接指标进行赋值,叠加得到总的人类活动强度[8,9]。不同方法间的区别主要在于指标的选择和赋值上,研究的空间尺度越小,一般选择的指标越多,更能反映当地特征[10,11,12];指标赋值多基于专家知识和经验知识[13,14],主观性较强,还有一些学者将所有指标纳入数学模型统一赋值[15,16]。

目前,已有很多方法被应用于评估或反映人类活动强度。Sanderson等[17]用人类足迹来指示人类活动对自然环境的压力,他选取了人口与基础设施中的4类指标（人口密度、土地转换、可达性、电力基础设施）,对4类指标的每个因子进行标准化赋值,并在1 km2的网格上叠加各因子值得到人类足迹指数。Theobald等[18]改进了人类足迹法,提出人类改造度模型。该模型在对人类活动各因子赋值时,考虑不同因子作用程度的差异,为每个因子赋予一定的权重值,进而得到了90 m分辨率的人类改造度数据。在国内,李士成等[19]参考了人类足迹法,根据高原人类活动特点,选取了人口密度、土地利用/土地覆被、道路和放牧密度4类指标,基于县级数据,采用专家打分法赋值,得到了青藏高原县域和1 km空间分辨率的人类活动强度值;李兰晖[16]以人类改造度模型为框架,选取了人类居住区、农牧业、交通运输、工矿和能源生产、电力基础设施5类人类活动压力源的12个相关指标,利用随机森林模型和模糊叠加模型求和,得到了1 km空间分辨率的青藏地区人类活动强度数据集。这些人类活动强度空间化结果,体现了人类活动的空间差异。但这些方法中评估指标的赋值多基于专家知识或经验模型,通过数理方法插值,缺乏对人类活动机理的理解和表达。比如说放牧强度,现实中并不是所有的草地都用作牧场,即便是适宜放牧的草场,也有部分草场因为环境保护或者生态建设的需要而禁止放牧[20];且放牧草场也不是进行同等程度的放牧,距离牧户或者离牧道较近的区域往往承载了更大的放牧压力[21]。而现有的空间化方法难以体现人类活动在空间分布上的这种机理性差异。

本文以青藏高原草地上的放牧活动为例,探讨人类活动强度空间化方法,刻画草地内部放牧强度的空间差异。并基于乡镇级牧业数据和自然地理基础信息,在高原典型牧业县—青海省泽库县进行实证研究。以期构建基于人类活动机理的高原放牧强度空间化方法,为土地变化机制分析提供参考。

2 模型构建

人类活动强度是指一定区域受人类活动影响而产生的扰动程度[6],放牧强度可以看作区域放牧活动对草地产生的扰动程度[22]。牧民的放牧活动是随草地单元移动的过程,但由于每个单元的草地资源状况各异,使不同单元放牧的优先等级存在差异[22],导致放牧对草地产生的扰动程度不均一。为此,构建放牧概率指标来反映区域内不同单元的放牧可能性,并引入放牧概率偏差,以此表示各草地单元放牧压力相对于区域整体放牧压力的偏离程度。

放牧强度可看作放牧密度与放牧概率偏差的乘积。

GIi=DiBi

式中：GIi代表第i个栅格单元的放牧强度;Di代表第i个栅格单元所在行政区域的放牧密度;Bi代表第i个栅格单元的放牧概率偏差。

2.1 放牧密度

放牧密度是单位放牧面积上的牲畜数量,因放牧活动要受一定的范围限制,放牧面积不是区域所有草地面积的总和。首先,青藏高原放牧草场一般会按季节划分,不同季节的放牧活动多只在对应草场内进行,随着季节演替,牧民对牲畜转场[23]。因此,要先明确各季节放牧草场的空间分布及居民点位置。其次,放牧活动多围绕居民点展开[24],在国家相关政策的实施下,牧民放牧多采取定居放牧的形式,以居民点为原点,向四周散开。再者,政府为保护草原生态,划定了一定的禁牧区域,对禁牧区之外的草场实施草畜平衡管理[25],放牧面积等同于草畜平衡面积。草原生态保护补助奖励政策实施之前,放牧范围为区内所有可利用草地;政策实施后,放牧范围以草畜平衡面积为控制,依据一定规则,逐步拓扑筛选确定。

在明确了放牧范围后,区域放牧密度按如下公式计算：

D=NS=∑1kraLaS

式中：D代表放牧密度;N代表转换为统一标准的牲畜数量总数;S代表放牧面积;k为该区域牲畜种类数;ra为第a种牲畜标准羊单位折算系数;La为第a种牲畜的数量。

2.2 放牧概率

放牧概率是指在放牧范围内各个草地单元上进行放牧的可能性大小。在放牧过程中,牲畜总会采用最经济有效的策略获取最大的采食效率[26],即牲畜会优先采食资源条件好且较易获取的草地植被,此草地上的放牧概率大。因此,放牧概率的大小可以根据草地资源的放牧适宜性来确定。

2.2.1 影响因子选择 草地放牧适宜性主要依据草地植被状况的好坏和草地植被获取的难易程度来确定[26]。土地覆被数据中草地被分为高覆盖度草地、中覆盖度草地、低覆盖度草地3种类型,这直接反映了草本植物的生长状况。此外,植被的生长与所处的地理环境密切相关,地貌是影响区域小气候、水文、土壤等生境要素的主要因子[27],本文选取海拔、坡度、坡向等地貌因子作为放牧概率的影响因子。放牧活动也是人为规划的过程[28],在放牧轨迹跟踪实验中观察到,牧民放牧一般从居民点出发,沿着道路转移牧群[29],牲畜除了采食和行走还需饮水[30],所以放牧概率也要考虑距居民点、道路、河流的距离,这影响着草地植被获取的难易程度。

基于上述分析,本文选取草地类型、海拔、坡度、坡向、距居民点的距离、距道路的距离、距河流的距离7个因子,作为影响放牧概率的因子。

2.2.2 因子特征分析 基于影响因子的特点和共性,将因子归为限制因素、便利因素和偏好因素3类。

限制因素包括海拔和坡度。海拔是制约草地植被生长的因子,海拔每升高100 m,年平均气温约下降0.56 ℃[27],水分条件、辐射强度、空气流动、土壤性质等亦随海拔不同而不同,从而使植被种类和生长状况呈垂直性变化[31]。坡度是同时制约植被生长和植被获取的因子。坡度影响太阳光照时长[32]、积温、单位面积的降水量[27],以及土壤保水保肥能力,高坡度区更容易出现草地退化。另一方面,坡度大,牲畜行走困难,采食难度加大,放牧概率小。

便利因素包括至居民点、道路和河流的距离。这些因子影响草地植被获取的难易。距居住地越近的草地,牲畜采食越方便。牲畜一般沿着交通路线迁移,越靠近道路的草地,越容易被牲畜采食[29]。牧群在采食过程中需要补充水分[30],草地越靠近河流,越便于牲畜在采食的同时补充水分。

偏好因素包括草地类型和坡向。类型较好的草地,草被生长茂密,覆盖度高,且水分条件好,往往被优先选择放牧[33]。坡向不同,光照时间、蒸散量、水分等自然条件均有差异[27, 34]。平地太阳辐射较强、光照时间长,且便于牲畜活动,平地的放牧适宜性高于坡地。

2.2.3 因子量化 为确定放牧概率的大小,需将影响因子量化。研究表明,草地变化与海拔、坡度、坡向、到居民点距离等存在一定的函数关系[35,36,37],可参考这些函数关系将放牧概率影响因子量化。放牧概率就是单元影响因子的影响力总值与区内所有单元影响力总值加和的比值,放牧概率偏差是这个单元的放牧概率与整个区域草地单元的放牧概率均值的比值。

计算放牧概率的公式如下：

Pi=MiM=Mi∑1nMi=∑1tuib∑i=1n∑b=1tuib

式中：Pi代表第i个栅格单元的放牧概率;Mi代表第i个栅格单元的影响因子的影响力总值;M代表行政区内放牧范围中所有草地栅格单元的影响力总值;n代表行政区域内放牧范围中草地栅格单元的数量;t代表影响因子的数量;uib代表第i个栅格单元第b个影响因子的影响力值。

计算放牧概率偏差的公式如下：

Bi=PiP¯=Pi∑1nPin=nPi∑1nMiM=nPi

式中：Bi代表第i个栅格单元的放牧概率偏差;Pi代表第i个栅格单元的放牧概率; P¯代表行政区内放牧范围中所有草地栅格单元的放牧概率的均值;n代表行政区内放牧范围中草地栅格单元的数量;Mi代表第i个栅格单元的影响因子的影响力总值;M代表行政区内放牧范围中所有草地栅格单元的影响力总值的加和。

2.3 放牧强度

经过上述分析,放牧范围中每个草地栅格单元的放牧强度可以表示为如下公式：

GIi=∑1kraLaS×n∑1tuib∑i=1n∑b=1tuib

式中：GIi代表第i个栅格单元的放牧强度;k为该区域牲畜种类数;ra为第a种牲畜标准羊单位折算系数;La为第a种牲畜的数量;n代表行政区域内放牧范围中草地栅格单元的数量;t代表影响因子的数量;uib代表第i个栅格单元第b个影响因子的影响力值。

3 实例分析

3.1 数据来源及处理方法

3.1.1 泽库县概况 泽库县位于青海省东南部（34°45′N~35°32′N, 100°34′E~102°08′E)。土地总面积为665853 hm2,其中草场面积652527 hm2,占土地总面积的98%。主要草地类型为山地草甸和高寒草甸,二者分别占全县草地总面积的46.65%、26.33%[38]。

该县是青海省的典型牧业县,全县辖6乡2镇,64个行政村,2018年末乡村人口6.64万,占总人口的85.28%[39]。畜牧业在全县国民经济中占主导地位,2018年地区生产总值为16.27亿元,其中,牧业总产值达8.53亿元。全县各类牲畜年未存栏数为53.96万头（只、匹）,其中,牛22.40万头,马1.02万匹,山羊0.28万只,绵羊30.26万只。牧民放牧的主要形式为定居放牧。

泽库县可利用草地面积为608747 hm2,自2011年开始实施草原生态保护补助奖励政策,对330080 hm2的可利用天然草地进行禁牧封育,放牧草场为278667 hm2,实施草畜平衡管理。

3.1.2 数据来源 DEM数据下载于地理空间数据云,空间分辨率为30 m;居民点、道路、河流等矢量数据来源于中国基础地理信息数据库,比例尺为1:25万,其中居民点结合Google Earth高清影像进行了核查补充。土地利用数据为中科院资源环境科学与数据中心的多期土地利用土地覆被数据集,时间跨度为2000—2018年,空间分辨率为30 m。年末牲畜存栏量来自于泽库县统计局编制的《泽库县国民经济统计资料》,草畜平衡面积由泽库县农牧科技局提供。植被指数数据采用NASA发布的EOS/MODIS数据中的MOD13Q1级数据产品,空间分辨率为250 m,时间序列为2000—2018年,采用最大值合成法（Maximum Value Composites, MVC）获取年最大NDVI值,用以表示当年植被生长的最好状况。

3.1.3 植被覆盖变化趋势分析 一元线性回归可以分析每个像元的年最大NDVI值的年际变化趋势,将线性回归方程的斜率视为植被覆盖变化的年际趋势率（Slope）[40]。计算公式如下：

Slope=n×∑1ni×Mi-∑1ni∑1nMin×∑1ni2-∑1ni2

式中：Slope为年最大NDVI与时间变量拟合的一元线性回归方程的斜率;n为年跨度;i为时间变量;Mi表示第i年的最大NDVI值。Slope > 0则说明年最大NDⅥ在n年间的变化呈现增加趋势,表明植被覆盖呈变好的趋势,反之则是减少,表明植被覆盖呈变差的趋势。

3.2 放牧强度计算

3.2.1 放牧范围的确定 2000年泽库县对天然草地进行划分并全部实现承包到户,放牧方式由游牧转为定居放牧,全县的放牧范围即为各乡镇的所有可利用天然草地。2011年起,泽施库县全面实草原生态保护补助奖励政策,以草畜平衡面积作为放牧草场面积（图1）。

图1

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图1   草原生态保护补助奖励政策实施前后放牧活动空间分布

Fig. 1   Spatial distribution of grazing activity before and after Grassland Ecological Compensation Policy (GECP) implementation


3.2.2 放牧概率的计算 青海省达日县长时间的放牧行为学研究结果表明[21],放牧概率与影响因子间存在较好的函数关系。泽库县和达日县在气候、草地类型、人口结构等方面具有一定的相似性,本文参考达日县草地变化与因子间的拟合函数为泽库县各影响因子量化赋值（表1）,并将各影响因子值进行叠加,计算放牧概率偏差（图2）。

表1   放牧概率影响因子分类赋值

Tab. 1  Classification and assignment of grazing probability influencing factors

因素因子分类方法函数
限制坡度将坡度≤ 35°的坡地分为10类,分别为0°~3°、3°~5°、5°~8°、8°~12°、12°~15°、15°~18°、18°~21°、21°~25°、25°~30°、30°~35°,坡度由小到大依次标号y=-0.0047x2+0.0503x+0.2279
海拔对4700 m以下的海拔高度以100 m为间隔分带,由泽库县海拔高度2789~4700 m划分为16个高程带,由低到高依次标号y=0.00009x4-0.0032x3+0.0391x2-0.1756x+0.6073
便利居民点以100 m为间隔在放牧范围内分带,由近及远依次标号y=-0.0175lnx+0.3808
道路以100 m为间隔对离道路最远距离5 km内分带,划分为50个带,由近及远依次标号y=-0.0358lnx+0.4132
河流以100 m为间隔对离河流最远距离1 km内分带,划分为10个带,由近及远依次标号y=0.0003x4-0.0056x3+0.0403x2-0.1295x+0.4375
偏好草地
类型		将高覆盖度草地、中覆盖度草地、低覆盖度草分别标记为1、2、3为保持同一数量级,1、2、3分别对应0.66667、0.50000、0.33333
坡向坡度≤ 3°的为平地,标号为1,将坡向分为东南、南、西南、北、东北、东、西和西北8类,分别标为2、3、4、5、6、7、8、9y=0.0021x4-0.0476x3+0.3469x2-0.8814x+0.8753

注：>35°的坡地因土层薄,植物生长差,石砾含量高,不宜放牧,不将其作为此次研究重点,故在≤ 35°的坡度范围内分类。泽库县最高海拔为4961 m,因海拔高于4700 m的区域分布着大面积的裸岩石砾地,植被稀疏,不宜放牧,不将其作为此次研究重点,故在低于4700 m的范围内分带。距河流、道路最远距离的确定依据文献[21, 23]的研究中进行放牧轨迹跟踪实验所得的数据,将分类标号分别带入函数进行赋值。

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图2

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图2   泽库县草原生态保护补助奖励政策实施前后放牧概率偏差空间分异

Fig. 2   Spatial variation of grazing probability deviation in Zeku County before and after GECP implementation


3.2.3 放牧密度的计算 因不同牲畜品种在草地上日采食量不同,需要将不同品种的牲畜折算成统一单位,依据2015年农业部颁布的《天然草地合理载畜量的计算》标准,将各类牲畜数量统一换算为羊单位。

3.3 放牧强度空间化结果分析

根据构建的放牧强度模拟模型得到2000年、2005年、2010年、2011年、2015年、2018年6个年份的泽库县放牧强度空间分布图（图3）。从研究结果看,泽库县放牧强度空间差异明显,北部乡镇整体放牧强度较低,中南部乡镇整体放牧强度较高,每个乡镇范围内,放牧强度也存在较大空间差异,放牧强度高的区域多沿河流和道路呈条带状分布或呈块状集中连片分布。

图3

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图3   2000—2018年泽库县放牧强度空间分布

Fig. 3   Spatial distribution of grazing intensity before and after GECP implementation


4 讨论

4.1 空间化结果的佐证

归一化植被指数（Normalized Difference Vegetation Index, NDVI）多用来反映地区植被生长状况[41,42,43]。分析泽库县2000年、2010年、2011年和2018年NDVI与放牧强度的对应关系（图4、表2）发现,2000—2010年泽库县放牧强度整体呈下降趋势,放牧强度均值减少0.414羊单位/hm2,而同期泽库县NDVI整体呈上升趋势,NDVI升高的单元格占99.14%。2000—2010年泽库县牲畜数量减少,可利用草地放牧压力减小,有利于草地植被的恢复和生长,这是草地NDVI上升的主要原因。2011—2018年泽库县放牧强度整体同样呈下降趋势,放牧强度均值减少0.789羊单位/hm2,此时,泽库县NDVI整体有所上升,NDVI增加的单元格占72.79%。放牧区NDVI值增加的面积占73.37%,高于非放牧区72.09%的对应比例。可以看出,放牧强度出现一定变动时,NDVI也出现对应变化,放牧强度变化与草地植被NDVI变化在时间和空间上均具有较好的一致性,且NDVI在放牧区变化的幅度更为明显,说明放牧强度变化可以在一定程度上解释NDVI的变化。

图4

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图4   2000—2010年和2011—2018年泽库县NDVI年际变化

Fig. 4   Trend of NDVI change in Zeku County from 2000 to 2010 and 2011 to 2018


表2   泽库县放牧强度与NDVI变化情况

Tab. 2  Changes of grazing intensity and NDVI in Zeku County

时段放牧强度变化NDVI变化
2000—2010年下降0.414羊单位/hm2上升占99.14%
2011—2018年下降0.789羊单位/hm2上升占72.79%

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4.2 与相关研究的比较

李兰晖[16]对青藏高原牲畜存栏量进行了空间化研究。截取其泽库县年末牲畜存栏量空间化结果发现,其年末牲畜存栏量与本文放牧强度结果在空间上具有较好的一致性（图5）：放牧强度和牲畜存栏量较大的区域主要集中在泽库县南部,而北部相对较低。

图5

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图5   泽库县年末牲畜存栏量空间化结果[16]

Fig. 5   The year-end livestock inventory in Zeku County[16]


相比而言,本文对放牧强度的空间细节展示更为细致。李兰晖的年末牲畜存栏量空间化研究基于县级的牲畜量数据,采用的是1 km分辨率的空间数据[16];而本文的放牧强度空间化牲畜量数据精确到乡镇级,采用的是30 m分辨率的空间数据。再者,李兰晖在研究中未考虑放牧草场和非放牧草场[16];本文结合草原生态保护补助奖励政策实施,辨识了放牧区和非放牧区。另外,本文在考虑放牧密度的同时,设置了放牧概率和放牧概率偏差,以此区分各栅格单元放牧强度的差异。

4.3 不足与展望

由于无法获取季节草场的分界图件及详细数据,本文未考虑季节草场居民点转场迁移问题。在实地调研过程中,通过牧户访谈,发现泽库县放牧草场有冬季草场和夏季草场之分,虽然不同牧户的冬、夏草场距离有远有近,但各乡镇牧户转场时间基本一致。未来可根据转场时间,结合NDVI值变化,确定季节草场的大致分界线,以更准确地确定不同时段的放牧范围。

本文中放牧居民点主要依据1:25万基础地理数据确定,并结合Google Earth高清影像核查补充,虽然基本上反映了研究区居民点总体状况,但由于区域内居民点分布相对零散,可能有部分放牧居民点未统计在内。另外,根据草地质量与牲畜数量的差异,不同乡镇实际放牧距离可能也不完全一致。这些都有待于进一步深入调研,以提升放牧强度空间化结果的准确性。

另外,本文仅探讨了高原牧区典型人类活动形式—放牧活动强度的空间化方法,而人类活动还涉及到耕作、城镇建设、交通、旅游、工矿开发等其他活动形式,如何全面掌握区域人类活动强度状况,需要对不同人类活动形式进行综合评估。希望本文能为相关人类活动的空间化提供参考和借鉴。

5 结论

本文以分布在青藏高原草地上最主要的人类活动形式—放牧活动为研究对象,依据放牧行为与居住点的关系,结合放牧管理策略,识别了放牧区和非放牧区;确定了放牧密度,以衡量区域整体放牧压力;分析高原草地生长状况和分布特征,选取了放牧影响因子,并根据牲畜采食行为与道路、河流、海拔、坡度等因子的函数关系,率定了单元格草地的放牧概率,表征放牧可能性;在此基础上,构建了放牧强度空间化模型,并在高原典型牧业县—泽库县进行了应用实证研究。放牧强度空间化结果较好地反映了泽库县放牧强度的空间差异,与相关研究结果具有很好的一致性,且能更清晰地表现出县域内部的差异;放牧强度变化过程与同期NDVI值变化具较好的统一性,反映出放牧是导致区域草地植被变化的重要原因。

本方法考虑了海拔、坡度、坡向等自然地理要素,以及围栏封育、草畜平衡等相关政策对放牧活动的限制,区分了放牧区和非放牧区。并依据相关研究成果中放牧活动影响因素与放牧行为间的函数关系对各影响因子进行赋值,这有效提高了放牧强度空间化结果的准确性。

人类活动强度空间化过程中,应尽量选取能够直接反映人类活动状况的指标,减少代用指标的应用;充分考虑人类活动的影响因子,从机理上分析各影响因子与人类活动强度的关联,避免简单机械地给因子赋值,这有望成为人类活动强度空间化研究的重要方向。

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Acta Ecologica Sinica, 2020, 40(23): 8753-8762.

[本文引用: 2]

[靳茗茗, 徐增让, 成升魁.

藏北高寒草地植被和土壤对不同放牧强度的响应

生态学报, 2020, 40(23): 8753-8762.]

[本文引用: 2]

Wen Wen, Zhou Dingyang, Li Guangyong, et al.

The research on grazing routes in Qinghai Lake watershed based on the grazing behavior of herdsmen: A case of Gangcha County

Journal of Arid Land Resources and Environment, 2016, 30(7): 95-100.

[本文引用: 1]

[文雯, 周丁扬, 李广泳, 等.

基于牧民放牧行为的青海湖流域放牧路线研究: 以刚察县为例

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Ren Jizhou.

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[任继周 . 放牧,

草原生态系统存在的基本方式: 兼论放牧的转型

自然资源学报, 2012, 27(8): 1259-1275.]

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Hu Zhentong, Kong Deshuai, Wei Tongyang, et al.

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[本文引用: 1]

[胡振通, 孔德帅, 魏同洋, 等.

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自然资源学报, 2015, 30(11): 1846-1859.]

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Wan Liqiang, Chen Weiwei, Li Xianglin, et al.

Effects of grazing intensities on foraging behaviors of goats on a cultivated pasture of southern China

Acta Prataculturae Sinica, 2013, 22(4): 275-282.

[本文引用: 2]

[万里强, 陈玮玮, 李向林, 等.

放牧强度对山羊采食行为的影响

草业学报, 2013, 22(4): 275-282.]

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Cai Hong, He Zhengwei, An Yanling, et al.

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Earth and Environment, 2014, 42(4): 518-524.

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[蔡宏, 何政伟, 安艳玲, 等.

基于RS和GIS的赤水河流域植被覆盖度与各地形因子的相关强度研究

地球与环境, 2014, 42(4): 518-524.]

[本文引用: 4]

Chen Yishi, Che Zhaobi, Cao Jiamin, et al.

Spatial and temporal characteristics of sheep displacement during grazing

Pratacultural Science, 2019, 36(12): 3175-3181.

[本文引用: 1]

[陈乙实, 车昭碧, 曹佳敏, 等.

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草业科学, 2019, 36(12): 3175-3181.]

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Liu Jinxin, Gong Yuanbo, Zuo Qin, et al.

A review of mountain grazing-paths and vegetation spatial heterogeneity

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[刘金鑫, 宫渊波, 左琴, 等.

山地牧道与植被空间异质性研究评述

草业学报, 2012, 21(1): 254-261.]

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Huai Huyin, Zhou Lihua.

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[淮虎银, 周立华.

青海湖湖盆南部的植被与海拔梯度

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Zhang Gang, Tang Guoan, Song Xiaodong, et al.

Spatial distribution of insolation-duration in Loess Plateau

Geomatics and Information Science of Wuhan University, 2015, 40(6): 834-840.

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[张刚, 汤国安, 宋效东, 等.

黄土高原可照时间空间分布特征研究

武汉大学学报: 信息科学版, 2015, 40(6): 834-840.]

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Zheng Wei, Li Shixiong, Dong Quanmin, et al.

Effects of grazing systems on the community characteristics of alpine steppe in Qinghai Lake region

Acta Agrestia Sinica, 2013, 21(5): 869-874.

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[郑伟, 李世雄, 董全民, 等.

放牧方式对环青海湖高寒草原群落特征的影响

草地学报, 2013, 21(5): 869-874.]

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Wang Yanlong, Wang Xiaoli, Ma Yushou.

Effect of slope aspect on vegetation growth and soil nutrient characteristics of alpine grassland in the source region of Yangtze River

Pratacultural Science, 2018, 35(10): 2336-2346.

[本文引用: 1]

[王彦龙, 王晓丽, 马玉寿.

坡向对长江源区高寒草地植被生长和土壤养分特征的影响

草业科学, 2018, 35(10): 2336-2346.]

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Zhang Yili, Ding Mingjun, Zhang Wei, et al.

Spatial characteristic of vegetation change in the source regions of the Yangtze River, Yellow River and Lancang River in China

Geographical Research, 2007, 26(3): 500-507, 639.

[本文引用: 1]

[张镱锂, 丁明军, 张玮, 等.

三江源地区植被指数下降趋势的空间特征及其地理背景

地理研究, 2007, 26(3): 500-507, 639.]

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Li Huixia, Liu Guohua, Fu Bojie.

Response of vegetation to climate change and human activity based on NDVI in the Three-River Headwaters region

Acta Ecologica Sinica, 2011, 31(19): 5495-5504.

[本文引用: 1]

[李辉霞, 刘国华, 傅伯杰.

基于NDVI的三江源地区植被生长对气候变化和人类活动的响应研究

生态学报, 2011, 31(19): 5495-5504.]

[本文引用: 1]

Zhang Yili, Li Lanhui, Ding Mingjun, et al.

Greening of the Tibetan Plateau and its drivers since 2000

Chinese Journal of Nature, 2017, 39(3): 173-178.

[本文引用: 1]

[张镱锂, 李兰晖, 丁明军, 等.

新世纪以来青藏高原绿度变化及动因

自然杂志, 2017, 39(3): 173-178.]

[本文引用: 1]

Yang Yongsheng, Zhang Li, Wei Yaxi, et al.

Effects of degradation degree on soil physicochemical properties and soil water-holding capacity in Zeku alpine meadow in the Headwater Region of Three Rivers in China

Chinese Journal of Grassland, 2017, 39(5): 54-61.

[本文引用: 1]

[杨永胜, 张莉, 未亚西, 等.

退化程度对三江源泽库高寒草甸土壤理化性质及持水能力的影响

中国草地学报, 2017, 39(5): 54-61.]

[本文引用: 1]

Qinghai Provincial Bureau of Statistics, Survey Office of the National Bureau of Statistics in Qinghai.

Qinghai Statistical Yearbook (2019)

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[本文引用: 1]

[青海省统计局, 国家统计局青海调查总队.

青海统计年鉴(2019)

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[本文引用: 1]

Liu Xianfeng, Ren Zhiyuan, Lin Zhihui, et al.

The spatial-temporal changes of vegetation coverage in the Three-River Headwater Region in recent 12 years

Acta Geographica Sinica, 2013, 68(7): 897-908.

[本文引用: 1]

The Three-River Headwater Region is the source areas of the Yangtze River, the Yellow River, and the Lancang River. The region is not only of key importance to the ecological security of China. Due to climate change and human activities, ecological degradation occurred in this region. Therefore, "The nature reserve of Three-River Source Regions" was established, and "The project of ecological protection and construction for the Three-River Headwater Nature Reserve" was implemented by Chinese government. This study, based on MODIS-NDVI and climate data, aims to analyze the spatial-temporal changes of vegetation coverage and its driving factors in the Three-River Headwater Region between 2000 and 2011 from three dimensions. Linear regression, Hurst index and partial correlation analysis were employed. The results showed that: (1) In the past 12 years (2000-2011), the NDVI of the study area increased, with a linear tendency being 1.2%/10a, of which the Yangtze and the Yellow river source regions presented an increasing trend, while the Lancang River source region showed a decreasing trend. (2) Vegetation coverage presented an obvious spatial difference in the Three-River Headwater Region, and NDVI frequency was featured by a bimodal structure. (3) The vegetation coverage improved area was larger than the degraded area, being 64.06% and 35.94% respectively in the 12 years, and presented an increase pattern in the north and a decrease one in the south. (4) The reverse characteristic of vegetation coverage change is significant. In future, the degradation trends will be mainly found in the Yangtze River Basin and north of the Yellow River, while the improving trend areas are mainly distributed in the Lancang River Basin. (5) The response of vegetation coverage to precipitation and potential evapotranspiration has time lag, while the temperature does not have. (6) The increased vegetation coverage is mainly attributed to the warm-wet climate change and the implementation of the ecological protection project.

[刘宪锋, 任志远, 林志慧, 等.

2000—2011年三江源区植被覆盖时空变化特征

地理学报, 2013, 68(7): 897-908.]

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Ma Linya, Cui Xia, Feng Qisheng, et al.

Dynamic changes of grassland vegetation coverage from 2001 to 2011 in Gannan Prefecture

Acta Prataculturae Sinica, 2014, 23(4): 1-9.

[本文引用: 1]

[马琳雅, 崔霞, 冯琦胜, 等.

2001—2011年甘南草地植被覆盖度动态变化分析

草业学报, 2014, 23(4): 1-9.]

[本文引用: 1]

Sun Y L, Yang Y L, Zhang L, et al.

The relative roles of climate variations and human activities in vegetation change in North China

Physics and Chemistry of the Earth, Parts A/B/C, 2015, 87/88: 67-78.

DOI:10.1016/j.pce.2015.09.017      URL     [本文引用: 1]

Zhou X, Yamaguchi Y, Arjasakusuma S.

Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau

Science of the Total Environment, 2018, 616/617: 730-743.

DOI:10.1016/j.scitotenv.2017.10.253      URL     [本文引用: 1]

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