冰川冻土 ›› 2021, Vol. 43 ›› Issue (5): 1582-1593.doi: 10.7522/j.issn.1000-0240.2021.0039
收稿日期:
2019-10-22
修回日期:
2021-01-11
出版日期:
2021-10-31
发布日期:
2021-12-09
通讯作者:
罗立辉
E-mail:duanqt@lzb.ac.cn;luolh@lzb.ac.cn
作者简介:
段群滔,硕士研究生,主要从事人类活动空间化及生态环境评估研究. E-mail: 基金资助:
Quntao DUAN1,2(),Lihui LUO1,2(
)
Received:
2019-10-22
Revised:
2021-01-11
Online:
2021-10-31
Published:
2021-12-09
Contact:
Lihui LUO
E-mail:duanqt@lzb.ac.cn;luolh@lzb.ac.cn
摘要:
地球系统受到气候变化和人类活动的双重影响,相比于气候变化研究相关的空间数据在数据采集、制备方法、制备速度、时空分辨率和数据质量等各方面的快速发展和提高,人类活动强度空间数据还处于探索发展阶段。人类活动强度空间数据有助于更好的理解人类活动影响的强度和范围,对于探究人类活动对地球系统各圈层及其变化的影响,以及促进全球和区域可持续发展具有重要意义。基于此,通过对人类活动强度空间数据相关研究进行梳理和分析,首先提出人类活动强度空间化的概念,然后归纳总结六类主要的空间化方法,并以青藏高原地区为例重点展示和分析四类应用较为广泛的方法,包括净初级生产力人类占用法、土地类型变化法、全球干扰指数法和人类足迹指数法等。最后讨论目前人类活动强度空间化研究中存在的问题,并对未来的发展提出建议。
中图分类号:
段群滔,罗立辉. 人类活动强度空间化方法综述与展望[J]. 冰川冻土, 2021, 43(5): 1582-1593.
Quntao DUAN,Lihui LUO. Summary and prospect of spatialization method of human activity intensity: taking the Qinghai-Tibet Plateau as an example[J]. Journal of Glaciology and Geocryology, 2021, 43(5): 1582-1593.
表1
直接空间化代表方法"
评价因子 | 计算方法 | 计算说明 | 提出时间 | 文献来源 |
---|---|---|---|---|
土地类型 | 式中:HAILS为陆地表层人类活动强度;SCLE为建设用地当量面积;S为区域总面积;SLi为第i种土地利用/覆被类型的面积;CIi为第i种土地利用/覆被类型的建设用地当量折算系数;n为区域内土地利用/覆被类型数 | 2015年 | 徐勇等[ | |
土地类型 | 式中:HAI代表人类活动强度指数;N为土地利用类型的数量;Ai为第i种土地利用类型的面积;Pi为第i种生态价值所反映的人类活动影响强度系数;TA为总面积 | 2001年 | 陈浮等[ 荣益等[ | |
自然、经济和社会 | 式中:Qh为总的人类活动强度;Ii、In、It分别为工业活动强度、农业活动强度和交通活动强度的权重值;Qi、Qn和Qt为经地形校正的工业活动强度、农业活动强度和交通活动强度 | 2011年 | 郑文武等[ | |
四类直接人类活动 | — | 首先将四类空间数据图层进行缓冲区分析和影响力赋值,然后将赋值后的图层进行叠加分析和分区归一化处理 | 2002年 | Sanderson等[ |
表2
Sanderson等的人类足迹指数方法中各图层影响力赋值方法和数据来源"
图 层 | 分值范围 | 影响力赋值方法 | 数据来源 |
---|---|---|---|
人口密度 | 0~10 | 0~10人?km-2时,0~10分线性增加; 大于10人?km-2为10分 | Gridded Population of the World (CIESIN) |
土地利用 | 0~10 | 建筑区域为10分;农业用地依据不同投入水平为6、7、8分;混合用地为4分;其余类型为0分 | Global Land Use/Land Cover version 2 (USGS/UNL/JRC) |
道路和铁路 | 4、8 | 2 km范围内为8分; 2~15km范围内为4分 | Vector Map Level 1 Roads and Railways (NIMA) |
居民点 | 8 | 各种类型居民点2 km范围内为8分 | Vector Map Level 0 Population Settlements (NIMA) |
建筑密集区 | 10 | 建筑密集区域为10分 | Vector Map Level 0 Built-Up Centers (NIMA) |
河 流 | 4 | 主要河流15 km范围内为4分 | Vector Map Level 0 Rivers |
海岸线 | 4 | 海岸线15 km范围内为4分 | Vector Map Level 0 Coastline |
夜间灯光 | 0、4、8、10 | 灯光频率超过89%为10分;40%~89%为8分; 低于40%为4分;等于0的区域为0分 | Defense Meteorological Satellite Program (NOAA/NGDC) |
表3
不同研究区内人类足迹数据指数方法对比"
研究区域 | 分辨率 | 主要调整内容 | 时间 | 文献来源 |
---|---|---|---|---|
全球 | 1 000 m | ①将人口密度影响阈值设为1 000人?km-2,并用对数模型来计算影响力分值;②使用等分方法来计算夜间灯光的影响;③将道路、铁路和通航水道的影响范围进一步细化并赋值 | 2009年 | Venter等[ |
阿卡迪亚 | 90 m | ①将道路分为四种等级,铁路分为三种类型,分别依据级别和类型进行影响力赋值;②计算工矿用地、再生森林、大坝和设施走廊等人类活动的影响 | 2008年 | Woolmer等[ |
青藏高原 | 1 000 m | ①设置人口密度影响阈值为50人/平方公里;②不计算通航河流的影响;③增加放牧密度影响图层 | 2017年 | Li等[ |
哥伦比亚 | 1 000 m | ①增加表示人类影响时间和生物物理脆弱性两方面的图层 | 2011年 | Etter等[ |
墨西哥火山带 | 100 m | ①增加表示人类影响时间、生物物理脆弱性和表示生境丧失和破碎化程度等三方面的图层 | 2017年 | Ayram等[ |
表4
人类活动强度空间化方法总结"
方法 | 优点 | 缺点 | 适用范围 | 样例数据 | |
---|---|---|---|---|---|
间接 空间化 | 植被指数法 | ①方法较为简单 ②可计算正反两面的影响 | ①存在使用条件限制 | 生态环境稳定的非城市区域 | - |
净初级生产力 人类占用法 | ①评价结果较好 ②可计算正反两面的影响 | ①结果存在不确定性 | 以行政单元为研究对象的区域 | ||
全球干扰指数法 | ①方法简单 ②空间精细度较高 | ①评价结果较差 ②可评价的人类活动种类较少 | 森林区域 | ||
直接 空间化 | 土地类型变化法 | ①方法简单 ②普适性较强 | ①评价结果较差 ②空间精细度不足 | 以行政单元为研究对象的区域 | |
综合指标法 | ①评价结果好 | ①空间精细度不足 | 以行政单元为研究对象的区域 | - | |
人类足迹指数法 | ①评价结果较好 ②空间精细度高 ③普适性强 | ①结果存在不确定性 | 所有陆地区域 |
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