肖轶1,
孙思琦1,
郭泺1,,
1.中央民族大学生命与环境科学学院 北京 100081
2.北京大学城市与环境学院 北京 100871
基金项目: 科技部重点研发计划课题2017YFC0505601
详细信息
作者简介:陈奕竹, 主要从事景观生态学研究。E-mail:baggieChen@126.com
通讯作者:郭泺, 主要从事景观生态学研究。E-mail:guoluo@muc.edu.cn
中图分类号:S181计量
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出版历程
收稿日期:2018-08-08
录用日期:2018-12-03
刊出日期:2019-04-01
Spatial-temporal changes of ecosystem service values in Xiangxi region based on terrain
CHEN Yizhu1, 2,,XIAO Yi1,
SUN Siqi1,
GUO Luo1,,
1. College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
2. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Funds: the Key Research and Development Project of Chinese Ministry of Science and Technology2017YFC0505601
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Corresponding author:E-mail:guoluo@muc.edu.cn
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摘要
摘要:为探究土地利用和生态系统服务价值（ecosystem service value，ESV）随不同地形因子变化的情况，本文利用湘西地区1990年、1995年、2000年、2005年、2010年及2015年6期遥感数据、野外调研数据和统计数据，分析了湘西不同土地利用类型在不同地形梯度上的变化、生态系统服务价值的时空变化及土地利用格局与ESV的相关性。研究结果表明：1）受人类活动影响较大的土地利用类型如耕地和建设用地主要分布在较低高度及坡度梯度上，山体南面和东面方向各类型土地面积较大。林地在不同地形梯度上均为最主要的土地利用类型。建设用地面积在不同地形梯度上随时间均明显呈增加趋势。2）ESV在海拔梯度上表现为先减少后增加，且海拔越高，ESV随时间变化的增长幅度越小；ESV随坡度增大而增大，且25年间最小坡度梯度上增长量最多；ESV随着地形位指数上升而增大；坡向变化中，东南方向出现ESV最低值，西北方向出现ESV最高值，东和西北方向ESV随时间增长量最大。3）相关性分析表明，林地、草地在坡度和地形位梯度上的变化与ESV变化呈正相关。湘西地区土地利用变化及生态系统服务价值随地形变化的差异明显。本研究为合理布局和保护我国南方丘陵山地区域的生态用地，发挥湘西地区的生态屏障作用提供了理论依据和决策支持。
关键词:土地利用变化/
生态系统服务价值/
地形梯度/
地形位指数/
湘西地区
Abstract:To understand the change of land use and ecosystem service value (ESV) on different terrain gradients, this study used remote sensing data, field data and statistic data of Xiangxi region in 1990, 1995, 2000, 2005, 2010 and 2015 to analyze the change of each land use types along the gradients of elevation, slope, aspect and terrain index, which were classified into five levels by quantile method, respectively. The study also evaluated the ESV with XIE Gaodi's method to explore the spatio-temporal change of ESV in Xiangxi region. And R Studio and Pearson correlations were used to analyze correlation between areas changes of different land use types and change of ESV with terrain gradients. The results showed that:1) areas of different land use types were significantly different on different terrain gradients. The human-activity-affected cropland and construction land mainly distributed on low gradients of elevation, slope and terrain index, while the natural systems, such as forestland and grassland, were mainly on high gradients of the three terrain factors. The south and east slopes were with larger areas of each land use type. Forestland occupied about 70% area on every terrain gradient, and the construction land area significantly increased on every terrain gradient over 25 years. 2) ESV decreased first and then increased with increasing elevation. From 1990 to 2015, ESV increased at lower elevation gradient, however, change was slighter at higher elevation, and it even decreased on the highest elevation gradient. ESV increased with slope growth. Over 25 years, ESV increased most on the lowest slope gradient. In a similar way, ESV evenly increased with increasing terrain index gradient. ESV was the lowest in southeast slope, highest in northwest slope, and increased most in east and northwest slopes from 1990 to 2015. 3) Correlations between change of land use types areas and ESV with gradients of elevation, slope, aspect, terrain index were different. On elevation gradient, grassland area change significantly positively correlated with ESV change. On slope gradient, forestland and grassland areas changes both had extremely positive correlation with ESV change. On aspect gradient, cropland and unused land areas changes had extremely negative correlation with ESV change, while forestland had a significant positive correlation. On terrain index gradient, areas changes of forestland and grassland were significantly correlated with change of ESV. In summary, the changes of land use and ESV were obviously different regarding topography. Forestland and grassland were two important land use types for improvement of ESV in mountain area of Xiangxi region.
Key words:Land use change/
Ecosystem service value/
Terrain gradient/
Terrain index/
Xiangxi region
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图11990—2015年湘西地区土地利用类型图
Figure1.Maps of land use in Xiangxi region from 1990 to 2015
下载: 全尺寸图片幻灯片
图2湘西地区随海拔、坡度、坡向、地形位指数梯度变化的生态系统服务价值
Figure2.Changes of ecosystem service values with elevation, slope, aspect, terrain index gradients in Xiangxi region
下载: 全尺寸图片幻灯片表1湘西地区海拔、坡度、坡向及地形位梯度设置情况
Table1.Gradients of elevation, slope, aspect and topographic index setting in Xiangxi region
| 海拔Elevation | 坡度Slope | 坡向Aspect | 地形位Terrain index | |||||||
| 梯度等级 Gradient | 梯度范围 Gradient range (m) | 梯度等级 Gradient | 梯度范围 Gradient range (°) | 梯度等级 Gradient | 梯度范围 Gradient range (°) | 梯度等级 Gradient | 梯度范围 Gradient range | |||
| 1 | 50~276 | 1 | 0~1.25 | 北North | 0~22.5 & 337.5~360 | 1 | 0~0.291 | |||
| 2 | 276~380 | 2 | 1.25~2.39 | 东北Northeast | 22.5~67.5 | 2 | 0.291~0.515 | |||
| 3 | 380~503 | 3 | 2.39~3.86 | 东East | 67.5~112.5 | 3 | 0.515~0.702 | |||
| 4 | 503~680 | 4 | 3.86~6.26 | 东南Southeast | 112.5~157.5 | 4 | 0.702~1.104 | |||
| 5 | 680~1 903 | 5 | 6.26~27.57 | 南South | 157.5~202.5 | 5 | 1.104~2.313 | |||
| 西南Southwest | 202.5~247.5 | |||||||||
| 西West | 247.5~292.5 | |||||||||
| 西北Northwest | 292.5~337.5 | |||||||||
下载: 导出CSV表2梯度变化的土地利用面积与地形梯度变化的生态系统服务价值相关性系数
Table2.Correlation coefficients between changes in land use area and ecosystem services value with terrain gradient change in Xiangxi region
| 地形因素Terrain factor | 土地利用面积变化Change in land use area | ||||||
| 耕地 Cropland | 林地 Forest | 草地 Grassland | 未利用地 Unused land | 水域 Water | 湿地 Wetland | ||
| 生态系统服务价值变化Change in ecosystem service value | 海拔梯度Elevation gradient | -0.763 | 0.632 | 0.938* | -0.561 | -0.481 | -0.598 |
| 坡度梯度Slope gradient | -0.997** | 0.994** | 0.958* | -0.180 | -0.989** | -0.941* | |
| 坡向梯度Aspect gradient | -0.888** | 0.842** | 0.598 | -0.828* | -0.570 | -0.295 | |
| 地形位梯度Terrain index gradient | -0.975** | 0.946* | 0.996** | -0.696 | -0.896* | -0.803 | |
| *0.05水平(双侧)显著相关。相关系数0.8~1.0为极强相关、0.6~0.8为强相关、0.4~0.6为中等相关、0.2~0.4为弱相关、0~0.2为极弱相关或不相关。* represents significant correlation at 0.05 level (both sides). Correlation coefficient of 0.8-1.0 represents extremely strong correlation, 0.6-0.8 represents strong correlation, 0.4-0.6 represents medium correlation, 0.2-0.4 represents weak correlation, 0-0.2 represents extremely weak correlation or non-correlation. | |||||||
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