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遥感技术与应用  2021, Vol. 36 Issue (3): 552-563    DOI: 10.11873/j.issn.1004-0323.2021.3.0552
森林遥感专栏     
西南地区2001~2019年森林损失特征遥感监测与时空分析
王淑静1,2(),赖佩玉1,2,郝斌飞3,马明国1,2,韩旭军1,2()
1.西南大学地理科学学院 重庆金佛山喀斯特生态系统教育部野外科学观测研究站,重庆 400715
2.西南大学地理科学学院 遥感大数据应用重庆市工程研究中心,重庆 400715
3.广东海洋大学电子与信息工程学院,广东 湛江 524088
Remote Sensing Monitoring and Spatio-temporal Pattern of Deforestation in Southwest China from 2001 to 2019
Shujing Wang1,2(),Peiyu Lai1,2,Binfei Hao3,Mingguo Ma1,2,Xujun Han1,2()
1.Chongqing Jinfo Mountain Karst Ecosystem Field Science Observation and Research Station,School of Geographical Sciences,Southwest University,Chongqing 400715,China
2.Chongqing Engineering Research Center for Remote Sensing Big Data Application;School of Geographical Sciences,Southwest University,Chongqing 400715,China
3.College of Electronics and Information Engineering,Guangdong Ocean University,Zhanjiang 524088,China
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摘要:

森林在生态系统服务中起着重要作用,例如提供清洁空气、保护生物栖息地以及减少全球温室气体的排放等。全球森林变化数据集(Global Forest Change,GFC)每年以30 m的高空间分辨率绘制森林覆盖变化图,成为监测森林覆盖时空变化特征的有效工具。利用谷歌地球引擎(Google Earth Engine,GEE),基于GFC产品,结合线性回归方法和空间自相关理论对西南地区2001~2019年森林变化情况进行研究,结果表明:近19 a来,西南地区森林损失面积为375.27万hm2,以2008年为拐点,2008年之前呈显著增加趋势(p<0.05),在此之后波动下降,损失主要集中分布在广西、贵州东南和云南南部地区;森林损失与地形分析的关系表明损失主要分布在海拔2 000 m以下、坡度小于40°的区域,并逐渐向海拔更低坡度更缓的地方转移;森林损失面积具有一定的空间关联性,2001~2019年来Moran’s I 指数均为正,平均值为0.406,空间高值聚集在广西和贵州南部,低值聚集在重庆、四川和云南北部;政策因素在土地利用方式转变过程中发挥着重要作用,林业活动和农业扩张是影响损失现象发生的主要驱动因素,今后制定森林保护和管理战略时应充分考虑多方面因素造成的森林损失现象,研究结果可以为森林监测和保护提供更科学的指导。

关键词: 森林变化西南地区遥感监测Google Earth Engine空间自相关    
Abstract:

Forests play an important role in ecosystem services, such as providing clean air, protecting biological habitats, and reducing global greenhouse gas emissions. Global Forest Change (GFC) maps the forest cover change with a high spatial resolution of 30 m every year, which has become an effective tool for monitoring the spatio-temporal changes of forest cover. Based on Google Earth Engine, using high-resolution global forest change data, we inspected the patterns and processes of deforestation combined with linear regression methods and spatial autocorrelation theory in Southwest China. The results show that in the past 19 years, the total forest loss in southwest China was 3.752 7 million ha. 2008 is a turning point, as the area of annual forest loss has a significant rising trend before(P<0.05), and a fluctuant decreasing trend afterwards. The losses were mainly distributed in Guangxi province, southeast Guizhou Province and southern Yunnan Province. While forest loss are mainly located on the mountains(elevation below 2 000 m, slope below 19°), the location of forest loss patches has moved to lower and flatter areas in recent years. From a spatial perspective, the Moran’s I index was positive, with an average value of 0.406 from 2001 to 2019.It also tells that most prefecture-level cities are neighboring by the cities with similar forest loss area. High-High clusters are mainly in Guangxi Province and southern Guizhou Province, and the aggregation of Low-Low are showed in Chongqing, Sichuan Province, and northern Yunnan Province. The potential factors that affect deforestation in Southwest China highlight the role of forestry activities and agricultural expansion, policy factors play an important role in the transformation of land use patterns. This study provides a basis for the future rational forest management and planning of forest resources, formulating more scientific and effective policies and programs.

Key words: Forest change    Southwest China    Remote sensing monitoring    Google Earth Engine    Spatial autocorrelation
收稿日期: 2020-08-03 出版日期: 2021-07-22
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41771361);西南大学博士基金(含引进人才计划)项目(SWU117035)
通讯作者: 韩旭军     E-mail: 1178725159@qq.com;hanxujun@swu.edu.cn
作者简介: 王淑静(1998-),女,河南焦作人,硕士研究生,主要从事遥感大数据分析研究。E?mail:1178725159@qq.com
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引用本文:

王淑静,赖佩玉,郝斌飞,马明国,韩旭军. 西南地区2001~2019年森林损失特征遥感监测与时空分析[J]. 遥感技术与应用, 2021, 36(3): 552-563.

Shujing Wang,Peiyu Lai,Binfei Hao,Mingguo Ma,Xujun Han. Remote Sensing Monitoring and Spatio-temporal Pattern of Deforestation in Southwest China from 2001 to 2019. Remote Sensing Technology and Application, 2021, 36(3): 552-563.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.3.0552        http://www.rsta.ac.cn/CN/Y2021/V36/I3/552

图1  研究区地理位置及森林分布审图号:GS(2016)1663(森林为2000年树冠覆盖度大于20%的区域)
数据产品(版本)代表年份空间分辨率来源
Global Forest Change(GFC) V1.72001~201930 mhttp://earthenginepartners.appspot.com/science-2013-global-forest
SRTM-DEM V3200730 mhttp://developers.google.com/earth-engine/datasets/catalog/USGS_SRTMGL1_003
Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) V0.1.3201710 mhttp://data.ess.tsinghua.edu.cn/fromglc10_2017v01.html
中国行政区划2015矢量http://www.resdc.cn/
表1  研究所使用的空间数据集
区域减少面积平均减少面积占森林面积比例/%最小年份及面积最大年份及面积占总损失百分比/%
广西2 280 203.52120 010.7116.682003201660.76
36 813.6188 205.03
重庆33 805.571 779.240.77200120170.9
279.715 527.98
云南889 675.7346 825.044.852001201223.71
10 563.4174 916.9
贵州314 547.3116 555.123.83200320088.38
5 828.2532 077.02
四川234 448.3912 339.391.37200120086.25
2 994.9229 904.78
共计3 752 680.5197 505.96.09200120161
70 234.03297 311.85
表2  2001~2019年西南地区森林损失面积统计(面积单位:hm2)
图2  2001~2019年西南地区不同行政区森林损失情况审图号:GS(2016)1663
图3  不同行政区森林损失区域地形特征变化趋势(蓝色虚线为相对地面高度,红色虚线为趋势线)
图4  西南地区2001~2019年森林损失面积的地形分布(以相对地面高度作为分割线,阴影部分为相对地面高度以下的森林损失面积)
图5  2001~2019年空间自相关分析结果
图6  2001~2019年西南地区森林损失LISA分布图(高—高型关联:损失面积高值地区被周围是高值的地区包围;高—低型关联:损失面积高值地区被周围是低值的地区包围;低—低型关联:损失面积低值地区被周围是低值的地区包围;低—高型关联:损失面积低值地区被周围是高值的地区包围)
图7  2001~2019年西南地区森林损失空间分布
图8  森林损失地区土地利用变化情况(a) 西南地区2017年土地利用类型 (b) 各行政区森林损失地区不同土地利用类型所占比例
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