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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 32-38     DOI: 10.6046/gtzyyg.2020.03.05
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Quality evaluation of forest cover products over China
WEN Yanan1,2(), CHE Yahui1,2, GUANG Jie1(), ZHANG Xiaomei1, LI Zhengqiang1
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Forest coverage is a common variable in forest management, monitoring and planning. It is also an important reference for biodiversity impact assessment and carbon storage quantification. Due to its objective, fast and macroscopic advantages, remote sensing technology has gradually been widely used in forest resources monitoring. In order to get better application of forest cover products based on remote sensing technology, validation and quality evaluation are particularly important. Two widely used forest cover products (UMD and JAXA FNF) were selected in this paper. UMD (University of Maryland) forest cover product was derived by EDENext Data Management Team from original datasets produced by UMD et al. 2013/UMD/Google/USGS/NASA. The forest/non-forest (FNF) product from ALSO/PALSAR data in 2015 was available on the Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA). Validation and quality evaluation were carried out by comparison between products and ground-based survey data. UMD and JAXA FNF forest cover products were compared through qualitative analysis and statistical analysis. The results show that the forest coverage rate of JAXA FNF in western, southern and central eastern China is significantly higher than that of UMD. In southeastern provinces of China, the consistency of UMD and JAXA FNF is better, but both of them are higher than ground-based data. In general, the accuracy of UMD is higher than that of JAXA FNF. Although the forest coverage data of UMD and JAXA FNF and GFW and CFGA are different in all provinces, the difference of JAXA FNF is larger, and the average absolute error of JAXA FNF is about 3 times of UMD.

Keywords forest cover product      UMD      JAXA FNF      validation      quality evaluation     
:  TP79  
Corresponding Authors: GUANG Jie     E-mail: m15611670108@163.com;guangjie@aircas.ac.cn
Issue Date: 09 October 2020
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Yanan WEN
Yahui CHE
Jie GUANG
Xiaomei ZHANG
Zhengqiang LI
Cite this article:   
Yanan WEN,Yahui CHE,Jie GUANG, et al. Quality evaluation of forest cover products over China[J]. Remote Sensing for Land & Resources, 2020, 32(3): 32-38.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.03.05     OR     https://www.gtzyyg.com/EN/Y2020/V32/I3/32
区域 森林面积/(万hm2) 森林覆盖率/% 清查年度
吉林 785 41.49 2014年
上海 9 14.04 2014年
浙江 605 59.43 2014年
安徽 396 28.65 2014年
湖北 736 39.61 2014年
湖南 1 053 49.69 2014年
陕西 887 43.06 2014年
山西 321 20.5 2015年
辽宁 572 39.24 2015年
黑龙江 1 990 43.78 2015年
广西 1 430 60.17 2015年
贵州 771 43.77 2015年
宁夏 66 12.63 2015年
江苏 156 15.2 2015年
河北 503 26.78 2016年
北京 72 43.77 2016年
江西 1 021 61.16 2016年
西藏 1 491 12.14 2016年
甘肃 510 11.33 2016年
新疆 802 4.87 2016年
山东 267 17.51 2017年
云南 2 106 55.04 2017年
广东 946 53.52 2017年
天津 14 12.07 2017年
重庆 355 43.11 2017年
四川 1 840 38.03 2017年
内蒙古 2 488 21.03 2013年
福建 801 65.95 2013年
河南 359 21.50 2013年
青海 406 5.63 2013年
海南 187 55.38 2013年
台湾 210 58.79 1993年
全国 22 034 22.96 2014—2018年
Tab.1  Main indicators in ninth national forest resources inventory
数据集 数据源 空间分辨率 森林定义
UMD Landsat 30 m 树高>5 m,郁闭度>20%
JAXA FNF ALSO PALSAR 25 m 树高>5 m,郁闭度>10%
GFW Landsat 30 m 树高>5 m,郁闭度>20%
CFGA 调查统计数据 省域 郁闭度>20%
Tab.2  General characteristics of forest cover products in this study
Fig.1  Absolute error of forest coverage between UMD and JAXA FNF
Fig.2  Forest coverage of UMD, JAXA FNF, GFW and CFGA in each province of China
Fig.3  Absolute error of forest coverage between UMD and GFW besides JAXA FNF and GFW
数据类型 绝对误差所在范围/% 省(区、市)个数 所占比例/%
UMD [0,5] 13 40.6
(5,10] 10 31.3
(10,20] 9 28.1
(20,40] 0 0
JAXA FNF [0,5] 2 6.2
(5,10] 4 12.5
(10,20] 10 31.3
(20,50] 16 50.0
Tab.3  Absolute error statistics of forest coverage data between UMD and GFW besides JAXA FNF and GFW
Fig.4  Absolute error of forest coverage between UMD and CFGA besides JAXA FNF and CFGA
数据类型 绝对误差所在范围/% 省(区、市)个数 所占比例/%
UMD [0,5] 9 28.2
(5,10] 12 37.5
(10,20] 10 32.0
(20,40] 1 3.1
JAXA FNF [0,5] 9 28.2
(5,10] 6 18.7
(10,20] 5 15.6
(20,50] 12 37.5
Tab.4  Absolute error statistics of forest coverage data between UMD and CFGA besides JAXA FNF and CFGA
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