全球历史森林数据中国区域的可靠性评估

doi:10.11821/dlxb201905007

地理学报 ›› 2019, Vol. 74 ›› Issue (5): 923-934.doi: 10.11821/dlxb201905007

全球历史森林数据中国区域的可靠性评估

杨帆^1,²,何凡能¹(),李美娇^1,²,李士成³

^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. 中国地质大学（武汉）公共管理学院,武汉 430074

收稿日期:2018-03-07 修回日期:2019-01-23 出版日期:2019-05-25 发布日期:2019-05-24
通讯作者: 何凡能
作者简介:杨帆(1991-), 男, 山西武乡人, 博士生, 主要从事历史土地利用变化研究。E-mail: yangf. 17b@igsnrr.ac.cn
基金资助:
中国科学院战略性先导科技专项(XDA19040101);国家自然科学基金项目(41671149);国家重点研发计划(2017YFA0603304)

Reliability assessment of global historical forest data in China

YANG Fan^1,²,HE Fanneng¹(),LI Meijiao^1,²,LI Shicheng³

^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. School of Public Administration, China University of Geosciences, Wuhan 430074, China

Received:2018-03-07 Revised:2019-01-23 Online:2019-05-25 Published:2019-05-24
Contact: HE Fanneng
Supported by:
The Chinese Academy of Sciences Strategic Priority Research Program(XDA19040101);National Natural Science Foundation of China(41671149);The National Key Research and Development Program of China(2017YFA0603304)

摘要/Abstract

摘要：

全球历史土地利用数据集对于深入理解全球或区域环境变化具有重要意义。历史森林数据作为其重要组成部分,在区域尺度上的可靠性至今鲜有评估。以中国区域为研究对象,依据中国学者基于历史文献资料重建的中国历史森林数据（CHFD）,采用趋势、数量和空间格局等对比法,对全球数据集（SAGE、PJ和KK10）中国森林数据的可靠性进行评估。结果表明：① 虽然全球数据集中国森林数据与CHFD在近300年的变化趋势上均呈减少态势,但数量上差异较大。其中,SAGE数据集对中国1700年以来的森林面积估算较CHFD高出约20%~40%;KK10数据集重建的1700-1850年森林数量则高出约32%~46%;而PJ数据集由于吸纳了区域性研究成果,其总量与CHFD较为接近,多数时点的数量差异低于20%。② 在省区尺度上,从总量与CHFD较为接近的PJ数据集来看,其与CHFD数据集森林变化趋势差异较大省区占到84%,而数量差异较大的省区占比高达92%。③ 在网格尺度上,PJ与CHFD数据集相对差异率> 70%的网格占比高达60%~80%,二者的时空动态格局差异明显。④ 全球数据集中国历史森林数据未能客观反映该区域森林变化的过程与格局特征,造成这一现象的原因在于全球与区域性数据集重建历史数据所依据的资料源不同,以及基于不同空间尺度构建的重建方法的差异等。

关键词: 历史森林, 全球数据集, 可靠性评估, 中国

Abstract:

Global historical land use datasets play a quite significant role in gaining a profound understanding of the global or regional environmental change. As an important component of these global datasets, the reliability of historical forest data at the regional scale is rarely assessed. Based on the Chinese historical forest dataset (CHFD) that is reestablished by Chinese scholars using historical documents, we conducted an evaluation of the reliability of China's forest data within these global datasets (SAGE, PJ and KK10) adopting comparative analysis from three aspects, including the change tendency of forest area, the area of forestland, and the differences at grid scale. The results indicated that: (1) Although Chinese forest area from multiple datasets was decreasing, there was a large difference in the quantity of forest area. Specifically, the forest area of China from 1700-1990 in SAGE dataset was 20%-40% greater than that in CHFD, while the forest area of China in KK10 dataset from 1700-1850 was approximately 32%-46% greater than that in CHFD. Due to the adoption of regional research results, the total forest area of China within PJ dataset was closen to CHFD, and the quantitative biases of most years were less than 20%. (2) At provincial scale, in terms of the PJ dataset which was relatively close to the CHFD, the proportion of the provinces with large difference in the changing trend was 84%, and the proportion of the provinces with large difference in the quantity could be up to 92%. (3) At grid cell scale, the percentage of grid cells having biases greater than 70% accounted for up to 60%-80%. Therefore, there was an apparent discrepancy of spatiotemporal dynamic patterns between PJ and CHFD datasets. (4) These global datasets failed to reveal the process and pattern characteristics of Chinese forest dynamics in an objective way. The major reasons were that different data sources were used in reconstructing historical forest data within global and regional datasets, and different reconstruction methods at different spatial scales were adopted.

Key words: historical forest, global datasets, reliability assessment, China

杨帆,何凡能,李美娇,李士成. 全球历史森林数据中国区域的可靠性评估[J]. 地理学报, 2019, 74(5): 923-934.

YANG Fan,HE Fanneng,LI Meijiao,LI Shicheng. Reliability assessment of global historical forest data in China[J]. Acta Geographica Sinica, 2019, 74(5): 923-934.

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参考文献 31

[1]	Watson R T N I R, Bolin B , et al. Land use, land-use change, and forestry//IPCC Special Report-Summary for Policymakers. IPCC, 2000.
[2]	Foley J A, Defries R, Asner G P , et al. Global consequences of land use. Science, 2005,309(5734):570-574. doi: 10.1126/science.1111772
[3]	Ellis E C, Kaplan J O, Fuller D Q , et al. Used planet: A global history. Proceedings of the National Academy of Sciences of the United States of America, 2013,110(20):7978-7985. doi: 10.1073/pnas.1217241110
[4]	Darby H C. The clearing of the woodland in Europe//Thomas J W L. Man's Role in Changing the Face of the Earth. Chicago: University of Chicago Press, 1956.
[5]	Hughes J D, Thirgood J V . Deforestation, erosion, and forest management in ancient Greece and Rome. Journal of Forest History, 1982,26(2):60-75.
[6]	Werf G R V D, Morton D C, Defries R S , et al. CO₂ emissions from forest loss. Nature Geoscience, 2009,2(11):737-738. doi: 10.1038/ngeo671
[7]	Houghton R A . Carbon Flux to the Atmosphere from Land-use Changes: 1850 To 1990. Office of Scientific & Technical Information Technical Reports, 2001.
[8]	House J I, Prentice I C, Quéré C L . Maximum impacts of future reforestation or deforestation on atmospheric CO₂. Global Change Biology, 2002,8(11):1047-1052. doi: 10.1046/j.1365-2486.2002.00536.x
[9]	Clawson M . Forests in the long sweep of american history. Science, 1979,204(4398):1168-1174. doi: 10.1126/science.204.4398.1168
[10]	Kaplan J O, Krumhardt K M, Zimmermann N . The prehistoric and preindustrial deforestation of Europe. Quaternary Science Reviews, 2009,28(27/28):3016-3034. doi: 10.1016/j.quascirev.2009.09.028
[11]	Tian H, Banger K, Bo T , et al. History of land use in India during 1880-2010: Large-scale land transformations reconstructed from satellite data and historical archives. Global and Planetary Change, 2014,121:78-88. doi: 10.1016/j.gloplacha.2014.07.005
[12]	He F, Li S, Zhang X . A spatially explicit reconstruction of forest cover in China over 1700-2000. Global and Planetary Change, 2015,131:73-81. doi: 10.1016/j.gloplacha.2015.05.008
[13]	Ramankutty N, Foley J A . Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global Biogeochemical Cycles, 1999,13(4):997-1027. doi: 10.1029/1999GB900046
[14]	Pongratz J, Reick C, Raddatz T , et al. A reconstruction of global agricultural areas and land cover for the last millennium. Global Biogeochemical Cycles, 2008,22(3):1-16.
[15]	Kaplan J O, Ruddiman W F, Crucifix M C , et al. Holocene carbon emissions as a result of anthropogenic land cover change. The Holocene, 2010,21(5):775-791.
[16]	Houghton R A, Hackler J L . Sources and sinks of carbon from land-use change in China. Global Biogeochemical Cycles, 2003,17(2):1034.
[17]	Brovkin V, Sitch S, Wvon B , et al. Role of land cover changes for atmospheric CO₂ increase and climate change during the last 150 years. Global Change Biology, 2004,10(8):1253-1266. doi: 10.1111/gcb.2004.10.issue-8
[18]	Kaplan J O, Krumhardt K M, Zimmermann N E . The effects of land use and climate change on the carbon cycle of Europe over the past 500 years. Global Change Biology, 2012,18(3):902-914. doi: 10.1111/j.1365-2486.2011.02580.x
[19]	He F, Vavrus S J, Kutzbach J E , et al. Simulating global and local surface temperature changes due to Holocene anthropogenic land cover change. Geophysical Research Letters, 2014,41(2):623-631. doi: 10.1002/2013GL058085
[20]	Ge Q S, Dai J H, He F N , et al. Land use changes and their relations with carbon cycles over the past 300 a in China. Science in China (Series D), 2008,51(6):871-884.
[21]	Li Beibei, Fang Xiuqi, Ye Yu , et al. Accuracy assessment of global historical cropland datasets based on regional reconstructed historical data: A case study in Northeast China. Science in China (Series D), 2010,40(8):1048-1059.
	[ 李蓓蓓, 方修琦, 叶瑜 , 等. 全球土地利用数据集精度的区域评估: 以中国东北地区为例. 中国科学(D辑), 2010,40(8):1048-1059.]
[22]	He Fanneng, Li Shicheng, Zhang Xuezhen , et al. Comparisons of reconstructed cropland area from multiple datasets for the traditional cultivated region of China in the last 300 years. Acta Geographica Sinica, 2012,67(9):1190-1200.
	[ 何凡能, 李士成, 张学珍 , 等. 中国传统农区过去300年耕地重建结果的对比分析. 地理学报, 2012,67(9):1190-1200.]
[23]	Zhang X, He F, Li S . Reconstructed cropland in the mid-eleventh century in the traditional agricultural area of China: Implications of comparisons among datasets. Regional Environmental Change, 2013,13(5):969-977. doi: 10.1007/s10113-012-0390-6
[24]	Ramankutty N, Foley J A, Hall F G , et al. ISLSCP II Historical Croplands Cover, 1700-1992. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/966. 2010.
[25]	Ramankutty N. Global Cropland and Pasture Data from 1700-2007. Available at: http://www.geog.mcgill.ca/nramankutty/Datasets/Datasets.html, 2012-06-01.
[26]	Mather A S, Fairbairn J, Needle C L . The course and drivers of the forest transition: The case of France. Journal of Rural Studies, 1999,15(1):65-90. doi: 10.1016/S0743-0167(98)00023-0
[27]	Bradshaw R H W . Past anthropogenic influence on European forests and some possible genetic consequences. Forest Ecology & Management, 2004,197(1-3):203-212.
[28]	He Fanneng, Ge Quansheng, Dai Junhu , et al. Quantitative analysis on forest dynamics of China in recent 300 years. Acta Geographica Sinica, 2007,62(1):30-40.
	[ 何凡能, 葛全胜, 戴君虎 , 等. 近300年来中国森林的变迁. 地理学报, 2007,62(1):30-40.]
[29]	Ye Y, Fang X . Spatial pattern of land cover changes across Northeast China over the past 300 years. Journal of Historical Geography, 2011,37(4):408-417. doi: 10.1016/j.jhg.2011.08.018
[30]	Wang Xiulan, Bao Yuhai . Study on the methods of land use dynamic change research. Progress in Geography, 1999,18(1):81-87. doi: 10.3969/j.issn.1007-6301.1999.01.012
	[ 王秀兰, 包玉海 . 土地利用动态变化研究方法探讨. 地理科学进展, 1999,18(1):81-87.] doi: 10.3969/j.issn.1007-6301.1999.01.012
[31]	Zhu Huiyi, Li Xiubin . Discussion on the index method of regional land use change. Acta Geographica Sinica, 2003,58(5):643-650.
	[ 朱会义, 李秀彬 . 关于区域土地利用变化指数模型方法的讨论. 地理学报, 2003,58(5):643-650.]

数据集	主要覆被类型	重建时段	时间分辨率(a)	空间分辨率
SAGE	耕地、森林、草地	1700-1992	1~50	0.5°×0.5°
PJ	耕地、牧草地、森林	800-1992	1	0.5°×0.5°
KK10	森林	8000-1850	1	5′×5′
CHFD	森林	1700-2000	5~50	10 km×10 km

年份	CHFD森林面积(万km²)	SAGE		PJ		KK10
年份	CHFD森林面积(万km²)	森林面积(万km²)	数量差异率 (%)	森林面积(万km²)	数量差异率 (%)	森林面积(万 km²)	数量差异率 (%)
1700	241.27	296.00	20.44	265.23	9.47	333.04	32.23
1720	235.58	286.40	19.53	256.30	8.43	339.63	36.58
1740	229.89	276.80	18.57	246.37	6.92	318.04	32.46
1760	222.81	267.20	18.17	236.41	5.92	311.20	33.41
1780	214.34	257.60	18.38	226.41	5.48	303.65	34.83
1800	205.87	248.00	18.62	216.35	4.97	299.10	37.35
1820	194.99	238.00	19.93	206.22	5.60	294.55	41.25
1840	184.11	228.00	21.38	195.91	6.21	292.50	46.29
1860	172.74	218.00	23.27	185.66	7.21
1880	160.88	208.00	25.69	176.21	9.10
1900	149.02	200.00	29.42	167.80	11.87
1920	133.48	190.00	35.31	158.20	16.99
1940	117.94	177.00	40.60	144.44	20.27
1960	87.88	169.00	65.39	143.98	49.37
1980	111.92	167.00	40.02	144.86	25.80

相对差异率绝对值(%)	1720年	1780年	1840年	1900年	1960年
< 10	9.32	7.59	6.47	4.81	3.05
10~30	17.14	14.51	11.73	9.47	7.18
30~50	7.97	9.70	9.85	8.05	5.04
50~70	5.71	6.39	6.24	7.14	4.81
70~90	4.66	4.81	4.74	4.21	4.66
> 90	55.19	56.99	60.98	66.32	75.27

全球历史森林数据中国区域的可靠性评估

Reliability assessment of global historical forest data in China

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