留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces

YUAN Cun YE Yu TANG Chanchan FANG Xiuqi

YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. 中国地理科学, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
引用本文: YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. 中国地理科学, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. Chinese Geographical Science, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
Citation: YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. Chinese Geographical Science, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1

Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces

doi: 10.1007/s11769-017-0862-1
基金项目: Under the auspices of National Natural Science Foundation of China (No. 41471156, 41501207), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA05080102), Special Fund of National Science and Technology of China (No. 2014FY130500)
详细信息
    通讯作者:

    YE Yu.E-mail:yeyuleaffish@bnu.edu.cn

Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces

Funds: Under the auspices of National Natural Science Foundation of China (No. 41471156, 41501207), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA05080102), Special Fund of National Science and Technology of China (No. 2014FY130500)
More Information
    Corresponding author: 10.1007/s11769-017-0862-1
  • 摘要: The spatial resolution of source data, the impact factor selection on the grid model and the size of the grid might be the main limitations of global land datasets applied on a regional scale. Quantitative studies of the impacts of rasterization on data accuracy can help improve data resolution and regional data accuracy. Through a case study of cropland data for Jiangsu and Anhui provinces in China, this research compared data accuracy with different data sources, rasterization methods, and grid sizes. First, we investigated the influence of different data sources on gridded data accuracy. The temporal trends of the History Database of the Global Environment (HYDE), Chinese Historical Cropland Data (CHCD), and Suwan Cropland Data (SWCD) datasets were more similar. However, different spatial resolutions of cropland source data in the CHCD and SWCD datasets revealed an average difference of 16.61% when provincial and county data were downscaled to a 10×10 km2 grid for comparison. Second, the influence of selection of the potential arable land reclamation rate and temperature factors, as well as the different processing methods for water factors, on accuracy of gridded datasets was investigated. Applying the reclamation rate of potential cropland to grid-processing increased the diversity of spatial distribution but resulted in only a slightly greater standard deviation, which increased by 4.05. Temperature factors only produced relative disparities within 10% and absolute disparities within 2 km2 over more than 90% of grid cells. For the different processing methods for water factors, the HYDE dataset distributed 70% more cropland in grid cells along riverbanks, at the abandoned Yellow River Estuary (located in Binhai County, Yancheng City, Jiangsu Province), and around Hongze Lake, than did the SWCD dataset. Finally, we explored the influence of different grid sizes. Absolute accuracy disparities by unit area for the year 2000 were within 0.1 km2 at a 1 km2 grid size, a 25% improvement over the 10 km2 grid size. Compared to the outcomes of other similar studies, this demonstrates that some model hypotheses and grid-processing methods in international land datasets are truly incongruent with actual land reclamation processes, at least in China. Combining the model-based methods with historical empirical data may be a better way to improve the accuracy of regional scale datasets. Exploring methods for the above aspects improved the accuracy of historical cropland gridded datasets for finer regional scales.
  • [1] Feng Yongheng, Zhang Shihuang, He Fanneng et al., 2014. Sep-arate reconstruction of Chinese cropland grid data in the 20th century. Progress in Geography, 33(11):1546-1555. (in Chi-nese)
    [2] Fuchs R, Herold M, Verburg P H et al., 2012. A high-resolution and harmonized model approach forreconstructing and analyz-ing historic land changes in Europe. Biogeosciences Discus-sions, 9(10):14823-14866. doi: 10.5194/bgd-9-14823-2012
    [3] Ge Quansheng, Dai Junhu, He Fanneng et al., 2003. Analysis of the quantitative change of cultivated land resources in China in the past 300 years and the driving factors analysis. Progress in Natural Science, 13(8):825-832. (in Chinese)
    [4] Goldewijk K K, Beusen A, Van Drecht G et al., 2011. The HYDE 3.1 spatially explicit database of human induced global land-use change over the past 12000 years. Global Ecology and Biogeography, 20(1):73-86. doi:10.1111/j.1466-8238. 2010.00587.x
    [5] Hall C A S, Tian H, Qi Y et al., 1995. Modelling spatial and temporal patterns of tropical land use change. Biogeography Journal, 22(4/5):753-757. doi: 10.2307/2845977
    [6] He Fanneng, Li Shicheng, Zhang Xuezhen, 2011. The reconstruc-tion of cropland area and its spatial distribution pattern in the Mid-northern Song Dynasty. Acta Geographica Sinica, 66(11):1531-1539. (in Chinese)
    [7] He Fanneng, Li Shicheng, Zhang Xuezhen, 2012. Comparisons of reconstructed cropland area from multiple datasets for the tra-ditional cultivated region of China in the last 300 years. Journal of Geographical Sciences, 67(9):1190-1200. (in Chinese)
    [8] He Fanneng, Li Shicheng, Zhang Xuezhen, 2014. Spatially explicit reconstruction of forest cover of Southwest China in the Qing Dynasty. Acta Geographica Sinica, 33(2):260-269. (in Chinese)
    [9] Kaplan J O, Krumhardt K M, Ellis E C et al., 2011. Holocene carbon emissions as a result of anthropogenic land cover change. The Holocene, 21(5):775-791. doi:10.1177/0959683 610386983
    [10] Li B B, Jansson U, Ye Y et al., 2013. The spatial and temporal change of cropland in the Scandinavian Peninsula during 1875-1999. Regional Environmental Change, 13(6):1325-1336. doi: 10.1007/s10113-013-0457-z
    [11] Li Beibei, Fang Xiuqi, Ye Yu et al., 2010. The global land use data set the accuracy of regional assessments, in the northeast of China as an example. Science China Earth Sciences, 40(8):1048-1059. (in Chinese)
    [12] Li Shicheng, He Fanneng, Chen Yisong, 2012. Gridding recon-struction of cropland spatial patterns in Southwest China in the Qing Dynasty. Progress in Geography, 31(9):1196-1203. (in Chinese)
    [13] Li Shicheng, He Fanneng, Zhang Xuezhen, 2014. An approach of spatially-explicit reconstruction of historical forest in China:a case study in Northeast China. Acta Geographical Sinica, 69(3):312-322. (in Chinese)
    [14] Li Shicheng, He Fanneng, Zhang Xuezhen, 2016. A spatially explicit reconstruction of cropland cover in China from 1661 to 1996. Regional Environmental Change, 16(2):417-428. doi: 10.1007/s10113-014-0751-4
    [15] Lin Shanshan, 2007. A Study on Cropland Gridding Data Recon-Struction over Chinese Traditional Agricultural Area in Qing Dynasty. Beijing:University of Chinese Academy of Sciences. (in Chinese)
    [16] Lin Shanshan, Zheng Jingyun, He Fanneng, 2009. Gridding cropland data reconstruction over the agricultural region of China in 1820. Journal of Geographical Sciences, 19:36-48. doi: 10.1007/s11442-009-0036-x
    [17] Long Ying, Jin Xiaobin, Li Miaoyi et al., 2014. A constrained cellular automata model for reconstructing historical arable land in Jiangsu province. Geographical Research, 33(12):2239-2250. (in Chinese)
    [18] Luo Jing, Chen Qiong, Liu Fenggui et al., 2015. Methods for reconstructing historical cropland spatial distribution of the Yellow River-Huangshui River valley in Tibetan Plateau. Progress in Geography, 34(2):207-216. (in Chinese)
    [19] Luo Jing, Zhang Yili, Liu Fenggui et al., 2014. Reconstruction of cropland spatial patterns for 1726 on Yellow River-Huangshui River Valley in northeast Qinghai-Tibet Plateau. Geographical Research, 33(7):1285-1296. (in Chinese)
    [20] Ramankutty N, Foley J A, 2010. ISLSCP II historical croplands cover, 1700-1992. In:Hall F G et al. (eds.). ISLSCP Initiative II Collection. Tennessee:Oak Ridge National Laboratory Dis-tributed Active Archive Center, 1-20. doi: 10.3334/ORNLDAAC/966
    [21] The agriculture of China, Anhui volume editor committee, 1998. The Agriculture of China, Anhui Volume. Beijing:China Ag-riculture Press. (in Chinese)
    [22] The agriculture of China, Jiangsu volume editor committee, 1998. The Agriculture of China, Jiangsu Volume. Beijing:China Agriculture Press. (in Chinese)
    [23] Wei Xueqiong, Ye Yu, Cui Yujuan et al., 2014. Review of Chi-na's historical land cover change reconstructions. Advances in Earth science, 29(9):1037-1045. (in Chinese)
    [24] Yang Xuhong, Guo Beibei, Jin Xiaobin et al., 2015. Recon-structing spatial distribution of historical cropland in China's traditional cultivated region:methods and case study. Chinese Geographical Science, 25(5):629-643. doi: 10.1007/s11769-015-0753-2
    [25] Ye Y, Fang X Q, Ren Y Y et al. 2009. Cropland cover change in Northeast China during the past 300 years. Science in China Series D:Earth Sciences, 52(8):1172-1182. doi:10. 1007/s11430-009-0118-8
    [26] Yuan Cun, 2015. Gridding and Accuracy Comparison of Cropland Data in Jiangsu and Anhui Provinces During the Past 300 years. Beijing:Beijing Normal University. (in Chi-nese)
    [27] Yuan Cun, Ye Yu, Fang Xiuqi, 2015. Rasterizing cropland data and accuracy comparison in Jiangsu and Anhui ProVinces in the Mid-Qing Dynasty. Progress in Geography, 34(1):83-91. (in Chinese)
    [28] Zhang Jiacheng, 1982. Possible impact of climate variation on agricultural in China. Geography Research, 1(2):8-15. (in Chinese)
    [29] Zhang Lijuan, Jiang Lanqi, Zhang Xuezhen et al., 2014. Recon-struction of cropland over Heilongjiang Province in the late 19th century. Acta Geographica Sinica, 69(4):448-458. (in Chinese)
    [30] Zhang Xuezhen, He fanneng, Li Shicheng, 2013. Reconstructed cropland in the mid-eleventh century in the traditional agri-cultural area of China:implications of comparisons among datasets. Regional Environmental Change, 13(5):969-977. doi: 10.1007/s10113-012-0390-6.
    [31] Zhao Yun, 2005. Jiangsu and Anhui Area Land Use and its Driv-ing Mechanism. Shanghai:Fudan University. (in Chinese)
    [32] Zhu Feng, Cui Xuefeng, Miu Lijuan, 2012. China's spatially-explicit historical land-use data and its reconstruction method-ology. Progress in Geography, 31(12):1563-1573. (in Chi-nese)
  • [1] Tianhao GUO, Jia ZHENG, Chunmei WANG, Zui TAO, Xingming ZHENG, Qi WANG, Lei LI, Zhuangzhuang FENG, Xigang WANG, Xinbiao LI, Liwei KE.  A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries . Chinese Geographical Science, 2023, 33(4): 649-663. doi: 10.1007/s11769-023-1365-x
    [2] Guixin ZHANG, Shisheng WANG, Shanyou ZHU, Yongming XU.  Spatial Distribution of High-temperature Risk with a Return Period of Different Years in the Yangtze River Delta Urban Agglomeration . Chinese Geographical Science, 2022, 32(6): 963-978. doi: 10.1007/s11769-022-1314-0
    [3] Wenwen QIU, Zhangbao ZHONG, Zhaoliang LI.  Agricultural Non-point Source Pollution in China: Evaluation, Convergence Characteristics and Spatial Effects . Chinese Geographical Science, 2021, 31(3): 571-584. doi: 10.1007/s11769-021-1200-1
    [4] SUO Anning, XU Jingping, LI Xuchun, WEI Baoquan.  Evaluation of Port Prosperity Based on High Spatial Resolution Satellite Remote Sensing Images . Chinese Geographical Science, 2020, 30(5): 889-899. doi: 10.1007/s11769-020-1153-9
    [5] JIANG Tao, ZHAO Kai, ZHENG Xingming, CHEN Si, WAN Xiangkun.  Dynamic bp in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval . Chinese Geographical Science, 2019, 20(2): 283-292. doi: 10.1007/s11769-019-1028-0
    [6] WEI Ye, CHEN Zuoqi, XIU Chunliang, YU Bailang, LIU Hongxing.  Siting of Dark Sky Reserves in China Based on Multi-source Spatial Data and Multiple Criteria Evaluation Method . Chinese Geographical Science, 2019, 29(6): 949-961. doi: 10.1007/s11769-019-1079-2
    [7] ZHANG Zengxiang, LIU Fang, ZHAO Xiaoli, WANG Xiao, SHI Lifeng, XU Jinyong, YU Sisi, WEN Qingke, ZUO Lijun, YI Ling, HU Shunguang, LIU Bin.  Urban Expansion in China Based on Remote Sensing Technology: A Review . Chinese Geographical Science, 2018, 28(5): 727-743. doi: 10.1007/s11769-018-0988-9
    [8] ZHU Huayou, DAI Zejuan, JIANG Ziran.  Industrial Agglomeration Externalities, City Size, and Regional Economic Development: Empirical Research Based on Dynamic Panel Data of 283 Cities and GMM Method . Chinese Geographical Science, 2017, 27(3): 456-470. doi: 10.1007/s11769-017-0877-7
    [9] Yu Dongsheng, Pan Yue, Zhang Haidong, Wang Xiyang, Ni Yunlong, Zhang Liming, Shi Xue­zheng.  Equality Testing for Soil Grid Unit Resolutions to Polygon Unit Scales with DNDC Modeling of Regional SOC Pools . Chinese Geographical Science, 2017, 27(4): 552-568. doi: 10.1007/s11769-017-0887-5
    [10] SHI Lifeng, ZHANG Zengxiang, LIU Fang, ZHAO Xiaoli, WANG Xiao, LIU Bin, HU Shunguang, WEN Qingke, ZUO Lijun, YI Ling, XU Jinyong.  City Size Distribution and Its Spatiotemporal Evolution in China . Chinese Geographical Science, 2016, 26(6): 703-714. doi: 10.1007/s11769-016-0832-z
    [11] YANG Xuhong, GUO Beibei, JIN Xiaobin, LONG Ying, ZHOU Yinkang.  Reconstructing Spatial Distribution of Historical Cropland in China's Traditional Cultivated Region: Methods and Case Study . Chinese Geographical Science, 2015, 25(5): 629-643. doi: 10.1007/s11769-015-0753-2
    [12] TAN Minghong, Guy M ROBINSON, LI Xiubin, XIN Liangjie.  Spatial and Temporal Variability of Farm Size in China in Context of Rapid Urbanization . Chinese Geographical Science, 2013, 23(5): 607-619. doi: 10.1007/s11769-013-0610-0
    [13] JIN Pingbin FU Zhiwei BAN Maosheng.  Industrial Arrangement of Large-scale, Non-grid-connected Wind Power Industrial Zones in Coastal Areas of China . Chinese Geographical Science, 2012, 22(1): 109-118.
    [14] SHI Xiaoliang, LI Ying, DENG Rongxin.  The Method Study of Spatial heterogeneity evaluation on the Landscape Pattern Of Farmland Shelterbelt Network . Chinese Geographical Science, 2011, 21(1): 48-56.
    [15] GONG Huili, JIAO Cuicui, ZHOU Demin, LI Na.  Scale Issues of Wetland Classification and Mapping Using Remote Sensing Images: A Case of Honghe National Nature Reserve in Sanjiang Plain, Northeast China . Chinese Geographical Science, 2011, 21(2): 230-240.
    [16] GU Lingjia, ZHAO Kai, ZHANG Shuwen et al.  Comparison Analysis of Microwave Brightness Temperature Data from AMSR-E and MWRI Based on Northeast China . Chinese Geographical Science, 2011, 21(1): 84-93.
    [17] DU Guoqing.  Development Mechanism of Urban System in Rapidly Changing Period in China . Chinese Geographical Science, 2007, 17(1): 10-18. doi: 10.1007/s11769-007-0010-4
    [18] WANG Peifa, DU Jinkang, FENG Xuezhi, KANG Guoding.  Effect of Uncertainty of Grid DEM on TOPMODEL:Evaluation and Analysis . Chinese Geographical Science, 2006, 16(4): 320-326.
    [19] ZHOU De-min, XU Jian-chun, John RADKE, MU Lan.  A SPATIAL CLUSTER METHOD SUPPORTED BY GIS FOR URBAN-SUBURBAN-RURAL CLASSIFICATION . Chinese Geographical Science, 2004, 14(4): 337-342.
    [20] ZHU Cheng, ZHANG Yun.  PALEO-ENVIRONMENTAL RECONSTRUCTION DURING THE PERIOD OF NANJING HOMO ERECTUS . Chinese Geographical Science, 2000, 10(3): 209-217.
  • 加载中
计量
  • 文章访问数:  393
  • HTML全文浏览量:  15
  • PDF下载量:  856
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-07-06
  • 修回日期:  2016-11-08
  • 刊出日期:  2017-04-27

Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces

doi: 10.1007/s11769-017-0862-1
    基金项目:  Under the auspices of National Natural Science Foundation of China (No. 41471156, 41501207), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA05080102), Special Fund of National Science and Technology of China (No. 2014FY130500)
    通讯作者: YE Yu.E-mail:yeyuleaffish@bnu.edu.cn

摘要: The spatial resolution of source data, the impact factor selection on the grid model and the size of the grid might be the main limitations of global land datasets applied on a regional scale. Quantitative studies of the impacts of rasterization on data accuracy can help improve data resolution and regional data accuracy. Through a case study of cropland data for Jiangsu and Anhui provinces in China, this research compared data accuracy with different data sources, rasterization methods, and grid sizes. First, we investigated the influence of different data sources on gridded data accuracy. The temporal trends of the History Database of the Global Environment (HYDE), Chinese Historical Cropland Data (CHCD), and Suwan Cropland Data (SWCD) datasets were more similar. However, different spatial resolutions of cropland source data in the CHCD and SWCD datasets revealed an average difference of 16.61% when provincial and county data were downscaled to a 10×10 km2 grid for comparison. Second, the influence of selection of the potential arable land reclamation rate and temperature factors, as well as the different processing methods for water factors, on accuracy of gridded datasets was investigated. Applying the reclamation rate of potential cropland to grid-processing increased the diversity of spatial distribution but resulted in only a slightly greater standard deviation, which increased by 4.05. Temperature factors only produced relative disparities within 10% and absolute disparities within 2 km2 over more than 90% of grid cells. For the different processing methods for water factors, the HYDE dataset distributed 70% more cropland in grid cells along riverbanks, at the abandoned Yellow River Estuary (located in Binhai County, Yancheng City, Jiangsu Province), and around Hongze Lake, than did the SWCD dataset. Finally, we explored the influence of different grid sizes. Absolute accuracy disparities by unit area for the year 2000 were within 0.1 km2 at a 1 km2 grid size, a 25% improvement over the 10 km2 grid size. Compared to the outcomes of other similar studies, this demonstrates that some model hypotheses and grid-processing methods in international land datasets are truly incongruent with actual land reclamation processes, at least in China. Combining the model-based methods with historical empirical data may be a better way to improve the accuracy of regional scale datasets. Exploring methods for the above aspects improved the accuracy of historical cropland gridded datasets for finer regional scales.

English Abstract

YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. 中国地理科学, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
引用本文: YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. 中国地理科学, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. Chinese Geographical Science, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
Citation: YUAN Cun, YE Yu, TANG Chanchan, FANG Xiuqi. Accuracy Comparison of Gridded Historical Cultivated Land Data in Jiangsu and Anhui Provinces[J]. Chinese Geographical Science, 2017, 27(2): 273-285. doi: 10.1007/s11769-017-0862-1
参考文献 (32)

目录

    /

    返回文章
    返回