Accuracy assessment of approaches to spatially explicit reconstruction of historical cropland in Songnen Plain, Northeast China

doi:10.1007/s11442-016-1264-5

Abstract

Abstract:

To understand historical human-induced land use/cover change (LUCC) and its climatic effects, it is essential to reconstruct historical land use/cover changes with explicit spatial information. In this study, based on the historically documented cropland area at county level, we reconstructed the spatially explicit cropland distribution at a cell size of 1 km × 1 km for the Songnen Plain in the late Qing Dynasty (1908 AD). The reconstructions were carried out using two methods. One method (hereafter, referred to as method I) allocated the cropland to cells ordered from a high agricultural suitability index (ASI) to a low ASI, but they were all within the domain of potential cropland area. The potential cropland area was created by excluding natural woodland, swamp, water bodies, and mountains from the study area. The other method (hereafter, method II) allocated the cropland to cells in the order from high ASI to low ASI within the domain of cropland area in 1959. This method was based on the hypothesis that the cropland area domain in 1959 resulted from enlargement of the cropland area domain in 1908. We then compared these two reconstructions. We found that the cropland distributions reconstructed by the two methods exhibit a similar spatial distribution pattern. Both reconstructions show that the cropland was mostly found in the southern and eastern parts of the Songnen Plain. The two reconstructions matched each other for about 68% of the total cropland area. By spatially comparing the unmatched cropland cells of the two reconstructions with the settlements for each county, we found that unmatched cropland cells from method I are closer to settlements than those from method II. This finding suggests that reconstruction using method I may have less bias than reconstruction with method II.

Key words: comparison of methods, cropland cover, late Qing Dynasty, Songnen Plain, Northeast China

Lanqi JIANG, Lijuan ZHANG, Shuying *ZANG, Xuezhen ZHANG. Accuracy assessment of approaches to spatially explicit reconstruction of historical cropland in Songnen Plain, Northeast China[J].Journal of Geographical Sciences, 2016, 26(2): 219-229.

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County	Area	County	Area	County	Area	County	Area
Hailun	5043.00	Changling	2198.51	Huaide	2381.91	Mulan	530.84
Dehui	4035.59	Hulan	2180.84	Yitong	1214.34	Tangyuan	265.42
Changchun	3538.94	Suihua	2087.98	Kaitong	1159.68	Nehe	176.95
Binzhou	2927.03	Xincheng	1821.08	Wuchang	1069.06	Anda	60.65
Yushu	2775.49	Lanxi	1602.72	Anguang	926.96	Nenjiang	30.79
Nongan	2770.65	Yuqing	1494.73	Dalai	912.20	Zhaozhou	26.54
Shuangcheng	2390.08	Jing’an	1451.42	Longjiang	814.36	Guoqianqi	―
Bayan	2300.59	Taonan	1405.50	Baiquan	582.15	Acheng	―
Xingdong	―	Binjiang	―			Total	49175.98

County	Settlement	Method I	Method II	County	Settlement	Method I	Method II
Wuchang	127.1, 44.8	127.3, 44.8	127.4, 44.9	Longjiang	124.0, 47.5	124.0, 47.5	124.1, 47.5
Yuqing	127.5, 46.9	127.7, 46.9	127.5, 46.9	Yitong	125.2, 43.5	125.2, 43.5	125.2, 43.5
Lanxi	126.2, 46.3	127.7, 46.9	127.5, 46.9	Nongan	124.7, 44.6	124.9, 44.7	125.1, 44.7
Shuangcheng	126.6, 45.3	126.4, 45.4	126.3, 45.4	Dalai	123.9, 45.8	123.8, 45.9	123.8, 45.9
Hulan	126.6, 46.1	126.8, 46.0	126.7, 45.9	Anguang	123.3, 45.5	123.3, 45.5	123.3, 45.5
Anda	124.2, 46.6	124.3, 46.7	124.0, 46.6	Kaitong	122.9, 44.8	123.0, 44.9	122.7, 44.9
Binzhou	127.5, 45.8	127.6, 45.7	127.7, 45.8	Dehui	125.8, 43.9	125.7, 43.9	125.9, 44.1
Bayan	127.4, 46.1	127.4, 46.3	127.6, 46.3	Huaide	124.6, 43.8	124.4, 44.0	124.3, 44.0
Baiquan	125.9, 47.4	126.0, 47.5	125. 7, 44.4	Xincheng	125.6, 45.1	125.3, 45.1	125.3, 45.1
Mulan	128.2, 46.0	127.9, 46.1	127.9, 46.1	Yushu	126.5, 45.0	126.6, 45.0	126.6, 44.8
Hailun	126.7, 47.1	126.8, 47.2	128.0, 47.6	Taonan	122.7, 45.4	122.5, 45.4	122.6, 45.3
Suihua	127.1, 46.9	127.0, 46.8	127.2, 47.1	Changling	123.6, 44.4	123.6, 44.4	123.5, 44.4
Zhaozhou	125.0, 45.7	125.5, 45.8	124.7, 45.9	Changchun	124.9, 44.2	125.0, 44.2	124.8, 44.3
Nehe	124.9, 48.5	124.9, 48.4	125.0, 48.4	Jing’an	122.8, 45.7	123.2, 45.9	122.9, 45.8
Acheng	127.0, 45.6	126.9, 45.8	127.0, 45.7	Average	125.5, 45.7	125.5, 45.7	125.5, 45.8

County	R₁	R₂	R₃	County	R₁	R₂	R₃	County	R₁	R₂	R₃
Nongan	0.60	0.47	0.6-1	Bayan	0.63	0.63	0.1-0.2	Lindian	0.00	0.00	0-0.05
Beian	0.07	0.02	0.2-0.6	Hulan	0.52	0.52	0.1-0.2	Tailai	0.01	0.00	0-0.05
Hailun	0.58	0.60	0.2-0.6	Shuangcheng	0.46	0.43	0.1-0.2	Zhenlai	0.29	0.27	0-0.05
Suiling	0.20	0.13	0.2-0.6	Wuchang	0.20	0.24	0.1-0.2	Zhaoyuan	0.03	0.01	0-0.05
Qing’an	0.21	0.28	0.2-0.6	Gannan	0.03	0.01	0.05-0.1	Anda	0.05	0.00	0-0.05
Wangkui	0.60	0.77	0.2-0.6	Longjiang	0.01	0.01	0.05-0.1	Zhaodong	0.10	0.09	0-0.05
Lanxi	0.46	0.53	0.2-0.6	Fuyu	0.09	0.08	0.05-0.1	Zhaozhou	0.00	0.01	0-0.05
Suihua	0.49	0.60	0.2-0.6	Taonan	0.35	0.37	0.05-0.1	Acheng	0.03	0.07	0-0.05
Mulan	0.26	0.24	0.2-0.6	Qian’an	0.05	0.08	0.05-0.1	Binxian	0.31	0.51	0-0.05
Da’an	0.09	0.09	0.2-0.6	Qianguo	0.28	0.40	0.05-0.1	Tongyu	0.18	0.18	0-0.05
Fuyu	0.25	0.23	0.2-0.6	Changling	0.51	0.41	0.05-0.1	Daqing	0.00	0.00	0-0.05
Yushu	0.57	0.56	0.2-0.6	Nehe	0.03	0.03	0-0.05	Baicheng	0.36	0.50	0-0.05
Dehui	0.52	0.26	0.2-0.6	Keshan	0.00	0.00	0-0.05	Gongzhuling	0.50	0.52	0.2-0.6
Jiutai	0.48	0.03	0.2-0.6	Kedong	0.00	0.00	0-0.05	Harbin	0.26	0.28	0-0.05
Changchun	0.52	0.48	0.2-0.6	Yian	0.00	0.00	0-0.05	Qiqihar	0.06	0.09	0.05-0.1
Yitong	0.14	0.14	0.2-0.6	Baiquan	0.12	0.13	0-0.05	Dorbot	0.01	0.00	0-0.05
Qinggang	0.15	0.14	0.1-0.2	Mingshui	0.03	0.03	0-0.05	Wudalianchi	0.00	0.00	0-0.05

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