Research on the index system of cultivated land quality grading based on random forest algorithm

LIN Zicong; REN Xiangning; ZHU Axing; ZHAO Xin; HU Yueming

doi:10.7671/j.issn.1001-411X.201909036

Journal of South China Agricultural University > 2020 > 41(4): 38-48. > DOI: 10.7671/j.issn.1001-411X.201909036

LIN Zicong, REN Xiangning, ZHU Axing, et al. Research on the index system of cultivated land quality grading based on random forest algorithm[J]. Journal of South China Agricultural University, 2020, 41(4): 38-48. DOI: 10.7671/j.issn.1001-411X.201909036

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Research on the index system of cultivated land quality grading based on random forest algorithm

LIN Zicong^1,,
REN Xiangning¹,
ZHU Axing^{1, 2},
ZHAO Xin¹,
HU Yueming^{1, 3, 4, ,}

1.
College of Natural Resources and Environment, South China Agricultural University/Institute of Geographic Information Engineering, South China Agricultural University/Key Laboratory of Construction Land Improvement, Ministry of Land and Resources/Guangdong Provincial Key Laboratory of Land Use and Consolidation, Guangzhou 510642, China
2.
Department of Geography, University of Wisconsin-Madison, Madison 53706, USA
3.
College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
4.
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 610054, China

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Received Date: September 17, 2019
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To analyze the difference of cultivated land quality in the study region, optimize the use and layout of cultivated land, and provide a reference for cultivated land protection.

Method
Taking the cultivated land in Gonghe County, Dulan County and Wulan County in Qinghai Province as the research object, the influencing factors of cultivated land quality were collected based on the history and existing literature, and the random forest algorithm and correlation analysis were used to screen the grading indicators and confirm the weight. We calculated the grading index and divided the levels by weighted sum method to get the grading result. We compared the results with the grading results of commonly used Delphi method.

Result
The value of variable importance I obtained by random forest algorithm ranged from 0.03 to 11.94. Correlation analysis showed that the correlation between most influencing factors was not significant, eight of which were significant correlation. The 14 rating indicators under four dimensions were astringed from 30 influencing factors. The main factors influencing the quality of cultivated land in the study area were ecosystem vulnerability, mean precipitation of growing season and annual solar radiation amount, with the weights of 0.11, 0.10 and 0.09, respectively.

Conclusion
Compared with Delphi method, the random forest algorithm has better stability and smaller level of index variation interval, which is more conducive to construct a comparable sequence of cultivated land levels at provincial spatial scale.
- cultivated land quality evaluation,
- grading indicators system,
- random forest algorithm,
- Delphi method

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References (36)

References

[1]	吴大放, 刘艳艳, 董玉祥, 等. 我国耕地数量、质量与空间变化研究综述[J]. 热带地理, 2010, 30(2): 108-113. doi: 10.3969/j.issn.1001-5221.2010.02.002
[2]	温良友, 孔祥斌, 辛芸娜, 等. 对耕地质量内涵的再认识[J]. 中国农业大学学报, 2019, 24(3): 156-164.
[3]	张超, 乔敏, 郧文聚, 等. 耕地数量、质量、生态三位一体综合监管体系研究[J]. 农业机械学报, 2017, 48(1): 1-6. doi: 10.6041/j.issn.1000-1298.2017.01.001
[4]	刘兴华, 孙鹏举, 刘学录. 甘肃省临夏县耕地资源社会保障价值测算[J]. 干旱区资源与环境, 2013, 27(1): 53-57.
[5]	中华人民共和国国土资源部. 农用地定级规程: GB/T 28405—2012 [S]. 北京: 中国标准出版社, 2012.
[6]	高中贵, 彭补拙. 我国农用地分等定级研究综述[J]. 经济地理, 2004, 24(4): 514-519. doi: 10.3969/j.issn.1000-8462.2004.04.020
[7]	金东海, 许皞, 秦文利. 基于分等成果的农用地定级新方法:两层七参数法[J]. 中国土地科学, 2004, 18(6): 34-39. doi: 10.3969/j.issn.1001-8158.2004.06.007
[8]	鲁明星, 贺立源, 吴礼树. 我国耕地地力评价研究进展[J]. 生态环境, 2006, 8(4): 866-871.
[9]	冯超. 中国谷物产出的"面积− 质量"导向因素分析[J]. 干旱区资源与环境, 2015, 29(8): 7-13.
[10]	沈仁芳, 陈美军, 孔祥斌, 等. 耕地质量的概念和评价与管理对策[J]. 土壤学报, 2012, 49(6): 1210-1217. doi: 10.11766/trxb201208130319
[11]	张凤荣, 安萍莉, 王军艳, 等. 耕地分等中的土壤质量指标体系与分等方法[J]. 资源科学, 2002, 24(2): 71-75. doi: 10.3321/j.issn:1007-7588.2002.02.014
[12]	付国珍, 摆万奇. 耕地质量评价研究进展及发展趋势[J]. 资源科学, 2015, 35(2): 226-236.
[13]	盛艳, 姚云峰, 秦富仓, 等. 基于GIS的耕地地力等级划分研究[J]. 干旱区资源与环境, 2014, 28(6): 27-32.
[14]	马瑞明, 马仁会, 韩冬梅, 等. 基于多层级指标的省域耕地质量评价体系构建[J]. 农业工程学报, 2018, 34(16): 249-257. doi: 10.11975/j.issn.1002-6819.2018.16.032
[15]	杜国明, 刘彦随, 于凤荣, 等. 耕地质量观的演变与再认识[J]. 农业工程学报, 2016, 32(14): 243-249.
[16]	马昊翔, 陈长成, 宋英强, 等. 青海省近10年草地植被覆盖动态变化及其驱动因素分析[J]. 水土保持研究, 2018, 25(6): 137-145.
[17]	保广裕, 张静, 周丹, 等. 青海省太阳辐射强度时空变化特征分析[J]. 冰川冻土, 2017, 39(3): 563-571.
[18]	樊杰. 中国主体功能区划方案[J]. 地理学报, 2015, 70(2): 186-201. doi: 10.11821/dlxb201502002
[19]	BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
[20]	CHEN X W, LIU M. Prediction of protein-protein interactions using random decision forest framework[J]. Bioinformatics, 2005, 21(24): 4394-4400. doi: 10.1093/bioinformatics/bti721
[21]	WARD M M, PAJEVIC S, DREYFUSS J, et al. Short-term prediction of mortality in patients with systemic lupus erythematosus: Classification of outcomes using random forests[J]. Arthrit Rheumat, 2006, 55(1): 74-80. doi: 10.1002/art.21695
[22]	OPARIN I, GLEMBEK O, BURGET L, et al. Morphological random forests for language modeling of inflectional languages[C/OL]//IEEE. 2008 IEEE Spoken Language Technology Workshop. Goa: IEEE, 2008: 189-192. [2019-08-25]. https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004777872/tab/summary. doi: 10.1109/SLT.2008.4777872.
[23]	ZHANG M, ZHANG H, WU P, et al. Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model[J]. Sci Total Environ, 2017, 592: 704-713. doi: 10.1016/j.scitotenv.2017.02.146
[24]	方匡南, 朱建平, 谢邦昌. 基于随机森林方法的基金收益率方向预测与交易策略研究[J]. 经济经纬, 2010, 27(2): 61-65. doi: 10.3969/j.issn.1006-1096.2010.02.015
[25]	董师师, 黄哲学. 随机森林理论浅析[J]. 集成技术, 2013, 2(1): 1-7.
[26]	张雷, 王琳琳, 张旭东, 等. 随机森林算法基本思想及其在生态学中的应用:以云南松分布模拟为例[J]. 生态学报, 2014, 24(3): 650-659.
[27]	刘斌, 郭星, 朱宇恩. 基于随机森林模型的土壤重金属源解析:以晋中盆地为例[J]. 干旱区资源与环境, 2019, 33(1): 106-111.
[28]	马玥, 姜琦刚, 孟治国, 等. 基于随机森林算法的农耕区土地利用分类研究[J]. 农业机械学报, 2016, 47(1): 297-303. doi: 10.6041/j.issn.1000-1298.2016.01.040
[29]	ZHU Z, WOODCOCK C E, ROGAN J, et al. Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data[J]. Rem Sens Environ, 2012, 117: 72-82. doi: 10.1016/j.rse.2011.07.020
[30]	VAN BEIJMA S, COMBER A, LAMB A. Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data[J]. Rem Sens Environ, 2014, 149: 118-129. doi: 10.1016/j.rse.2014.04.010
[31]	LIAW A, WIENER M. Classification and regression by random forest[J]. R News, 2002, 2(3): 18-22.
[32]	刘欢, 吴克宁, 宋文, 等. 耕地质量定级方法改进研究:以农安县为例[J]. 北京师范大学学报(自然科学版), 2018, 54(3): 315-320.
[33]	赵璐, 郑新奇, 闫弘文, 等. 基于地统计学的县域农用地定级方法[J]. 农业工程学报, 2008, 24(S1): 99-103.
[34]	黄居茂. 青海省农作物生产发展的科学技术探讨[J]. 青海农林科技, 1984, 14(4): 18-26.
[35]	朱文江, 康素珍. 柴达木盆地春小麦高产的气候因素[J]. 中国农业科学, 1978, 19(2): 51-56.
[36]	张玮, 李江. 青海省菜田盐渍化形成及治理[J]. 青海农技推广, 2015, 20(2): 32-33. doi: 10.3969/j.issn.1008-7117.2015.02.012

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Research on the index system of cultivated land quality grading based on random forest algorithm

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