Effects of two spatial data structures on soil erosion assessment at county scale
LI Wenlong1, YUAN Li2, WEI Wenjie1, LIU Zhuohao1, GAO Ruiyu1, NIU Yong1, ZHAO Chuanpu2, ZHANG Ronghua1
1. Mountain Tai Forest Ecosystem Research Station of State Forestry Administration, College of Forestry, Shandong Agricultural University, 271018 Tai'an, Shandong, China; 2. Huaihe River Basin Soil and Water Conservation Monitoring Center of Huaihe River Water Conservancy Commission, 233001 Bengbu, Anhui, China
Abstract：[Background] The existing county-scale soil erosion evaluation method mostly uses raster structure as the basic calculation unit, leading to multiple raster numbers and multiple erosion intensities in a single vector plot, which makes it difficult to directly apply to vector plot-based soil erosion control and planning. Therefore, studying the impact of different spatial data structures of raster and vector on soil erosion calculation and evaluation is of great significance to the dynamic monitoring of soil erosion and the application of results, and to serve soil and water conservation planning and soil erosion control. [Methods] This study used Ruzhou city, Henan province as the research area, based on two different spatial data structures of raster and vector, combined with China's soil loss equation and remote sensing monitoring, the differences in soil erosion among 5 methods were compared and analyzed. [Results] 1) The raster statistics method based on raster calculation took 8min, which quickly and accurately calculated the soil erosion area and intensity, and accurately described the erosion area and intensity of different land uses, but the mapping effect of aggregation index was bad. This method was suitable for soil erosion evaluation at county scale that did not require mapping. 2)The software judgment method presented the smallest difference in calculation results, and demonstrated the best effect in drawing. Compared with the raster statistics method, the software judgment method had a difference of 0.05km2 in the erosion area, with a difference rate of 0.01%, and it had a small difference in the erosion area of different erosion intensity and different land use, which took 13min. The aggregation index was 96.81%, which was good mapping effect. It is suitable for soil erosion evaluation and mapping at county level. 3)The calculation time of the land parcel evaluation method was moderate, which was 16min. But the erosion area difference was 64.63km2, the difference rate was 16.02% and the area of different erosion intensity cannot be provided. It was not suitable for soil erosion evaluation at the county scale. 4) There were large differences in soil erosion area, intensity and erosion area of different land use between two vector calculation methods. The large amount of vector data led to longer calculation time, but the advantage was that the mapping effect was better. These two methods were suitable for calculation and evaluation of soil erosion in plain areas with relatively consistent soil types, terrain slopes, and vegetation coverage. [Conclusions] The research results may provide reference for water erosion evaluation, dynamic monitoring of soil and water loss and application of the results at county level.
李智广,姜学兵,刘二佳,等.我国水土保持监测技术和方法的现状与发展方向[J].中国水土保持科学,2015,13(4):144. LI Zhiguang, JIANG Xuebing, LIU Erjia, et al. Present status and future development of technologies and methods for soil and water conservation monitoring in China[J]. Science of Soil and Water Conservation,2015,13(4):144.
[2]
LIU B, XIE Y, LI Z, et al. The assessment of soil loss by water erosion in China[J]. International Soil and Water Conservation Research, 2020, 8(4): 430.
[3]
赵辉,李琦.区域水土流失动态监测评价的大数据分析基础与对策[J].中国水土保持科学,2016,14(4):68. ZHAO Hui, LI Qi. Foundations and countermeasures on big data analysis for dynamic monitoring and evaluation of regional soil and water loss[J]. Science of Soil and Water Conservation,2016,14(4):68.
[4]
赵辉,黎家作,李晶晶.中国水土流失动态监测与评价的现状与对策[J].水土保持通报,2016,36(1):115. ZHAO Hui, LI Jiazuo, LI Jingjing. Present situation and countermeasures of dynamic monitoring and evaluation of soil and water loss in China[J]. Bulletin of Soil and Water Conservation,2016,36(1):115.
[5]
李智广.《全国水土流失动态监测规划(2018—2022年)》的编制原则与目标任务[J].中国水土保持,2018(5):20. LI Zhiguang. Principle and objective of nationwide soil and water loss dynamic monitoring planning (2018-2022)"[J]. Soil and Water Conservation in China,2018(5):20.
[6]
水利部水土保持监测中心.2020年度水土流失动态监测技术指南[R].北京:水利部,2020:15. The Center of Soil and Water Conservation Monitoring, Ministry of Water Resources. 2020 technical guide for dynamic monitoring of soil erosion[R]. Beijing: Ministry of Water Resources, 2020:15.
[7]
吴思颖,汪小钦,曾舒娇,等.基于CSLE的安溪县土壤侵蚀估算与分析[J].中国水土保持科学,2019,17(4):112. WU Siying, WANG Xiaoqin, ZENG Shujiao, et al. Estimation and analysis of soil erosion in Anxi county based on CSLE[J]. Science of Soil and Water Conservation,2019,17(4):112.
[8]
陈峰,李红波.基于GIS和RUSLE的滇南山区土壤侵蚀时空演变:以云南省元阳县为例[J].应用生态学报,2021,32(2):629. CHEN Feng, LI Hongbo. Spatial-temporal variations of soil erosion in southern Yunnan Mountainous Area using GIS and RUSLE: A case study in Yuanyang county, Yunnan province, China [J]. Chinese Journal of Applied Ecology, 2021, 32(2): 629.
[9]
邹海天,李子轩,张彩云.基于栅格像元和矢量地块的区域土壤侵蚀计算结果对比分析:以怀来县外井沟小流域为例[J].海河水利,2020(4):16. ZOU Haitian, LI Zixuan, ZHANG Caiyun. Contrastive analysis on computed result of regional soil erosion based on grid cell and vector plot[J]. Haihe Water Resources, 2020(4): 16.
[10]
符素华,刘宝元,周贵云,等.坡长坡度因子计算工具[J].中国水土保持科学,2015,13(5):105. FU Suhua, LIU Baoyuan, ZHOU Guiyun, et al. Calculation tool of topographic factors[J]. Science of Soil and Water Conservation,2015,13(5):105.
[11]
中华人民共和国水利部.土壤侵蚀分类分级标准:SL190—2007[S].北京:中国水利水电出版社,2008:8. Ministry of Water Resources of the People's Republic of China. Soil erosion classification and grading standard: SL190—2007[S]. Beijing: China Water and Power Press, 2008:8.
[12]
孔凡强. 矢、栅土地利用图斑综合的算法及应用分析[D].辽宁大连:辽宁师范大学,2014:11. KONG Fanqiang. Vector and raster land use map synthesis algorithm and application analysis[D]. Dalian, Liaoning:Liaoning Normal University, 2014:11.
[13]
刘晓君,李占斌,李鹏,等.基于土地利用/覆被变化的流域景观格局与水沙响应关系研究[J].生态学报,2016,36(18):5691. LIU Xiaojun, LI Zhanbin, LI Peng, et al. Land use/cover change based relationship between landscape, runoff, and sedimentation[J]. Acta Ecologica Sinica,2016,36(18):5691.
[14]
白燕,廖顺宝,孙九林.栅格化属性精度损失的评估方法及其尺度效应分析:以四川省1∶25万土地覆被数据为例[J].地理学报,2011,66(5):709. BAI Yan, LIAO Shunbao, SUN Jiulin. Evaluating methods and scale effects of attribute information loss in rasterization: A case study of 1∶250000 land cover data of Sichuan[J]. Acta Geographica Sinica,2011,66(5): 709.
[15]
崔步礼,常学礼,左登华,等.沙地景观中矢量数据栅格化方法及尺度效应[J].生态学报,2009, 29(5):2463. CUI Buli, CHANG Xueli, ZUO Denghua, et al. Rasterizing vector data and its scaling effect for a sand landscape[J]. Acta Ecologica Sinica, 2009, 29(5): 2463.
[16]
游晋卿.土地利用数据小图斑制图综合及质量评价的研究[D].南昌:东华理工大学,2015:13. YOU Jinqing. Study on the synthesis and quality evaluation of small pattern mapping of land use data[D]. Nanchang:East China University of Technology,2015:13.
2022, Vol. 20 
