沂蒙山区不同抽样密度对土壤侵蚀因子估算精度的影响
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摘要
[目的]探讨分层系统抽样方法下不同抽样密度对土壤侵蚀因子估算精度的影响,为区域水土流失动态监测抽样方法和抽样密度的选取提供数据支撑。[方法]以沂蒙山泰山国家级重点治理区蒙阴县为对象,基于2013年SPOT5遥感影像和1∶1万地形图,采用人机交互解译、野外调查、统计分析等方法,以全县土壤侵蚀因子为基准值,对1%和4%密度土壤侵蚀因子进行精度评价。[结果]①1%抽样密度下,S,E,K因子相对误差较大,分别为33.48%,23.46%,20.64%,主要受坡度、土地利用和土壤类型影响;L,B,T相对误差均小于11%;六者平均14.44%。②4%抽样密度下,E,K,B相对误差较大,分别为15.07%,13.94%和10.69%,主要受土地利用和土壤类型影响;L,S,T相对误差均小于10%;6者平均7.89%。③以栅格计算法结果为基准值,采用单元插值外推法推算全县水土流失面积,1%密度下水土流失面积比偏高19.73%,4%密度下水土流失面积比偏高11.77%。[结论]蒙阴县1%和4%密度各因子均有不同程度的精度损失,并对水土流失估算结果造成一定影响,在区域水土流失动态监测过程中可根据需求选取合适的抽样密度。
[Objective] In order to provide data support for sampling method and sampling density selection in regional dynamic monitoring of soil erosion, the influence of sampling densities on estimation precision of soil erosion factors was studied. [Methods] The paper took Mengyin County, in the Yimeng and Tai Mountains national key control areas, as the research object to calculate the precision loss of soil erosion factors estimation under 1% and 4% sampling densities. The precision loss of soil erosion factors estimation under 1% and 4% sampling densities, compared to the soil erosion factors of the county was evaluated by human-computer interaction interpretation, field investigation and statistical analysis, based on the SPOT5 remote sensing images in 2013 and 1∶10 000 topographic maps. [Results] ① Under the 1% sampling density, the relative errors of S, E and K factors were 33.48%, 23.46% and 20.64% respectively, which are mainly influenced by slope, land use and soil types, while the relative errors of L, B and T factors were less than 11%. The average relative error of soil erosion factors was 14.44% in the field investigation units. ② Under the 4% sampling density, the relative errors of E, K and B factors were 15.07%, 13.94% and 10.69% respectively, which were mainly affected by land use and soil types, while the relative errors of L, S and T factors were less than 10%. The average relative error of soil erosion factors in the field survey units was 7.89%. ③ The results calculated by the element interpolation extrapolation method under the two sampling densities were higher than that of the grid calculation method. The area of soil erosion under the 1% sampling density was 19.73% higher than that of the grid calculation method, and under the 4% sampling density, it was 11.77% higher. [Conclusion] The 1% and 4% density factors of Mengyin County had different degree of precision loss, which had a certain influence on the results of soil erosion estimation. In the process of dynamic monitoring of regional soil erosion, the appropriate sampling density can be selected according to the demand.
[Objective] In order to provide data support for sampling method and sampling density selection in regional dynamic monitoring of soil erosion, the influence of sampling densities on estimation precision of soil erosion factors was studied. [Methods] The paper took Mengyin County, in the Yimeng and Tai Mountains national key control areas, as the research object to calculate the precision loss of soil erosion factors estimation under 1% and 4% sampling densities. The precision loss of soil erosion factors estimation under 1% and 4% sampling densities, compared to the soil erosion factors of the county was evaluated by human-computer interaction interpretation, field investigation and statistical analysis, based on the SPOT5 remote sensing images in 2013 and 1∶10 000 topographic maps. [Results] ① Under the 1% sampling density, the relative errors of S, E and K factors were 33.48%, 23.46% and 20.64% respectively, which are mainly influenced by slope, land use and soil types, while the relative errors of L, B and T factors were less than 11%. The average relative error of soil erosion factors was 14.44% in the field investigation units. ② Under the 4% sampling density, the relative errors of E, K and B factors were 15.07%, 13.94% and 10.69% respectively, which were mainly affected by land use and soil types, while the relative errors of L, S and T factors were less than 10%. The average relative error of soil erosion factors in the field survey units was 7.89%. ③ The results calculated by the element interpolation extrapolation method under the two sampling densities were higher than that of the grid calculation method. The area of soil erosion under the 1% sampling density was 19.73% higher than that of the grid calculation method, and under the 4% sampling density, it was 11.77% higher. [Conclusion] The 1% and 4% density factors of Mengyin County had different degree of precision loss, which had a certain influence on the results of soil erosion estimation. In the process of dynamic monitoring of regional soil erosion, the appropriate sampling density can be selected according to the demand.
引文
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[13] 赵维军,朱清科,马欢,等.不同抽样比例的土壤侵蚀因子调查精度比较[J].西北林学院学报,2012,28(3):168-171.
[14] 邹丛荣,齐斐,张庆红,等.CSLE模型应用中不同抽样密度和推算方法的比较[J].中国水土保持科学,2016,14(3):130-138.
[15] Zhang Hongda,Zhang Ronghua,Qi Fei,et al.The CSLE model based soil erosion prediction:Comparisons of sampling density and extrapolation method at the county level [J].Catena,2018,165:465-472.
[16] Liu Baoyuan.,Zhang Keli,Xie Yun.An empirical soil loss equation[C]//Proceedings-Process of soil erosion and its environment effect,12th international soil conservation organization conference,Beijing,Tsinghua University Press,2002.
[17] 章文波,谢云,刘宝元.利用日雨量计算降雨侵蚀力的方法研究[J].地理科学,2002,22(6):705-711.
[18] 杨韶洋.基于CSLE模型的沂蒙山国家级重点治理区土壤侵蚀格局分析[D].山东泰安:山东农业大学,2014.
[2] 章文波,刘宝元.基于GIS的中国土壤侵蚀预报信息系统[J].水土保持学报,2003,17(2):89-92.
[3] 刘宝元,郭索彦,李智广,等.中国水力侵蚀抽样调查[J].中国水土保持,2013(10):26-34.
[4] 郭索彦.土壤侵蚀调查与评价[M].北京:中国水利水电出版社,2014.
[5] 杨勤科,李锐,曹明明.区域土壤侵蚀定量研究的国内外进展[J].地球科学进展,2006,21(8):849-856.
[6] 程琳,杨勤科,谢红霞,等.基于GIS和CSLE的陕西省土壤侵蚀定量评价方法研究[J].水土保持学报,2009,23(5):61-66.
[7] 中华人民共和国水利部.SL190-96 土壤侵蚀分类分级标准[S].北京:中国水利水电出版社,1997.
[8] 刘震.我国水土保持情况普查及成果运用[J].中国水土保持科学,2013,11(2):1-5.
[9] 李智广,刘宪春,刘建祥,等.第一次全国水利普查水土保持普查方案[J].水土保持通报,2010,30(3):87-91.
[10] 国务院第一次全国水利普查领导小组办公室.第一次全国水利普查培训教材之六:水土保持情况普查[M].北京:中国水利水电出版社,2010.
[11] 李智广,符素华,刘宝元.我国水力侵蚀抽样调查方法[J].中国水土保持科学,2012,10(1):77-81.
[12] 刘淑珍,刘斌涛,苏正安,等.对我国水土流失调查评价方法若干问题的思考[J].山地学报,2014,32(2):150-153.
[13] 赵维军,朱清科,马欢,等.不同抽样比例的土壤侵蚀因子调查精度比较[J].西北林学院学报,2012,28(3):168-171.
[14] 邹丛荣,齐斐,张庆红,等.CSLE模型应用中不同抽样密度和推算方法的比较[J].中国水土保持科学,2016,14(3):130-138.
[15] Zhang Hongda,Zhang Ronghua,Qi Fei,et al.The CSLE model based soil erosion prediction:Comparisons of sampling density and extrapolation method at the county level [J].Catena,2018,165:465-472.
[16] Liu Baoyuan.,Zhang Keli,Xie Yun.An empirical soil loss equation[C]//Proceedings-Process of soil erosion and its environment effect,12th international soil conservation organization conference,Beijing,Tsinghua University Press,2002.
[17] 章文波,谢云,刘宝元.利用日雨量计算降雨侵蚀力的方法研究[J].地理科学,2002,22(6):705-711.
[18] 杨韶洋.基于CSLE模型的沂蒙山国家级重点治理区土壤侵蚀格局分析[D].山东泰安:山东农业大学,2014.