Analysis of factors affecting the extraction of relief amplitude by mean change- point method:taking the Yellow River Basin in Shanxi as an example

doi:10.13474/j.cnki.11-2246.2022.0063

Abstract

Abstract: Relief amplitude factor plays an important role in macro-scale ecological assessments. The mean change-point method is the mainstream method for determining the best fit window (BFW) of relief amplitude, but its influencing factors are still lacking in research. Taking the Yellow River Basin in Shanxi as an example, this paper extracts its relief amplitude based on DEM data and the mean change-point method, and explores the effects of three factors, namely number of analysis window samples, DEM resolution and landform type, respectively. The results show that: ① Number of analysis window samples has an important effect on the value of the best fit window. As the number of samples increases, the area of the best fit window where the change-point located also increases. ② DEM resolution has a certain effect on the best fit window value. When the analysis window area has the same value range, the best fit window area calculated based on 30 m ASTER GDEM is always smaller than the best fit window area based on 90 m SRTM DEM. ③ Landform type has little effect on the value of the best fit window. When the number of analysis window samples is the same, the best fit window for different landform areas and the entire study area is always same or similar. In short, number of analysis window samples is the key influencing factor.

Key words: digital elevation model, best fit window, number of analysis window samples, spatial resolution, landform type

CLC Number:

P237

NING Ting, CUI Wei, MA Xiaoyong. Analysis of factors affecting the extraction of relief amplitude by mean change- point method:taking the Yellow River Basin in Shanxi as an example[J]. Bulletin of Surveying and Mapping, 2022, 0(2): 159-163.

References

[1] RENARD K G, FOSTER G R, WEESIES G A, et al. Predicting soil erosion by water:a guide to conservation planning with the revised universal soil loss equation (RUSLE)[M].Washington:US Department of Agriculture, 1997:404-407.
[2] WANG T. Land use and sandy desertification in the North China[J]. Journal of Desert Research, 2000, 20(2):103-107.
[3] 刘新华,杨勤科,汤国安.中国地形起伏度的提取及在水土流失定量评价中的应用[J].水土保持通报, 2001, 21(1):57-59, 62.
[4] 舒天竹,王晓红.基于3S技术的地形起伏度与区域土壤侵蚀的相关性研究[J].水土保持研究, 2017, 24(4):127-132.
[5] 李该霞,宋蒙亚,谢丽芳,等.江苏省水土保持功能的重要性评价[J].水土保持通报, 2016, 36(1):236-241.
[6] 封志明,李文君,李鹏,等.青藏高原地形起伏度及其地理意义[J].地理学报, 2020, 75(7):1359-1372.
[7] 王让虎,张树文,蒲罗曼,等.基于ASTER GDEM和均值变点分析的中国东北地形起伏度研究[J].干旱区资源与环境, 2016, 30(6):49-54.
[8] 王志恒,胡卓玮,赵文吉,等.应用累积和分析算法的地形起伏度最佳统计单元确定[J].测绘科学, 2014, 39(6):59-64.
[9] 钟静,卢涛.中国西南地区地形起伏度的最佳分析尺度确定[J].水土保持通报, 2018, 38(1):175-181.
[10] 贺晓娟,韩锦涛.山西省水土流失空间分布和现状[J].江西农业学报, 2007, 19(3):97-98, 105.
[11] 陈学兄,毕如田,刘正春,等.基于ASTER GDEM数据的山西地形起伏度分析研究[J].山西农业大学学报(自然科学版), 2016, 36(6):417-421.
[12] 王宁,刘万青,姚志宏,等.多源DEM数据地形因子表达准确性分析[J].测绘通报, 2018(9):39-44.
[13] 张伟,李爱农.基于DEM的中国地形起伏度适宜计算尺度研究[J].地理与地理信息科学, 2012, 28(4):8-12.
[14] 张竞,杜东,白耀楠,等.基于DEM的京津冀地区地形起伏度分析[J].中国水土保持, 2018(9):33-37.
[15] 涂汉明,刘振东.中国地势起伏度最佳统计单元的求证[J].湖北大学学报(自然科学版), 1990, 12(3):266-271.
[16] 南希,李爱农,景金城.中国山地起伏度计算中地形自适应滑动窗口获取与验证[J].地理与地理信息科学, 2017, 33(4):34-39.
[17] 张锦明,游雄.地形起伏度最佳分析区域预测模型[J].遥感学报, 2013, 17(4):728-741.
[18] 郑鑫鑫.基于DEM的山西省地貌信息与土地利用分形特征研究[D].晋中:山西农业大学, 2014.
[19] 康立,王国梁.基于GIS的山西省地形起伏度及其与人口分布相关性的研究[J].山西师范大学学报(自然科学版), 2016, 30(4):98-102.