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基于DEM的小班坡度自动提取算法及其验证 被引量:4

Automatic Extraction Algorithm and Verification of Subcompartment Slope Based on DEM
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摘要 针对现有的小班坡度计算方法存在的复杂地形适应能力较差、提取结果准确度不高的问题,提出了一种基于DEM的小班坡度自动提取算法,通过与其他2种传统方法进行对比分析,检验了该方法的准确性与实用性。结果表明:该方法剔除了小班内的异常的高低起伏点,并获取了小班所在的斜平面模型,综合考虑了小班整体情况。该算法得到的结果更准确、更实用,能够适应地形复杂度较高的区域,替代人工完成小班坡度值的测量,节省时间、人力和物力。 Aiming at the problem that the existing subcompartment gradient calculation method has poor adaptability to complex terrain and low accuracy of extraction results, a subcompartment automatic extraction algorithm based on DEM was proposed. The accuracy and practicability of the method were tested by comparison with other 2 traditional methods. Results show that the method eliminates the abnormal high and low fluctuation points in the subcompartment, and obtains the oblique plane model of the subcompartment, taking into account the overall situation of the subcompartment. The result obtained by the algorithm is more accurate and practical, and can adapt to the area with high terrain complexity, instead of manually completing the measurement of the subcompartment value, saving time, manpower and material resources.
作者 陈晨 陈永刚 徐文兵 梁丹 Chen Chen;Chen Yonggang;Xu Wenbing;Liang Dan(Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration,Zhejiang A &F University, Hangzhou Zhejiang 311300,China;School of Surveying and Geoinformatics,Tongji University,Shanghai 200092,China)
出处 《西南林业大学学报(自然科学)》 CAS 北大核心 2019年第4期83-88,共6页 Journal of Southwest Forestry University:Natural Sciences
基金 浙江省自然科学基金项目(LY16D010009)资助 浙江省基础公益研究计划项目(LGN18C160004)资助
关键词 DEM 小班 坡度 异常的高低起伏点 斜平面模型 digital elevation model subcompartment slope abnormal highs and lows inclined plane model
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