[1]魏智海,张志超,李崇贵*. 基于GF-1PMS影像的森林郁闭度定量估测模型研究[J].西北林学院学报,2022,37(4):231-237.[doi:10.3969/j.issn.1001-7461.2022.04.31]
　WEI Zhi-hai,ZHANG Zhi-chao,LI Chong-gui*. Quantitative Estimation of Forest Canopy Density Based on GF-1 PMS Images[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2022,37(4):231-237.[doi:10.3969/j.issn.1001-7461.2022.04.31]
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 基于GF-1PMS影像的森林郁闭度定量估测模型研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第37卷
期数:
2022年第4期
页码:
231-237
栏目:
出版日期:
2022-07-31

文章信息/Info

Title:
 Quantitative Estimation of Forest Canopy Density Based on GF-1 PMS Images
文章编号:
1001-7461(2022)04-0231-07
作者:
 魏智海1; 张志超2; 李崇贵2*
 （1.国家林业和草原局 西北调查规划设计院，陕西 西安 710048；2.西安科技大学 测绘科学与技术学院，陕西 西安 710054）
Author(s):
 WEI Zhi-hai1; ZHANG Zhi-chao2; LI Chong-gui2*
 (1.Northwest Surveying,Planning and Designing Institute of National Forestry and Grassland Administration,Xi’an 710048,Shaanxi,China; 2.College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,Shaanxi,China)
关键词:
 ; k-最近邻法; 稳健估计; 偏最小二乘法; 森林郁闭度
Keywords:
 ; k-nearest neighbor;  robust estimate;  partial least square;  forest canopy density
分类号:
S771
DOI:
10.3969/j.issn.1001-7461.2022.04.31
文献标志码:
A
摘要:
 森林郁闭度是我国森林资源二类调查中的重要林分因子之一，为探索高分一号影像在森林郁闭度定量估测的可行性，以内蒙古自治区东北部柴河林业局为研究区，基于GF-1 PMS多光谱影像和DEM数据，以纹理特征、光谱信息和地形因子为自变量，采用k-最近邻法（k-Nearest Neighbor,k-NN）、稳健估计以及偏最小二乘法3种方法构建研究区域的森林郁闭度估测模型，辅之以二类调查小班数据和现地实测数据进行模型精度评价。结果表明：1）3种郁闭度估测方法的应用效果均能满足实际需求，这说明GF-1 PMS多光谱影像在森林郁闭度定量估测方面具有一定的潜力；2）k-NN法和稳健估计的实际应用效果明显优于偏最小二乘法，且2种模型的估测精度均>80%；3）3种郁闭度估测稳定性分析，结果显示k-NN法稳定性较好，而稳健估计法和偏最小二乘法估测模型不稳定。
Abstract:
 Forest density is one of the important forest stand factors in China’s forest resource class II surveys.In order to explore the feasibility of the GF-1 images in quantitative estimation of forest density,based on GF-1 PMS multispectral images and DEM data,this study adopted k-nearest neighbor (k-NN),robust estimation,and partial least square methods to construct a model for estimating forest density in the study area by using texture features,spectral information,and topographic factors as independent variables.The model accuracy was evaluated with small-group data and in situ measurement data from the Type II survey.The results showed that 1) the application of the three methods for estimating forest density could meet the practical needs,which indicated that GF-1 PMS multispectral images had certain potential for quantitative estimation of forest canopy density; 2) the practical applications of k-NN and robust estimation methods were significantly better than partial least square method,and the rates of estimation accuracy of both models were higher than 80%; 3) the stability analysis of the three depression estimations showed that the k-NN method was more stable,while the other two methods were unstable.

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备注/Memo

备注/Memo:
 收稿日期：2021-06-17修回日期：2021-09-08
基金项目：国家自然科学基金项目（42077073）；国家重点研发计划（2017YFC0504704）。
第一作者：魏智海，高级工程师。研究方向：森林资源调查监测。E-mail:439405345@qq.com
*通信作者：李崇贵，教授。研究方向：遥感定量估算和3S集成技术。E-mail：864958361@qq.com
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