南京林业大学学报(自然科学版) ›› 2023, Vol. 47 ›› Issue (1): 23-30.doi: 10.12302/j.issn.1000-2006.202108045

所属专题: 智慧林业之森林参数遥感估测

• 专题报道Ⅰ:智慧林业之森林参数遥感估测 • 上一篇    下一篇

基于4-Scale模型的人工林郁闭度遥感估测

何萍(), 于颖(), 范文义, 杨曦光   

  1. 森林生态系统可持续经营教育部重点实验室,东北林业大学林学院,黑龙江 哈尔滨 150040
  • 收稿日期:2021-08-26 接受日期:2021-10-28 出版日期:2023-01-30 发布日期:2023-02-01
  • 通讯作者: 于颖
  • 基金资助:
    国家自然科学基金面上项目(31870621);国家自然科学基金面上项目(31971580)

Remote sensing estimation of plantation canopy closure based on 4-Scale model

HE Ping(), YU Ying(), FAN Wenyi, YANG Xiguang   

  1. Key Laboratory of Sustainable Forest Ecosystem Management, Ministry of Education, College of Forestry, Northeast Forestry University, Harbin 150040, China
  • Received:2021-08-26 Accepted:2021-10-28 Online:2023-01-30 Published:2023-02-01
  • Contact: YU Ying

摘要: 【目的】 为了寻找受区域影响较小、精度较高且鲁棒性较好的郁闭度遥感估算模型,采用4-Scale几何光学模型估算人工林树冠孔隙率及郁闭度。【方法】 选择内蒙古旺业甸林场和广西高峰林场为实验区,首先对4-Scale模型进行参数敏感性分析,模拟林分在不同敏感性参数下的树冠孔隙率Pvg_c(树冠为刚体时的冠间孔隙率)和Pvg(考虑树冠内部孔隙的孔隙率),建立Pvg_cPvg与敏感性参数的一一对应关系数据库。其次,根据数据库建立Pvg_cPvg与敏感性参数的统计关系模型。然后根据获得的敏感性参数估算Pvg_cPvg,进而估算林分郁闭度。最后,分别采用样线法与鱼眼相机测定法测量的郁闭度检验基于Pvg_cPvg估算的林分郁闭度。【结果】 基于 Pvg_cPvg估算的人工林郁闭度精度分别为88.17%和92.8%。Pvg_c与敏感性参数林木株数和冠半径相关性更高,模型的R2和均方根误差(RMSE)分别为0.814和0.043。Pvg与敏感性参数LAI的相关性更高,模型的R2和RMSE分别为0.795和0.040。【结论】 Pvg_cPvg均可以用来估算人工林郁闭度,虽然Pvg估算郁闭度的精度更高,但是其估算的郁闭度不是林业上定义的郁闭度,林业上定义的郁闭度指树冠为刚体时冠层垂直投影面积比。因此,采用Pvg_c估算人工林郁闭度更加准确。应在获得树木株数与树冠半径的基础上,采用Pvg_c估算人工林郁闭度。

关键词: 4-Scale模型, 郁闭度, 鱼眼相机, 样线法, 叶面积指数

Abstract:

【Objective】 Canopy closure is important for forest management planning. To find methods and models for estimating canopy closure that are less affected by the region, with higher accuracy and better robustness, the study adopted a 4-Scale geometric-optical model to estimate the canopy closure of plantations. 【Method】 Wangyedian Forest Farm in Inner Mongolia and Gaofeng Forest Farm in Guangxi Autonomons Region were selected as the study areas. First, a parameter sensitivity analysis of the 4-Scale model was performed, and the canopy gap fraction Pvg_c (the gap fraction when the canopy was a rigid body) and Pvg (the gap fraction of the gaps in a crown were considered) under different sensitivity parameters were simulated. Then, a one-to-one correspondence database of Pvg_c, Pvg and the sensitivity parameters was established. Second, statistical relationship models between Pvg_c and Pvg and the sensitivity parameters were established based on the database. Then, Pvg_c and Pvg were estimated based on the sensitivity parameters, and the stand canopy closure was estimated. Finally, the canopy closure measured by the transects and the fish eye camera measurement method were used to test the canopy closure based on Pvg_c and Pvg respectively. 【Result】 The accuracy of plantation canopy closure estimated by Pvg_c and Pvg was 88.17% and 92.8%, respectively. Pvg_c had a higher correlation with the number of trees and crown radius. The R2 and RMSE values of the model are 0.814 and 0.043, respectively. The correlation between Pvg and LAI was high, and the R2 and RMSE of the model were 0.795 and 0.040, respectively. 【Conclusion】 Both Pvg_c and Pvg can be used to estimate canopy closure in plantations. Although Pvg has higher accuracy in estimating canopy closure, the estimated canopy closure is not the canopy closure defined in forestry. Canopy closure is defined as the ratio of the vertical projection area of the canopy when it is a rigid body. Therefore, the use of Pvg_c to estimate the canopy closure of plantations was more accurate. To obtain the number of trees and radius of the crown, Pvg_c was used to estimate the canopy closure of the plantation.

Key words: 4-Scale model, canopy closure, fisheye camera, transects, leaf area index (LAI)

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