林业科学 ›› 2020, Vol. 56 ›› Issue (8): 47-54.doi: 10.11707/j.1001-7488.20200806
巫娟1,2,胡姝珍1,茅思雨1,2,邹凯1,2,郑淇元1,邱啟璜1,施建敏1,2,*
收稿日期:
2019-06-19
出版日期:
2020-08-25
发布日期:
2020-09-15
通讯作者:
施建敏
基金资助:
Juan Wu1,2,Shuzhen Hu1,Siyu Mao1,2,Kai Zou1,2,Qiyuan Zheng1,Qihuang Qiu1,Jianmin Shi1,2,*
Received:
2019-06-19
Online:
2020-08-25
Published:
2020-09-15
Contact:
Jianmin Shi
摘要:
目的: 毛竹叶片小、易卷曲的特点增加了叶面积测量的难度和测量误差,本研究旨在建立快速、便捷、准确测量毛竹叶片面积的模型。方法: 采集7个省份的毛竹叶片,对毛竹叶片形态进行分类,利用叶长、叶宽数据和扫描所得实际叶面积进行建模,并采用均方根误差、卡方、赤池信息量准则和预测精度检验模型的精度,同时与叶面积仪测定结果进行精度比较。结果: 1)根据长宽比,毛竹叶片可以分为3类,分别是类三角形叶片(长宽比≤7.2)、长椭圆叶片(7.2 <长宽比≤ 8.3)和细长条叶片(长宽比>8.3);2)对毛竹叶面积和叶形态学指标的相关分析显示,叶片长度与宽度的积对叶面积影响最大,相关系数为0.993;3)建立以叶片长宽积为自变量的叶形分类拟合模型,其决定系数为0.990 1、均方根误差为0.159 6、卡方值为10.368 1、赤池信息量准则为-6 317.10、预测精度为97.73%,其预测结果最佳,优于叶形不分类的整体拟合和叶面积仪测量结果。结论: 基于叶片形态分类的毛竹叶面积拟合模型仅需测定叶片的长和宽,便可准确预测叶片面积,其精度不但优于叶面积仪与整体拟合结果,而且测量过程快速、便捷。该模型可解决长期困扰的毛竹叶片面积测量难题。
中图分类号:
巫娟,胡姝珍,茅思雨,邹凯,郑淇元,邱啟璜,施建敏. 基于叶片形态的毛竹单叶叶面积模型[J]. 林业科学, 2020, 56(8): 47-54.
Juan Wu,Shuzhen Hu,Siyu Mao,Kai Zou,Qiyuan Zheng,Qihuang Qiu,Jianmin Shi. Single Leaf area Model of Phyllostachys edulis Based on Leaf Morphology[J]. Scientia Silvae Sinicae, 2020, 56(8): 47-54.
表1
毛竹叶片取样概况"
采样地 Sampling site | 经度 Longitude(E) | 纬度 Latitude(N) | 海拔 Altitude/ m | 建模样本 Modeling samples | 检验样本 Test samples | 样本量 Sample size |
江西南昌Nanchang, Jiangxi | 115°54′40″ | 28°40′13″ | 30 | 977 | 424 | 1 221 |
江西龙南Longnan, Jiangxi | 114°53′31″ | 25°16′0″ | 200 | 240 | 60 | 300 |
福建福州Fuzhou, Fujian | 119°14′10″ | 26°5′11″ | 52 | 120 | 30 | 150 |
浙江临安Lin’an, Zhejiang | 119°41′48″ | 30°15′33″ | 40 | 120 | 30 | 150 |
湖北武汉Wuhan, Hubei | 114°16′18″ | 30°36′28″ | 42 | 120 | 30 | 150 |
江苏连云港Lianyungang, Jiangsu | 119°11′4″ | 34°36′53″ | 44 | 120 | 30 | 150 |
湖南娄底Loudi, Hunan | 112°0′18″ | 27°44′54″ | 338 | 120 | 30 | 150 |
四川宜宾Yibin, Sichuan | 104°37′51″ | 28°45′36″ | 314 | 120 | 30 | 150 |
合计Total | / | / | / | 1 937 | 484 | 2 421 |
表2
模型检验的统计量①"
统计量Statistic | 符号Symbol | 公式Fomula | 理想值Ideal value |
均方根误差 Root mean square error | E1 | | 0 |
卡方 Chi-square test | E2 | | -∞ |
赤池信息量准则 Chi information criterion | E3 | 2k+nln(RSS/n) | -∞ |
预测精度 Predicted precision | E4 | | 1 |
表4
毛竹叶片形态学特性相关(Pearson)分析④"
指标Measurements | LL/cm | LW/cm | LL*LW/cm2 | LL-LW/cm | LL+LW/cm | LL2/cm2 | LW2/cm2 | LA/cm2 |
LL/cm | ||||||||
LW/cm | 0.343** | |||||||
LL×LW/cm2 | 0.840** | 0.790** | ||||||
LL-LW/cm | 0.993** | 0.231** | 0.772** | |||||
LL+LW/cm | 0.994** | 0.441** | 0.892** | 0.975** | ||||
LL2/cm2 LW2/cm2 | 0.993** 0.332** | 0.337** 0.996** | 0.839** 0.785** | 0.987** 0.221** | 0.987** 0.431** | 0.328** | ||
LA/cm2 | 0.864** | 0.749** | 0.993** | 0.802** | 0.910** | 0.862** | 0.745** |
表5
3种测量方式的误差统计量"
方法 Method | 均方根误差 Root mean square error (E1) | 卡方检验 Chi square test(E2) | 赤池信息准则 Chi information criterion(E3) | 预测精度 Predicted precision(E4) | n |
整体拟合 Whole fitting | 0.228 8 | 14.352 6 | -5 709.97 | 97.36 | 1 937 |
分类拟合 Classified fitting | 0.195 6 | 10.368 1 | -6 317.10 | 97.73 | 1 937 |
叶面积仪 Leaf area meter | 0.916 4 | 131.440 8 | -474.59 | 85.64 | 1 937 |
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