基于无人机高光谱遥感的枣树多参数健康评价

IF 3.3 2区 农林科学 Q1 AGRONOMY
Yuzhen Wu, Qingzhan Zhao, Xiaojun Yin, Yuan-Ming Wang, Wenzhong Tian
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引用次数: 1

摘要

针对目前枣椰树健康评价参数单一、指标体系不完善、精度低等问题,对枣椰树健康状况进行科学评价。本文以新疆生产建设兵团“昆嵛城”第十四师224团枣树为研究对象,利用无人机高光谱影像对枣树果实膨大期的各种理化参数(冠层叶绿素含量、叶面积指数(LAI)、树高、冠层面积)进行了反演研究。并提出了基于多种理化参数的枣树健康评价模型。首先,计算叶绿素含量反演的6个光谱指数和LAI反演的4个光谱指数,分析与枣树冠层叶绿素含量和LAI相关性较高的光谱指数,构建叶绿素含量和LAI的反演模型。其次,利用Mask R-CNN模型实现枣树的冠层分割和面积提取,并将分割后的冠层与冠层高度模型(canopy Height model, CHM)进行匹配,提取枣树的高度。最后,基于反演的4个理化参数,构建了偏最小二乘回归分析(PLSR)、随机森林(RF)、支持向量机(SVM)和决策树(DT) 4种枣树健康评价模型。结果表明:基于叶绿素含量、叶面积、树高、冠层面积等多理化参数构建的PLSR树木健康评价模型的R2为0.853,RMSE为0.3;与采用RF、SVM和DT构建的枣树健康评价模型相比,R2分别提高了0.127、0.386和0.165,RMSE分别降低了0.04、0.175和0.063。本文借助无人机高光谱影像实现了对枣树多理化参数的快速准确反演,基于多理化参数构建的PLSR模型可以准确评估枣树健康状况,为科学合理评价枣树健康提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing
To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.
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来源期刊
Agriculture-Basel
Agriculture-Basel Agricultural and Biological Sciences-Food Science
CiteScore
4.90
自引率
13.90%
发文量
1793
审稿时长
11 weeks
期刊介绍: Agriculture (ISSN 2077-0472) is an international and cross-disciplinary scholarly and scientific open access journal on the science of cultivating the soil, growing, harvesting crops, and raising livestock. We will aim to look at production, processing, marketing and use of foods, fibers, plants and animals. The journal Agriculturewill publish reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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