Evaluación del estado sanitario de individuos de Araucaria araucana a través de imágenes hiperespectrales

IF 0.4 Q4 REMOTE SENSING Revista de Teledeteccion Pub Date : 2018-12-26 DOI:10.4995/RAET.2018.10916
N. Medina, P. Vidal, R. Cifuentes, J. Torralba, F. Keusch
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引用次数: 3

Abstract

The Araucaria araucana is an endemic species from Chile and Argentina, which has a high biological, scientific and cultural value and since 2016 has shown a severe affection of leaf damage in some individuals, causing in some cases their death. The purpose of this research was to detect, from hyperspectral images, the individuals of the Araucaria species (Araucaria araucana (Molina and K. Koch)) and its degree of disease, by isolating its spectral signature and evaluating its physiological state through indices of vegetation and positioning techniques of the inflection point of the red edge, in a sector of the Ralco National Reserve, Biobío Region, Chile. Seven images were captured with the HYSPEX VNIR-1600 hyperspectral sensor, with 160 bands and a random sampling was carried out in the study area, where 90 samples of Araucarias were collected. In addition, from the remote sensing techniques applied, spatial data mining was used, in which Araucarias were classified without symptoms of disease and with symptoms of disease. A 55.11% overall accuracy was obtained in the classification of the image, 53.4% in the identification of healthy Araucaria and 55.96% in the identification of affected Araucaria. In relation to the evaluation of their sanitary status, the index with the best percentage of accuracy is the MSR (70.73%) and the one with the lowest value is the SAVI (35.47%). The positioning technique of the inflection point of the red edge delivered an accuracy percentage of 52.18% and an acceptable Kappa index.
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利用高光谱图像评估阿劳卡纳阿劳卡纳个体的健康状况
Araucaria araucana是智利和阿根廷的特有物种,具有很高的生物、科学和文化价值,自2016年以来,一些个体的叶子受到严重损害,在某些情况下导致它们死亡。本研究的目的是在智利Biobío地区Ralco国家保护区的一个区域,通过分离其光谱特征并通过植被指数和红边拐点定位技术评估其生理状态,从高光谱图像中检测Araucaria物种(Araucaria araucana (Molina and K. Koch))的个体及其疾病程度。利用HYSPEX VNIR-1600高光谱传感器采集7幅图像,共160个波段,在研究区进行随机抽样,共采集到90份沙蚕标本。此外,从所应用的遥感技术来看,还使用了空间数据挖掘,其中将无疾病症状和有疾病症状的沙蚕进行了分类。图像分类的总体准确率为55.11%,对健康鼠兔的识别准确率为53.4%,对患病鼠兔的识别准确率为55.96%。在卫生状况评价指标中,准确率最高的指标为MSR(70.73%),最低的指标为SAVI(35.47%)。采用红边拐点定位技术,准确率达52.18%,Kappa指数可接受。
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来源期刊
Revista de Teledeteccion
Revista de Teledeteccion REMOTE SENSING-
CiteScore
1.80
自引率
14.30%
发文量
11
审稿时长
10 weeks
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