کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10113974 | 1621183 | 2018 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The increased frequency and intensity of insect-induced forest disturbances necessitates effective methods to precisely monitor and map the degree of disaster. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technology for surveying and monitoring forest health. In this article, a novel framework that utilizes a UAV-based hyperspectral image is proposed to identify the degree of damage caused by Dendrolimus tabulaeformis Tsai et Liu (D.tabulaeformis) in Jianping county of Liaoning province, China. First, data reduction of the hyperspectral image is achieved by comparing three waveband selection algorithms: principal components analysis (PCA), the successive projection algorithm (SPA), and the instability index between classes (ISIC). On this basis, a joint algorithm, ISIC-SPA, which demonstrates the best waveband selection efficiency and good cross-validation accuracy, is proposed. ISIC-SPA is used to select only three sensitive wavebands from 125 original wavebands with a root mean square error of 0.1535. Then, according to analysis of the three sensitive wavebands' reflectance and the corresponding defoliation rate, the piecewise index (PI, B(710â¯+â¯738 - 522)) was constructed and the threshold of PI was found to divide the defoliation level. Finally, a piecewise partial least-squares regression model was established to quantitatively estimate the defoliation using the optimal wavebands to identify and demarcate the damage level to individual trees. The assessment accuracy of damage caused by D.tabulaeformis at the tree level reached 95.23% using the ISIC-SPA-P-PLSR framework.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 217, November 2018, Pages 323-339
Journal: Remote Sensing of Environment - Volume 217, November 2018, Pages 323-339
نویسندگان
Ning Zhang, Xiaoli Zhang, Guijun Yang, Chenghao Zhu, Langning Huo, Haikuan Feng,