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Assessing the Defoliation of Pine Forests in a Long Time-Series and Spatiotemporal Prediction of the Defoliation Using Landsat Data

文献类型： 外文期刊

作者： Zhu, Chenghao 1 ; Zhang, Xiaoli 1 ; Zhang, Ning 2 ; Hassan, Mohammed Abdelmanan 1 ; Zhao, Lin 1 ;

作者机构： 1.Beijing Forestry Univ, Forestry Coll, Beijing Key Lab Precis Forestry, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： pine defoliation; long time-series; assessing; meteorology; topography; spatiotemporal analysis; damage prediction; remote sensing

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2018 年 10 卷 3 期

页码：

收录情况： SCI

摘要： Pine forests (Pinus tabulaeformis) have been in danger of defoliation by a caterpillar in the west Liaoning province of China for more than thirty years. This paper aims to assess and predict the degree of damage to pine forests by using remote sensing and ancillary data. Through regression analysis of the pine foliage remaining ratios of field plots with several vegetation indexes of Landsat data, a feasible inversion model was obtained to detect the degree of damage using the Normalized Difference Infrared Index of 5th band (NDII5). After comparing the inversion result of the degree of damage to the pine in 29 years and the historical damage record, quantized results of damage assessment in a long time-series were accurately obtained. Based on the correlation analysis between meteorological variables and the degree of damage from 1984 to 2015, the average degree of damage was predicted in temporal scale. By adding topographic and other variables, a linear prediction model in spatiotemporal scale was constructed. The spatiotemporal model was based on 5015 public pine points for 24 years and reached 0.6169 in the correlation coefficient. This paper provided a feasible and quantitative method in the spatiotemporal prediction of forest pest occurrence by remote sensing.

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