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森林食叶害虫空间分布格局遥感定量反演研究

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  • 维普
以Sentinel-2A多光谱影像为数据源，利用卷积神经网络模型提取的受害树种空间分布和多时相PROSAIL模型叶面积指数反演差值确定的失叶率耦合的虫口密度，定量获取长白山南麓虫害空间格局。结果表明:2018-2020年共7个时相LAI反演整体精度在88％以上;红松的适宜参考时相为2019年6月，预测与实测拟合R2为0。82，其余树种及全样本2018年6月最佳;虫口密度与失叶率耦合采用线性函数，R2为0。755;落叶松遭虫害面积6174 hm2最大，云杉受害面积比65。19％最大。虫害导致失叶率计算采用的参考时相为受灾前一年6月;虫口密度与失叶率呈线性关系;不同树种受灾空间格局不同，常绿树种重度灾害比例普遍高于落叶树种。
Study on Quantitative Inversion of Spatial Pattern of Forest Leaf Eating Pest Disaster
Using Sentinel-2A multi-spectral image as the data source,the spatial pattern of pest damage at the southern foot of Changbai Mountain was quantitatively obtained by coupling the insect mouth density using spa-tial distribution of injured tree species extracted using a convolutional neural network model and leaf foliation rate by the difference of the leaf area index reversed by the PROSAIL model at multiple time points.Results show that:the overall accuracy of 7 LAI inversion in 2018-2020 was above 88％;the optimal reference phase of red pine was in June 2019,R2 is 0.82 and other species in June 2018;linear function,R2 is 0.755;larch pest area of 6174 hm2,and spruce damage area ratio of 65.19％.The reference phase of the leaf loss rate is June of the year before the disaster;the relationship between the pest density and the leaf loss rate is linear;the spatial pattern of different tree species is different,and the proportion of evergreen trees is generally higher than that of deciduous tree species.

leaf eating pestLAIconifer speciesremote sensingleaf loss rateinsect mouth density

姜雪菲、包广道、翟畅、刘婷、任志彬、丁铭铭、张微、杜云霞

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吉林省林业科学研究院,吉林长春 130033

北京林业大学,北京 100083

长春大学,吉林长春 130022

中国科学院东北地理与农业生态研究所,吉林长春 130102

长白朝鲜族自治县林业局,吉林白山 134400

海南师范大学,海南海口 571158

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食叶害虫 叶面积指数 针叶树种 遥感 失叶率 虫口密度

吉林省科技厅重点研发项目吉林省发改委创新能力建设项目吉林省自然科学基金吉林省自然科学基金吉林省自然科学基金中国科学院先导专项海南省自然科学基金吉林省科技厅重大专项

20230202098NC2021C044-9YDZJ202201ZYTS446YDZJ202102CXJD04620220101315JCXDA28080303422QN30620200503001SF

2024

10.11929/j.swfu.202210021

西南林业大学学报
西南林业大学

西南林业大学学报

CSTPCD北大核心
影响因子：0.773
ISSN：2095-1914
年,卷(期)：2024.44(1)
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