Remote sensing of insect pests in larch forest based on physical model

Author

Wang, Lei ; Huang, Huaguo ; Luo, Youqing

Author_Institution

Key Lab. for Silviculture & Conservation of Minist. of Educ., Beijing Forestry Univ., Beijing, China

fYear

2010

fDate

25-30 July 2010

Firstpage

3299

Lastpage

3302

Abstract

A physical decision method was proposed here to monitor Larch forest insect pests at early stage. Three remote sensing indicators were defined, which are CWC (canopy water content), TVDI (Temperature/Vegetation Dryness Index) and LAI (Leaf Area Index). The Five-scale model and artificial neural network (ANN) were combined to inverse the three factors from Landsat data. Based on training samples of health or attacked pixels, a decision tree was built to classify pest-infected pixels. Field validation showed that the prediction of forest compartments with insect pest were highly consistent with the ground field data.

Keywords

forestry; geophysical image processing; geophysical techniques; image classification; neural nets; remote sensing; China; Landsat data; Larch forest insect pests; Temperature/Vegetation Dryness Index; artificial neural network; canopy water content; decision tree; leaf area index; pest-infected pixels; physical model; pixel classification; remote sensing; Earth; Insects; Monitoring; Needles; Remote sensing; Satellites; Temperature measurement; Insect pest; Larch forest; early monitoring; remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International

Conference_Location

Honolulu, HI

ISSN

2153-6996

Print_ISBN

978-1-4244-9565-8

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2010.5649528

Filename

5649528