NDLI: The methodology of detailed vegetation classification based on environmental knowledge and remote sensing images

Content Provider	IEEE Xplore Digital Library
Author	Zhigang Xu Dafang Zhuang
Copyright Year	2007
Description	Author affiliation: Graduate Univ. of CAS, Beijing (Zhigang Xu)
Abstract	Detailed vegetation types information is required for many ecology and hydrology models. We can obtain the information using remote sensing image classification. However, the accuracy of the traditional classification is limited and unsatisfied. Knowledge of the relationship between vegetation and environmental factors can be utilized to assist remote sensing classification. The paper proposes a method combining this kind of knowledge and feature extracted from remote sensing images to classify vegetation types. The method consists of three steps. The first is to extract feature such as spectrum information, texture, and geometry shape from fusion of aerial and TM images. In order to extract the information more precisely, we combined the high spatial resolution aerial image with the multi-spectral TM of satellite remote sensing image, and employed a remote sensing image fusion method based on wavelet packet transform. The second step was to derive environmental knowledge which control vegetation growth. Soil types, temperature, precipitation and landform were chosen as the main environmental factors controlling vegetation growth. Landform indexes such as elevation, slope and aspect were derived from high resolution DEM. The third step was to classify vegetation types using decision tree method with environmental knowledge and feature extracted from remote sensing images. In our research, we calculated the eigenvalue of every feature through training sample. The features included spectrum information, texture, geometry shape, soil types, temperature, precipitation, elevation, slope and wetness index. Adopting the threshold method to delaminate and extract the vegetation information according to definite threshold range, knowledge rules and decision tree. After that we classified vegetation information. A case study was done in Beijing suburb area. We used field point data to validate our classification maps. The point sampling strategy was used to obtain field vegetation data for the assessment of the overall accuracy. For point sampling, a stratification technique was employed. The final accuracy is 85%. By comparison with maximum likelihood classification the accuracy was increased by about 10%. This study showed that our method has a high accuracy and high spectral and spatial resolutions. It was able to provide more accurate vegetation classification information than traditional method.
Starting Page	2074
Ending Page	2077
File Size	1665591
Page Count	4
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423241
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation mapping Remote sensing Data mining Environmental factors Feature extraction Information geometry Shape Spatial resolution Soil Temperature sensors decision tree detailed vegetation classification remote sensing image environmental knowledge object parcel
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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