Extrapolation of in situ data from 1-km squares to adjacent squares using remote sensed imagery and airborne lidar data for the assessment of habitat diversity and extent
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- 作者：M. Lang ; A. Vain ; R. G. H. Bunce…
- 关键词：Plant life forms ; General habitat categories ; Lidar ; Landsat ; 7 Enhanced Thematic Mapper Plus ; Iterative self organising clustering ; Maximum likelihood classification
- 刊名：Environmental Monitoring and Assessment
- 出版年：2015
- 出版时间：March 2015
- 年：2015
- 卷：187
- 期：3
- 全文大小：2,291 KB
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A. Vain (2)
R. G. H. Bunce (2)
R. H. G. Jongman (3)
J. Raet (2)
K. Sepp (2)
V. Kuusemets (2)
T. Kikas (2)
N. Liba (1)
1. Estonian University of Life Sciences, Institute of Forestry and Rural Engineering, Kreutzwaldi 5, 51014, Tartu, Estonia
4. Tartu Observatory, Observatooriumi 1, 61602, T?ravere, Estonia
2. Estonian University of Life Sciences, Institute of Agricultural and Environmental Sciences, Kreutzwaldi 5, 51041, Tartu, Estonia
3. Alterra, Wageningen UR, P.O. Box 47, 6700 AA, Wageningen, The Netherlands - 刊物类别：Earth and Environmental Science
- 刊物主题：Environment
Monitoring, Environmental Analysis and Environmental Ecotoxicology
Ecology
Atmospheric Protection, Air Quality Control and Air Pollution
Environmental Management - 出版者：Springer Netherlands
- ISSN：1573-2959
文摘
Habitat surveillance and subsequent monitoring at a national level is usually carried out by recording data from in situ sample sites located according to predefined strata. This paper describes the application of remote sensing to the extension of such field data recorded in 1-km squares to adjacent squares, in order to increase sample number without further field visits. Habitats were mapped in eight central squares in northeast Estonia in 2010 using a standardized recording procedure. Around one of the squares, a special study site was established which consisted of the central square and eight surrounding squares. A Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image was used for correlation with in situ data. An airborne light detection and ranging (lidar) vegetation height map was also included in the classification. A series of tests were carried out by including the lidar data and contrasting analytical techniques, which are described in detail in the paper. Training accuracy in the central square varied from 75 to 100?%. In the extrapolation procedure to the surrounding squares, accuracy varied from 53.1 to 63.1?%, which improved by 10?% with the inclusion of lidar data. The reasons for this relatively low classification accuracy were mainly inherent variability in the spectral signatures of habitats but also differences between the dates of imagery acquisition and field sampling. Improvements could therefore be made by better synchronization of the field survey and image acquisition as well as by dividing general habitat categories (GHCs) into units which are more likely to have similar spectral signatures. However, the increase in the number of sample kilometre squares compensates for the loss of accuracy in the measurements of individual squares. The methodology can be applied in other studies as the procedures used are readily available.