Abstract
This study focuses on the forest cover-type classification using remote sensing techniques. The SPOT4 and SPOT5 images, which cover the Chilanshan mountainous area located in the northeastern part of Taiwan, are applied to classify forest cover types into three classes using supervised classification approach. The classified three classes are natural Chamaecyparis formosensis forests, coniferous plantations, and nature broadleaf forests, respectively. To improve the classification accuracy of forest cover types, SPOT5 image (spatial resolution is 10m) and SPOT4 image (spatial resolution is 20m) were integrated together by the mosaic technique. In addition, the ground permanent samples established for continuous forest inventory were also used to assess the classification accuracy of forest cover types. The result indicates that the classification accuracy in SPOT5 is better than in SPOT4 because of the finer resolution. However, among these three classes, the natural broadleaf forests have low accuracy due to the complicated stand structure.
| Original language | English |
|---|---|
| Title of host publication | Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006 |
| Pages | 985-989 |
| Number of pages | 5 |
| Publication status | Published - 2006 Dec 1 |
| Event | 27th Asian Conference on Remote Sensing, ACRS 2006 - Ulaanbaatar, Mongolia Duration: 2006 Oct 9 → 2006 Oct 13 |
Publication series
| Name | Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006 |
|---|
Other
| Other | 27th Asian Conference on Remote Sensing, ACRS 2006 |
|---|---|
| Country | Mongolia |
| City | Ulaanbaatar |
| Period | 06-10-09 → 06-10-13 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
Cite this
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Forest cover-type classification using SPOT4 and SPOT5 images. / Wang, Su Fen; Cheng, Chi Chuan; Chen, Yeong Kuan.
Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006. 2006. p. 985-989 (Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Forest cover-type classification using SPOT4 and SPOT5 images
AU - Wang, Su Fen
AU - Cheng, Chi Chuan
AU - Chen, Yeong Kuan
PY - 2006/12/1
Y1 - 2006/12/1
N2 - This study focuses on the forest cover-type classification using remote sensing techniques. The SPOT4 and SPOT5 images, which cover the Chilanshan mountainous area located in the northeastern part of Taiwan, are applied to classify forest cover types into three classes using supervised classification approach. The classified three classes are natural Chamaecyparis formosensis forests, coniferous plantations, and nature broadleaf forests, respectively. To improve the classification accuracy of forest cover types, SPOT5 image (spatial resolution is 10m) and SPOT4 image (spatial resolution is 20m) were integrated together by the mosaic technique. In addition, the ground permanent samples established for continuous forest inventory were also used to assess the classification accuracy of forest cover types. The result indicates that the classification accuracy in SPOT5 is better than in SPOT4 because of the finer resolution. However, among these three classes, the natural broadleaf forests have low accuracy due to the complicated stand structure.
AB - This study focuses on the forest cover-type classification using remote sensing techniques. The SPOT4 and SPOT5 images, which cover the Chilanshan mountainous area located in the northeastern part of Taiwan, are applied to classify forest cover types into three classes using supervised classification approach. The classified three classes are natural Chamaecyparis formosensis forests, coniferous plantations, and nature broadleaf forests, respectively. To improve the classification accuracy of forest cover types, SPOT5 image (spatial resolution is 10m) and SPOT4 image (spatial resolution is 20m) were integrated together by the mosaic technique. In addition, the ground permanent samples established for continuous forest inventory were also used to assess the classification accuracy of forest cover types. The result indicates that the classification accuracy in SPOT5 is better than in SPOT4 because of the finer resolution. However, among these three classes, the natural broadleaf forests have low accuracy due to the complicated stand structure.
UR - http://www.scopus.com/inward/record.url?scp=84865637016&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865637016&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84865637016
SN - 9781604231380
T3 - Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006
SP - 985
EP - 989
BT - Asian Association on Remote Sensing - 27th Asian Conference on Remote Sensing, ACRS 2006
ER -