Abstract
The geomorphology of Taiwan is characterized by marked changes in terrain, geological fractures, and frequent natural disasters. Because of sustained economic growth, urbanization and land development, the land cover in Taiwan has undergone frequent use changes. Among the various technologies for monitoring changes in land cover, remote sensing technologies, such as LiDAR, are efficient tools for collecting a broad range of spectral and spatial data. Two types of airborne LiDAR systems exist; full-waveform (FW) LiDAR and traditional discrete-echo LiDAR. Because reflected waveforms are affected by the land object material type and properties, the waveform features can be applied to analyze the characteristics specifically associated with land-cover classification (LCC). Five types of land cover that characterize the volcanic Guishan Island were investigated. The automatic LCC method was used to elucidate the spectral, geomorphometric and textural characteristics. Interpretation keys accompanied by additional information were extracted from the FW LiDAR data for subsequent statistical and separation analyses. The results show that the Gabor texture and geomorphometric features, such as the normalized digital surface model (nDSM) and slopes can enhance the overall LCC accuracy to higher than 90%. Moreover, both the producer and user accuracy can be higher than 92% for forest and built-up types using amplitude and pulse width. Although the waveform characteristics did not perform as well as anticipated due to the waveform data sampling rate, the data provides suitable training samples for testing the waveform feature effects.
| Original language | English |
|---|---|
| Pages (from-to) | 169-181 |
| Number of pages | 13 |
| Journal | Terrestrial, Atmospheric and Oceanic Sciences |
| Volume | 29 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2015 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Oceanography
- Atmospheric Science
- Earth and Planetary Sciences (miscellaneous)
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Land cover classification accuracy assessment using full-waveform LiDAR data. / Chang, Kuan Tsung; Yu, Feng Chi; Chang, Yi; Hwang, Jin Tsong; Liu, Jin King; Hsu, Wei Chen; Shih, Peter Tian Yuan.
In: Terrestrial, Atmospheric and Oceanic Sciences, Vol. 29, No. 2, 01.01.2015, p. 169-181.Research output: Contribution to journal › Article
TY - JOUR
T1 - Land cover classification accuracy assessment using full-waveform LiDAR data
AU - Chang, Kuan Tsung
AU - Yu, Feng Chi
AU - Chang, Yi
AU - Hwang, Jin Tsong
AU - Liu, Jin King
AU - Hsu, Wei Chen
AU - Shih, Peter Tian Yuan
PY - 2015/1/1
Y1 - 2015/1/1
N2 - The geomorphology of Taiwan is characterized by marked changes in terrain, geological fractures, and frequent natural disasters. Because of sustained economic growth, urbanization and land development, the land cover in Taiwan has undergone frequent use changes. Among the various technologies for monitoring changes in land cover, remote sensing technologies, such as LiDAR, are efficient tools for collecting a broad range of spectral and spatial data. Two types of airborne LiDAR systems exist; full-waveform (FW) LiDAR and traditional discrete-echo LiDAR. Because reflected waveforms are affected by the land object material type and properties, the waveform features can be applied to analyze the characteristics specifically associated with land-cover classification (LCC). Five types of land cover that characterize the volcanic Guishan Island were investigated. The automatic LCC method was used to elucidate the spectral, geomorphometric and textural characteristics. Interpretation keys accompanied by additional information were extracted from the FW LiDAR data for subsequent statistical and separation analyses. The results show that the Gabor texture and geomorphometric features, such as the normalized digital surface model (nDSM) and slopes can enhance the overall LCC accuracy to higher than 90%. Moreover, both the producer and user accuracy can be higher than 92% for forest and built-up types using amplitude and pulse width. Although the waveform characteristics did not perform as well as anticipated due to the waveform data sampling rate, the data provides suitable training samples for testing the waveform feature effects.
AB - The geomorphology of Taiwan is characterized by marked changes in terrain, geological fractures, and frequent natural disasters. Because of sustained economic growth, urbanization and land development, the land cover in Taiwan has undergone frequent use changes. Among the various technologies for monitoring changes in land cover, remote sensing technologies, such as LiDAR, are efficient tools for collecting a broad range of spectral and spatial data. Two types of airborne LiDAR systems exist; full-waveform (FW) LiDAR and traditional discrete-echo LiDAR. Because reflected waveforms are affected by the land object material type and properties, the waveform features can be applied to analyze the characteristics specifically associated with land-cover classification (LCC). Five types of land cover that characterize the volcanic Guishan Island were investigated. The automatic LCC method was used to elucidate the spectral, geomorphometric and textural characteristics. Interpretation keys accompanied by additional information were extracted from the FW LiDAR data for subsequent statistical and separation analyses. The results show that the Gabor texture and geomorphometric features, such as the normalized digital surface model (nDSM) and slopes can enhance the overall LCC accuracy to higher than 90%. Moreover, both the producer and user accuracy can be higher than 92% for forest and built-up types using amplitude and pulse width. Although the waveform characteristics did not perform as well as anticipated due to the waveform data sampling rate, the data provides suitable training samples for testing the waveform feature effects.
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U2 - 10.3319/TAO.2014.12.02.02(EOSI)
DO - 10.3319/TAO.2014.12.02.02(EOSI)
M3 - Article
VL - 29
SP - 169
EP - 181
JO - Terrestrial, Atmospheric and Oceanic Sciences
JF - Terrestrial, Atmospheric and Oceanic Sciences
SN - 1017-0839
IS - 2
ER -