TY - JOUR
T1 - Detecting and modelling dynamic landuse change using multitemporal and multi-sensor imagery
AU - Zhou, Qiming
AU - Li, B.
AU - Zhou, C.
N1 - Publisher Copyright:
© 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
PY - 2004
Y1 - 2004
N2 - It is now common to use data from two or more sensors for land cover change detection. Since the spatial and spectral resolutions of different sensors vary significantly, the ability to discriminate the land cover also varies greatly. In this paper the applications of landuse change detection including area statistics, temporal trajectories and spatial pattern are discussed. The area statistics show the general landuse change pattern, but with quite significant uncertainty. The results of this study show that if the area of detected landuse change accounts for less than 5% of the total area, the uncertainty of change detection can be very significant. Temporal trajectory analysis was also conducted with the particular focus on the analysis of unchanged and "stable" change trajectories, because they generally show the trend of landuse change that is irreversible. Unstable change trajectories, on the other hand, show relatively less significance since they largely contain reversible temporary changes (e.g. seasonal cropping and bare ground) and classification errors. The study results show overall accuracy of 85-90% with Kappa coefficients of 0.66-0.78 in classification and change detection. On spatial patterns, the landuse pattern metrics demonstrate a reasonable result, but most other patch metrics do not show recognisable patterns.
AB - It is now common to use data from two or more sensors for land cover change detection. Since the spatial and spectral resolutions of different sensors vary significantly, the ability to discriminate the land cover also varies greatly. In this paper the applications of landuse change detection including area statistics, temporal trajectories and spatial pattern are discussed. The area statistics show the general landuse change pattern, but with quite significant uncertainty. The results of this study show that if the area of detected landuse change accounts for less than 5% of the total area, the uncertainty of change detection can be very significant. Temporal trajectory analysis was also conducted with the particular focus on the analysis of unchanged and "stable" change trajectories, because they generally show the trend of landuse change that is irreversible. Unstable change trajectories, on the other hand, show relatively less significance since they largely contain reversible temporary changes (e.g. seasonal cropping and bare ground) and classification errors. The study results show overall accuracy of 85-90% with Kappa coefficients of 0.66-0.78 in classification and change detection. On spatial patterns, the landuse pattern metrics demonstrate a reasonable result, but most other patch metrics do not show recognisable patterns.
KW - Change Detection
KW - Land Cover
KW - Modelling
KW - Monitoring
KW - Multisensor
KW - Multitemporal
UR - http://www.scopus.com/inward/record.url?scp=67649330726&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:67649330726
SN - 1682-1750
VL - 35
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
T2 - 20th ISPRS Congress on Technical Commission VII
Y2 - 12 July 2004 through 23 July 2004
ER -