TY - GEN
T1 - Land cover change detection
T2 - 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008
AU - Boriah, Shyam
AU - Kumar, Vipin
AU - Steinbach, Michael S
AU - Potter, Christopher
AU - Klooster, Steven
PY - 2008/12/1
Y1 - 2008/12/1
N2 - The study of land cover change is an important problem in the Earth Science domain because of its impacts on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Most well-known change detection techniques from statistics, signal processing and control theory are not well-suited for the massive high-dimensional spatio-temporal data sets from Earth Science due to limitations such as high computational complexity and the inability to take advantage of seasonality and spatio-temporal autocorrelation inherent in Earth Science data. In our work, we seek to address these challenges with new change detection techniques that are based on data mining approaches. Specifically, in this paper we have performed a case study for a new change detection technique for the land cover change detection problem. We study land cover change in the state of California, focusing on the San Francisco Bay Area and perform an extended study on the entire state. We also perform a comparative evaluation on forests in the entire state. These results demonstrate the utility of data mining techniques for the land cover change detection problem.
AB - The study of land cover change is an important problem in the Earth Science domain because of its impacts on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Most well-known change detection techniques from statistics, signal processing and control theory are not well-suited for the massive high-dimensional spatio-temporal data sets from Earth Science due to limitations such as high computational complexity and the inability to take advantage of seasonality and spatio-temporal autocorrelation inherent in Earth Science data. In our work, we seek to address these challenges with new change detection techniques that are based on data mining approaches. Specifically, in this paper we have performed a case study for a new change detection technique for the land cover change detection problem. We study land cover change in the state of California, focusing on the San Francisco Bay Area and perform an extended study on the entire state. We also perform a comparative evaluation on forests in the entire state. These results demonstrate the utility of data mining techniques for the land cover change detection problem.
KW - Change detection
KW - Land cover
KW - Land use
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=65449165703&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=65449165703&partnerID=8YFLogxK
U2 - 10.1145/1401890.1401993
DO - 10.1145/1401890.1401993
M3 - Conference contribution
AN - SCOPUS:65449165703
SN - 9781605581934
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 857
EP - 865
BT - KDD 2008 - Proceedings of the 14th ACMKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 24 August 2008 through 27 August 2008
ER -