TY - GEN
T1 - Importance of vegetation type in forest cover estimation
AU - Karpatne, Anuj
AU - Blank, MacE
AU - Lau, Michael
AU - Boriah, Shyam
AU - Steinhaeuser, Karsten
AU - Steinbach, Michael S
AU - Kumar, Vipin
PY - 2012
Y1 - 2012
N2 - Forests are an important natural resource that play a major role in sustaining a number of vital geochemical and bioclimatic processes. Since damage to forests due to natural and anthropogenic factors can have long-lasting impacts on the health of the planet, monitoring and estimating forest cover and its losses at global, regional and local scales is of primary concern. Developing forest cover estimation techniques that utilize remote sensing datasets offers global applicability at high temporal frequencies. However, estimating forest cover using satellite observations is challenging in the presence of heterogeneous vegetation types, each having its unique data characteristics. In this paper, we explore techniques for incorporating information about the vegetation type in forest cover estimation algorithms. We show that utilizing the vegetation type improves performance regardless of the choice of input data or forest cover learning algorithm. We also provide a mechanism to automatically extract information about the vegetation type by partitioning the input data using clustering.
AB - Forests are an important natural resource that play a major role in sustaining a number of vital geochemical and bioclimatic processes. Since damage to forests due to natural and anthropogenic factors can have long-lasting impacts on the health of the planet, monitoring and estimating forest cover and its losses at global, regional and local scales is of primary concern. Developing forest cover estimation techniques that utilize remote sensing datasets offers global applicability at high temporal frequencies. However, estimating forest cover using satellite observations is challenging in the presence of heterogeneous vegetation types, each having its unique data characteristics. In this paper, we explore techniques for incorporating information about the vegetation type in forest cover estimation algorithms. We show that utilizing the vegetation type improves performance regardless of the choice of input data or forest cover learning algorithm. We also provide a mechanism to automatically extract information about the vegetation type by partitioning the input data using clustering.
UR - http://www.scopus.com/inward/record.url?scp=84872375778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872375778&partnerID=8YFLogxK
U2 - 10.1109/CIDU.2012.6382203
DO - 10.1109/CIDU.2012.6382203
M3 - Conference contribution
AN - SCOPUS:84872375778
SN - 9781467346252
T3 - Proceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
SP - 71
EP - 78
BT - Proceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
T2 - 2012 Conference on Intelligent Data Understanding, CIDU 2012
Y2 - 24 October 2012 through 26 October 2012
ER -