TY - JOUR
T1 - Assessing relationships between forest spatial patterns and fire history with fusion of optical and microwave remote sensing
AU - Henry, Mary C.
AU - Yool, Stephen R.
N1 - Funding Information:
This research was funded by EPA Science to Achieve Results (STAR) Fellowship number U915601 awarded to Mary C. Henry, University of Arizona. The authors wish to thank Dr. Stuart Marsh, Dr. Tom Swetnam, Dr. Susan Moran, Dr. Mal Zwolinski, and several anonymous reviewers for comments and suggestions on the manuscript. The abundant feedback greatly helped to improve this paper. We would also like to thank Kathy Schon and Pam Anning of the National Park Service (Saguaro National Park) for supplying the fire atlas used in this study.
PY - 2004
Y1 - 2004
N2 - In this paper, we tested the use of active and passive sensor fusion for relating forest fire history to landscape spatial patterns. Principal Components Analysis (PCA) was implemented to combine Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) data from October 1994. Resulting PCs were converted to landscape patch maps. Plots with known fire history were delineated using a fire atlas of the study area. These plots came from four fire history categories: unburned (nine plots), once burned (three plots), twice burned (three plots), and multiple burned (three plots). Landscape metrics were calculated for each plot, including a shape index, mean patch size, Shannon's Diversity Index, and Shannon's Evenness Index. Spearman's Rank Correlation Analysis was used to compare the patch map-derived landscape metrics to fire history characteristics, such as average fire-free interval and number of fire-free years in different time periods. Results showed that landscape patterns derived from fused data were significantly (p < 0.05) related to fire history and typically performed better than SIR-C data (a greater number of significant correlations), but not as well as TM data.
AB - In this paper, we tested the use of active and passive sensor fusion for relating forest fire history to landscape spatial patterns. Principal Components Analysis (PCA) was implemented to combine Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) data from October 1994. Resulting PCs were converted to landscape patch maps. Plots with known fire history were delineated using a fire atlas of the study area. These plots came from four fire history categories: unburned (nine plots), once burned (three plots), twice burned (three plots), and multiple burned (three plots). Landscape metrics were calculated for each plot, including a shape index, mean patch size, Shannon's Diversity Index, and Shannon's Evenness Index. Spearman's Rank Correlation Analysis was used to compare the patch map-derived landscape metrics to fire history characteristics, such as average fire-free interval and number of fire-free years in different time periods. Results showed that landscape patterns derived from fused data were significantly (p < 0.05) related to fire history and typically performed better than SIR-C data (a greater number of significant correlations), but not as well as TM data.
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U2 - 10.1080/10106040408542304
DO - 10.1080/10106040408542304
M3 - Article
AN - SCOPUS:18444401820
SN - 1010-6049
VL - 19
SP - 25
EP - 37
JO - Geocarto International
JF - Geocarto International
IS - 2
ER -