A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

被引：666

作者：

Hilker, Thomas ^{[1
]}

Wulder, Michael A. ^{[2
]}

Coops, Nicholas C. ^{[1
]}

Linke, Julia ^{[3
]}

McDermid, Greg ^{[3
]}

Masek, Jeffrey G. ^{[4
]}

Gao, Feng ^{[4
]}

White, Joanne C. ^{[2
]}

机构：

[1] Univ British Columbia, Integrated Remote Sensing Studio, Dept Forest Resource Management, Vancouver, BC V6T IZ4, Canada

[2] Nat Resources Canada, Canadian Forest Serv, Pacific Forestry Ctr, Victoria, BC V8Z 1M5, Canada

[3] Univ Calgary, Dept Geog, Foothills Facil Remote Sensing & GI Sci, Calgary, AB T2N 1N4, Canada

[4] NASA, Goddard Space Flight Ctr, Biospher Sci Branch, Greenbelt, MD 20771 USA

来源：

REMOTE SENSING OF ENVIRONMENT | 2009年 / 113卷 / 08期

基金：

美国国家航空航天局;

关键词：

Landsat; MODIS; Change detection; Disturbance; Synthetic imagery; STARFM; STAARCH; Data blending; EOSD; MOUNTAIN PINE-BEETLE; RED ATTACK DAMAGE; SATELLITE DATA; SURFACE REFLECTANCE; LANDSCAPE METRICS; COVER; IMAGERY; CARBON; PATTERN; CLASSIFICATION;

D O I：

10.1016/j.rse.2009.03.007

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 x 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques. (C) 2009 Elsevier Inc. All rights reserved.

引用

页码：1613 / 1627

页数：15

共 70 条

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