A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS
A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS
复制标题
DOI：
10.1016/j.rse.2009.03.007
复制
发表时间：
2009-08-01
影响因子：
13.5
通讯作者：
White, Joanne C.
中科院分区：
文献类型：
--
作者：
Hilker, Thomas;Wulder, Michael A.;White, Joanne C.
研究景观干扰的时间和空间模式是对生态系统特征进行建模的重要要求，包括了解陆生碳循环的变化或映射野生动植物栖息地的质量和丰度。来自Landsat系列卫星的数据已成功地用于以30 m的空间分辨率绘制一系列生物物理植被参数。然而，由于云覆盖而通常扩展的Landsat 16天的重新访问周期可能是监测短期干扰和随时间变化的主要障碍。数据融合技术的开发有助于改善时间的时间分辨率通过将空间和时间特征不同的传感器观测到观察结果来融合观察结果。这项研究介绍了一种用于产生合成图像的新数据融合模型，并检测用于映射反射率变化（STAARCH）的空间时间自适应算法所谓的变化。该算法旨在使用Landsat TM/ETM和MODIS反射率数据的流共帽转换来检测反射率的变化，表示干扰。该算法已在加拿大艾伯塔省中西部的185 x 185公里研究区域进行了测试。结果表明，Staarch能够识别出高度细节的景观中的空间和时间变化。与验证数据集相比，受干扰面积的空间准确性为93％，而景观的时间变化正确地估计为87％至89％的总体情况。从Staarch得出的变化序列也用于在每个可用的MODIS图像日期为研究期间生成合成的Landsat图像。与现有的Landsat观测值相比，与先前发布的数据融合技术相比，从Staarch得出的变化序列有助于改善预测结果。 （c）2009 Elsevier Inc.保留所有权利。
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.