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Evaluating uncertainty in Landsat-derived postfire recovery metrics due to terrain, soil, and shrub type variations in southern California
GIScience & Remote Sensing ( IF 6.0 ) Pub Date : 2019-12-17 , DOI: 10.1080/15481603.2019.1703287
Emanuel A Storey 1 , Douglas A Stow 1 , Dar A Roberts 2
Affiliation  

ABSTRACT Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial variation in shrub type, soil substrate, or illumination differences associated with topography. We evaluate the effects of these variables in Landsat-derived metrics of FSC and postfire recovery, based upon three chaparral sites in southern California which contain shrub community ecotones, complex terrain, and soil variations. Detailed validations of prefire and postfire FSC are based on high spatial resolution ortho-imagery; cross-stratified random sampling is used for variable control. We find that differences in the composition and structure of shrubs (inferred from ortho-imagery) can substantially influence FSC-NDVI relations and impact recovery metrics. Differences in soil type have a moderate effect on the FSC-NDVI relation in one of the study sites, while no substantial effects were observed due to variation of terrain illumination among the study sites. Arithmetic difference recovery metrics – based on NDVI values that were not normalized with unburned control plots – correlate in a moderate but significant manner with a change in FSC (R 2 values range 0.47–0.59 at two sites). Similar regression coefficients resulted from using Landsat visible reflectance data alone. The lowest correlations to FSC resulted from Soil-Adjusted Vegetation Index (SAVI) and are attributed to the effects of the soil-adjustment factor in sparsely vegetated areas. The Normalized Burn Ratio and Normalized Burn Ratio 2 showed a moderate correlation to FSC. This study confirms the utility of Landsat NDVI data for postfire recovery evaluation and implies a need for stratified analysis of postfire recovery in some chaparral landscapes.

中文翻译:

由于南加州地形、土壤和灌木类型的变化,评估 Landsat 衍生的火灾后恢复指标的不确定性

摘要 基于多年代 Landsat 图像系列的表观植被丰度的时间轨迹提供了有关丛林灌木丛火灾后恢复的宝贵信息,这些灌木丛往往在 10 年内成熟。由于灌木类型、土壤基质或与地形相关的光照差异的空间变化,从时间序列归一化差异植被指数 (NDVI) 数据中提取的部分灌木覆盖 (FSC) 变化信号可能会出现系统偏差。我们评估了这些变量对 FSC 和火灾后恢复的 Landsat 衍生指标的影响,基于南加州的三个丛林场地,其中包含灌木群落交错带、复杂的地形和土壤变化。火前和火后 FSC 的详细验证基于高空间分辨率正射影像;交叉分层随机抽样用于变量控制。我们发现灌木组成和结构的差异(从正射影像推断)可以显着影响 FSC-NDVI 关系并影响恢复指标。土壤类型的差异对研究地点之一的 FSC-NDVI 关系有中等影响,而由于研究地点之间地形照明的变化,没有观察到实质性影响。算术差异恢复指标——基于未使用未燃烧控制图归一化的 NDVI 值——以适度但显着的方式与 FSC 的变化相关(两个站点的 R 2 值范围为 0.47-0.59)。单独使用 Landsat 可见反射率数据会产生类似的回归系数。土壤调整植被指数 (SAVI) 与 FSC 的相关性最低,这归因于植被稀疏地区土壤调整因子的影响。Normalized Burn Ratio 和 Normalized Burn Ratio 2 显示出与 FSC 的中等相关性。这项研究证实了 Landsat NDVI 数据在火灾后恢复评估中的实用性,并意味着需要对某些丛林景观中的火灾后恢复进行分层分析。
更新日期:2019-12-17
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