Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume
Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume
复制标题
DOI：
10.1016/j.foreco.2006.01.014
复制
发表时间：
2006-04
影响因子：
3.7
通讯作者：
R. Hall;R. Skakun;E. Arsenault;B. Case
中科院分区：
文献类型：
--
作者：
R. Hall;R. Skakun;E. Arsenault;B. Case
地上生物量（AGB）和林分蓄积量的地图是感兴趣的，以确定其规模和空间分布在森林地区，并需要输入预测碳预算和生态系统生产力。得出AGB和体积的估计需要关于物种组成和林分结构的信息。本文介绍了一种称为生物量估计从林分结构的方法，这是基于地理参考现场样地生成的经验关系的森林结构属性的连续估计值和遥感图像数据表示为光谱响应变量。在这项研究中，高度和树冠郁闭度属性建模Landsat ETM+图像和现场绘图数据。这些建模的属性，然后被用作输入的AGB和体积的林分水平模型。图像高度模型的ETM+波段3、4和5的调整R2为0.65。同样，牙冠闭合模型使用ETM+条带3、4和7调整后的R2为0.57。平均AGB估计值在40吨/公顷以内，林分蓄积量在4 m3/公顷以内，与AGB（p=0.61）和林分蓄积量（p=0.65）的验证样本数据集在统计学上相似，并且在以前发表的研究范围内。现场图分布，误差传播，并在多个图像上扩展模型被确定为需要进一步调查的因素，以便在更大的地理区域应用BioCT。
Maps of aboveground biomass (AGB) and stand volume are of interest to determine their magnitude and spatial distribution over forested areas, and required for input to forecasting carbon budgets and ecosystem productivity. Deriving estimates of AGB and volume requires information about species composition and forest stand structure. This paper introduces a method called BioSTRUCT (Biomass estimation from stand STRUCTture), which is based on georeferenced field plots to generate empirical relationships between continuous estimates of forest structure attributes and remote sensing image data represented as spectral response variables. In this study, height and crown closure attributes were modeled from Landsat ETM+ image and field plot data. These modeled attributes were then used as inputs to stand-level models of AGB and volume. The image height model had an adjusted R2of 0.65 from ETM+ bands 3, 4, and 5. Likewise, the crown closure model had an adjusted R2of 0.57 using ETM+ bands 3, 4, and 7. Average AGB estimates were within 4tonnes/ha and stand volume was within 4m3/ha of field plot values, statistically similar to a validation sample data set for both AGB (p=0.61) and stand volume (p=0.65), and within the range of previous published studies. Field plot distribution, error propagation, and extending models over multiple images were identified as factors requiring further investigation in order to apply BioSTRUCT over larger geographic areas.