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首页> 外文期刊>Journal of Applied Remote Sensing >Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System
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Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System

机译：Landsat时间序列和地球科学激光高度计系统对亚马逊次生林生物量积累速率和老龄林生物量的影响

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We estimate the age of humid lowland tropical forests in Rondonia, Brazil, from a somewhat densely spaced time series of Landsat images (1975-2003) with an automated procedure, the Threshold Age Mapping Algorithm (TAMA), first described here. We then estimate a landscape-level rate of aboveground woody biomass accumulation of secondary forest by combining forest age mapping with biomass estimates from the Geoscience Laser Altimeter System (GLAS). Though highly variable, the estimated average biomass accumulation rate of 8.4 Mg ha~(-1) yr~(-1) agrees well with ground-based studies for young secondary forests in the region. In isolating the lowland forests, we map land cover and general types of old-growth forests with decision tree classification of Landsat imagery and elevation data. We then estimate aboveground live biomass for seven classes of old-growth forest. TAMA is simple, fast, and self-calibrating. By not using between-date band or index differences or trends, it requires neither image normalization nor atmospheric correction. In addition, it uses an approach to map forest cover for the self-calibrations that is novel to forest mapping with satellite imagery; it maps humid secondary forest that is difficult to distinguish from old-growth forest in single-date imagery; it does not assume that forest age equals time since disturbance; and it incorporates Landsat Multispectral Scanner (MSS) imagery. Variations on the work that we present here can be applied to other forested landscapes. Applications that use image time series will be helped by the free distribution of coregistered Landsat imagery, which began in December 2008, and of the Ice Cloud and land Elevation Satellite (ICESat) Vegetation Product, which simplifies the use of GLAS data. Finally, we demonstrate here for the first time how the optical imagery of fine spatial resolution that is viewable on Google Earth provides a new source of reference data for remote sensing applications related to land cover.
机译：我们使用自动化程序“阈值年龄映射算法（TAMA）”（此处首次描述），通过一定间隔的Landsat图像时间序列（1975-2003年）估算巴西Rondonia湿润的低地热带森林的年龄。然后，我们通过将森林年龄图与地球科学激光测高仪系统（GLAS）的生物量估计值结合起来，估算次生森林地上木质生物量积累的景观水平速率。尽管变化很大，但估计的平均生物量积累速率为8.4 Mg ha〜（-1）yr〜（-1）与该地区年轻次生林的地面研究非常吻合。在隔离低地森林时，我们使用Landsat影像的决策树分类和高程数据来绘制土地覆盖和旧森林的一般类型。然后，我们估算了七类旧林的地上生物量。 TAMA简单，快速且可自我校准。通过不使用日期之间的区间或指数差异或趋势，它既不需要图像标准化也不需要大气校正。此外，它使用一种方法对森林覆盖率进行自校准，这对于使用卫星图像进行森林制图是一种新颖的方法;它绘制了很难在单日图像中与老龄林区分开的潮湿次生林;它不假设森林年龄等于扰动以来的时间;并且包含Landsat多光谱扫描仪（MSS）图像。我们在此展示的作品变化可以应用于其他森林景观。从2008年12月开始，免费分发共同注册的Landsat影像以及冰云和陆地高程卫星（ICESat）植被产品，从而简化了GLAS数据的使用，从而有助于使用图像时间序列的应用程序。最后，我们首次在这里展示如何在Google Earth上看到的高分辨率的光学图像如何为与土地覆盖相关的遥感应用提供新的参考数据源。

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