Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.
Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.
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DOI：
10.1371/journal.pone.0133583
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
O Halloran J
中科院分区：
文献类型：
--
作者：
Devaney J;Barrett B;Barrett F;Redmond J;O Halloran J
森林覆盖的空间和时间变化的量化是森林监测方案的一个基本组成部分。由于其无云的能力，合成孔径雷达（SAR）是一个理想的信息来源，在几乎恒定的云层覆盖的国家的森林动态。然而，很少有研究调查使用合成孔径雷达的森林覆盖估计景观高度稀疏和分散的森林覆盖。在这项研究中，潜在的使用L-波段SAR的森林覆盖估计在两个地区（朗福德和斯莱戈）在爱尔兰的调查和森林覆盖估计来自三个国家（Forestry 2010年，Prime 2，国家森林清查），一个泛欧（森林地图2006年）和一个全球森林覆盖（全球森林变化）产品。评估了两种机器学习方法（随机森林和极端随机树）。随机森林和极端随机树的分类准确率都很高（98.1-98.5%），两个分类器之间的差异很小（<0.5%）。分类后过滤水平的提高导致估计森林面积的减少和SAR衍生森林覆盖图的总体准确性的提高。所有森林覆盖产品都是使用独立的验证数据集进行评价的。对于朗福德地区，Forestry 2010数据集（97.42%）记录的总体准确度最高，而在斯莱戈，Prime 2数据集（97.43%）获得的总体准确度最高，尽管SAR衍生森林地图的准确度相当。我们的研究结果表明，星载雷达可以帮助在破碎景观中森林覆盖率低的地区进行库存。与国家和SAR衍生的森林地图相比，全球和泛大陆森林覆盖地图的准确性有所降低，这表明在将这些数据集用于国家报告时应谨慎行事。
Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.