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首页> 外文期刊>Remote Sensing >High-Resolution Mapping of Redwood ( Sequoia sempervirens ) Distributions in Three Californian Forests
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High-Resolution Mapping of Redwood ( Sequoia sempervirens ) Distributions in Three Californian Forests

机译：加利福尼亚三大森林中红杉（红杉）分布的高分辨率制图

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High-resolution maps of redwood distributions could enable strategic land management to satisfy diverse conservation goals, but the currently-available maps of redwood distributions are low in spatial resolution and biotic detail. Classification of airborne imaging spectroscopy data provides a potential avenue for mapping redwoods over large areas and with high confidence. We used airborne imaging spectroscopy data collected over three redwood forests by the Carnegie Airborne Observatory, in combination with field training data and application of a gradient boosted regression tree (GBRT) machine learning algorithm, to map the distribution of redwoods at 2-m spatial resolution. Training data collected from the three sites showed that redwoods have spectral signatures distinct from the other common tree species found in redwood forests. We optimized a gradient boosted regression model for high performance and computational efficiency, and the resulting model was demonstrably accurate (81–98% true positive rate and 90–98% overall accuracy) in mapping redwoods in each of the study sites. The resulting maps showed marked variation in redwood abundance (0–70%) within a 1 square kilometer aggregation block, which match the spatial resolution of currently-available redwood distribution maps. Our resulting high-resolution mapping approach will facilitate improved research, conservation, and management of redwood trees in California.
机译：高分辨率的红木分布图可以使战略土地管理满足各种保护目标，但是目前可用的红木分布图的空间分辨率和生物细节都很低。机载成像光谱数据的分类为在大面积和高可信度上绘制红木图提供了潜在的途径。我们使用了卡内基空中天文台在三个红木森林上收集的机载成像光谱数据，结合野外训练数据和应用梯度增强回归树（GBRT）机器学习算法，以2米空间分辨率绘制了红木的分布图。从这三个地点收集的训练数据表明，红木的光谱特征与红木森林中发现的其他常见树种不同。我们优化了梯度增强回归模型，以实现高性能和计算效率，并且在每个研究地点对红木进行制图时，所得到的模型都具有明显的准确性（81-98％的真实阳性率和90-98％的总体准确性）。生成的地图显示，在1平方公里的聚合块内，红木丰度（0–70％）有明显变化，与当前可用的红木分布图的空间分辨率匹配。我们产生的高分辨率制图方法将有助于改善加利福尼亚州红木树的研究，保护和管理。

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