Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification
Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification
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DOI：
10.3390/rs12091414
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发表时间：
2020-04
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影响因子：
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通讯作者：
Victoria M. Scholl;M. Cattau;M. Joseph;J. Balch
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作者：
Victoria M. Scholl;M. Cattau;M. Joseph;J. Balch
准确绘制树种组成和多样性是实现空间明确和特定物种生态理解的关键一步。国家生态观测站网络 (NEON) 是全美开放生态数据的宝贵来源。免费提供的 NEON 数据包括单棵树木的现场测量，包括茎位置、物种和树冠直径，以及 NEON 机载观测平台 (AOP) 机载遥感图像，包括高光谱、多光谱以及光探测和测距 (LiDAR) 数据产品。使用遥感数据预测物种的一个重要方面是创建高质量的训练集以实现最佳分类目的。最终，手动创建训练数据是一项昂贵且耗时的任务，依赖于人类分析师的决策，并且可能需要外部数据集或信息。我们结合现场和机载遥感 NEON 数据来评估自动训练集准备和新颖的数据预处理工作流程对美国科罗拉多州尼沃特岭山研究站森林 NEON 站点的四种主要亚高山针叶树种分类的影响。我们使用一系列训练数据集以及遥感栅格数据作为描述性特征来训练基于像素的随机森林 (RF) 机器学习模型。基于内部射频误差评估和独立验证集，使用每棵树最大树冠直径一半创建的圆形树冠多边形获得了最高的分类精度，分别为 69% 和 60%。激光雷达衍生的数据产品是物种分类最重要的特征，其次是植被指数。这项工作有助于使用公开可用的 NEON 数据来开放开发标记良好的森林成分图训练数据集，而无需外部数据收集、手动描绘步骤或特定于地点的参数。
Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.