论文标题
在美国东北部农业后景观中的低温“灌木丛”覆盖类型的分类和映射
Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast
论文作者
论文摘要
新颖的植物社区重塑景观，并对土地覆盖分类和映射构成挑战，这可以限制研究和管理工作。在美国东北部，野外景观中的低温木质植被或灌木丛的出现，而不是次生森林，这是有充分记录在农业景观中的，但从景观的角度来看，这限制了系统地研究和管理这些土地的能力。为了解决它们历史上很少见的低温覆盖类型的分类/映射的差距，我们开发了模型来预测纽约州（NYS）30m分辨率的灌木丛分布，使用堆叠的集合结合了随机的森林，梯度提升机，和人工神经网络，以整合结构性的（空中流动性Lidar）和Optical（Satellite）的远程感应（Satellite）和Satellite（Satellite）和satellite（Satellite telliete and satellite and satellite and satelliete and satelliete and oftery Imageys of satelliete and satellite and terlige and tellime nige）。我们首先将1M冠层高度模型（CHM）分类为从可用的LIDAR覆盖范围的拼布中得出，以定义灌木丛的存在/不存在。接下来，这些非连续图用于基于时间分段的图像来训练模型集成，以预测整个研究格局（NYS）的灌木概率。 CHM覆盖面积约2.5％被归类为灌木丛。使用在分类的CHM上训练的Landsat预测变量的模型可有效识别灌木丛（测试集AUC = 0.893，现实世界AUC = 0.904），即使在超越了原始培训数据的范围之外，也可以区分灌木/Young Forest和其他覆盖类别，并产生有性明智的地图。我们的结果表明，即使是从不连续的覆盖范围中，机载激元的纳入也可以改善历史上罕见但越来越普遍的灌木丛栖息地的土地覆盖分类。
Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict shrubland distributions at 30m resolution across New York State (NYS), using a stacked ensemble combining a random forest, gradient boosting machine, and artificial neural network to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1m canopy height model (CHM), derived from a patchwork of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally-segmented imagery to predict shrubland probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC=0.893, real-world AUC=0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. Our results suggest that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve land cover classification of historically rare but increasingly prevalent shrubland habitats across broader areas.
