Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast

• Published in: arXiv.org 2022-12
• Main Authors: Mahoney, Michael J, Johnson, Lucas K, Guinan, Abigail Z, Beier, Colin M
• Format: Article
• Language: English
• Subjects: Airborne sensing; Classification; Land cover; Landsat satellites; Landscape; Lidar; Machine learning; Mapping; Optical properties; Remote sensing; Satellite imagery; Vegetation; Wildlife conservation; Wildlife management
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2205.05047

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.