  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.
