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

arXiv: https://arxiv.org/abs/2205.05047 · HTML: https://mlsanity.com/paper/2205.05047/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 76 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 1 of 1)

- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)

## Built on by (most cited first, 3 of 3)

- [Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages](https://mlsanity.com/paper/2205.08530/) (2022)
- [Mapping historical forest biomass for stock-change assessments at parcel to landscape scales](https://mlsanity.com/paper/2304.02632/) (2023)
- [New allometric models for the USA create a step-change in forest carbon estimation, modeling, and mapping](https://mlsanity.com/paper/2405.04507/) (2024)

## Abstract (arXiv)

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.