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Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast
- Source :
- The International Journal of Remote Sensing 43(19-24), (2022), 7117-7138
- Publication Year :
- 2022
-
Abstract
- 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.<br />Comment: 43 pages (35 main text, 8 supplementary materials); 11 figures (10 main text, 1 supplementary materials), 10 tables (4 main text, 6 supplementary materials)
Details
- Database :
- arXiv
- Journal :
- The International Journal of Remote Sensing 43(19-24), (2022), 7117-7138
- Publication Type :
- Report
- Accession number :
- edsarx.2205.05047
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1080/01431161.2022.2155086