Combining machine learning and publicly available aerial data (NAIP and NEON) to achieve high-resolution remote sensing of grass-shrub-tree mosaics in the Central Great Plains (U.S.A.)

Abstract

Woody plant encroachment (WPE)--a phenomenon similar to species invasion--is shifting many grasslands and savannas into shrub and evergreen-dominated ecosystems. Tracking WPE is difficult because shrubs and small trees are much smaller than the coarse resolution of common remote sensing platforms (> 10 m2) and the impassibility of encroaching woody thickets slows ground-based approaches. Many agencies have been investing in fine resolution (< 2 m2) remote sensing through programs such as the United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) and the National Ecological Observatory Network (NEON). Both use low-flying planes and provide data to end users in an easy-to-use format at large spatial extents. By removing entry barriers, these publicly available open-source programs could increase the accessibility and extent of remote sensing. We compared two common methods of machine learning classification of land cover (random forests and support vector machines) factorially crossed with these two freely available remote sensing platforms to determine if we could quickly and accurately develop remote sensing of major vegetation types in a tallgrass prairie landscape undergoing encroachment by shrubs and trees. Our work took place at Konza Prairie Biological Station--a landscape scale experiment that results in a wide range of land cover types. All models had very high overall classification accuracy (>90%), with the NEON-based models a few percent more accurate than NAIP. A model using both inputs had the highest accuracy. However, the accuracies of NAIP and NEON models differed for woody vegetation: compared to NEON, NAIP accuracy was, 82-93% compared to 94-98% for shrubs, 72-92% compared to 93-98% for deciduous trees, and 52-78% compared to 83-86% for evergreen trees (specifically Juniperus virginiana). NEON-based models relied on canopy height (LiDAR) to make classifications, whereas the several bands of light make similar contributions to accuracy in the NAIP models. Finally, we found that both machine learning approaches had similar accuracy, but random forests ran substantially faster. We conclude that with large training datasets, publicly available aerial imagery and similar products (e.g., UAVs, micro-satellites) can produce fine-scale, high-accuracy remote sensing of WPE in this region with low up-front costs.