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Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches

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Abstract

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis—[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.

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Acknowledgement

The authors would like to thank K. Ward and E. LaPoint for their thoughtful reviews on earlier versions of the manuscript. We also thank R.P. Kollasch for his help with independent components analysis, S. Uriarte for her valuable assistance with ICA processing and aerial photo interpretation (PI) work for the accuracy assessments, and C. Olson who also assisted with PI work.

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  1. Northern Research Station, USDA Forest Service, 1992 Folwell Avenue, St. Paul, MN, 55108, USA

    Dacia M. Meneguzzo, Greg C. Liknes & Mark D. Nelson

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  1. Dacia M. Meneguzzo
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Correspondence to Dacia M. Meneguzzo.

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Meneguzzo, D.M., Liknes, G.C. & Nelson, M.D. Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches. Environ Monit Assess 185, 6261–6275 (2013). https://doi.org/10.1007/s10661-012-3022-1

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