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Remote Sens. 2017, 9(8), 766; https://doi.org/10.3390/rs9080766

Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys

1
Department of Landscape Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, Birmensdorf 8903, Switzerland
2
Department of Forest Resources and Management, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zuercherstrasse 111, Birmensdorf 8903, Switzerland
*
Author to whom correspondence should be addressed.
Received: 14 June 2017 / Revised: 13 July 2017 / Accepted: 24 July 2017 / Published: 27 July 2017
(This article belongs to the Section Forest Remote Sensing)
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Abstract

Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km2 that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of −3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector. View Full-Text
Keywords: airborne digital sensor; NFI; independent validation data; random forest classifier; high automation; conifers; broadleaved airborne digital sensor; NFI; independent validation data; random forest classifier; high automation; conifers; broadleaved
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Waser, L.T.; Ginzler, C.; Rehush, N. Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys. Remote Sens. 2017, 9, 766.

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