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Open AccessArticle

Mapping Physiognomic Types of Indigenous Forest using Space-Borne SAR, Optical Imagery and Air-borne LiDAR

Manaaki Whenua—Landcare Research, Palmerston North 4410, New Zealand
Manaaki Whenua—Landcare Research, Wellington 6143, New Zealand
Manaaki Whenua—Landcare Research, Lincoln 7608, New Zealand
Department of Soil Science, University of Tehran, Tehran 1417, Iran
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1911;
Received: 15 July 2019 / Revised: 4 August 2019 / Accepted: 12 August 2019 / Published: 15 August 2019
(This article belongs to the Section Forest Remote Sensing)
Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from the European Space Agency (ESA) Sentinel-1 and 2 missions, Advanced Land Orbiting Satellite (ALOS) PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest. A five-fold cross-validation (repeated 100 times) of ground data showed that the highest classification accuracy of 80.5% is achieved for bands 2, 3, 4, 8, 11, and 12 from Sentinel-2, the ratio of bands VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) from Sentinel-1, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on optical bands alone was 72.7% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.4%. The classification accuracy is sufficient for many management applications for indigenous forest, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management. View Full-Text
Keywords: forest types; forest mapping; Sentinel-2; SAR; LiDAR; canopy metrics forest types; forest mapping; Sentinel-2; SAR; LiDAR; canopy metrics
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MDPI and ACS Style

Dymond, J.R.; Zörner, J.; Shepherd, J.D.; Wiser, S.K.; Pairman, D.; Sabetizade, M. Mapping Physiognomic Types of Indigenous Forest using Space-Borne SAR, Optical Imagery and Air-borne LiDAR. Remote Sens. 2019, 11, 1911.

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