Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Remote Sens. 2012, 4(11), 3462-3480; doi:10.3390/rs4113462
Article

Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data

* ,
,
 and
Received: 28 August 2012; in revised form: 25 October 2012 / Accepted: 6 November 2012 / Published: 13 November 2012
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
View Full-Text   |   Download PDF [2401 KB, updated 19 June 2014; original version uploaded 19 June 2014]   |   Browse Figures
Abstract: Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges—reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas—enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.
Keywords: species mapping; SVM; crown segmentation; CAO; Carnegie Airborne Observatory; Kruger National Park; South Africa species mapping; SVM; crown segmentation; CAO; Carnegie Airborne Observatory; Kruger National Park; South Africa
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.

Export to BibTeX |
EndNote


MDPI and ACS Style

Colgan, M.S.; Baldeck, C.A.; Féret, J.-B.; Asner, G.P. Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data. Remote Sens. 2012, 4, 3462-3480.

AMA Style

Colgan MS, Baldeck CA, Féret J-B, Asner GP. Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data. Remote Sensing. 2012; 4(11):3462-3480.

Chicago/Turabian Style

Colgan, Matthew S.; Baldeck, Claire A.; Féret, Jean-Baptiste; Asner, Gregory P. 2012. "Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data." Remote Sens. 4, no. 11: 3462-3480.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert