Sensors 2008, 8(5), 3020-3036; doi:10.3390/s8053020
Article

Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object- Oriented Approach

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Received: 31 January 2008; Accepted: 29 April 2008 / Published: 6 May 2008
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
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Abstract: The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (Gleditsia triacanthos, Acer saccharum, Tilia Americana, Quercus palustris, Pinus strobus and Picea glauca) were used in the classification. The results from this analysis did not show any major difference in overall accuracy between the two seasons. Overall accuracy was approximately 57% for the Summer dataset and 56% for the Fall dataset. However, the Fall dataset provided more consistent results for all tree species while the Summer dataset had a few higher individual class accuracies. Further, adding LiDAR into the classification improved the results by 19% for both fall and summer. This is mainly due to the removal of shadow effect and the addition of elevation data to separate low and high vegetation.
Keywords: remote sensing; object oriented; hyperspectral; LiDAR; tree species; urban
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.

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MDPI and ACS Style

Voss, M.; Sugumaran, R. Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object- Oriented Approach. Sensors 2008, 8, 3020-3036.

AMA Style

Voss M, Sugumaran R. Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object- Oriented Approach. Sensors. 2008; 8(5):3020-3036.

Chicago/Turabian Style

Voss, Matthew; Sugumaran, Ramanathan. 2008. "Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object- Oriented Approach." Sensors 8, no. 5: 3020-3036.


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