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This paper focuses on the use of multispectral measurements to classify remotely sensed radiance and reflectance information into three tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.), and birch (Betula pubescens Ehrh., Betula pendula Roth), using a Support Vector Machine (SVM) algorithm. The features used for the classifier are radiometric involving different viewing angles, but without textural information. At-sensor radiance (ASR) signals used here were obtained using a four-band Leica ADS40-SH52 airborne sensor. The experiments were carried out in a forest area at Hyytiälä, in southern Finland (61 ^\circ50^\prime\ \N, 24^\circ20 ^\prime\ \E), which has been widely used for similar purposes, so that detailed tree-level information has been reported previously. The flight was carried out on August 23, 2008. ADS40 ASR measurements can be converted to ground reflectance signatures in two viewing directions using atmosphere and BRDF modeling implemented in Leica XPro 4.2 software. Taking into account the assumptions entailed in the radiometric model, the classification performance of the ground reflectance is evaluated only for the pixel values under sunlit conditions and is compared with the performance of the ASR data. The sunlit and shaded parts of the tree crown were extracted based on the use of LiDAR data for crown shape modeling. The classification results for the real multispectral measurements are compared with the earlier results obtained with simulated Leica ADS40 at-sensor radiance response values which were based on the ground-level high-resolution ground reflectance factor measurements using a single viewing direction. The simulated classification accuracy was 75–79% with the original four bands, while it was up to 85–88%, using the simulated fifth channel. It was found here that the classification accuracy using comparable real ADS40-SH52 four-band data and one viewing angle was 75–79% and increased to 78–82% with two viewing angles. The results show that the best-case classification accuracy with real data can reach 88% if trees are modeled as objects with sunlit and shaded areas, and multiple measurements are available for every tree. The results suggest that ground reflectance estimation with normalization of anisotropic reflectance behavior leads to similar classification performance to ASR data, but can in some cases improve the generalization properties of training data. [ABSTRACT FROM AUTHOR]

The information content of remotely sensed data depends primarily on the spatial and spectral properties of the imaging device. This paper focuses on the classification performance of the different spectral features (hyper- and multispectral measurements) with respect to three tree species. The Support Vector Machine was chosen as the classification algorithm for these features. A simulated optical radiation model was constructed to evaluate the identification performance of the given multispectral system for the tree species, and the effects of spectral-band selection and data preprocessing were studied in this setting. Simulations were based on the reflectance measurements of the pine (Pinus sylvestris L.), spruce [Picea abies (L.) H. Karst.], and birch trees (Betula pubescens Ehrh. and Betula pendula Roth). Leica ADS80 airborne sensor with four spectral bands (channels) was used as a fixed multispectral sensor system that leads to response values for the at-sensor radiance signal. Results suggest that this four-band system has inadequate classification performance for the three tree species. The simulations demonstrate on average a 5-15 percentage points improvement in classification performance when the Leica system is combined with one additional spectral band. It is also demonstrated for the Leica data that feature mapping through a Mahalanobis kernel leads to a 5-10 percentage points improvement in classification performance when compared with other kernels. [ABSTRACT FROM AUTHOR]

Abstract: Airborne multispectral remote sensing devices have been used in automatic identification of tree species, and the spatial and spectral properties of the sensors affect the remote sensing measurement results. Previous work based on a simulation model with ground-level measured reflectance data of pine (Pinus sylvestris L.), spruce (Picea abies (L.) H. Karst.), and birch (Betula pubescens Ehrh. and Betula pendula Roth) tree species and idealized Leica ADS80 sensitivities suggested that the addition of a fifth sensitivity band in the red edge wavelength region to the existing Leica ADS80 system significantly improves the classification performance. In this paper, we extend this analysis using a simulated model with accurate spectral sensitivity information and airborne AisaEAGLE II hyperspectral data for these three tree species. We simulated multispectral responses using spectral sensitivity characteristics of the Leica ADS40, the Vexcel UltraCam-D, the Intergraph-Z/I Digital mapping camera and the Leica ADS40 system with an added band in the 691–785nm region. We evaluated the tree species classification performance of these simulated responses using Discriminant Analysis and Support Vector Machine classifiers. The classification experiment result showed that the simulated responses of the 5-band multispectral system yielded the most robust classification performance with approximately 98% accuracy. This result was similar to the accuracy obtained with the hyperspectral data. Although differences were observed in the sensitivity functions of the 4-band systems, there were no large differences observed in the classification performances between them. With the simulated 5-band system, there was an increase of 5–13% points in classification accuracy when compared to the accuracies of the 4-band systems. The results obtained via proposed 5-band system support results from previous studies suggesting that there is a need for a sensitivity band in the red edge wavelength region for applications in tree species classification. [Copyright &y& Elsevier]