Urban tree species classification on pixel and object level with worldview-2 image, using maximum likelihood classifier and support vector machine

Urban forests play a significant role in improving air quality and climate protection, energy saving, recreation and human connection with nature. To maximize on the mentioned benefits, urban trees inventory is necessary to identify tree location, gain species information and their spatial distributions. Urban areas do have a mixed environment of land cover. In addition, traditional techniques like ground survey, aerial photography are time-consuming, costly and limited. This study researched the classification of urban tree species using maximum likelihood classification and support vector machine on the WorldView-2 satellite image. This entailed using object and pixels in both methods. The main objective of this study was to classify urban tree species using Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM). Object based and pixel based analysis was used in both methods. MLC performed better than SVM in both object and pixel based calssification. MLC pixel based overall accuracy was 66.93% with Kappa of 0.54 and 51.24% with Kappa of 0.36 for SVM pixel based. MLC object based overall accuracy was 71.17% with Kappa of 0.66 and 44.62% with Kappa of 0.31 for SVM object based analysis. Even though MLC perfoms better than SVM, the accuracy is still low compared to generally accepted accuracy. This indicates that both methods are still not satisfactory techniques of classifying high resolution images for Delft city. MLC, however, has been used for many years in image classification. It is straightforward and does not require extreme expert skills to apply. MLC algorithm can be found in most of the remote sensing application software. Examples of this software are ERDAS, ENVI and ILWIS among others. This makes MLC an easy available method for classification. MLC pixel based classification is effective in classifying medium and large trees (e.g. Plantanus Spp. and Fagus Spp.). MLC relies on mean and covariance of samples hence calculation of covariance matrix in small tree crowns (> 10 pixels) could not be determined. Class separability using J-M distance measure and NDVI mean and variation evaluation were the same. This gives possibilty of use of NDVI in separating tree species. SVM does not operate based on data distribution making it applicable to any type of data (i.e. normal or non-normal distribution. Its performance relies on kernel parameters. In this study, C value of 5 and value 5 for δ were used. Experimenting on optimum parameters values of C and δ can give satisfactory classification results. Though the study area is in The Netherlands, classification of tree species using MLC and SVM brings up possibilities to apply the same approach in other urban areas and to other tree species.