Image Classification of Individual Trees: Case Study of Wanjohi Area, Re-Charge Source of Menengai Geothermal Field, Kenya

Abstract

Kenya is endowed with geothermal resources mainly located in the Rift Valley. Menengai Geothermal Field is one of these fields with its re-charge source being Wanjohi area in Aberdares region. From the hydro-geologic controls, it is evident that there are three distinct geologic formations that physically translate into the different water yield levels i.e. highest to the east and lowest to the west. Perhaps this is because the east is characterized by a regional groundwater aquifer likely to be recharged from a larger area with high rainfall or through deep and expansive faults such as those found at the edges of the Rift Valley. A methodology of Object-Oriented Image Classification method of remote sensing is a useful and promising method of classifying objects from high resolution satellite images. The method segments the image pixel into objects and utilizes the texture and contexture information of the object rather than only using spectral information relied upon by traditional methods. This paper, using high resolution multispectral satellite imagery from WorldView-2, sought to explore ways to identify and extract accurate trees of varying crown sizes. IMAGINE Objective tools from ERDAS IMAGINE software were used to define individual trees model parameters by employing different feature detection and extraction techniques. These enable geospatial data layers to be created and maintained through the use of remotely sensed imagery. The results show that IMAGINE Objective provides a high accuracy function for tree extraction especially when one is dealing with cluster of individual tree crowns. By using cue parameters like color, tone, orientation, texture, etc. the spectral differences between tree and others features were able to be detected. Based on training samples, the trees were quantitatively extracted by means of probability of Bayesian Network on Single Feature Probability (SFP) function. In conclusion, Object-oriented analysis proves a successful method of identifying and extracting individual trees of varying crown sizes. A high accuracy is achieved compared to other pixel-based classification techniques.