Identification of urban tree crown in a tropical environment using WorldView-2 data: problems and perspectives

Abstract

With the availability of high-resolution satellite data, much research has been focused on the automatic detection and classification of individual tree crowns. Most of these studies were applied to temperate climates of the northern hemisphere, especially for forests of coniferous. Very few studies have been applied to the detection of trees in the tropical regions, least of all in the urban environment. Urban trees play a major role in maintaining or even improving the quality of life in cities by their contribution to the quality of the air, by absorbing rain water, by refreshing the air through transpiration and providing shadow. In this study we explored the potential of high-resolution WorldView-2 satellite data for the identification of urban individual tree crowns in the city of Belo Horizonte, Minas Gerais, Brazil, through an object-oriented approach. Irrelevant areas were masked (e.g. buildings, asphalt, shadows, exposed soil) using a threshold of NDVI. Three different approaches were tested to isolate and delineate individual tree crowns: region growing, watershed and template matching. For the first two approaches several parameters were tested to find the best result for the isolation of the individual tree crowns. An in-house program has been developed for template matching using a set of seven different templates of different species. A set of 300 individual tree crowns were visually interpreted in the WorldView-2 image to serve as validation and to compare the performance of the three different approaches. Then, the comparison was performed between the visual interpretation and the results of each approach by calculating the difference between the areas as a ratio of the validated area. Our results show that the region growing approach provided the best results, with an accuracy of over 80%.

Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach

“…In the context of ITC detection, TM methods has been exploited in some previous studies [37,52,[99][100][101][102][103][104][105]. A comparative study [52] involving six different ITC detection methods (TM, local maxima, valley following, region-growing, scale-space theory, and techniques based on stochastic frameworks) showed that TM had the best performance among other methods for the detection of isolated tree crowns, which are usually the predominant pattern of trees located in orchards [103].…”

“…The position of the occurrence of the best matches on the similarity image indicate the position of the pixels with the highest probability of being a tree crown centroid. Thus, to find the tree crown centroids in the adopted TM algorithm, the NCC values were thresholded (for more details see [52,102,143]). The template match threshold value should be selected with care to meet the requirements of the map users, as setting the threshold value too high leads to a large number of false negative (FN) errors (i.e., undetected tree crowns), while setting the threshold value too low leads to a high number of false positive (FP) errors (i.e., misdetection of non-tree features as tree crowns).…”

“…A more sophisticated solution for enhancing the performance of tree mapping by TM-CS in this area is to use a multi-template matching approach [194] by generating a specific template for each class of trees and then applying the TM algorithm to the entire study area by using the multiple templates (to find the best match). Previous studies [100,102,105] have demonstrated that creating multiple templates may improve the tree detection performance of the TM algorithm in both single-species and mixed-species stands because doing so helps to compensate for variations in viewing geometry and tree size in both types of stands as well as tree shape and spectral characteristics in mixed-species stands. The multi-template matching solution is more suitable for case studies where there is at least one crowdsourced sample for each of the main classes of trees present in the study area (i.e., the crowdsourced data is complete in terms of thematic completeness (regarding to the tree classes)).…”

The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring

“…The level of detail in very high spatial resolution sensors, such as WorldView-2, is often appreciated for object detection such as tree crown mapping (e.g. Karlson et al, 2014;Gomes and Maillard, 2013). However, the narrow multispectral bands provide also great potential for robust extraction of forest structural param- eters (Ozdemir and Karnieli, 2011), improved forest biomass and carbon estimations (Eckert, 2012), and to discriminate forest tree species (Peerbhay et al, 2014).…”

Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows

“…Further examples of object recognition using satellite and aerial images are aircraft and crater detection [25,26], tree crown detection, oil palm and species detection, and counting in forestry [27][28][29][30][31] and dwelling extraction [32].…”