14:10:51 >>BAHARAK: Okay.
14:10:51 Thank you very much.
14:11:07 Good afternoon my name is Bahark Razaghirad and today my presentation is part of the mass sustainability at Brock University on urban tree canopy assessment using geospatial technologies.
14:11:22 While largest cities are gradually becoming interested in the veiled of the benefits of urban tree canopy assessment.
14:11:40 Due to high cost and require special expertise usually small communities are deprived of opportunities that are prepared by UTC assessment as very good management and planning tool.
14:12:01 In order to propose an alternative convenient method to UTC assessment for small communities, we compared a cost and time intensive sophisticated image classification method with a free web-based application that uses image interpretation.
14:12:10 Pursuing the objectives of this study required software and data from different sources.
14:12:27 For UTC assessment using image classification, we used World War II multispectral satellite image and GIS data sets and for the software, we used it in VI arc map and Google Earth Pro.
14:12:35 For the UTC assessment using image interpretation we used a canopy which is an open source web-based application designed by the United States department of agriculture.
14:12:43 Here you can see the flowcharts of the first approach of UTC assessment.
14:12:56 After acquiring the datum and implementing the preprocessing, random points were generated from 70 percent was used for training data.
14:13:13 Using maximum likelihood classification, the land covers available in the study we are assigned to six previous some predefined classes including trees and shrubs low-lying visitation, souls, impervious roads, impervious buildings and water.
14:13:30 Actually Sally us was the next suspect in our study following the COVID 19 safety protocols available reference data including previous land classifications and Google Earth images were used for accuracy assessment.
14:13:41 One of the challenges we have in this study was the abundance of agricultural land, especially vineyards and orchards that created mixed coverages.
14:13:52 In theory these two groups of plants should be classified as trees and shrubs, but we subtracted them from the classification due to some concentration.
14:14:12 First, because of their small canopy size, they could increase the possibility of being misclassified mainly as a low-lying visitation, and second, considering trees for the ecosystem services, fruit trees are less efficient than shade and ornamental trees.
14:14:15 Because of the small average size.
14:14:32 So using polygons we presented vineyards and orchards, these two land-use types were clipped from the classification using arc map.
14:14:49 After finalizing the canopy by overlaying the canopy layer in different boundaries we presented geographic areas to show the area of the canopy for each of the seven small urban areas into Greenbelt and (name?) were calculated.
14:14:56 Here you can see we classified map of the study area using (name?) in the distribution of trees between different geographic areas.
14:15:08 Estimation of the canopy coverage using our canopy was more straightforward than the (term unknown).
14:15:16 This automated method that works on the Google maps interface, you have just to assign the random points created by the software to appropriate predefined classes.
14:15:30 Given our time constraints, I only show a comparison of the results obtained from the two different methods here.
14:15:40 This diagram clearly illustrates how similar the results are exempt for low-lying vegetation class.
14:15:58 But I have to mention here that the comparison of the eye tree canopy results was made with the result of MLC before subtracting every cultural.
14:15:59 The study showed that the use of eye tree canopy for assessing UTC should be examined from various perspectives.
14:16:08 I tree canopy is not cost and time intensive and does not require any specific expertise.
14:16:12 In assessing homogeneous land covers, I tree canopy can achieve close result to complex methods of image processing.
14:16:24 And I tree canopy presents the assessment results in numeric values of area and percentage, and does not provide any coverage maps.
14:16:39 So instead is requiring spatial information, that I tree canopy is not a good equivalent to image processing and image classification.
14:16:48 And finally, the suitability of this method depends on the technical characteristics of Google imageries such as a special resolution, available updates and time of acquisition.
14:16:56 That may vary from different countries and can affect the assessment.
14:17:00 Technically you cannot generalize of the suitability of I tree canopy to other parts of the world.
14:17:00 The key very much.