Is ward-level calculation of urban green space availability important?—A case study on Vellore city, India, using the histogram-based spectral discrimination approach - Gaikadi, Sangeetha; Kumar, S. Vasantha (Frontiers in Sustainable Cities, 2024)
How much green space is available for individuals is a major question that city planners are generally interested in and the present study tried to address this for a case study area of Vellore, India through two approaches, namely the per capita and the geographical area approach. In existing studies, urban green space (UGS) was calculated at the macro level, i.e., for the city as a whole. Micro or ward level analysis was not attempted before and the present study carried out the same to get a clear picture of how much greenery is available in each ward of a city. For this, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland and crop land were spectrally different. Then the classification techniques like ISODATA, maximum likelihood, support vector machine (SVM) and object-based were applied on the GE image. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that if we consider the city as a whole, the amount of UGS available is 42% of the total area which is more than the recommended range of 20-40%. Similarly, the available UGS per person is 97.84m2 which is far above the recommended 12m2/person. However, the micro level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at ward level than city level as the former gives more closer look into the surplus and deficit areas. The present study also showed how to utilize the LiDAR data to extract information like type of tree, height, diameter, crown area and temperature at individual tree level. The results revealed that if trees are there adjacent to buildings or roads, then it results in lesser heat islands when compared to that of no trees case.