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RSS FeedsRemote Sensing, Vol. 14, Pages 4888: Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions (Remote Sensing)

 
 

30 september 2022 12:56:34

 
Remote Sensing, Vol. 14, Pages 4888: Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions (Remote Sensing)
 


The importance of small urban green areas has increased in the context of rapid urbanization and the densification of the urban tissue. The analysis of these areas through remote sensing has been limited due to the low spatial resolution of freely available satellite images. We propose a timeseries analysis on 3 m resolution Planet images, using GEOBIA and vegetation indices, with the aim of extracting and assessing the quality of small urban green areas in two different climatic and biogeographical regions: temperate (Bucharest, Romania) and mediterranean (Athens, Greece). Our results have shown high accuracy (over 91%) regarding the extraction of small urban green areas in both cities across all the analyzed images. The timeseries analysis showed consistency with respect to location for around 55% of the identified surfaces throughout the entire period. The vegetation indices registered higher values in the temperate region due to the vegetation characteristics and city plan of the two cities. For the same reasons, the increase in the vegetation density and quality, as a result of the distance from the city center, and the decrease in the density of built-up areas, is more obvious in Athens. The proposed method provides valuable insights into the distribution and quality of small urban green areas at the city level and can represent the basis for many analyses, which is currently limited by poor spatial resolution.


 
99 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 4885: Unifying Deep ConvNet and Semantic Edge Features for Loop Closure Detection (Remote Sensing)
Remote Sensing, Vol. 14, Pages 4887: Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery (Remote Sensing)
 
 
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