Abstract
Urban forest and vegetation conditions are an important variable in urban ecosystem management decision-making. However, it is difficult to evaluate and monitor solely on the basis of field measurements. Remote sensing technologies can greatly contribute to the faster extraction and mapping of vegetation health status indicators, on the basis of which agronomy and forestry experts can draw conclusions about the condition of urban vegetation in larger areas. A new remote sensing-based urban forest and vegetation cover monitoring framework is presented and applied to a case study of the city of Zagreb, Croatia. In this study, Sentinel-2 multi-temporal imagery was used to derive and analyze the current state of urban forest cover. Vegetation indices (NDVI, RVI, and GRVI) were calculated. K-means unsupervised classification of the vegetation indices was conducted. In this way, the dimensionality of the vegetation indices was reduced, while all the data contained in it were used to represent their graded values. Vegetation that was in a poor condition stood out better that way. Finally, PCA-based change detection was performed on the vegetation indices graded values, and a map of change was produced. These results need to be interpreted and validated by foresters and agronomists in further research.
Highlights
Principal components analysis (PCA)-based change detection was performed on the vegetation indices graded values, and a map of change was produced
Urban forests and vegetation are of crucial importance for modern towns, because they provide a wide range of ecosystem services that increase citizens’ quality of life
Urban vegetation reduces the potential for creating surface urban heat islands, while the removal of vegetation areas contributes to creating them [9,10]
Summary
Urban forests and vegetation are of crucial importance for modern towns, because they provide a wide range of ecosystem services that increase citizens’ quality of life They are an important part of the urban ecological system, which plays an important role in protecting the urban ecological environment [1]. The spatial distribution and density of forest and vegetation cover within city borders is recognized as a major factor influencing numerous biophysical processes in the urban environment [11,12]. In contrast to such analyzes, in this framework, these results were used to. Unsupervised classifiSeptembercation of vegetation indices0.159 provides insight0.897 into the spatial 0.093 distribution of their values
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