Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia

Abstract

Mapping and identifying allergenic tree species in densely urbanized regions is vital for understanding their distribution and prevalence. However, accurately detecting individual allergenic tree species in urban green spaces remains challenging due to their smaller site and patchiness. To overcome these issues, PlanetScope (PS) satellite imagery offers significant benefits compared with moderate or high-resolution RS imagery due to its daily temporal resolution and 3 m spatial resolution. Therefore, the primary objectives of this research were to: assess the feasibility of mapping allergenic tree species in the highly urbanized area using high-resolution PS imagery; evaluate and compare the performance of the most important machine learning and feature selection methods for accurate detection of individual allergenic tree species. The research incorporated three classification scenarios based on ground truth data: The first scenario (CS1) used single-date PS imagery with vegetation indices (VI), while the second and third scenarios (CS2 and CS3) used multitemporal PS imagery with VI, and GLCM and VI, respectively. The study demonstrated the feasibility of using multitemporal eight-band PlanetScope imagery to detect allergenic tree species, with the XGB method outperforming others with an overall accuracy of 73.13% in CS3. However, the classification accuracy varied between the scenarios and species, revealing limitations including the inherent heterogeneity of urban green spaces. Future research should integrate high-resolution satellite imagery with aerial photography or LiDAR data along with deep learning methods. This approach has the potential to classify dominant tree species in highly complex urban environments with increased accuracy, which is essential for urban planning and public health.

Comparing WorldView-2 and PlanetScope Imagery to Mapping Housing Types Using GEOBIA

“…These capabilities of Planetscope imagery extend to the extraction of urban features, as demonstrated in building inventory information extraction and the rapid identification of urban green spaces. Nevertheless, it's noteworthy that identifying urban features in Planetscope imagery encounters challenges arising from the relatively small and patchy nature of urban areas within cities [15]. To address these challenges, researchers have combined machine learning techniques with Planetscope data to discern tree species in compact urban zones [15].…”

“…Nevertheless, it's noteworthy that identifying urban features in Planetscope imagery encounters challenges arising from the relatively small and patchy nature of urban areas within cities [15]. To address these challenges, researchers have combined machine learning techniques with Planetscope data to discern tree species in compact urban zones [15]. They advocate for the integration of multitemporal eight-band Planetscope imagery in future studies, citing its potential to significantly enhance the accuracy of urban feature detection.…”