Assessing tree decay in an urban park using PlanetScope images: the case of Cerro Almodovar Park.

Abstract

Green urban areas play a major role in the quality of life of the citizens in terms of public health, environment and recreation. They help to improve air quality, control temperature and provide ecosystem services that contribute, like forests, to climate change mitigation. The health of urban tree stands is, therefore, essential to maintain the benefits that the urban and peri-urban parks provide to people and cities. Early detection of a reduction in vegetation health is essential to apply measures in order to prevent tree mortality or damage. The use of remote sensing technology for forest health monitoring has been widely demonstrated but the number of published papers using these techniques applied to urban parks is significantly lower. Nevertheless, the use of remote sensing offers excellent opportunities for monitoring the state of health of urban and peri-urban parks. It is a tool that can be very useful to complement on-site visits and make them more efficient by warning of suspected areas of damage. The main objective of this work was to evaluate the potential of PlanetScope images to estimate the degree of tree vegetation decay in the Cerro Almodovar urban park. It is located in the neighborhood of Aluche, in the Latina district, Madrid (Spain), and it had been observed that there were individuals that were beginning to show defoliation problems. Machine learning techniques were used to generate a mapping of the damage levels in the park as well as information on the uncertainty of the estimates. The good performance of the model obtained encourages further development of remote sensing health monitoring in urban green areas.