A novel artificial neural network methodology to produce high-resolution bioclimatic maps using Earth Observation data: A case study for Cyprus

Kostas Philippopoulos; Katerina Pantavou; Constantinos Cartalis; Ilias Agathangelidis; Thaleia Mavrakou; Anastasios Polydoros; Georgios Nikolopoulos

doi:10.1016/j.scitotenv.2023.164734

A novel artificial neural network methodology to produce high-resolution bioclimatic maps using Earth Observation data: A case study for Cyprus

Sci Total Environ. 2023 Oct 1:893:164734. doi: 10.1016/j.scitotenv.2023.164734. Epub 2023 Jun 9.

Authors

Kostas Philippopoulos¹, Katerina Pantavou², Constantinos Cartalis³, Ilias Agathangelidis³, Thaleia Mavrakou³, Anastasios Polydoros³, Georgios Nikolopoulos²

Affiliations

¹ National and Kapodistrian University of Athens, Department of Physics, 15784 Athens, Greece. Electronic address: kphilip@phys.uoa.gr.
² Medical School, University of Cyprus, P.O.Box 20537, 1678 Nicosia, Cyprus.
³ National and Kapodistrian University of Athens, Department of Physics, 15784 Athens, Greece.

PMID: 37302587
DOI: 10.1016/j.scitotenv.2023.164734

Abstract

The aim of this research is to propose a novel methodology that exploits Earth Observation (EO) data to accurately produce high-resolution bioclimatic maps at large spatiotemporal scales. This method directly links EO products (i.e., land surface temperature - LST and Normalized Difference Vegetation Index - NDVI) to air temperature (Tair) and such thermal indices as the Universal Thermal Climate Index (UTCI), and the Physiologically Equivalent Temperature (PET) to produce large-scale high-quality bioclimatic maps at a spatial resolution of 100 m. The proposed methodology is based on Artificial Neural Networks (ANNs), and the bioclimatic maps are developed with the use of Geographical Information Systems. High-resolution LST maps are produced from the spatial downscaling of EO images and the application of the methodology in the case of the island of Cyprus highlights the ability of EO parameters to estimate accurately T_air as well as the above mentioned thermal indices. The results are validated for different conditions and the overall Mean Absolute Error for each case ranges from 1.9 °C for Tair to 2.8 °C for PET and UTCI. The trained ANNs could be used in near real-time for estimating the spatial distribution of outdoor thermal conditions and for assessing the relationship between human health and the outdoor thermal environment. On the basis of the developed bioclimatic maps, high-risk areas were identified. Furthermore, the study examines the relationship between land cover and Tair, UTCI, and PET, and the results provide evidence of the suitability of the method to monitor the dynamics of the urban environment and the effectiveness of urban nature-based solutions. Studies on bioclimate analysis monitor thermal environment, raise awareness and enhance the capacity of national public health systems to respond to thermally-induced health risks.

Keywords: Big data; Bioclimatic maps; Earth observation; Physiologically equivalent temperature; Universal thermal climate index.