Publication Type
Journal Article
Publication Date (Issue Year)
2024
Journal Name
Remote Sensing
Abstract
Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda’s C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR’s performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats.
Keywords
depolarization ratio (DR), fuzzy logic algorithm, fall armyworm (FAW) detection, weather radar, FAW early warning system
Rsif Scholar Name
Fidele Maniraguha
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Rwanda (UR), Rwanda
Recommended Citation
Maniraguha, F., Vodacek, A., Kim, K. S., Ndashimye, E., & Rushingabigwi, G. (2024). Enhancing Identification of Meteorological and Biological Targets Using the Depolarization Ratio for Weather Radar: A Case Study of FAW Outbreak in Rwanda. Remote Sensing, 16 (14), 2509. https://doi.org/10.3390/rs16142509