A general grass growth model for urban green spaces management in tropical regions: A case study with bahiagrass in southeastern Brazil

Author(s)

Escobar-Silva, Elton Vicente ; Bourscheidt, Vandoir ; Daughtry, Craig S. T. ; Kiniry, Jim R. ; Backes, Andre R. ; Chaves, Michel E. D.

• Document type: Journal article
• Source: URBAN FORESTRY & URBAN GREENING; v. 73, p. 12-pg., 2022-07-01.

Abstract

Urban green spaces (UGS) positively impact the population, providing essential ecosystem services and improving public health. Urban vegetation management needs to optimize mowing process costs and reducing impacts on the natural ecosystem. Thus, we implemented a general grass growth model suitable for UGS management in tropical areas, focused on lawns, public parks and squares, roadsides, and around waterways. The model incorporates local edaphoclimatic conditions to simulates the daily dynamics of leaf area index (LAI), biomass, evapotranspiration, and soil water content, going under mowing processes or not, with spatialization capability which might be integrated within geographic information system (GIS) environments. A case study assessing bahiagrass (Paspalum notatum Flugge ') vegetation species in Sa similar to o Carlos, southeastern Brazil, is presented, considering two scenarios to demonstrate the spatial capabilities of the model: (i) UGS as a single area, and (ii) several areas independently. For model validation, vegetation indices calculated based on data from an unmanned aerial vehicle (UAV) and CubeSat imagery (PlanetScope) were used to retrieve LAI time series, calibrated with spectral signatures from leaf ground sampling. For performance analysis, LAI time series from the model and LAI retrieved from both sensors were compared via determination coefficient (R2) and root mean square error (RMSE). Our findings suggest that the proposed model is accurate, and due to its spatialization capability and integration with a GIS, its application may help government administrations to optimize UGS mowing processes. (AU)

• FAPESP's process: 21/07382-2 - Use of dense Sentinel-2/MSI time series and machine learning algorithms to improve crop monitoring in the Cerrado biome
• Grantee: Michel Eustáquio Dantas Chaves
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/12428-9 - Development of a vegetation growth model adapted to urban areas
• Grantee: Elton Vicente Escobar Silva
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• FAPESP's process: 17/24038-8 - A new approach to vegetation growth modelling in urban areas using geographical Information systems
• Grantee: Elton Vicente Escobar Silva
• Support Opportunities: Scholarships in Brazil - Master