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

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

扫码查看
? 2022 Elsevier GmbHUrban 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 Flüggé) vegetation species in S?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.

BahiagrassPublic open spacesUrban planningUrban vegetation managementVegetation growth modeling

Escobar-Silva E.V.、Chaves M.E.D.、Bourscheidt V.、Daughtry C.S.T.、Kiniry J.R.、Backes A.R.

展开 >

Earth Observation and Geoinformatics Division National Institute for Space Research (INPE)

Department of Environmental Science – DCAm Federal University of S?o Carlos

USDA Agricultural Research Service Beltsville Agricultural Research Center

USDA Agricultural Research Service Grassland Soil and Water Research Laboratory

School of Computer Science – Facom Federal University of Uberlandia

展开 >

2022

Urban Forestry & Urban Greening

Urban Forestry & Urban Greening

SCI
ISSN:1618-8667
年,卷(期):2022.73
  • 1
  • 66