Using the Negative Soil Adjustment Factor of Soil Adjusted Vegetation Index (SAVI) to Resist Saturation Effects and Estimate Leaf Area Index (LAI) in Dense Vegetation Areas.

Saturation effects limit the application of vegetation indices (VIs) in dense vegetation areas. The possibility to mitigate them by adopting a negative soil adjustment factor X is addressed. Two leaf area index (LAI) data sets are analyzed using the Google Earth Engine (GEE) for validation. The first one is derived from observations of MODerate resolution Imaging Spectroradiometer (MODIS) from 16 April 2013, to 21 October 2020, in the Apiacás area. Its corresponding VIs are calculated from a combination of Sentinel-2 and Landsat-8 surface reflectance products. The second one is a global LAI dataset with VIs calculated from Landsat-5 surface reflectance products. A linear regression model is applied to both datasets to evaluate four VIs that are commonly used to estimate LAI: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), transformed SAVI (TSAVI), and enhanced vegetation index (EVI). The optimal soil adjustment factor of SAVI for LAI estimation is determined using an exhaustive search. The Dickey-Fuller test indicates that the time series of LAI data are stable with a confidence level of 99%. The linear regression results stress significant saturation effects in all VIs. Finally, the exhaustive searching results show that a negative soil adjustment factor of SAVI can mitigate the SAVIs' saturation in the Apiacás area (i.e., X = -0.148 for mean LAI = 5.35), and more generally in areas with large LAI values (e.g., X = -0.183 for mean LAI = 6.72). Our study further confirms that the lower boundary of the soil adjustment factor can be negative and that using a negative soil adjustment factor improves the computation of time series of LAI.

Multilayer urban canopy modelling and mapping for traffic pollutant dispersion at high density urban areas.

A semi-empirical multilayer urban canopy model is developed to estimate the vertical dispersion of traffic emissions in high density urban areas. It is motivated by the heterogeneity of urban morphology in real urban cities and the need of quick urban design and planning. The urban canopy is divided into multiple layers, to include the impact of building height variance on pollutant dispersion. The model is derived by mass conservation within each layer through adopting a box model. To validate the model, results in several cases with uniform and non-uniform building height distributions are compared with CFD simulations. The validation study indicates that the assumption of zero pollutant concentration over the modeled canopy and no horizontal pollutant transfer has increasingly negligible influence with increasing urban densities. The new multilayer model performs well to model the vertical pollutant transport, and modelling results can mostly follow the trend of the CFD simulations. The present paper conducts two case studies in metropolitan areas in Singapore and Hong Kong to illustrate how to implement this multilayer urban canopy model in the planning practice. With an in-house GIS team using available data, the multilayer model provides planners a way to understand air pollutant dispersion in high-density urban areas.