Improving the Estimation of Leaf Area Index from Multispectral Remotely Sensed Data

dc.contributor.advisorLiang, Shunlinen_US
dc.contributor.advisorPrince, Stephen D.en_US
dc.contributor.advisorTownshend, John R.en_US
dc.contributor.advisorWeismiller, Richarden_US
dc.contributor.authorFang, Hongliangen_US
dc.description.abstractLeaf Area Index (LAI) is an important structural property of surface vegetation. Many algorithms use LAI in regional and global biogeochemical, ecological, and meteorological applications. This dissertation reports several new, improved methods to estimate LAI from remotely sensed data. To improve LAI estimation, a new atmospheric correction algorithm was developed for the Enhanced Thematic Mapper Plus (ETM+) imagery. It can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance under general atmospheric and surface conditions. This method was validated using ground measurements at Beltsville, Maryland. Several examples are given to correct AVIRIS (Airborne Visible/Infrared Imaging Spectrometer), MODIS (Moderate Resolution Imaging Spectroradiometer) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data using the new algorithm. Next, a genetic algorithm (GA) was incorporated into the optimization process of radiative transfer (RT) model inversion for LAI retrieval. Different ETM+ band combinations and the number of "genes" employed in the GA were examined to evaluate their effectiveness. The LAI estimates from ETM+ using this method were reasonably accurate when compared with field measured LAI. A new hybrid method, which integrates both the RT model simulation and the non-parametric statistical methods, was developed to estimate LAI. Two non-parametric methods were applied, the neural network ((NN) algorithms and the projection pursuit regression (PPR) algorithms. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Both atmospherically corrected surface reflectances and raw top-of-atmosphere (TOA) radiances from ETM+ were tested. It was found that the best way to estimate LAI was to use the red and near infrared band combination of surface reflectance. In an application of this hybrid method to MODIS, the PPR and NN methods were compared. MODIS LAI standard products (MOD15) were found to have larger values than my results in the study area.en_US
dc.format.extent11247507 bytes
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.subject.pqcontrolledBiology, Ecologyen_US
dc.titleImproving the Estimation of Leaf Area Index from Multispectral Remotely Sensed Dataen_US


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