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Light Scattering Reviews 5
Light scattering by densely packed inhomogeneous media is a particularly ch- lenging optics problem. In most cases, only approximate methods are used for the calculations. However, in the case where only a small number of macroscopic sc- tering particles are in contact (clusters or aggregates) it is possible to obtain exact results solving Maxwell’s equations. Simulations are possible, however, only for a relativelysmallnumberofparticles,especiallyiftheirsizesarelargerthanthewa- length of incident light. The ?rst review chapter in PartI of this volume, prepared by Yasuhiko Okada, presents modern numerical techniques used for the simulation of optical characteristics of densely packed group...
Satellite Aerosol Remote Sensing Over Land
Aerosols have a significant influence on the Earth's radiation budget, but there is considerable uncertainty about the magnitude of their effect on the Earth's climate. Currently, satellite remote sensing is being increasingly utilized to improve our understanding of the effect of atmospheric aerosols on the climate system. Satellite Aerosol Remote Sensing Over Land is the only book that brings together in one volume the most up-to-date research and advances in this discipline. As well as describing the current academic theory, the book presents practical applications, utilizing state-of-the-art instrumentation, invaluable to the work of environmental scientists. With contributions by an international group of experts and leaders of correspondent aerosol retrieval groups, the book is an essential tool for all those working in the field of climate change.
2005 Joint Assembly
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Spring Meeting
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Meetings Abstracts
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Spatial Validation Reveals Poor Predictive Performance of Large-scale Ecological Mapping Models
Abstract: Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations
