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Remote sensing is a potentially powerful technology with which to extrapolate eddy covariance-based gross primary production (GPP) to continental scales. In support of this concept, we used meteorological and flux data from the AmeriFlux network and Support Vector Machine (SVM), an inductive machine learning technique, to develop and apply a predictive GPP(More)
—Application of remote sensing data to extrapolate evapotranspiration (ET) measured at eddy covariance flux towers is a potentially powerful method to estimate continental-scale ET. In support of this concept, we used meteorological and flux data from the AmeriFlux network and an inductive machine learning technique called support vector machine (SVM) to(More)
Running head: Adaptive approach to selecting flow partition exponent for multiple flow direction determination 3 Acknowledgements: the University of Wisconsin-Madison is also appreciated. Abstract Most Multiple Flow Direction algorithms (MFD) use a flow partition coefficient (exponent) to determine the fractions draining to all downslope neighbors. The(More)
When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of(More)
A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes When two spatial point processes are overlaid, the one with the higher rate is shown as clustered points, and the other one with the lower rate is often perceived to be background. Usually, we consider the clustered points as feature and the(More)
a r t i c l e i n f o Detailed information on the spatial variation of soils is desirable for many agricultural and environmental applications. This research explores three approaches that use soil fuzzy membership values to predict detailed spatial variation of soil properties. The first two are weighted average models with which the soil property value at(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o Transition between slope positions (e.g., ridge,(More)
a r t i c l e i n f o Keywords: Multi-scale digital terrain analysis Feature selection Spatial data mining Digital soil mapping ANOVA Principal components analysis Random subsets Decision trees Terrain attributes are the most widely used predictors in digital soil mapping. Nevertheless, discussion of techniques for addressing scale issues and feature(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: Terrain variables have been used widely in soil organic matter (SOM) content mapping(More)