Christopher D. Lloyd

TL;DR: In this article, a range of interpolation methods were applied to map monthly precipitation in Great Britain from sparse point data using a variety of data sources, such as moving window regression (MWR), inverse distance weighting (IDW), ordinary kriging (OK), simple Kriging with a locally varying mean (SKlm), and krigging with an external drift (KED).

TL;DR: Local Models and Methods: Local models and methods What is local? Spatial Dependence Spatial Scale Stationarity Spatial Data Models Data Sets Used for Illustrative Purposes A Note on Notation Overview Local Modeling Approaches to Local Adaptation Stratification or Segmentation of spatial data Moving Window/Kernel Methods Locally Varying Model Parameters Transforming and Detrending Spatial data as discussed by the authors.

TL;DR: The accuracy with which land cover could be classified in this region was maximised at 89% by using a per-field, ANN approach in which semivariance at a lag of 1 pixel was incorporated as textural information, 15% greater than the accuracy achieved using a standard per-pixel ML classification.

TL;DR: In this article, the advantages of using a trend model (KT) for the construction of digital surface models from LiDAR point data is discussed. But, the advantages become more apparent as the number of data points decrease (and the sample spacing increases).

TL;DR: Maximum likelihood (ML) and artificial neural network (ANN) classifiers were applied to three Landsat Thematic Mapper image sub-scenes (termed urban, agricultural and semi-natural) of Cukurova, Turkey, and the addition of texture measures increased classification accuracy for the urban sub-scene but decreased classification accuracyFor Mediterranean land, texture classification applied to Landsat TM imagery may be appropriate for the classification of urban areas only.