STATISTICS FOR PATCH OBSERVATIONS
Autor: | Kassel Hingee |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
0106 biological sciences
lcsh:Applied optics. Photonics 010504 meteorology & atmospheric sciences Closed set Stochastic process lcsh:T Estimator lcsh:TA1501-1820 Land cover computer.software_genre 010603 evolutionary biology 01 natural sciences lcsh:Technology Field (geography) Geography lcsh:TA1-2040 Statistics Data mining lcsh:Engineering (General). Civil engineering (General) Stochastic geometry computer Spatial analysis Categorical variable 0105 earth and related environmental sciences |
Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLI-B6, Pp 235-242 (2016) |
ISSN: | 2194-9034 |
Popis: | In the application of remote sensing it is common to investigate processes that generate patches of material. This is especially true when using categorical land cover or land use maps. Here we view some existing tools, landscape pattern indices (LPI), as non-parametric estimators of random closed sets (RACS). This RACS framework enables LPIs to be studied rigorously. A RACS is any random process that generates a closed set, which encompasses any processes that result in binary (two-class) land cover maps. RACS theory, and methods in the underlying field of stochastic geometry, are particularly well suited to high-resolution remote sensing where objects extend across tens of pixels, and the shapes and orientations of patches are symptomatic of underlying processes. For some LPI this field already contains variance information and border correction techniques. After introducing RACS theory we discuss the core area LPI in detail. It is closely related to the spherical contact distribution leading to conditional variants, a new version of contagion, variance information and multiple border-corrected estimators. We demonstrate some of these findings on high resolution tree canopy data. |
Databáze: | OpenAIRE |
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