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Titolo:
A geostatistical approach for mapping thematic classification accuracy andevaluating the impact of inaccurate spatial data on ecological model predictions
Autore:
Kyriakidis, PC; Dungan, JL;
Indirizzi:
Stanford Univ, Dept Geol & Environm Sci, Palo Alto, CA 94304 USA Stanford Univ Palo Alto CA USA 94304 nvironm Sci, Palo Alto, CA 94304 USA Calif State Univ Monteray Bay, NASA, Ames Res Ctr, Moffett Field, CA USA Calif State Univ Monteray Bay Moffett Field CA USA Moffett Field, CA USA
Titolo Testata:
ENVIRONMENTAL AND ECOLOGICAL STATISTICS
fascicolo: 4, volume: 8, anno: 2001,
pagine: 311 - 330
SICI:
1352-8505(2001)8:4<311:AGAFMT>2.0.ZU;2-H
Fonte:
ISI
Lingua:
ENG
Soggetto:
GROUND-BASED RADIOMETRY; SOIL PROPERTIES; KRIGING APPROACH; INFORMATION; SATELLITE; STATISTICS; MAPS;
Keywords:
biogeochemical cycles; classification uncertainty; geographic information systems; indicator kriging; land cover map quality; net primary production; remote sensing; stochastic simulation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
49
Recensione:
Indirizzi per estratti:
Indirizzo: Kyriakidis, PC Stanford Univ, Dept Geol & Environm Sci, Palo Alto, CA 94304 USA Stanford Univ Palo Alto CA USA 94304 lo Alto, CA 94304 USA
Citazione:
P.C. Kyriakidis e J.L. Dungan, "A geostatistical approach for mapping thematic classification accuracy andevaluating the impact of inaccurate spatial data on ecological model predictions", ENV ECOL ST, 8(4), 2001, pp. 311-330

Abstract

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparsehigher accuracy (hard) class labels is presented. Such assessment leads tolocal indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematicmap. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responsesprovides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps fora Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).

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Documento generato il 20/01/21 alle ore 01:18:14