Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Ecography ◽  
2007 ◽  
Vol 30 (4) ◽  
pp. 526-536 ◽  
Author(s):  
J. Angus Webb ◽  
Nicholas R. Bond ◽  
Stephen R. Wealands ◽  
Ralph Mac Nally ◽  
Gerry P. Quinn ◽  
...  
Keyword(s):  
Bayesian Clustering ◽  
Landscape Classification

Multivariate Bayesian clustering using covariate‐informed components with application to boreal vegetation sensitivity

Biometrics ◽  
2021 ◽  
Author(s):  
Henry R. Scharf ◽  
Ann M. Raiho ◽  
Sierra Pugh ◽  
Carl A. Roland ◽  
David K. Swanson ◽  
...  
Keyword(s):  
Bayesian Clustering

scholarly journals Slope Landscape Classification and Application Security in the Special Section of Western

2012 ◽  
Vol 12 ◽  
pp. 146-151 ◽  
Author(s):  
Wang Song ◽  
Zhao yongguo ◽  
Gu xiaoxu ◽  
Liu xiaoning
Keyword(s):  
Special Section ◽  
Landscape Classification ◽  
Application Security

scholarly journals An ecologically based landscape classification system for monitoring and assessment of pastures

2014 ◽  
Vol 69 (1) ◽  
pp. 17A-21A ◽  
Author(s):  
S. Goslee ◽  
M. Sanderson ◽  
K. Spaeth ◽  
J. Herrick ◽  
K. Ogles
Keyword(s):  
Classification System ◽  
Landscape Classification

scholarly journals Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment

2011 ◽  
Vol 15 (11) ◽  
pp. 3275-3291 ◽  
Author(s):  
S. Gharari ◽  
M. Hrachowitz ◽  
F. Fenicia ◽  
H. H. G. Savenije
Keyword(s):  
Conceptual Models ◽  
Overland Flow ◽  
Surface Flow ◽  
Landscape Classification ◽  
Surface Slope ◽  
Central European ◽  
Digital Elevation ◽  
Elevation Model ◽  
Meso Scale ◽  
Hydrological Regimes

Abstract. This paper presents a detailed performance and sensitivity analysis of a recently developed hydrological landscape classification method based on dominant runoff mechanisms. Three landscape classes are distinguished: wetland, hillslope and plateau, corresponding to three dominant hydrological regimes: saturation excess overland flow, storage excess sub-surface flow, and deep percolation. Topography, geology and land use hold the key to identifying these landscapes. The height above the nearest drainage (HAND) and the surface slope, which can be easily obtained from a digital elevation model, appear to be the dominant topographical controls for hydrological classification. In this paper several indicators for classification are tested as well as their sensitivity to scale and resolution of observed points (sample size). The best results are obtained by the simple use of HAND and slope. The results obtained compared well with the topographical wetness index. The HAND based landscape classification appears to be an efficient method to ''read the landscape'' on the basis of which conceptual models can be developed.

Sign in / Sign up

Export Citation Format

Share Document