Classification of Different Vegetation Types Combining Two Information Sources Through a Probabilistic Segmentation Approach

2015 ◽  
pp. 382-392
Author(s):  
Francisco E. Oliva ◽  
Oscar S. Dalmau ◽  
Teresa E. Alarcón ◽  
Miguel De-La-Torre
Keyword(s):  
Information Sources ◽  
Vegetation Types ◽  
Segmentation Approach

scholarly journals A proposed procedure for the analysis of large phytosociological data sets in the classification of South African grasslands

Koedoe ◽  
1995 ◽  
Vol 38 (1) ◽  
Author(s):  
G.J. Bredenkamp ◽  
H. Bezuidenhout
Keyword(s):  
South African ◽  
Plant Communities ◽  
Vegetation Type ◽  
Second Step ◽  
Data Sets ◽  
Vegetation Types ◽  
Related Plant ◽  
Single Cluster ◽  
Synoptic Table

A procedure for the effective classification of large phytosociological data sets, and the combination of many data sets from various parts of the South African grasslands is demonstrated. The procedure suggests a region by region or project by project treatment of the data. The analyses are performed step by step to effectively bring together all releves of similar or related plant communities. The first step involves a separate numerical classification of each subset (region), and subsequent refinement by Braun- Blanquet procedures. The resulting plant communities are summarised in a single synoptic table, by calculating a synoptic value for each species in each community. In the second step all communities in the synoptic table are classified by numerical analysis, to bring related communities from different regions or studies together in a single cluster. After refinement of these clusters by Braun-Blanquet procedures, broad vegetation types are identified. As a third step phytosociological tables are compiled for each iden- tified broad vegetation type, and a comprehensive abstract hierarchy constructed.

scholarly journals Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values

2007 ◽  
Vol 18 (4) ◽  
pp. 605-612 ◽  
Author(s):  
Jan‐Philip M. Witte ◽  
Rafał B. Wójcik ◽  
Paul J.J.F. Torfs ◽  
Martin W.H. Haan ◽  
Stephan Hennekens
Keyword(s):  
Gaussian Mixture ◽  
Bayesian Classification ◽  
Vegetation Types ◽  
Indicator Values ◽  
Mixture Density ◽  
Density Fitting

A phytosociological classification of the Rustenburg Nature Reser ve

Bothalia ◽  
1975 ◽  
Vol 11 (4) ◽  
pp. 561-580 ◽  
Author(s):  
B. J. Coetzee
Keyword(s):  
South Africa ◽  
Nature Reserve ◽  
Vegetation Types ◽  
Habitat Features ◽  
Community Types

I he vegetation of the Rustenburg Nature Reserve, situated on the Magaliesberg in Acocks’s (1953) Sour Bushveld veld Type ot South Africa, is classified by the Braun-Blanquet Method. Five major vegetation types, including mam subtypes, basic community types, variations and sub-variations are described floristically, physiognomically and in terms of habitat features. The vegetation is mapped at community tvpe and variation level, at a scale of 1 : 30 000.

A Parallel Classification of Climates and Vegetation Types.

1932 ◽  
Vol 20 (1) ◽  
pp. 211
Author(s):  
W. B. Turrill ◽  
B. Stefanoff
Keyword(s):  
Vegetation Types

scholarly journals Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

2018 ◽  
Vol 10 (10) ◽  
pp. 1647 ◽  
Author(s):  
Ramses Molijn ◽  
Lorenzo Iannini ◽  
Paco López Dekker ◽  
Paulo Magalhães ◽  
Ramon Hanssen
Keyword(s):  
Soil Type ◽  
A Priori ◽  
The State ◽  
Vegetation Types ◽  
A Priori Information ◽  
Vegetation Growth ◽  
Temporal Fluctuations ◽  
Priori Information ◽  
Precipitation Events

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

Classification of montane forest community types in the Cedar River drainage of western Washington, U.S.A.

1977 ◽  
Vol 7 (2) ◽  
pp. 217-225 ◽  
Author(s):  
Roger Del Moral ◽  
James N. Long
Keyword(s):  
Discriminant Analysis ◽  
Large Scale ◽  
Classification Scheme ◽  
Forest Community ◽  
Vegetation Types ◽  
Agglomerative Clustering ◽  
Lower Elevation ◽  
Community Types ◽  
Western Washington

Vegetation of the Cedar River watershed, located in the Cascade Mountains of western Washington, was analyzed by an agglomerative clustering method followed by discriminant analysis. Stepwise mutliple discriminant analysis provided a means to reallocate stands and assists in the production of a classification scheme and a key to the vegetation types. Ten types are recognized, six from upper-elevation older-growth stands, and four seral types from lower elevation stands logged since 1900. Each type can be identified in the field with a simple key based on cover percentage. The key provides a means for large-scale vegetation mapping with a limited amount of effort.

How Pharmacists are Rated as a Source of Drug Information by Physician Assistants

1986 ◽  
Vol 20 (5) ◽  
pp. 379-383 ◽  
Author(s):  
Jack E. Fincham
Keyword(s):  
Information Sources ◽  
Drug Information ◽  
Physician Assistants ◽  
The Other ◽  
Journal Articles ◽  
Coefficient Of Concordance ◽  
The Difference

A study to examine physician assistants' (PAs) views of drug information sources was undertaken in a sample of practicing PAs in Georgia. Analysis of Kendall's coefficient of concordance statistic indicated Pharm.D.s were ranked highest as being good sources of drug information of the six categories listed. Next in descending order of classification as good sources of drug information were the categories of journal articles, physicians, non-Pharm.D. pharmacists, detail persons, and physician assistants. A significant correlation was found between PA contact with pharmacists and the classification of pharmacists as good sources of drug information. The difference in rating between Pharm.D.s and journal articles and physicians was not significantly different, but the rating differences between these categories and each of the other sources were statistically significant. Pharm.D. pharmacists were rated higher as a source of drug information than were non-Pharm.D. pharmacists. Results indicate pharmacists are viewed positively as sources of drug information for PAs, and that view increases with contact with pharmacists.

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