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.

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.

scholarly journals Plant Communities of Travertine Outcrops of the Saturnia Area in Southern Tuscany (Central Italy)

Hacquetia ◽  
2013 ◽  
Vol 12 (1) ◽  
pp. 141-164
Author(s):  
Daniele Viciani ◽  
Barbara Maffei ◽  
Federico Selvi
Keyword(s):  
Central Italy ◽  
Deciduous Forests ◽  
Forest Community ◽  
Vegetation Types ◽  
Conservation Priority ◽  
Field Sampling ◽  
Significant Interest ◽  
Community Types ◽  
Ecological Specificity ◽  
Dynamic Relationships

Abstract A phytosociological survey was carried out in a poorly known travertine area of southern Tuscany harbouring a rich vegetation mosaic with chamaephytic garrigues, species-rich xerophytic grasslands, chasmophytic coenoses, annual species-dominated communities, shrublands and thermophilous deciduous forests. Field sampling and data analysis allowed to identify and characterize several community types, some of which of significant interest due to their ecological specificity and rarity in peninsular Italy. In particular, our data confirm the associations Pistacio terebinthi-Paliuretum spinosae and Pistacio terebinthi-Quercetum pubescentis, respectively a shrub and forest community type previously unknown for Tuscany. In addition, a new therophytic association of travertine debris named Sedetum hispanico-caespitosi and placed in the Hypochoerion achyrophori alliance (Brachypodietalia distachyi order, Tuberarietea class) is also described. Finally, dynamic relationships between the vegetation types are highlighted and the presence of conservation priority habitats in the area are pointed out.

scholarly journals Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images

2018 ◽  
Vol 10 (7) ◽  
pp. 1144 ◽  
Author(s):  
Wimala van Iersel ◽  
Menno Straatsma ◽  
Hans Middelkoop ◽  
Elisabeth Addink
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Accurate Solution ◽  
Vegetation Types ◽  
Time Step ◽  
Floodplain Vegetation ◽  
Herbaceous Vegetation ◽  
Single Time ◽  
Uav Images

The functions of river floodplains often conflict spatially, for example, water conveyance during peak discharge and diverse riparian ecology. Such functions are often associated with floodplain vegetation. Frequent monitoring of floodplain land cover is necessary to capture the dynamics of this vegetation. However, low classification accuracies are found with existing methods, especially for relatively similar vegetation types, such as grassland and herbaceous vegetation. Unmanned aerial vehicle (UAV) imagery has great potential to improve the classification of these vegetation types owing to its high spatial resolution and flexibility in image acquisition timing. This study aimed to evaluate the increase in classification accuracy obtained using multitemporal UAV images versus single time step data on floodplain land cover classification and to assess the effect of varying the number and timing of imagery acquisition moments. We obtained a dataset of multitemporal UAV imagery and field reference observations and applied object-based Random Forest classification (RF) to data of different time step combinations. High overall accuracies (OA) exceeding 90% were found for the RF of floodplain land cover, with six vegetation classes and four non-vegetation classes. Using two or more time steps compared with a single time step increased the OA from 96.9% to 99.3%. The user’s accuracies of the classes with large similarity, such as natural grassland and herbaceous vegetation, also exceeded 90%. The combination of imagery from June and September resulted in the highest OA (98%) for two time steps. Our method is a practical and highly accurate solution for monitoring areas of a few square kilometres. For large-scale monitoring of floodplains, the same method can be used, but with data from airborne platforms covering larger extents.

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

The Swiss Prison Study (SWIPS): Protocol for Establishing a Public Health Registry of Prisoners in Switzerland (Preprint)

2020 ◽  
Author(s):  
Thomas Gaisl ◽  
Naser Musli ◽  
Patrick Baumgartner ◽  
Marc Meier ◽  
Silvana K Rampini ◽  
...  
Keyword(s):  
Public Health ◽  
Medical History ◽  
Large Scale ◽  
International Classification Of Diseases ◽  
Disease Prevalence ◽  
Vulnerable Population ◽  
International Classification ◽  
Classification Of Diseases ◽  
Health Registry

BACKGROUND The health aspects, disease frequencies, and specific health interests of prisoners and refugees are poorly understood. Importantly, access to the health care system is limited for this vulnerable population. There has been no systematic investigation to understand the health issues of inmates in Switzerland. Furthermore, little is known on how recent migration flows in Europe may have affected the health conditions of inmates. OBJECTIVE The Swiss Prison Study (SWIPS) is a large-scale observational study with the aim of establishing a public health registry in northern-central Switzerland. The primary objective is to establish a central database to assess disease prevalence (ie, International Classification of Diseases-10 codes [German modification]) among prisoners. The secondary objectives include the following: (1) to compare the 2015 versus 2020 disease prevalence among inmates against a representative sample from the local resident population, (2) to assess longitudinal changes in disease prevalence from 2015 to 2020 by using cross-sectional medical records from all inmates at the Police Prison Zurich, Switzerland, and (3) to identify unrecognized health problems to prepare successful public health strategies. METHODS Demographic and health-related data such as age, sex, country of origin, duration of imprisonment, medication (including the drug name, brand, dosage, and release), and medical history (including the International Classification of Diseases-10 codes [German modification] for all diagnoses and external results that are part of the medical history in the prison) have been deposited in a central register over a span of 5 years (January 2015 to August 2020). The final cohort is expected to comprise approximately 50,000 to 60,000 prisoners from the Police Prison Zurich, Switzerland. RESULTS This study was approved on August 5, 2019 by the ethical committee of the Canton of Zurich with the registration code KEK-ZH No. 2019-01055 and funded in August 2020 by the “Walter and Gertrud Siegenthaler” foundation and the “Theodor and Ida Herzog-Egli” foundation. This study is registered with the International Standard Randomized Controlled Trial Number registry. Data collection started in August 2019 and results are expected to be published in 2021. Findings will be disseminated through scientific papers as well as presentations and public events. CONCLUSIONS This study will construct a valuable database of information regarding the health of inmates and refugees in Swiss prisons and will act as groundwork for future interventions in this vulnerable population. CLINICALTRIAL ISRCTN registry ISRCTN11714665; http://www.isrctn.com/ISRCTN11714665 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/23973

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Research on Methods of Parsing and Classification of Internet Super Large-scale Texts

2021 ◽  
Vol 1757 (1) ◽  
pp. 012121
Author(s):  
Mengting Song ◽  
Hang Zheng ◽  
Zhen Tao ◽  
Jia Jiang ◽  
Bin Pan
Keyword(s):  
Large Scale
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