scholarly journals Mapping alien and indigenous vegetation in the KwaZulu-Natal Sandstone Sourveld using remotely sensed data

Bothalia ◽  
2016 ◽  
Vol 46 (2) ◽  
Author(s):  
John Odindi ◽  
Onisimo Mutanga ◽  
Mathieu Rouget ◽  
Nomcebo Hlanguza
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Learning Algorithm ◽  
Remotely Sensed ◽  
Woody Vegetation ◽  
Machine Learning Algorithm ◽  
Grassland Ecosystem ◽  
Remotely Sensed Data ◽  
Kwazulu Natal ◽  
New Generation

Background: The indigenous KwaZulu-Natal Sandstone Sourveld (KZN SS) grassland is highly endemic and species-rich, yet critically endangered and poorly conserved. Ecological threats to this grassland ecosystem are exacerbated by encroachment of woody plants, with severe negative environmental and economic consequences. Hence, there is an increasing need to reliably determine the extent of encroached or invaded areas to design optimal mitigation measures. Because of inherent limitations that characterise traditional approaches like field surveys and aerial photography, adoption of remotely sensed data offer reliable and timely mapping of landscape processes.Objectives: We sought to map the distribution of woody vegetation within the KZN SS using remote sensing approaches.Method: New generation RapidEye imagery, characterised by strategically positioned bands, and the advanced machine learning algorithm Random Forest (RF) were used to determine the distribution and composition of alien and indigenous woody vegetation within the KZN SS.Results: Results show that alien and indigenous encroachment and invasion could be mapped with over 86% accuracy whilst the dominant indigenous and alien tree species could be mapped with over 74% accuracy. These results highlight the potential of new generation RapidEye satellite data in combination with advanced machine learning technique in predicting the distribution of alien and indigenous woody cover within a grassland ecosystem. The successful discrimination of the two classes and the species within the classes can be attributed to the additional strategically positioned bands, particularly the red-edge in the new generation RapidEye image.Conclusion: Results underscore the potential of new generation RapidEye satellite data with strategically positioned bands and an advanced machine learning algorithm in predicting the distribution of woody cover in a grassland ecosystem.

Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery

2021 ◽  
Vol 180 ◽  
pp. 105880
Author(s):  
Daniele De Rosa ◽  
Bruno Basso ◽  
Matteo Fasiolo ◽  
Johannes Friedl ◽  
Bill Fulkerson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Learning Algorithm ◽  
Remotely Sensed ◽  
Machine Learning Algorithm ◽  
Remotely Sensed Imagery

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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