Using the random forest algorithm to integrate hydroacoustic data with satellite images to improve the mapping of shallow nearshore benthic features in a marine protected area in Jamaica

2019 ◽  
Vol 56 (7) ◽  
pp. 1065-1092 ◽  
Author(s):  
Kurt McLaren ◽  
Karen McIntyre ◽  
Kurt Prospere
Keyword(s):  
Random Forest ◽  
Protected Area ◽  
Satellite Images ◽  
Marine Protected Area ◽  
Random Forest Algorithm

scholarly journals Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image

2021 ◽  
Vol 10 (5) ◽  
pp. 313
Author(s):  
Salma Benmokhtar ◽  
Marc Robin ◽  
Mohamed Maanan ◽  
Hocein Bazairi
Keyword(s):  
Random Forest ◽  
Satellite Images ◽  
Atlantic Coast ◽  
Satellite Image ◽  
High Tide ◽  
Quantitative Relationship ◽  
Biomass Estimation ◽  
Random Forest Algorithm ◽  
Zostera Noltei ◽  
Eelgrass Beds

The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers.

scholarly journals Comparative analysis of classification algorithms for critical land prediction in agricultural cultivation areas

2020 ◽  
Vol 8 (4) ◽  
pp. 270-275
Author(s):  
Deden Istiawan
Keyword(s):  
Information System ◽  
Random Forest ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Random Forest Algorithm ◽  
K Nearest Neighbor

Currently, the identification of critical land, that has been physically, chemically, and biologically damaged, uses a geographic information system. However, it requires a high cost to get the high resolution of satellite images. In this study, a comparison framework is proposed to determine the performance of the classification algorithms, namely C.45, ID3, Random Forest, k-Nearest Neighbor, and Naïve Bayes. This research aims to find out the best algorithm for the classification of critical land in agricultural cultivation areas. The results show that the highest accuracy Random Forest algorithm was 93.10 % in predicting critical land, and the naïve Bayes has the lowest performance, with 89.32 % of accuracy in predicting critical land.

scholarly journals Analysis of the Spatio-Temporal Evolution of Dredging from Satellite Images: A Case Study in the Principality of Asturias (Spain)

2021 ◽  
Vol 9 (3) ◽  
pp. 267
Author(s):  
Vanesa Mateo-Pérez ◽  
Marina Corral-Bobadilla ◽  
Francisco Ortega-Fernández ◽  
Vicente Rodríguez-Montequín
Keyword(s):  
Random Forest ◽  
Satellite Images ◽  
Random Forest Algorithm ◽  
Past Behavior ◽  
Before And After ◽  
Real Behavior ◽  
Environmental Requirements ◽  
Spatio Temporal ◽  
Sentinel 2A

One of the fundamental tasks in the maintenance of port operations is periodic dredging. These dredging operations facilitate the elimination of sediments that the coastal dynamics introduce. Dredging operations are increasingly restrictive and costly due to environmental requirements. Understanding the condition of the seabed before and after dredging is essential. In addition, determining how the seabed has behaved in recent years is important to consider when planning future dredging operations. In order to analyze the behavior of sediment transport and the changes to the seabed due to sedimentation, studies of littoral dynamics are conducted to model the deposition of sediments. Another methodology that could be used to analyze the real behavior of sediments would be to study and compare port bathymetries collected periodically. The problem with this methodology is that it requires numerous bathymetric surveys to produce a sufficiently significant analysis. This study provides an effective solution for obtaining a dense time series of bathymetry mapping using satellite data, and enables the past behavior of the seabed to be examined. The methodology proposed in this work uses Sentinel-2A (10 m resolution) satellite images to obtain historical bathymetric series by the development of a random forest algorithm. From these historical bathymetric series, it is possible to determine how the seabed has behaved and how the entry of sediments into the study area occurs. This methodology is applied in the Port of Luarca (Principality of Asturias), obtaining satellite images and extracting successive bathymetry mapping utilizing the random forest algorithm. This work reveals how once the dock was dredged, the sediments were redeposited and the seabed recovered its level prior to dredging in less than 2 months.

Seagrass amphipod assemblages in a Mediterranean marine protected area: a multiscale approach

2014 ◽  
Vol 506 ◽  
pp. 175-192 ◽  
Author(s):  
N Sturaro ◽  
G Lepoint ◽  
A Pérez-Perera ◽  
S Vermeulen ◽  
P Panzalis ◽  
...  
Keyword(s):  
Protected Area ◽  
Marine Protected Area ◽  
Multiscale Approach

Habitat use of toothed whales in a marine protected area based on point process models

2019 ◽  
Vol 609 ◽  
pp. 239-256 ◽  
Author(s):  
TL Silva ◽  
G Fay ◽  
TA Mooney ◽  
J Robbins ◽  
MT Weinrich ◽  
...  
Keyword(s):  
Habitat Use ◽  
Point Process ◽  
Protected Area ◽  
Marine Protected Area ◽  
Process Models ◽  
Point Process Models

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

What Values Do Tourists Place on a Marine Protected Area? White Shark Cage-Dive Tourists and the Neptune Islands

2019 ◽  
Vol 14 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Kirin Apps ◽  
Kay Dimmock ◽  
David J. Lloyd ◽  
Charlie Huveneers
Keyword(s):  
Protected Area ◽  
Marine Protected Area ◽  
White Shark
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