Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models

2012 ◽  
Vol 7 (6) ◽  
pp. 492-509 ◽  
Author(s):  
Victor F. Rodriguez-Galiano ◽  
Mario Chica-Rivas
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Mediterranean Area ◽  
Land Cover Mapping ◽  
Digital Terrain Models ◽  
Landsat Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Digital Terrain ◽  
Terrain Models

scholarly journals Utilizing Sentinel-1A Radar Images for Large-Area Land Cover Mapping with Machine-learning Methods

2019 ◽  
Vol 45 (2) ◽  
pp. 163-175
Author(s):  
Mohammad Imangholiloo ◽  
Jussi Rasinmäki ◽  
Yrjö Rauste ◽  
Markus Holopainen
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Large Area ◽  
Learning Methods ◽  
Radar Images ◽  
Machine Learning Methods

scholarly journals Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

2018 ◽  
Vol 7 (9) ◽  
pp. 342 ◽  
Author(s):  
Adam Salach ◽  
Krzysztof Bakuła ◽  
Magdalena Pilarska ◽  
Wojciech Ostrowski ◽  
Konrad Górski ◽  
...  
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Point Clouds ◽  
Data Sources ◽  
Digital Terrain Models ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Terrain Models ◽  
Airborne Laser ◽  
Vertical Accuracy

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

scholarly journals Development and Application of Earth Observation Based Machine Learning Methods for Characterizing Forest and Land Cover Change in Dilijan National Park of Armenia between 1991 and 2019

2021 ◽  
Vol 13 (15) ◽  
pp. 2942
Author(s):  
Nathalie Morin ◽  
Antoine Masse ◽  
Christophe Sannier ◽  
Martin Siklar ◽  
Norman Kiesslich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
National Park ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Anthropogenic Pressure ◽  
Learning Methods ◽  
Forest Density ◽  
Machine Learning Methods

Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R2 between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system.

scholarly journals Estimating Surface Downward Longwave Radiation Using Machine Learning Methods

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1147 ◽  
Author(s):  
Chunjie Feng ◽  
Xiaotong Zhang ◽  
Yu Wei ◽  
Weiyu Zhang ◽  
Ning Hou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Time Scale ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Downward Longwave Radiation

The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient boosting regression tree (GBRT), random forest (RF), and multivariate adaptive regression spline (MARS), to estimate Ld using ground measurements collected from 27 Baseline Surface Radiation Network (BSRN) stations. Ld measurements in situ were used to validate the accuracy of Ld estimation models on daily and monthly time scales. A comparison of the results demonstrated that the estimates on the basis of the GBRT method had the highest accuracy, with an overall root-mean-square error (RMSE) of 17.50 W m−2 and an R value of 0.96 for the test dataset on a daily time scale. These values were 11.19 W m−2 and 0.98, respectively, on a monthly time scale. The effects of land cover and elevation were further studied to comprehensively evaluate the performance of each machine learning method. All machine learning methods achieved better results over the grass land cover type but relatively worse results over the tundra. GBRT, RF, and MARS methods were found to show good performance at both the high- and low-altitude sites.

scholarly journals Large-scale Mapping of Arctic Coastal Infrastructure using Copernicus Sentinel Data and Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Georg Pointner ◽  
Annett Bartsch ◽  
Thomas Ingeman-Nielsen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Land Cover ◽  
Mitigation Strategies ◽  
The Arctic ◽  
Gradient Boosting ◽  
Land Cover Mapping ◽  
Learning Methods ◽  
Sar Data ◽  
Sentinel 2

<p>The climate change induced increased warming of the Arctic is leading to an accelerated thawing of permafrost, which can cause ground subsidence. In consequence, buildings and other infrastructure of local settlements are endangered from destabilization and collapsing in many Arctic regions. The increase of the exploitation of Arctic natural resources has led to the establishment of large industrial infrastructures that are at risk likewise. Most of the human activity in the Arctic is located near permafrost coasts. The thawing of coastal permafrost additionally leads to coastal erosion, which makes Arctic coastal settlements even more vulnerable.</p><p>The European Union (EU) Horizon2020 project “Nunataryuk” aims to assess the impacts of thawing land, coast and subsea permafrost on the climate and on local communities in the Arctic. One task of the project is to determine the impacts of permafrost thaw on coastal Arctic infrastructures and to provide appropriate adaptation and mitigation strategies. For that purpose, a circumpolar account of infrastructure is needed.</p><p>During recent years, the two polar-orbiting Sentinel-2 satellites of the Copernicus program of the EU have been acquiring multi-spectral imagery at high spatial and temporal resolution. Sentinel-2 data is a common choice for land cover mapping. Most land cover products only include one class for built-up areas, however. The fusion of optical and Synthetic Aperture Radar (SAR) data for land cover mapping has gained more and more attention over the last years. By combining Sentinel-2 and Sentinel-1 SAR data, the classification of multiple types of infrastructure can be anticipated. Another emerging trend is the application machine learning and deep learning methods for land cover mapping.</p><p>We present an automated workflow for downloading, processing and classifying Sentinel-2 and Sentinel-1 data in order to map coastal infrastructure with circum-Arctic extent, developed on a highly performant virtual machine (VM) provided by the Copernicus Research and User Support (RUS). We further assess the first classification results mapped with two different methods, one being a pixel-based classification using a Gradient Boosting Machine and the other being a windowed semantic segmentation approach using the deep-learning framework keras.</p>

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