Wetland landscape based on Sentinel-2 images and geo-tagged photographs in Centla, Tabasco

This is an analysis of the geographic landscape in the Centla wetlands of Tabasco, Mexico. A map shows the use of remote sensing data combined with easily understood and conveyed visual descriptive data which show the ecological conditions of the landscape. The central map of this article presents a land use and land cover study, obtained from Sentinel-2 MSI data for the Centla wetland zones. The support vector machine algorithm is used to classify Sentinel-2 images. The results show a high general precision of 90.0%, as well as high precision in separating types of wetlands. Information obtained during fieldwork at the ground level is inserted in the map, comprised of photos taken with a mobile application along the Grijalva and the San Pedro y San Pablo rivers. These photos provide visual verification of the map.

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Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽

2019 ◽

Author(s):

Juan Carlos Valdiviezo-Navarro ◽

Adan Salazar-Garibay ◽

Karla Juliana Rodríguez-Robayo ◽

Lilián Juárez ◽

María Elena Méndez-López ◽

...

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Food Sovereignty ◽

Region Of Interest ◽

Remote Sensing Data ◽

Support Vector ◽

Multispectral Images ◽

Crop Fields ◽

Remote Sensing Techniques ◽

Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

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Parallel Multiclass Support Vector Machine for Remote Sensing Data Classification on Multicore and Many-Core Architectures

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2017.2713126 ◽

2017 ◽

Vol 10 (10) ◽

pp. 4387-4398 ◽

Cited By ~ 5

Author(s):

Weijia Li ◽

Haohuan Fu ◽

Yang You ◽

Le Yu ◽

Jiarui Fang

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Remote Sensing Data ◽

Data Classification ◽

Support Vector ◽

Sensing Data ◽

Multiclass Support Vector Machine ◽

Many Core

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Remote sensing data fusion using support vector machine

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 ◽

10.1109/igarss.2004.1369823 ◽

2004 ◽

Cited By ~ 3

Author(s):

Shuhe Zhao

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Data Fusion ◽

Remote Sensing Data ◽

Support Vector ◽

Sensing Data

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APPROACHES BASED ON SUPPORT VECTOR MACHINE TO CLASSIFICATION OF REMOTE SENSING DATA

Handbook of Pattern Recognition and Computer Vision ◽

10.1142/9789814273398_0014 ◽

2009 ◽

pp. 329-352 ◽

Cited By ~ 2

Author(s):

Lorenzo Bruzzone ◽

Claudio Persello

Keyword(s):

Remote Sensing ◽

Support Vector Machine ◽

Remote Sensing Data ◽

Support Vector ◽

Sensing Data

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EXPLORATION OF IOCG MINERALIZATIONS USING INTEGRATION OF SPACE-BORNE REMOTE SENSING DATA WITH AIRBORNE GEOPHYSICAL DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-9-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 9-16

Author(s):

M. Abdolmaleki ◽

T. M. Rasmussen ◽

M. K. Pal

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Principal Component ◽

Support Vector ◽

Mineral Potential Mapping ◽

Band Ratio ◽

Sensing Data ◽

Mineral Potential ◽

Potential Mapping ◽

Sentinel 2

Abstract. Nowadays, remote sensing technologies are playing a significant role in mineral potential mapping. To optimize the exploration approach along with a cost-effective way, narrow down the target areas for a more detailed study for mineral exploration using suitable data selection and accurate data processing approaches are crucial. To establish optimum procedures by integrating space-borne remote sensing data with other earth sciences data (e.g., airborne magnetic and electromagnetic) for exploration of Iron Oxide Copper Gold (IOCG) mineralization is the objective of this study. Further, the project focus is to test the effectiveness of Copernicus Sentinel-2 data in mineral potential mapping from the high Arctic region. Thus, Inglefield Land from northwest Greenland has been chosen as a study area to evaluate the developed approach. The altered minerals, including irons and clays, were mapped utilizing Sentinel-2 data through band ratio and principal component analysis (PCA) methods. Lineaments of the study area were extracted from Sentinel-2 data using directional filters. Self-Organizing Maps (SOM) and Support Vector Machines (SVM) were used for classification and analysing the available data. Further, various thematic maps (e.g., geological, geophysical, geochemical) were prepared from the study area. Finally, a mineral prospectively map was generated by integrating the above mentioned information using the Fuzzy Analytic Hierarchy Process (FAHP). The prepared potential map for IOCG mineralization using the above approach of Inglefield Land shows a good agreement with the previous geological field studies.

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Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

Forest Systems ◽

10.5424/fs/2018273-13686 ◽

2018 ◽

Vol 27 (3) ◽

pp. eSC03 ◽

Cited By ~ 2

Author(s):

Miguel Garcia-Hidalgo ◽

Ángela Blázquez-Casado ◽

Beatriz Águeda ◽

Francisco Rodriguez

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Digital Terrain Model ◽

Image Data ◽

Tree Height ◽

Support Vector ◽

Lidar Data ◽

Support Vector Machine Algorithm ◽

Terrain Model ◽

Sentinel 2

Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types. Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out. Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable. Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable. SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD). SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively. SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables. Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest.

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