scholarly journals Sinergia de imágenes Sentinel 1 y Sentinel 2 A para la delimitación de humedales a partir de un enfoque ecohidrogeomórfico

2021 ◽  
Vol 60 (3) ◽  
pp. 233-252
Author(s):  
Nicolás Emanuel Vidal Quini ◽  
Alejandra Mabel Geraldi
Keyword(s):  
Buenos Aires ◽  
Alos Palsar ◽  
Sentinel 2

En los últimos años se desarrolla un creciente interés en detectar, delimitar e inventariar humedales bajo un enfoque ecohidrogeomórfico para ampliar el conocimiento de estos ecosistemas y posibilitar la elaboración de lineamientos de gestión para su aprovechamiento. El objetivo de esta investigación es detectar y delimitar aquellos sectores que reflejan condiciones ecohidrogeomórficas favorables para definirlos como humedales en un sector de la cuenca Alsina ubicada en el sudoeste de la provincia de Buenos Aires, Argentina. Se aplicó una metodología complementaria entre imágenes ópticas y radar que consiste en la utilización de un modelo digital de elevación Alos Palsar e imágenes Sentinel 2 A y Sentinel 1. Se abordó un pulso de inundación en la laguna ocurrido en el año 2017. Los eventos húmedos reflejan el funcionamiento de la laguna y los sistemas de humedales. Se analizaron cambios biofísicos que ocurrieron en los sectores perilagunares en referencia al ingreso de lluvias. Los resultados verificaron la presencia de diferentes tipologías de humedales entre la laguna y su área perilagunar que durante una inundación funcionan como un ecosistema híbrido.

scholarly journals Evidências Geomorfológicas de Mudanças Ambientais na Baía Esperança, Península Antártica

2019 ◽  
Vol 37 ◽  
pp. 137-149
Author(s):  
Rafaela Mattos Costa ◽  
Carina Petsch ◽  
Maria Eliza Sotille ◽  
Katia Kellem da Rosa ◽  
Jefferson Cardia Simões ◽  
...  
Keyword(s):  
Little Ice Age ◽  
Buenos Aires ◽  
Environmental Changes ◽  
Ice Age ◽  
Physical Weathering ◽  
Geomorphological Mapping ◽  
Transport Distance ◽  
Coarse Grains ◽  
Geomorphological Features ◽  
Sentinel 2

A geomorphological interpretation of the glacial geomorphology of the proglacial environments of the Buenos Aires, Kenney and Flora glaciers in Hope Bay, Antarctica, between the coordinates 63° 23'S and 63° 26'S latitude and 56° 8'W and 57° 4'W longitude. Sedimentary, granulometric and morphological analyzes were carried out on 15 samples collected in 2017 for the identification of depositional geomorphological features and subsequent geomorphological mapping. The glacier fronts were delineated from Sentinel-2 and Quickbird images of 1988, 2008 and 2017 using the ArcGIS® software to identify the landforms chronology of the shapes. The higher number of coarse grains, low selection, and high values of C40 in all samples indicate modification by erosive processes in supraglacial environment and/or transport distance after substrate quarrying process and grain fracturing by post-depositional physical weathering. The reconstruction of the Hope Bay’s Holocene landscape of indicates glaciers with tens of meters of advance compared to the current front during the Little Ice Age (LIA). The geomorphological mapping and sedimentary analyzes showed recent environmental changes in the proglacial system, with formation of hummocky moraines indicating the retreat/stabilization of the glaciers in the LIA. Furthermore, they set evidences of recent recession moraines, which prevail in the Buenos Aires Glacier predominate in the 2008-2017 phase, on the Flora Glacier in 1988-2008 period and the Kenney Glacier in the 2008-2017 period.

scholarly journals Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

scholarly journals Discriminación de trigo y cebada empleando imágenes satelitales ópticas y radar

2021 ◽  
Author(s):  
Mario Fabián Marini
Keyword(s):  
Support Vector Machines ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Buenos Aires ◽  
Support Vector ◽  
Vector Machines ◽  
Sentinel 2

El partido de Coronel Rosales (Buenos Aires, Argentina) se halla localizado dentro de la región pampeana austral, una de las de mayor relevancia agro productiva del país. En este contexto, el conocimiento de la superficie cultivada adquiere significativa importancia para la posterior planificación agrícola y económica. En tal sentido, la discriminación de cultivos mediante teledetección se dificulta cuando se trata de los de ciclo fenológico muy similar, como el trigo y la cebada. En este estudio se realizó una discriminación de dichos cultivos empleando imágenes de Radar de Apertura Sintética (SAR) Sentinel-1A SLC, imágenes ópticas Sentinel-2 y una combinación de ambos tipos de datos. Se incorporaron medidas de coherencia, textura e intensidad de retrodispersión extraídas de los datos SAR durante el ciclo fenológico completo. Sobre cada escena Sentinel-2 se obtuvo el Índice de Diferencia Normalizada de Vegetación (Normalized Difference Vegetation Index - NDVI). Se emplearon tres algoritmos de clasificación: Máxima Verosimilitud (Maximum Likelihood - MLC), Máquinas de Soporte Vectorial (Support Vector Machines - SVM) y Random Forest (RF). Los mejores resultados se obtuvieron al combinar imágenes ópticas y SAR empleando el clasificador RF. La combinación de las retrodispersiones VV y VH junto a la coherencia y la textura de las imágenes SAR, sumada al apilado de NDVI de imágenes ópticas, arrojó los máximos valores de precisión de la clasificación. El valor de F1 fue de 87.27% para el trigo y de 89.20% para la cebada.

scholarly journals MODELLING THE AMOUNT OF CARBON STOCK USING REMOTE SENSING IN URBAN FOREST AND ITS RELATIONSHIP WITH LAND USE CHANGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1051-1058
Author(s):  
N. Tavasoli ◽  
H. Arefi ◽  
S. Samiei-Esfahany ◽  
Q. Ronoud
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Stock ◽  
Urban Forest ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Alos Palsar ◽  
Radar Images ◽  
Wide Range ◽  
Sentinel 2

Abstract. The estimation of biomass has been highly regarded for assessing carbon sources. In this paper, ALOS PALSAR, Sentinel-1, Sentinel-2 and ground data are used for estimating of above ground biomass (AGB) with SVM-genetic model Moreover Landsat satellite data was used to estimate land use change detection. The wide range of vegetation, textural and principal component analysis (PCA) indices (using optical images) and backscatter, decomposition and textural features (from radar images) are derived together with in situ collected AGB data into model to predict AGB. The results indicated that the coefficient of determination (R2) for ALOS PALSAR, Sentinel-1, Sentinel-2 were 0.51, 0.50 and 0.60 respectively. The best accuracy for combining all data was 0.83. Afterwards, the carbon stock map was calculated. Landsat series data were acquired to document the spatiotemporal dynamics of green spaces in the study area. By using a supervised classification algorithm, multi-temporal land use/cover data were extracted from a set of satellite images and the carbon stock time series simulated by using carbon stock maps and green space (urban forest) maps.

scholarly journals Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8

2018 ◽  
pp. 47 ◽  
Author(s):  
J. Delegido ◽  
A. Pezzola ◽  
A. Casella ◽  
C. Winschel ◽  
E. P. Urrego ◽  
...  
Keyword(s):  
Buenos Aires ◽  
Low Cost ◽  
Estimation Error ◽  
Fire Severity ◽  
Inverse Correlation ◽  
Landsat 8 ◽  
Buenos Aires Province ◽  
Before And After ◽  
The Difference ◽  
Sentinel 2

<p>Assessment of rural fire severity is fundamental to evaluate fire damages and to analyze recovery processes in a low-cost and efficient way. Burnt areas covering shrubs and grasslands were estimated in more than 30,000 km<sup>2</sup>  in Argentina from December 2016 to January 2017. The study area presented in this work is located in the South of the Buenos Aires province, and it covers a semiarid area with the presence of xerophilous shrubs and grasslands. This is one of the most abundant ecosystem in Central and Southern Argentina. Field campaigns were carried out over the area affected by the fire in order to georreference the burnt plots and characterized the fire severity in 5 levels. The objective of this work is to analyze the feasibility of new satellites Sentinel-2 for fire studies, as well as provide a comparison to Landsat-8 derived results, because this mission has been one of the most used in it. Pre-fire and postfire Sentinel-2 and Landsat-8 imagery were used to analyze different band combinations to compute a Normalized Difference Spectral Index (NDSI), as well as the difference of this index before and after the fire (dNDSI). Results show a significant correlation (R<sup>2</sup> =0.72 and estimation error of 0.77) between dNDSI derived from Sentinel-2 and the severity levels obtained in the field campaign using bands 8a and 12 (NIR and SWIR), the same bands as used in the Normalized Burn Ratio (NBR). Moreover, results derived from Sentinel-2 are better than results derived from Landsat-8 (R<sup>2</sup> =0.63 and estimation error of 0.92). Furthermore, it is observed that the correlation is improved when Sentinel-2 bands 6 and 5 (located in the Red-Edge region) are considered (R<sup>2</sup> =0.74 and estimation error of 0.76). An inverse correlation has been observed between the recovery of vegetation four months after the fire and the fire severity level.</p>

scholarly journals TOWARD A MULTI-SOURCE REMOTE SENSING WETLAND INVENTORY OF THE USA: PRELIMINARY RESULTS ON WETLAND INVENTORY OF MINNESOTA

2021 ◽  
Vol V-3-2021 ◽  
pp. 97-100
Author(s):  
S. Adeli ◽  
B. Salehi ◽  
M. Mahidanpari ◽  
L. J. Quackenbush
Keyword(s):  
Field Data ◽  
Cost Effective ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Wetland Delineation ◽  
Observation Data ◽  
Alos Palsar ◽  
The Usa ◽  
Wetland Inventory ◽  
Sentinel 2

Abstract. Wetlands are highly productive ecosystems that offer unique services on regional and global scales including nutrient assimilation, carbon reduction, geochemical cycling, and water storage. In recent years, however, they are being lost or exploited as croplands due to natural or man-made stressors (1.4 percent in 5 years within the USA). This decline in the extent of wetlands began legislative activity at a national scale that mandate the regulate use of wetlands. As such, the need for cost-effective, robust, and semi-automated techniques for wetland preservation is ever-increasing in the current era. In this study, we developed a workflow for wetland inventorying on a state-wide scale using optimal incorporation of dual-polarimetry Sentinel-1, multi-spectral Sentinel-2 and dual polarimetry ALOS-PALSAR with the Random Forest (RF) classifier in Google Earth Engine (GEE). A total of 45 features from a stack of multi-season/multi-year SAR and Optical imagery (included more than 5000 imagery) was extracted over Minnesota state, USA. We followed the Cowardin classification scheme for clustering the field data. The classification was performed in two levels in 5 different ecozones that cover the Minnesota state. Depending on the availability field data for each ecozone overall accuracies changed from 77% to 85%. The variable importance analysis suggests that Sentinel-2 spectral features are dominant in terms of their capability for wetland delineation. Sentinel-1 backscattering coefficient was also superior among other SAR features. Ultimately, the results of this study shall illustrate the applicability of free of charge earth observation data coupled with the advanced machine learning techniques that are available in GEE for better restoration and management of wetlands.

scholarly journals Multi-Source and Multi-Temporal Image Fusion on Hypercomplex Bases

2020 ◽  
Vol 12 (6) ◽  
pp. 943
Author(s):  
Andreas Schmitt ◽  
Anna Wendleder ◽  
Rüdiger Kleynmans ◽  
Maximilian Hell ◽  
Achim Roth ◽  
...  
Keyword(s):  
Image Fusion ◽  
Optical Sensors ◽  
Remote Sensing Data ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Alos Palsar ◽  
Data Set ◽  
Multi Temporal ◽  
The Stability ◽  
Sentinel 2

This article spanned a new, consistent framework for production, archiving, and provision of analysis ready data (ARD) from multi-source and multi-temporal satellite acquisitions and an subsequent image fusion. The core of the image fusion was an orthogonal transform of the reflectance channels from optical sensors on hypercomplex bases delivered in Kennaugh-like elements, which are well-known from polarimetric radar. In this way, SAR and Optics could be fused to one image data set sharing the characteristics of both: the sharpness of Optics and the texture of SAR. The special properties of Kennaugh elements regarding their scaling—linear, logarithmic, normalized—applied likewise to the new elements and guaranteed their robustness towards noise, radiometric sub-sampling, and therewith data compression. This study combined Sentinel-1 and Sentinel-2 on an Octonion basis as well as Sentinel-2 and ALOS-PALSAR-2 on a Sedenion basis. The validation using signatures of typical land cover classes showed that the efficient archiving in 4 bit images still guaranteed an accuracy over 90% in the class assignment. Due to the stability of the resulting class signatures, the fuzziness to be caught by Machine Learning Algorithms was minimized at the same time. Thus, this methodology was predestined to act as new standard for ARD remote sensing data with an subsequent image fusion processed in so-called data cubes.

scholarly journals Clasificación de usos del suelo a partir de imágenes Sentinel-2

2017 ◽  
pp. 55 ◽  
Author(s):  
J. Borràs ◽  
J. Delegido ◽  
A. Pezzola ◽  
M. Pereira ◽  
G. Morassi ◽  
...  
Keyword(s):  
Land Use ◽  
Buenos Aires ◽  
Relevant Information ◽  
Earth Observation ◽  
Kappa Index ◽  
Excellent Spatial Resolution ◽  
The One ◽  
Agricultural Areas ◽  
Sentinel 2 ◽  
Do So

<p>Sentinel-2 (S2), a new ESA satellite for Earth observation, accounts with 13 bands which provide high-quality radiometric images with an excellent spatial resolution (10 and 20 m) ideal for classification purposes. In this paper, two objectives have been addressed: to determine the best classification method for S2, and to quantify its improvement with respect to the SPOT operational mission. To do so, four classifiers (LDA, RF, Decision Trees, K-NN) have been selected and applied to two different agricultural areas located in Valencia (Spain) and Buenos Aires (Argentina). All classifiers were tested using, on the one hand, all the S2 bands and, on the other hand, only selecting those bands from S2 closer to the four bands from SPOT. In all the cases, between 10%-50% of samples were used to train the classifier while remaining the rest for validation. As a result, a land use map was generated from the best classifier, according to the Kappa index, providing scientifically relevant information such as the area of each land use class.</p>

scholarly journals A Hierarchical Clustering Method for Land Cover Change Detection and Identification

2020 ◽  
Vol 12 (11) ◽  
pp. 1751
Author(s):  
Tuomas Häme ◽  
Laura Sirro ◽  
Jorma Kilpi ◽  
Lauri Seitsonen ◽  
Kaj Andersson ◽  
...  
Keyword(s):  
Land Cover ◽  
Hierarchical Clustering ◽  
Unsupervised Clustering ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Change Analysis ◽  
Detection And Identification ◽  
Multi Temporal ◽  
Sentinel 2

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis.

scholarly journals Complémentarité des images optiques SENTINEL-2 avec les images radar SENTINEL-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique.

2021 ◽  
Vol 223 ◽  
pp. 143-158
Author(s):  
Siham ACHARKI ◽  
Pierre Louis FRISON ◽  
Mina AMHARREF ◽  
Hanna KHOJ ◽  
Samed BERNOUSSI
Keyword(s):  
Nous Avons ◽  
Alos Palsar ◽  
Climat Méditerranéen ◽  
Sentinel 2

Dans cet article, nous évaluons les performances de classification de trois algorithmes non paramétriques (kNN, RF et SVM) en utilisant les données multi-temporelles de trois satellites (Sentinel-1, Alos-Palsar-2 et Sentinel-2) et de leurs combinaisons. La zone d'étude choisie se caractérise par un climat méditerranéen subhumide et une topographie très accidentée qui rend la classification d’occupation du sol particulièrement difficile. En outre, elle contient une aire protégée nommée Jbel Moussa et présente une diversité biologique exceptionnelle. Afin de suivre le couvert végétal de cette dernière, nous avons acquis et prétraités les images satellitaires optiques et radar pour la période du 1er janvier au 31 décembre 2017. Ensuite, nous avons combiné les trois satellites, soit douze scénarios produits. Des cartes de classifications illustrent notre approche. Un total de trente-six classifications a été obtenu, en se basant sur sept classes : eau, bâtiment et infrastructures, sol nu, végétation peu dense, prairies, forêt peu dense et forêt dense. Les résultats ont montré que pour tous les scénarios, la précision globale la plus élevée a été produite par RF (53,03%-93,06%), suivie de kNN (49,16%-89,63%), tandis que SVM (47,86%-86,08%) a produit la précision de classification la plus faible. L'étude a également montré une similitude entre les performances de la combinaison des trois satellites et celles de Sentinel-2 seul.  Les estimations de la superficie pour les différentes classes vont de 0,85 km2 (0,11% de la zone d'étude) à 326,84 km2 (41,31% de la zone d'étude)

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