Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images

Abstract Background: Forests have an extremely important place in the ecosystem in terms of ensuring social and environmental balance. The biggest danger for forests that have this importance is forest fires due to various reasons. It is extremely important to estimate the formation and behavior characteristics of fires in terms of combating forest fires. Using the satellite images obtained with the developing technology for this purpose provides great convenience in the detection of the fire areas and the severity of the fire affected. In this study, forest fire that occurred in the Zeytinköy region of Muğla province was investigated using remotely sensed images. According to the reference data provided by the General Directorate of Forestry (GDF), 425 hectares of area was destroyed by fire. In this study, it is aimed to extract burn scar by applying seven vegetation indexes on Sentinel-2 and Landsat-8 satellite images. Additionally, forest fire areas have been determined with the object-based classification technique. Results: As a result of the study, when the obtained results are compared with the values obtained from GDF, it is determined that object based analysis of Sentinel-2 provided the highest accuracy with 98.36% overall accuracy and 0.976 kappa statistics. Comparing the results of spectral indices of Sentinel-2 and Landsat-8, Sentinel-2 resulted better results in all indices. Among the indices RdNBR and dNDVI obtained better results than other indices with Sentinel-2 and Landsat-8, respectively. Conclusions: In general, it has been determined that Sentinel-2 data is more suitable than Landsat-8 satellite images for determining Turkish red pine forest fired areas. Red and near infrared based images can be used for rapid mapping of fired areas. The results also indicated that the indices provided by multi-temporal Sentinel-2 data can assist forest management for rapid monitoring of fire scars and also for evolution of reforestation after fire.

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Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network

Remote Sensing ◽

10.3390/rs13163319 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3319

Author(s):

Nan Ma ◽

Lin Sun ◽

Chenghu Zhou ◽

Yawen He

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Satellite Images ◽

Detection Algorithm ◽

Landsat 8 ◽

Cloud Detection ◽

Spectral Library ◽

Remote Sensing Images ◽

Different Types ◽

Sentinel 2

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.

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Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest

Journal of Sustainable Forestry ◽

10.1080/10549811.2019.1598443 ◽

2019 ◽

Vol 38 (7) ◽

pp. 615-628 ◽

Cited By ~ 2

Author(s):

Leila Soleimannejad ◽

Sami Ullah ◽

Roya Abedi ◽

Matthias Dees ◽

Barbara Koch

Keyword(s):

Random Forest ◽

Tree Species ◽

Satellite Images ◽

Landsat 8 ◽

Hyrcanian Forest ◽

Species Classification ◽

Tree Species Classification ◽

Sentinel 2

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The urban areas classification methodology according to multi-zone images of Sentinel 2 and Landsat 8 (on the example of the city of Thanh Hoa, Vietnam)

Geodesy and Cartography ◽

10.22389/0016-7126-2020-956-2-40-49 ◽

2020 ◽

Vol 956 (2) ◽

pp. 40-49

Author(s):

Le Hung Trinh ◽

Dinh Sinh Mai ◽

V.R. Zablotskii

Keyword(s):

Land Cover ◽

Urban Areas ◽

Satellite Images ◽

Near Infrared ◽

Landsat 8 ◽

Land Use Classification ◽

Infrared Band ◽

The Status ◽

The Impact ◽

Sentinel 2

In recent years, land cover changes very quickly in urban areas due to the impact of population growth and socio-economic development. The authors present the method of land cover/land use classification based on the combination of Sentinel 2 and Landsat 8 multi-resolution satellite images. A middle infrared band (band 11), a near infrared (band 8) of Sentinel 2 image and a thermal infrared one (band 10) of Landsat 8 image were used to calculate EBBI (Enhanced Built-up and Barreness Index). The EBBI index and Sentinel 2 spectral bands with spatial resolution 10 m (band 2, 3, 4, 8) were used to classify the land cover. The obtained results showed that, the method of land cover classification based on combination of Sentinel 2 and Landsat 8 satellite images improves the overall accuracy by about 5 % compared with the one using only Sentinel 2 data. The results obtained at the study can be used for the management, assessment and monitoring the status and dynamics of land cover in urban areas.

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Automatic Detection of Dominant Crop Types in Poland Based on Satellite Images

Artificial Satellites ◽

10.2478/arsa-2020-0013 ◽

2020 ◽

Vol 55 (4) ◽

pp. 185-208

Author(s):

Joanna Pluto-Kossakowska ◽

Magdalena Pilarska ◽

Paulina Bartkowiak

Keyword(s):

European Union ◽

Random Forests ◽

Satellite Images ◽

Agricultural Practices ◽

Automatic Detection ◽

Landsat 8 ◽

The European Union ◽

Large Area ◽

Agricultural Structure ◽

Sentinel 2

AbstractThe assumption of the European Union Common Agricultural Policy is to maintain good agricultural practices for sustainability in the environment. A number of requirements are imposed on farmers, including the maintenance of permanent grassland, fallow land or crop diversification. To meet these requirements, the European Union guarantees subsidies, but at the same time fields must be monitored focusing on crop identification. The limitation of field inspection and substituting it with crop recognition using satellite images could increase the effectiveness of this procedure. The application of satellite imagery in automatic detection and identification of dominant crops over a large area seems to be technically and economically sound. The paper discusses the concept and the results of automatic classification based on a Random Forests classifier performed on multitemporal images of Sentinel-2 and Landsat-8. A test site was established in a complex agricultural structure with long and narrow parcels in the south-eastern part of Poland. Time-series images acquired during the growing season 2016 were used for multispectral classification in different configurations: for Sentinel-2 and Landsat-8 separately and for both sensors integrated. Different Random Forests approaches and post-processing methods were examined based on independent data from farmers’ declarations records, reaching the best accuracy of over 90% for crops like winter or spring cereals. Overall accuracy of the classification ranged from 72% to 91% depending on the classification variant. The elaborated scheme is novel in the context of Polish complex agricultural structure and smallholders.

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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

Revista de Teledetección ◽

10.4995/raet.2018.8934 ◽

2018 ◽

pp. 47 ◽

Cited By ~ 2

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

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 km2 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 (R2 =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 (R2 =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 (R2 =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.

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ON-LINE GIS ANALYSIS AND IMAGE PROCESSING FOR GEOPORTAL KIELCE/POLAND DEVELOPMENT

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

10.5194/isprs-archives-xli-b2-197-2016 ◽

2016 ◽

Vol XLI-B2 ◽

pp. 197-200

Author(s):

B. Hejmanowska ◽

E. Głowienka ◽

R. Florek-Paszkowski

Keyword(s):

Web Application ◽

Satellite Images ◽

Complex Analysis ◽

Landsat 8 ◽

Gis Analysis ◽

Is Implementation ◽

Multi Criteria Evaluation ◽

On Line ◽

Python Programming ◽

Sentinel 2

GIS databases are widely available on the Internet, but mainly for visualization with limited functionality; very simple queries are possible i.e. attribute query, coordinate readout, line and area measurements or pathfinder. A little more complex analysis (i.e. buffering or intersection) are rare offered. Paper aims at the concept of Geoportal functionality development in the field of GIS analysis. Multi-Criteria Evaluation (MCE) is planned to be implemented in web application. OGC Service is used for data acquisition from the server and results visualization. Advanced GIS analysis is planned in PostGIS and Python programming. In the paper an example of MCE analysis basing on Geoportal Kielce is presented. Other field where Geoportal can be developed is implementation of processing new available satellite images free of charge (Sentinel-2, Landsat 8, ASTER, WV-2). Now we are witnessing a revolution in access to the satellite imagery without charge. This should result in an increase of interest in the use of these data in various fields by a larger number of users, not necessarily specialists in remote sensing. Therefore, it seems reasonable to expand the functionality of Internet's tools for data processing by non-specialists, by automating data collection and prepared predefined analysis.

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