Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Close, Odile; Petit, Sophie; Beaumont, Benjamin; Hallot, Éric

doi:10.3390/land10010055

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…In the case of optical products, image differencing was applied, a widely used technique for many applications involving change detection. Close et al [28] showed that image differencing achieves one of the highest overall accuracy scores (94%).…”

Section: Change Detectionmentioning

confidence: 99%

An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data, for Monitoring Historic Clusters in Cypriot Cities

Tzima¹,

Αγαπίου²,

Lysandrou³

et al. 2023

Preprint

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In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can bridge technological innovation with conserving and restoring heritage buildings. The paper presents ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-discipline study of heritage buildings towards the creation of an online HBIM platform for urban monitoring.

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Section: Change Detectionmentioning

confidence: 99%

An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data, for Monitoring Historic Clusters in Cypriot Cities

Tzima¹,

Αγαπίου²,

Lysandrou³

et al. 2023

Preprint

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“…Due to the potentially large number of earthquake-induced landslides, covering a wide area, multispectral Sentinel-2 imagery was selected. To identify landslides in pre-and post-Sentinel-2 Earthquake Imagery we used the Image Differencing approach (Lu et al, 2004) as detailed in Close et al (2021). Based on the assumption that earthquake-induced landslides would be highlighted on different spectral bands, we used a range of different approaches to attempt an automatic landslide detection.…”

Section: Optical-based Landslide Detectionmentioning

confidence: 99%

Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

Whitworth

Giardina

Penney

et al. 2022

Front. Built Environ.

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On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission.

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“…Since 2015, multispectral images of Sentinel-2 (S-2), Copernicus program are available with very good temporal (up to 2-3 days for middle latitudes) and spatial (10 m, 20 m) resolutions [14]. Moreover, the spectral characteristics of S-2 (13 spectral bands) make it a very valuable dataset for quasi-continuous monitoring of environmental changes [15], agricultural management [16] as well as for studying the structure and characteristics of forest communities [17,18].…”

Section: Introductionmentioning

confidence: 99%

Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images

Głowienka

Zembol²

2022

GaEE

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The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper ), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer’s accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.

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Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Cited by 16 publications

References 36 publications

An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data, for Monitoring Historic Clusters in Cypriot Cities

An Application of Machine Learning Algorithms by Synergetic Use of SAR and Optical Data, for Monitoring Historic Clusters in Cypriot Cities

Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images

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