Semi-automated detection of landslide timing using harmonic modelling of satellite imagery, Buckinghorse River, Canada

Deijns, Axel; Bevington, Alexandre; Zadelhoff, Feiko van; Jong, S.M. de; Geertsema, Marten; McDougall, Scott

doi:10.1016/j.jag.2019.101943

Cited by 27 publications

(42 citation statements)

References 29 publications

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“…Most approaches of recent TSA studies that use LANDSAT or Sentinel-2 data, apply harmonic regressions (ordinary least square models) on the generated time series to characterise seasonality of vegetation canopy (Zhu and Woodcock 2014;Jönsson et al 2018;Shimizu et al 2019;Deijns et al 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The cumulative sum (extensive integral) of the difference between the BL and the smoothed index course within the DP, serves as a proxy of how disturbances emerge and manifest themselves (Verbesselt et al 2010a, b;Deijns et al 2020). Periods with an insu cient number of data points (often winter times) are ignored for the cumulative sum calculation.…”

Section: Anomaly Detectionmentioning

confidence: 99%

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Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

Löw

Tatjana

2020

Preprint

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Background Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or an altering natural environment. It is decisive to trace the intra- to trans-annual dynamics of these forest ecosystems. National to local forest communities request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. Methods We developed a remote sensing based time series analysis (TSA) framework that comprises data access, data management, image pre-processing, and an advanced but flexible TSA. The data basis is a dense time series of multispectral Sentinel-2 images with a spatial resolution of 10 metres. We use a dynamic Savitzky-Golay-filtering approach to reconstruct robust but sensitive phenology courses. Deviations from the latter are further used to derive spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. Finally, we use zonal statistics on different spatial scales to provide aggregated information on the extent of forest disturbances between 2018 and 2019.Results and Conclusion The outcomes are a) individual phenology models and deduced phenology metrics for each 10 metres by 10 metres forest pixel in Austria and b) forest disturbance maps useful to investigate the occurrence, development and extent of bark beetle infestation. The phenology modelling results provide area-wide consistent data, also useful for downstream analyses (e.g. forest type classification). Results of the forest disturbance detection demonstrate that the TSA is capable to systematically delineate disturbed forest areas. Information derived from such a forest monitoring tool is highly relevant for various stakeholders in the forestry sector, either for forest management purposes or for decision-making processes on different levels.

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Section: Resultsmentioning

confidence: 99%

Section: Anomaly Detectionmentioning

confidence: 99%

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

Löw

Tatjana

2020

Preprint

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“…NDVI is used to quantify the vegetation density, the areas with low NDVI values are featured with bare rock and soil, and bad water and soil conservation capacity, resulting in formulations of HLDs easily [ 41 , 42 ].…”

Section: Susceptibility Evaluation Indexes Of Hldsmentioning

confidence: 99%

Susceptibility mapping and zoning of highway landslide disasters in China

Yin

Li²,

Che

et al. 2020

PLoS ONE

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Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

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“…continuamente calibrados contribuem fortemente para a disseminação de técnicas que fazem o uso de geotecnologias. Sobre isso, Deijns et al (2020), conseguiram detectar eventos naturais catastróficos utilizando processos semiautomatizados de obtenção de imagens do satélite Landsat, adicionando uma detecção mais rápida e escalável no tempo de detecção de distúrbios ambientais em grandes conjuntos de dados. O modelo do banco de dados constitui de tabelas interligadas via chave estrangeira, a princípio as entidades foram implementadas seguindo um padrão de pedidos, onde há uma solicitação de um ou vários pedi-Figura 5: Fluxograma base de recorte dos de regiões específicas, após isso o protocolo executa esses pedidos de forma assíncrona e periódica.…”

Section: Aplicações Que Utilizam Imagens De Satéliteunclassified

Automatização do protocolo de aquisição e pré-processamento de imagens térmicas do sensor TIRS do satélite Landsat-8

Oliveira

Souza

2020

RBCA

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A coleta de imagens térmicas de satélites para o estudo de uma determinada região é feita acessando sistemas na internet como o Serviço de Levantamento Geológico Americano (USGS). A cada novo estudo etapas de aquisição e recorte de imagens de satélites para uma determinada região são feitas de forma individualizada, sendo um protocolo bem comum em pesquisas de imagens de satélites orbitais. Este trabalho apresenta um protocolo automatizado de aquisição e pré-processamento de imagens de satélites do sensor térmico TIRS do Landsat-8 que possa contribuir para a otimização da performance de pesquisas geográcas. Para o desenvolvimento desse protocolo foram analisados os processos manuais comuns de coleta e recorte em pesquisas geográcas que fazem o uso de imagens térmicas do Landsat-8. Foi observado que é possível obter e pre-processar imagens térmicas do Landsat-8 automaticamente, possibilitando a junção com outras técnicas automatizadas e acoplamento com plataformas SIG que contenham processamento térmico de regiões.

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Semi-automated detection of landslide timing using harmonic modelling of satellite imagery, Buckinghorse River, Canada

Cited by 27 publications

References 29 publications

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

Susceptibility mapping and zoning of highway landslide disasters in China

Automatização do protocolo de aquisição e pré-processamento de imagens térmicas do sensor TIRS do satélite Landsat-8

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