Multitemporal Satellite Image Time Series analysis of urban development in Bucharest and Ilfov areas

Costachioiu, Teodor; Constantinescu, Rodica; Datcu, Mihai

doi:10.1109/iccomm.2014.6866702

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The city has undergone several social, economic and cultural transformations in the past thirty years. Some of the most prominent urban changes which occurred in this period are the replacement of existing structures, such as the re-purposing of old industrial areas, and the reclaiming of agricultural land in the suburban areas [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Bucharest'S Land Cover Evolution Over A Period Of 33 Years Using Multi-Sensor Data

Grivei

Datcu

2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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Past and current EO (Earth Observation) satellite missions have gathered huge amount of data during the past decades. This offers the opportunity to retrieve significant information concerning the evolution of land cover for almost any point of interest (POI) on Earth's surface. This paper presents the evolution of land cover in the administrative area of Bucharest, Romania, over a time span of 33 years. In order to achieve this goal we use data acquired by multiple EO missions such as: Landsat 5 TM (Thematic Mapper), 7 ETM+ (Enhanced Thematic Mapper Plus), 8 OLI/TIRS (Operational Land Imager/Thermal InfraRed Sensor) and Sentinel-2 MSI (Multi-Spectral Instrument). We compute several spectral indexes in order to obtain information regarding the surface coverage evolution for categories such as vegetation, water bodies and build up.

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Section: Introductionmentioning

confidence: 99%

Analysis of Bucharest'S Land Cover Evolution Over A Period Of 33 Years Using Multi-Sensor Data

Grivei

Datcu

2018

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

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“…They reveal the Earth's changes over time which allows monitoring, analyzing, and characterizing the dynamics of various phenomena over the years. A number of previous research studies in land-cover and landuse analysis have demonstrated the necessity of long-term SITS for characterizing natural variability trends in seasonal to decennial scales [1]- [3]. Díaz-Delgado et al [1] used a longterm time series of Landsat-5 and Landsat-7 images during 1974 to 2014 for assessing the hydroperiod trends of the marshes (i.e., a wetland) in Doñana National Park.…”

Section: Introductionmentioning

confidence: 99%

“…Tan et al [2] applied a classification-based approach to a long-term SITS dataset for monitoring land-cover and land-use dynamics over Zigui County, China. Costachioiu et al [3] showed the benefits of long-term SITS (over 25 years) in urban development monitoring. They classified intra-annual image stacks in a supervised manner and compared the results to population dynamics.…”

Section: Introductionmentioning

confidence: 99%

Land-cover evolution class analysis in Image Time Series of Landsat and Sentinel-2 based on Latent Dirichlet Allocation

Espinoza-Molina

Bahmanyar

Datcu

et al. 2017

2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

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Satellite Image Time Series (SITS) are widely used in monitoring the Earth's changes for various applications such as land-cover evolution analysis. In this paper, we propose an approach based on Latent Dirichlet Allocation (LDA) which considers spatial and spectral information to measure the landcover changes in multispectral SITS. For our experiments, we focus on the vegetation dynamics of the Doñana National Park (in southwestern Spain) using a Landsat and a Sentinel-2 SITS dataset. The proposed approach represents each image by Normalized Difference Vegetation Index (NDVI) and tiles it into smaller patches. The patches are then modeled as Bag-of-Words (BoW) and LDA is applied to them in order to discover the latent structure of the image. The divergence between the latent structures of any two consecutive images is then considered as the measure of change. Results show that the changes measured by the proposed approach can represent the vegetation dynamics of the region of interest. Moreover, comparing the results obtained from the two datasets demonstrates that using highlevel information allows the proposed approach to measure the changes independent of the sensor. This will support long-term monitoring through combining various available data.

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Land-cover change detection using local feature descriptors extracted from spectral indices

Espinoza-Molina

Bahmanyar

Dı́az-Delgado

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Multitemporal Satellite Image Time Series analysis of urban development in Bucharest and Ilfov areas

Cited by 4 publications

References 10 publications

Analysis of Bucharest'S Land Cover Evolution Over A Period Of 33 Years Using Multi-Sensor Data

Analysis of Bucharest'S Land Cover Evolution Over A Period Of 33 Years Using Multi-Sensor Data

Land-cover evolution class analysis in Image Time Series of Landsat and Sentinel-2 based on Latent Dirichlet Allocation

Land-cover change detection using local feature descriptors extracted from spectral indices

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