Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

Aleksandrowicz, Sebastian; Turlej, Konrad; Lewiński, Stanisław; Bochenek, Zbigniew

doi:10.3390/rs6075976

Cited by 27 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A review of such methodologies is provided by [Coppin et al, 2004]; object-based change detection techniques are commented in (Hussain et al, 2013). When using HR/VHR images, an operational methodology is suggested in (Aleksandrowicz et al,, 2014), where the need of algorithms able to automate the change detection is also pinpointed. For detecting urban sprawl precursors, such algorithms have to rely both on production of vegetation indices and on the assessment of the phenological trajectories of the areas of interest.…”

Section: Monitoring the Urban Sprawlmentioning

confidence: 99%

Temporal Trajectories of HR/VHR Pixels and Detection of Land Take Processes

Iannucci¹

2016

AOL

View full text Add to dashboard Cite

An increasing share of people and economic activities are attracted by the cities. This fact shows positive aspects and at the same time causes challenges, mainly in reference to the soil whose ecosystem services can be disrupted when the land cover is modified. Therefore, urbanization is a critical issue for the land management.Contrasting the urban sprawl (i.e. the spontaneous, unplanned process transforming vegetated land covers into artificial ones) is relevant for soil protection in terms of minimizing the land take. Remote sensing technologies have provided support (with an ex-post approach) to understand urban sprawl as a process and to assess its impacts on the sustainability of land management. The current availability of high-resolution/ very-high-resolution (HR/VHR) satellite data suggests to explore a different approach, aiming to deploy timely adequate countermeasures.The analysis of the urban sprawl processes pinpoints how the induced land cover changes show some specific patterns; moreover, distinctive trajectories in the space of multitemporal / multispectral imagery data can be elicited, relying also upon vegetation indices as the Normalized Difference Vegetation Index (NDVI). Accordingly, suitable precursors of urban sprawl processes can be detected. Such precursors can support a novel ex-ante approach in preventing the consolidation of the outcomes of the urban sprawl processes. KeywordsSustainable land management, urbanization, land cover change, remote sensing, satellite imagery, spatio-temporal trajectories, urban sprawl.

show abstract

Section: Monitoring the Urban Sprawlmentioning

confidence: 99%

Temporal Trajectories of HR/VHR Pixels and Detection of Land Take Processes

Iannucci¹

2016

AOL

View full text Add to dashboard Cite

show abstract

“…Therefore, the change information in M (2) , M (3) , and M (4) is robust for extraction by spectral distortion. Because certain spectral distortion effects have occurred in the fused images using multi-temporal images, M (1) compensates for these effects in M (2) , M (3) , and M (4) . In this study, based on the combination of M (1) , M (2) , M (3) , and M (4) , the final change detection index Z j for pixel j is calculated using Equation (11):…”

Section: Application Of Modified Ir-mad By Cross-fused Imagementioning

confidence: 99%

“…where M (1) are the MAD variates of the fused image with the same temporal data. Using the same multispectral image with high spatial resolution, the fused image (F 1 , F 2 ) and cross-fused image (F 3 , F 4 ) in M (2) and M (4) are generated.…”

Section: Application Of Modified Ir-mad By Cross-fused Imagementioning

confidence: 99%

“…International and national institutions require information about the landscape and environment to support decision-makers, and Earth observation techniques are advanced tools that can meet this requirement [1]. As the landscape is not static, Earth observations should focus not only on mapping the static environment, but also on detecting changes, in order to meet the needs of contemporary users.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Wang

Choi

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD) method-a measure for the spectral distortion of change information-is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

show abstract

“…(Aleksandrowicz, Turlej, Lewinski, & Bochnenk, 2014). This interest stems for the fast and easy classification of multi-temporal satellite data.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Using Object-Based Classification Technique: A Case Study Along The Kosi River, Bihar

Modi

Kumar

Shankar

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Land use/land cover (LULC) is dynamic in nature and can affect the ability of land to sustain human activities. The Indo-Gangetic plains of north Bihar in eastern India are prone to floods, which have a significant impact on land use / land cover, particularly agricultural lands and settlement areas. Satellite remote sensing techniques allow generating reliable and near-realtime information of LULC and have the potential to monitor these changes due to periodic flood. Automated methods such as object-based techniques have better potential to highlight changes through time series data analysis in comparison to pixel-based methods, since the former provides an opportunity to apply shape, context criteria in addition to spectral criteria to accurately characterise the changes. In this study, part of Kosi river flood plains in Supaul district, Bihar has been analysed to identify changes due to a flooding event in 2008. Object samples were collected from the post-flood image for a nearest neighbourhood (NN) classification in an object-based environment. Collection of sample were partially supported by the existing 2004-05 database. The feature space optimisation procedure was adopted to calculate an optimum feature combination (i.e. object property) that can provide highest classification accuracy. In the study, for classification of post-flood image, best class separation was obtained by using distance of 0.533 for 28 parameters out of 34. Results show that the Kosi flood has resulted in formation of sandy riverine areas.

show abstract

Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

Cited by 27 publications

References 31 publications

Temporal Trajectories of HR/VHR Pixels and Detection of Land Take Processes

Temporal Trajectories of HR/VHR Pixels and Detection of Land Take Processes

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Land Cover Change Detection Using Object-Based Classification Technique: A Case Study Along The Kosi River, Bihar

Contact Info

Product

Resources

About