Urban impervious surfaces

Owned by Maiwenn Herpe
Last updated: May 31, 2021 by Frederika Mourot
2 min read

Satellite(s)

e.g., Sentinel-1A and Sentinel-2.

Monitoring element

Land surface reflectance, SAR backscatter.

Satellite(s)

e.g., Sentinel-1A and Sentinel-2.

Monitoring element

Land surface reflectance, SAR backscatter.

Description technique

The approach proposed by Sun et al. (2019) utilises the fusion of optical and SAR data to improve the characterisation of impervious surfaces.

Zhang et al. (2020) developed a hybrid method to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates local ancillary datasets to generate training and validation samples in a semi-automatic manner to reduce the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes.

Accuracy / Resolution

Overall accuracy: 88%.

Case study

Sun et al. (2019) have applied this method to characterise the development of a large-area of urban land in China.
Gong et al. (2020) developed the Global Artificial Impervious Areas (GAIA) dataset mapping changes from 1985 to 2018 (dataset available in Google Earth Engine).

Benefits

The technique proposed by Sun et al. (2019) uses advantages of both optical and SAR data (e.g., SAR add the cloud free day and night data acquisition capability).

Limitations

  • Some urban applications required finer spatial mapping/details.

  • Potential over/under-estimation of urban land (e.g., regions with very high-brightness of SAR-data can be misclassified in urban). Determination of specified classification thresholds could be optimised/automatised.

Applicability for Northland

Yes.

Local image classification thresholds would have to be determined in the Northland context. The spatial resolution might limit some applications. Local ancillary datasets would help with classification.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publications references

Sun Z, Xu R, Du W, Wang L, Lu D. 2019. High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine. Remote Sensing. 11(7). doi:10.3390/rs11070752.

https://www.mdpi.com/2072-4292/11/7/752

Gong P, Li X, Wang J, Bai Y, Chen B, Hu T, Liu X, Xu B, Yang J, Zhang W, et al. 2020. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018. Remote Sensing of Environment. 236:111510. doi: https://doi.org/10.1016/j.rse.2019.111510

Ettehadi Osgouei P, Kaya S, Sertel E, Alganci U. 2019. Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery. Remote Sensing. 11(3). doi:10.3390/rs11030345.

https://www.mdpi.com/2072-4292/11/3/345

Zhang H, Gorelick SM, Zimba PV. 2020. Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability. Remote Sensing. 12(3):506.

https://www.mdpi.com/2072-4292/12/3/506