Extracting Built-Up Features in Complex Biophysical Environments by Using a Landsat Bands Ratio

Abstract

Abstract. This paper addresses the remote sensing challenging field of urban mixed pixels on a medium spatial resolution satellite data. The tentatively named Normalized Difference Built-up and Surroundings Unmixing Index (NDBSUI) is proposed by using Landsat-8 Operational Land Imager (OLI) bands. It uses the Shortwave Infrared 2 (SWIR2) as the main wavelength, the SWIR1 with the red wavelengths, for the built-up extraction. A ratio is computed based on the normalization process and the application is made on six cities with different urban and environmental characteristics. The built-up of the experimental site of Yaoundé is extracted with an overall accuracy of 95.51% and a kappa coefficient of 0.90. The NDBSUI is validated over five other sites, chosen according to Cameroon’s bioclimatic zoning. The results are satisfactory for the cities of Yokadouma and Kumba in the bimodal and monomodal rainfall zones, where overall accuracies are up to 98.9% and 97.5%, with kappa coefficients of 0.88 and 0.94 respectively, although these values are close to those of three other indices. However, in the cities of Foumban, Ngaoundéré and Garoua, representing the western highlands, the high Guinea savannah and the Sudano-sahelian zones where built-up is more confused with soil features, overall accuracies of 97.06%, 95.29% and 74.86%, corresponding to 0.918, 0.89 and 0.42 kappa coefficients were recorded. Difference of accuracy with EBBI, NDBI and UI are up to 31.66%, confirming the NDBSUI efficiency to automate built-up extraction and unmixing from surrounding noises with less biases.

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

“…Noise can be categorized as brighter and darker bodies, in and around the built-up. To assess its influence, the most accurate bare soil index, BSI [30] for Bangui and NDSI [31] for Yaoundé, and the shadow index [36] are each regressed by four urban/built-up, spectral indices, i.e., UI [6], NDBI [7], Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise MNBI [32] and NDBSUI [33]. These regressions are positive with the BSI/NDSI and negative with the shadow index.…”