Abstract
Abstract. One of the most basic classification tasks is to distinguish bare-soil areas from urban region. Bare-soil plays an important role in the ecosystem. It could be the reason of dust storms and the indicator of urban expansion. It is also important to monitor the bare-soil areas, but there was no good idea to automatically extraction bare-soil areas using existing method. In this work, a new bareness index (BI) has been developed and applied to map developing region in Pearl River Delta using Landsat OLI/TIRS data in 2013. The BI based on the logical combination of the Tasseled Cap transformation (TCB) and Normalized Difference Bareness Index (NDBaI). Results show that the BI not only has a good effect on the enhancement of bare soil information, but also on the inhibition of the background information, and improve the accuracy of detection. The results of this study could be of scientific and practical merits in regional remote sensing monitoring and improve the accuracy of land use classification.
Highlights
Land use and land cover change (LUCC) is regarded as the single most important variable of global change affecting ecosystems with an impact on the environment that is at least as large as that associated with climate change (Vitousek, 1994; Skole, 1994)
Researchers have developed many indexes based on the different land surface types, commonly used are Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Normalized Difference Water Index (NDWI), and Normalized Built-up Index (NDBI) and so on
We build a new methodology based on a logical combination of the two indices Tasseled Cap Brightness (TCB) and Normalized Difference Bareness Index (NDBaI) for bare-soil areas mapping. This combined formulation is called the baresoil index (BI). It is applied within a Landsat 8 data (WRS2: Path/Row =122/44, acquired on August 9, 2013) covering part of the Pearl River Delta of P.R
Summary
Land use and land cover change (LUCC) is regarded as the single most important variable of global change affecting ecosystems with an impact on the environment that is at least as large as that associated with climate change (Vitousek, 1994; Skole, 1994). Southworth (2004) find that the thermal infrared band (TIR) of Landsat TM measures the emission of energy from the Earth’s surface and, as this is a function of the surface cover, it can be used as a determinant of land cover type based on the temperatures measured According to this principle, Zhao & Chen (2005) build a normalized difference bareness index (NDBaI) for mapping of the bare-soil areas from the satellite images. The Landsat 8 carries two instruments: the Operational Land Imager (OLI), collects image data for nine shortwave spectral bands (OLI1~ OLI7, OLI9) over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band (OLI8); the Thermal Infrared Sensor (TIRS), collects image data for two thermal bands with a. The results are very promising following extensive empirical, ground measurements and statistical comparisons
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