Interpretation of land cover using spectral modulation pattern- an example with Landsat 8 OLIimage

Abstract

Spectral signature, in general, can be defined as characteristics of surface objects of transmission, absorption and reflection of electromagnetic radiation. Spectral signature is expected stable and unique for given surface material. Spectral signature can be graphically represented in two-dimensional space in the form of spectral curves. Spectral signature has been used long time for object detection and classification mostly by spectral matching methods. Spectral matching is time-consuming and requires reference spectra that is not always available in hand. Spectral modulation pattern is a simplified form of spectral curve shapes. We define this pattern by pairwise comparison of reflectance between two spectral bands. The aim of this research is to point out the use of spectral modulation patterns for land cover mapping. The experiment has been carried out with Landsat 8 OLI image data, which has six reflective bands of 30 m spatial resolution. The Landsat 8 OLI is an excellent data source for land cover mapping in both local and global scale. Due to very huge data volume, automated analysis is crucial when we need accomplish land cover maps in a short time. In this paper, the authors introduce the concept to use spectral modulation patterns of spectral signatures for automated interpretation of land cover. The first step in this concept is to understand correctly meaning of each modulation pattern of spectral signatures and their relation to land cover classes. In this study, we use three Landsat 8 OLI images of Vietnam in 2013 and 2014. Ground GPS field photos were collected to support interpretation of land cover.

Mapping land cover using multi-temporal sentinel-1a data: A case study in Hanoi

“…The accuracy of proposed method achieved 84.7%.Optical satellite imagery plays an important part in mapping land cover. Recognition of the land cover is based on spectral reflectance characteristics of land cover categories (Abdalla and Abdulaziz, 2012;Nguyen Dinh Duong et al, 2014;Li et al, 2004) or NDVI time series (Lambin et al, 1999;Myneni et al, 1995). However, optical imagery has many disadvantages due to weather condition and cloudiness.…”