Airborne multispectral and hyperspectral remote sensing is a powerful tool for environmental monitoring applications. In this paper we describe a new system (ASPIS) composed by a 4-CCD spectral sensor, a thermal IR camera and a laser altimeter that is mounted on a flexible Sky-Arrow airplane. A test application of the multispectral sensor to estimate durum wheat quality is also presented.

Monitoring mine waste from sulfide deposits by hyperspectral remote sensing can be used to predict surface water quality by quantitatively estimating acid drainage and metal contamination on a yearly basis. In addition, analysis of the mineralogy of surface crusts rich in soluble salts can provide a record of annual humidity and temperature. In fact, temporal monitoring of salt efflorescence fr...

To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size ...

Hyperspectral imagery is a class of image data which is used in many scientific areas, most notably, medical imaging and remote sensing. It is characterized by a wealth of spatial and spectral information. Over the last years, many algorithms have been developed with the purpose of finding “spectral endmembers,” which are assumed to be pure signatures in remotely sensed hyperspectral data sets....

Remote sensing is viewed as a cost-effective alternative to intensive field surveys in assessing site factors that affect growth of Eucalyptus grandis over broad areas. The objective of this study was to assess the utility of hyperspectral remote sensing to discriminate between site qualities in E. grandis plantation in KwaZulu-Natal, South Africa. The relationships between physiology-based hyp...

Sparse unmixing of hyperspectral images aims to separate the endmembers and estimate abundances mixed pixels. This approach is essential step for many applications involving images. The multi-scale spatial sparse algorithm (MUA) could achieve higher accuracy than state-of-the-art algorithms. regularization parameters, whose combinations markedly influence accuracy, are determined by manually se...

Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverag...