In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree mo...

Research in time-series remote sensing data is receiving increasing attention. With the availability of relatively short repeat cycle and high spatial resolution satellite data, the construction and application of high spatiotemporal remote sensing time-series data is promising. In this paper, we proposed a method to construct complete spatial time series data, with Savitzky-Golay filter for sm...

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and te...

Although nano/microsatellites have great potential as remote sensing platforms, the spatial and spectral resolutions of an optical payload instrument are limited. In this study, a high spatial resolution multispectral sensor, the High-Precision Telescope (HPT), was developed for the RISING-2 microsatellite. The HPT has four image sensors: three in the visible region of the spectrum used for the...

Hyperspectral remote sensing provides great potential to monitor and study biodiversity of tropical forests through species identification and mapping. In this study, five species were selected to examine crown-level spectral variation within and between species using HYperspectral Digital Collection Experiment (HYDICE) data collected over La Selva, Costa Rica. Spectral angle was used to evalua...

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyper...

Dry grassland sites are amongst the most species rich habitats of Central Europe. In Switzerland, they are home to a large number of plant and animal species that are classified as endangered or threatened. A key component for designing optimal and effective management schemes ensuring the sustainability of these ecosystems, is knowledge of their biomass production. In this study we explored th...

© 2012 Wen and Yang, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An Unsupervised Classification Method for Hyperspectral Remote Sensing Image Based on ...

This paper investigates the extent to which the accuracy and speed of classifying Hyperspectral remote sensing images are affected by the application of varying degrees of dimensionality reduction. Three methods have been used for dimensionality reduction: PCA, ICA and random band selection; SVM has been used for classification. The results have been evaluated on both natural and man-made scena...

Atmospheric profile variability impacts the effectiveness of hyperspectral remote sensing systems to identify geological materials from space. This variability alters the "truth" spectrum (e.g. reflective or emissive) by modifying its spectrum through such atmospheric factors as transmissivity, upwelling radiance, and downwelling radiance. Incomplete characterization of these effects can lead t...