The prime purpose of the research study was to elucidate the potential of remotely sensed data for estimation of water quality parameters (WQPs) in inland and coastal waters. The useful application of remotely sensed data for operational monitoring of water bodies demand for improved algorithms and methodology. The in situ hyperspectral Spectroradiometer data, water quality data and Airborne Im...

Fuzzy methods in remote sensing have received growing interest for their particular value in situations where the geographical phenomena are inherently fuzzy. A fuzzy approach is investigated for the classi® cation of sub-urban land cover from remote sensing imagery and the evaluation of classi® cation accuracy. Under the fuzzy strategy, fuzziness, intrinsic to both remotely sensed data and gro...

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth’s interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth’s stethoscope. In a similar manner remote sensing is the rock hammer of the planetary sc...

This presentation will discuss statistical methods for use in the analysis of remotely sensed data. Many of the methods represent enhancements of traditional means for processing remotely sensed data that have been developed by the CSIRO Mathematical and Information Sciences Remote Sesning and Monitoring Group (CMIS-RSM). The presentation will discuss statistical methods in the context of parti...

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variabili...

Clustering is unsupervised classification of patterns into groups. Neural networks are very useful tools for performing clustering. In this paper, we propose a new model for using artificial neural networks to perform clustering tasks on remotely sensed imagery. This model generates self-organizing maps (SOM) based on remotely sensed imagery and such related data as yield, nitrate, and moisture...