Using Satellite Imagery for Mapping Forest Types or Changes

Mats Rosengren has been working in the remote sensing field for 25-years and has a Degree in Physical Engineering. METRIA Miljoanalys is a consulting group in Sweden that has carried out work for the National Board of Forestry, Environmental Protection Agency (EPA), and Agency for Fishery along with many more governmental agencies that deal with the environment.

Mr. Rosengren has been using NeuroSolutions and NeuroSolutions for Excel for the past 6 years and has also attended one of our Neural Network Courses in Orlando, Florida. Using remote sensing satellite imagery, multi-spectral sensor data is used for applications to map forest types or changes in a forest or to extract statistical information from the images together with available maps. In the case of mapping of forest types and changes, the information is essential for forestry companies in order to keep their databases up-to-date for planning and forest management. In addition, the imagery has been used to do wall-to-wall mapping of land use and vegetation from space as well as measuring the depth in the sea water, mapping along the coastlines and measuring the vegetation at the bottom of the sea.

The primary benefit of using NeuroSolutions over other methods is not having to set up a mathematical model for the classification and regression applications. Common classification methods used in remote sensing is based on the assumption of normal distributed classes. However, in Mr. Rosengren's applications, the classes often have multimodal distributions. Using NeuroSolutions rather than other classification and regression methods, often save as much as 25% of the time and cost of the project!

Majority of the problem types Mr. Rosengren has dealt with are Classification of 10 different classes, which are often overlapping and sometime low separability. Using various neural network topologies such as Self-Organizing Maps (SOM) for forest classification problems as well as Principal Component Analysis (PCA), Radial Basis Function (RBF) networks and Multi-Layer Perceptron (MLP) networks help solve his problems by creating generalized models. Mr. Rosengren chose NeuroSolutions for his consulting firm because he found it "easy to start working with" and also the Interactive Book helped him "understand neural networks and start working directly with NeuroSolutions".

We would like to thank Mr. Rosengren for sharing his successes with NeuroSolutions and we hope to continue to see great things from Mr. Rosengren and METRIA Miljoanalys in the near future!

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