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A Robust Forest Height Estimation by Using EBPNN by Utilizing Morphological Estimation

neeraj

9876543217

D 33 OLD MINAL BHOPAL

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07-December-2018

Forest above-ground dry biomass (AGB, herewith simply referred to as biomass) is an important variable for the global carbon budget, not only due to the uptake of carbon dioxide in the process of photosynthesis, but also because forests store huge amounts of carbon, which are eventually released into the atmosphere following a disturbance [1]. Accurate and timely mapping of forest AGB is therefore crucial to support carbon cycle modeling. Traditional methods based on forest inventories and aerial photography, and more recently, LiDAR campaigns, give accurate estimates of AGB; however, such methods are expensive and become inefficient whenever frequent and large-scale mapping is needed. Therefore, there is a need for development of alternative methods for frequent and large-scale biomass mapping [2]. One of the more promising techniques for above-ground dry biomass mapping is Synthetic Aperture Radar (SAR), cf. [3]. Being an active sensor, radar is independent of weather and external illumination

Here proposed work focus on classifying the vegetation and forest region from the input data image. Classification is done on the basis of filter image and input vector. Error Back propagation neural network is use for classification. Whole work is explained in below block diagram.

In this paper a new approach of forest height estimation techniques is explain with their requirement area. Here use of neural network help in reading the new areas of the input image and identify the vegetation region. Here this paper has made the necessary changes in previous work for proper training of the neural network. Experiment is done on real images of TandemX, Biotope, etc.  Results shows that proposed work is better as compare to previous work on different evaluation parameters. There is always work remain in future work as height estimation accuracy can be further be increase by using color correction algorithms.

 

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Software Requirement :   MATLAB

Hardware Requirement :   • Intel Processor 2.0 GHz or above. • 2 GB RAM or more. • 160 GB or more Hard Disk Drive or above.


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