Measuring the Mangroves

Measuring the Mangroves

Problem being addressed

Mangrove swamps play important roles as coastal carbon sequestrators. However​,​ in order to quantify the carbon storage capacity of mangroves accurate measurements of biomass are required. Can machine learning play a role?

Solution

Machine learning methods that use imaging data, particularly satellite imaging, were applied to a 151 hectare section of Mangrove ecosystem off of Thailand. The basic idea was to measure the biomass of the region using a combination of botanical allometric models and biomass estimation techniques. Satellite image data was used to find counts of different tree species in a grided area. Based on the size estimates of trees and known density values a biomass estimate was created using machine learning algorithms, particularly those from the open source WEKA package. Both above and below ground biomass estimates were done with support vector machine approaches providing the most accurate results when compared to the samples.

Advantages of this solution

Since carbon is becoming an increasingly important question with respect to the climate crisis, carbon estimates that are easy to compute will play a role in conservation. This research provides a tool for scalable carbon estimates.

Solution originally applied in these industries

environment

Environment Sector

Possible New Application of the Work

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Agriculture Industry

Agricultural biomass estimates is an emerging field, but most of these estimates are for mono-cultured crops. In this research multiple species of tree are considered and then mass is estimated. In more complex, permaculture, agriculture settings these techniques could be used for yield estimation.

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Food Industry

Food is often packed and then sold by weight. Perhaps imaging and mass estimates could be used as the weighing methods, rather than physical weighing? This would save time in food processing businesses.

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

Transport and Logistics Industry

This research uses imaging to develop mass estimates. A useful tool for transport companies would be an imaging device that could give approximate weight readouts. This could help optimize transportation loads.

Source DOI: #############check-icon

Source URL: #############check-icon


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