IT SHOWED HOW IMPORTANT IT IS

TO CONTINUE

TO DEVELOP SOLUTIONS LIKE THIS SO THAT WE CAN EFFECTIVELY

DEAL WITH THIS

AND PROVIDE SOLUTIONS THAT ALLOW

INFORMED CONSERVATION

DECISION- MAKING .

What progress has the project made over the past year and how has the Proof-of-Concept application supported this ?

Van Hoang : We have been applying so many different conservation technologies . For example , we have been using a very basic GPS device to record our patrol work and to record data when patrolling . We then turn it into Smart data and now we are using a Smart Connect . That means all of our data from when we are patrolling the site can be sent to our centre much more easily . We have an immediate response to whatever is happening on the ground .

We also recently used a thermal drone to monitor the habitat . We are using the thermal drone to conduct our diversity survey and our primate survey . We use our camera trap to record our data for privacy monitoring . We use the camera in the forest to monitor what is happening in the forest .

We also used the remote sensing to monitor the change in habitat because of local community encroachment to the forestry resources . For example , the cardamom is underneath the forest canopy so we would not be able to use a human on the ground to monitor everything . So , we applied the remote sensing to measure that change in the forest habitat and measure the degradation of the area .

What were the specific challenges inherent in both instigating and sustaining a remote sensing and Machine Learning solution for such a project and how were they overcome ?

Branson : There are a number of challenges . The first that we initially came across was accessing good-quality satellite imagery . Over our region it was very cloudy , and we had about 5 – 10 dates per year where we had usable imagery and they were usually in very small time clusters where you had a good weather period .

Acquiring that good-quality data can also be expensive as you move into purchasing satellite imagery . We overcame that by implementing drone imagery as well into this project and using that to reference and label the cardamom locations in our satellite imagery . This brought about a second challenge as well ; using the drone imagery , we then had to implement a labelling solution where we labelled the cardamom . This particular area is quite mountainous and involved very accurate ortho-rectifying to ensure that these images could be accurately overlayed with the satellite imagery and the drone imagery , so that the two cardamom locations lined up . These had to be done manually to ensure it was as accurate as possible .

This is a solution that we are hoping to overcome in phase 2 of the project as we look to introduce a lot more accurate orthorectifying processing .

Similarly with developing the algorithm , this was definitely a trial and error process of using different approaches to train the algorithm and the classifications to produce better accuracies . So , looking at cardamom that was bright in the imagery and cardamom that was dark in the imagery and using those two classes to enable an output that classified two different types of cardamom and increasing the accuracy that way as well .

The last challenge we are looking at in phase 2 is deploying this product and enabling implementation across a much wider area than the Proof-of-Concept allowed .

Was there anything that stood out from the project in terms of how technology was used ?

Branson : One thing I definitely took from it was how difficult it can be , especially when you have got situations where there is activity beneath the canopy . This is increasingly difficult to monitor , especially when you are working in very thick and dense forests and you are trying to identify something that grows on the ground at a bush level . It was difficult to do even via remote sensing and using staff on the ground . It showed how important it is to continue to develop solutions like this so that we can effectively deal with this and provide solutions that allow informed conservation decision-making . �

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