Can We Catch Them At It: Making Deforestation Monitoring a Tactical Activity
Deforestation Early Warning Gets a Space-AI Boost
“You can’t solve the problem of climate change if you don’t solve the problem of deforestation” - William Boyd.
Early Detection - VISIBLE TO THE SKY!
According to the 2022 Global Annual Tree Cover Loss by Dominant Driver - Global Forest Watch, 2,28,190 sq. km of deforestation occurred due to anthropogenic activities like urbanization and commodity-driven deforestation.
In Mongabay's investigative piece "Meeting an Illegal Logger" by Robert Eshelman from 2014 , the illegal Indonesian logger revealed that logging to load a complete tractor with wood can span approximately 6-8 hours. Logs are then transported via river using boats. (We recommend Robert Eshelman's article to immerse yourself in the real and tough conditions that push one into this profession.) The signs of logging activity, like sawmills and log rafts, are often clearly visible from the sky!
Early detection of such activities is crucial for authorities to take action and reduce deforestation.
Capacity Building to Combat Deforestation
Increasing the frequency of monitoring and reducing the time between early warning analysis (using satellite images with predictive models) and early warning alerts is crucial to building local capacity to combat deforestation and promoting transparency globally.
Unsurprisingly, forest patrolling has human resource shortfalls and logistical bottlenecks. On the data side, satellite imagery available to teams has temporal gaps, producing an alert has high computational costs and needs specialized experts.
In light of the data limitations, researchers have started modeling deforestation activity to provide a more strategic approach as depending on overhead imagery (drones or satellites) for tactical response becomes expensive very quickly. By analyzing spatio-temporal patterns of illegal logging, we can find proxies for logging activities with the available data (open-access USGS and Copernicus satellites). Correlating these proxies with single data source pixel pattern differences and time-series analysis of multi-source data could be used to trigger alerts, albeit with temporal blind-spots.
Annotating satellite imagery using patterns intrinsic to illegal logging provides a foundation for building effective early warning systems. These systems rapidly detect these signatures in every new image, providing an alarm for illegal and unsustainable activities that might otherwise go unseen.
Temporal gaps in satellite imagery, and prohibitive computational costs have an indirect effect on the models themselves. You can see only what you can afford to - and in that lies an artificial limit to the currently active early-warning systems.
Take the example of Forest Alert a real-world example of the work done in partnership between the University of Leicester and the Kenya Forest Service (KFS). Built upon the data collected by the Sentinel-2 constellation, Forest Alert detects incidents every five days and generates notifications within 24 hours. If we assume a clearing rate of four hectares per day, we can potentially lose 24 hectares of forest area by the time the alert hits the patrol team's device.
The effectiveness of early warning systems depends on the frequency and availability of satellite data, and any compromise will hinder its performance.
Our primary need is timely alerts!
As soon as the system detects deforestation and triggers an alert, the rate of deforestation should decrease.
To detect illegal logging activity more effectively, we require satellite data that is acquired more frequently.
By utilizing a combination of satellites such as Landsat and Sentinel, each with different revisit days, we can align our observation calendar to access more satellite data at no cost.
If our budget permits, we can procure commercial satellite images to guarantee that each day,a new satellite image will be appended to our database.
However, obtaining more satellite data results in a significant need for greater storage and computational capacity since the image capacity increases manifold when the spatial resolution of the satellite image rises.When the resolution of satellite images doubles, the pixel size increases fourfold.
Nonetheless, the requirements for data storage and computational infrastructure remain high.
Even if we receive an image every 24 hours, the analysis required to trigger the alert can take an additional 24 hours. So, we lose up to 2 days from the incident to even thinking about on-ground actions.
Leveraging Space Segment
The two major segments of the deforestation early warning system are ground operations and the imaging satellite. The former serves as the instrument to enforce laws and neutralize threats, while the latter enables rapid monitoring and detection over areas larger than the ability of a ground team. The space segment feeds data to the ground segment, enabling timely warnings and interventions.
On-board edge computing in focus
While the revisit can be improved through access to more satellites, the processing latency cannot be sustainably solved by just increasing raw computing power. In industries like manufacturing and transportation, this has been achieved by bringing compute and algorithms to the sensors, thereby speeding up decision-making. In our scenario, this would mean that our AI model needs to run on the imaging satellite literally! This is actually possible, and the experts call it satellite edge computing.
If trained models are deployed on satellites equipped with on-board edge computing, tailored monitoring of high-risk regions susceptible to deforestation can be achieved. This capability is essential as the net latency in monitoring of deforestation incidents is critical for timely intervention.
By processing data directly on the satellite, the system minimizes delays associated with transmitting raw data to ground stations.
Moreover, this technology allows for customization such as receiving only area of interest images, only the affected area boundary, or just an alert message, ensuring that transmissions from the satellite deliver precisely what is needed for the teams on ground.
In the given scenario, the model deployed onboard is purposefully engineered to generate deforestation early warning alert messages directly from the satellite. With the ability to deal with only boundary files or text alerts, actual procurement of satellite imagery is, for all practical purposes, eliminated. Need for resident experts in data analysis can be reduced and so is the cost of storage and compute while still achieving the critical goal of delivering timely early warning alerts.
Figure 4 (A sample scenario) illustrates how on-board edge processing can empower daily deforestation monitoring by increasing the number of alerts, leading to more field interventions. This approach can reduce temporal gaps to mere hours, while simultaneously addressing data storage, computation, and associated cost limitations.
Value additions from On-board edge computing to deforestation early warning systems
On-time alerts: Faster analysis enables immediate notification of deforestation activity, allowing for quicker response by field teams and authorities.
Efficient resource allocation: Field teams can be deployed quickly to areas with confirmed deforestation activity.
Improved trend monitoring: Continuous analysis allows for early detection of emerging deforestation patterns.
Empowering local authorities with transparency: Faster alerts enable timely enforcement and build trust with communities.
Global collaboration: Timely data empowers stakeholders like scientists and NGOs to monitor deforestation trends and track the effectiveness of conservation efforts.
Time to trial Space-AI for combating deforestation?
Deforestation undeniably contributes to global warming, with a cumulative annual release of approximately 4.8 billion tons of carbon dioxide into the Earth's atmosphere.
Carbon offsets, intended to mitigate greenhouse gas emissions, particularly those stemming from deforestation, are facing scrutiny due to their limited effectiveness in combating global warming. only 5.4 million out of 89 million credits (only 6%), actually resulted in carbon reduction through forest preservation,scientists reported in Science (Aug, 2023).
Early warning systems should be viewed as a technology enabler to make programs such as REDD+ work.
As we explore the potential of On-board edge computing in deforestation monitoring, it is time to trial this innovative approach on a larger scale to achieve more sustainable forest conservation outcomes.