Tracking Ecosystem Changes in the Amazon
Satellite imagery has long been used by scientists to track the deforestation and ecosystem changes in the Amazon rainforest — now, data science allows us to go further. But how exactly does Ecology 4.0 help us solve a problem of land use in one of Earth’s most biodiverse places?
The Amazon, a Hotspot of Biodiversity — and Deforestation
Home to more flora and fauna species than any other terrestrial ecosystem, the Amazon jungle in South America is the World’s largest continuous rainforest. Moreover, the Amazon is at the center of Earth’s climate system, affecting regional and global dynamics through the water and carbon cycles. And of course, this tropical forest is home to vast resources, which are in turn under immense pressure from deforestation, land-use changes, and global warming.
During the 1990s and 2000s, the Brazilian Amazon was losing more than 17,000 square kilometers (6,500 square miles) per year, an area nearly the size of Lake Ontario. In 2004, the national government adopted an aggressive policy that, among other actions, strengthened satellite monitoring systems. However, there are also some other hotspots in the region.
As strange as it might sound, the pace of deforestation in Colombia has been rising rapidly since the civil war ended in 2015. This event made ecosystem changes for subsistence farming, coca production, timber, and other uses more viable. At the same time, gold mining, highways, and oil palm plantations in Peru have all helped push annual clearing rates to record levels.
Satellite Imagery and Machine Learning at the Crossroads
4.0 tools provide us with hands-on experience applying new technologies to a problem of environmental conservation. They present an opportunity to study any ecosystem anywhere on the planet without leaving home or the office — one of the biggest concerns within the field is finding ways to study the planet without always traveling by airplane to field sites and conferences.
Every ecosystem-tracking project starts from a challenging task: gathering the data. Developers construct scripts to filter and download images for an area of interest within Amazonia, typically employing machine-readable interfaces or APIs. Programming interfaces are also critical resources for labeling and formatting large datasets from the most common imagery sources, such as Landsat and MODIS.
Pre-Processed image collections are later run to train a neural network — GPUs are incredible tools for that. Neural networks “learn” by recalling patterns in image features; for each image, a feature is a pixel. Then, a modeling pipeline is built to accommodate for multiple models and cross-validation. Deeper neural networks have functions set up to streamline this process.
Currently, several projects use satellite images to classify scenes from the Amazon based on weather, land-use, and land-cover. With improved satellite technology from companies, startups, government agencies, and universities, we can find ways to monitor this precious region remotely. With machine learning models, we can track ecosystem changes in real time, alerting local authorities and conservation organizations of illegal forestry operations.
Major Outcomes and Next Steps
Advanced technologies have opened up pandora’s box of new questions, methods, and projects for scientific exploration and policy development. The impact of land-use change and unsustainable agriculture can now be tracked to account for ecosystem changes, biodiversity, land cover, and other vital ecological services.
Researchers admit that the trend toward smaller patch size is something that they now see all over the Amazon, maybe a strategy to avoid satellite monitoring and enforcement. For instance, a recent study found that most of the newer clear-cutting patches in Brazil were below the 25-hectares threshold detected by the satellite-based system used by environmental authorities.
There could be a benefit to incorporating smaller-plot satellite imagery and integrating more radar data into forest monitoring systems. Like those embedded in the synthetic aperture radar (SAR), radar sensors can detect deforestation through clouds, make observations by day and night, and expose subtle changes in forest canopies. That kind of move toward transparency and accountability proves crucial because it makes it possible for the science community, NGOs, and the public to engage.
Matias Verdu is a technical writer with a Master’s degree in Life Science and a BSc in Natural Resource Engineering. Working more than 8 years in the energy industry he has extensive expertise in the evaluation and execution of energy and water infrastructure projects.
Sources
Tracking Amazon Deforestation from Above. NASA Earth Observatory. https://earthobservatory.nasa.gov/images/145988/tracking-amazon-deforestation-from-above
Butler, R.A. What’s the deforestation rate in the Amazon? Mongabay. https://rainforests.mongabay.com/amazon/deforestation-rate.html
Bronstein, C. Land-Use and Deforestation in the Brazilian Amazon. Using Satellite Imagery and Deep Learning for Environmental Conservation. Towards Data Science Inc. https://towardsdatascience.com/land-use-and-deforestation-in-the-brazilian-amazon-5467e88933b
Kalamandeen, M., Gloor, E., Mitchard, E. et al. Pervasive Rise of Small-scale Deforestation in Amazonia. Sci Rep 8, 1600 (2018). https://doi.org/10.1038/s41598-018-19358-2
SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation. SERVIR. https://servirglobal.net/Global/Articles/Article/2674/sar-handbook-comprehensive-methodologies-for-forest-monitoring-and-biomass-estimation
