ENVIRONMENT
THE HUMAN EFFECT Tracking and understanding the impact human beings have on the environmental. By Mike Lane
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egardless of which side of the aisle you sit on, it’s hard to ignore the growing amount of scientific research and data on the environmental impact human beings have on Earth. And while scientists are just beginning to understand many of these effects, they need more evidence and information to minimize and mitigate our impact. Some degree of change is natural. It can be slow, gradual and continual — rivers flow and create wear on the surrounding land, deserts shift as air circulates around and through them, and trees grow and affect their environments with subterranean root systems and overhead canopies. At other times, change is sudden and drastic. Hurricanes are devastating when they make landfall, and floods and fires wreak destruction wherever they occur. While these may seem like natural occurrences, the degree to which such environmental disasters are influenced by human activities is still being studied. What’s certain is that humans do change the world in significant ways. We build cities, we dig mines, we plant fields of agricultural crops. Sometimes it’s easy to see these and other small, fast changes — like when land is cleared to make room for a new school — but it’s harder to track the wider, slower changes happening to the planet. Enterprising researchers are using geospatial technologies to collect, monitor and understand all the ways human activities are changing the planet. From the migration of river deltas to the construction of man-made islands in Dubai, the transformations are constant. It’s impossible to address every avenue of change, but three specific university research projects are providing data and insight that can be applied to assess and better understand our environmental impact on Earth.
Protecting elephants in Africa As the human population continues to grow in Africa, elephants’ habitats are being repurposed for agriculture and settlement. To plan long-term, sustainable solutions, it is necessary to have accurate data on elephant numbers, migration patterns and locations. A monitoring system that uses remote sensing and Machine Learning can identify
Croatia’s Pag Island suffers from extreme soil erosion due to a combination of harsh climatic conditions and intensive grazing and deforestation caused by humans
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www.geospatialworld.net | Mar-Apr 2020
herds and better protect them from land use conflicts. Human residents use several techniques to deter elephants from entering their cropland and inhabited areas. These techniques include chili fences and bombs, bee fences, and technological solutions designed as early warning systems for communities. But the elephants need to be kept safe too. High-resolution satellite imagery combined with remote sensing and Machine Learning can be used to monitor and protect herds, and a group of researchers at the University of Oxford is doing just that. They partnered with the Elephants Without Borders non-profit organization, which contributed GPS tracking collars for herds in the Kavango Zambezi Transfrontier Conservation Area in Sub-Saharan Africa. The researchers accessed multispectral imagery and used remote sensing to locate areas of interest where a concentration of elephants could be identified. Images were then processed, and the researchers developed a convolutional neural network (CNN), an algorithm that automates the detection of herds in satellite images. To do this, the GPS collar data was cross-referenced with image coordinates, which provided the algorithm with exact examples of what elephants look like from space. Additionally, training data was collected from Addo Elephant National Park in South Africa, where there is a high concentration of elephants in a relatively small area. After labelling satellite images by drawing bounding boxes around individual elephants and non-elephant objects (e.g. trees) in the landscape, Machine Learning was applied to teach the machine the difference. Eventually, the algorithm will be smart enough to detect wild elephant populations in images without relying on GPS collar data. There are already a number of CNNs that are more accurate than human detection in challenging image classification and object detection areas. With enough training data, CNNs can learn complex, distinctive features of objects in a short time. Developing a close to real-time monitoring technique will allow scientists to track herds more accurately and efficiently, enabling more effective conservation planning and management.
