In the heart of Borneo and Sumatra, a technological revolution is unfolding, one that could reshape how we understand and protect tropical biodiversity, with significant implications for the energy sector. A groundbreaking study led by Patrick Burns from the School of Informatics, Computing, and Cyber Systems at Northern Arizona University has harnessed the power of space-based lidar technology to map the intricate three-dimensional structure of forests, providing unprecedented insights into the habitats of tropical mammals.
The research, published in the journal ‘Frontiers in Remote Sensing’ (which translates to ‘Frontiers in the Science of Remote Sensing’), combines data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar with time-series data from Landsat satellites. This fusion of technologies has enabled Burns and his team to create dynamic models of forest structure, stretching back to the year 2001. These models are not just static snapshots but living, breathing maps that change over time, reflecting the dynamic nature of forest ecosystems.
“The beauty of this approach is that it allows us to look back in time and see how changes in forest structure have affected species distributions,” Burns explains. “This is crucial for understanding the impacts of deforestation, habitat fragmentation, and climate change on biodiversity.”
For the energy sector, this research opens up new avenues for sustainable development. Tropical forests are not just biodiversity hotspots; they are also crucial carbon sinks. Understanding how forest structure influences species distributions can help energy companies plan infrastructure projects that minimize environmental impact. For instance, by identifying key habitats for endangered species, companies can avoid or mitigate potential disruptions to these ecosystems.
The study focused on 47 mammal species in Borneo and Sumatra, using a large camera trap dataset to predict the probability of occurrence of these species. The researchers found that while the addition of GEDI-based predictors did not always significantly improve model performance, they did enhance the ecological interpretability of the models. This means that the models not only predict where species are likely to be found but also provide insights into why they are found there.
“When we look at the importance of different predictors, we can see that GEDI-based canopy structure predictors play a significant role,” Burns notes. “This helps us understand the specific habitat requirements of different species, which is invaluable for conservation planning.”
The implications of this research are far-reaching. As the energy sector increasingly focuses on sustainability, tools that can predict and mitigate environmental impacts will become ever more valuable. Moreover, the methods developed in this study could be applied to other regions and species, providing a global framework for biodiversity conservation.
The study has produced a catalog of probability of occurrence maps for all 47 mammal species at a spatial resolution of 90 meters for the years 2001 and 2021. These maps are not just scientific artifacts; they are tools for conservation, tools that can guide policy, inform management practices, and drive sustainable development.
As we look to the future, the fusion of lidar and satellite data holds immense promise. It offers a window into the past, a lens through which to view the present, and a roadmap for the future. For the energy sector, it represents an opportunity to balance development with conservation, to build a future that is both sustainable and biodiverse. And for the forests of Borneo and Sumatra, it offers a chance to thrive, to adapt, and to endure.