Forests play a vital role in the wellbeing of our planet. Large- and small-scale deforestation across the globe is threatening the stability of our climate, forest biodiversity, and thus the preservation of fragile ecosystems and our natural habitat as a whole. With increasing public interest in climate change issues and forest preservation, a large demand for carbon offsetting, carbon footprint ratings, and environmental impact assessments is emerging. Satellite remote sensing is the only method that can provide global coverage at frequent revisit times and is therefore the standard method for global forest monitoring. Most often, deforestation maps are created from optical data such as Landsat and MODIS. Although such maps are of generally good quality, they cannot quantify biomass and are not typically available at less than annual intervals due to persistent cloud cover in many parts of the world, especially the tropics where most of the world's forest biomass is concentrated. Synthetic Aperture Radar (SAR) can fill this gap as it penetrates clouds and interacts with the three-dimensional structure on the ground in a way that scales with volume and therefore biomass. While longer wavelengths are better for deeper penetration of the canopy and therefore biomass estimation, one of the most readily available data sources is Sentinel-1, a shorter wavelength C-band SAR. We have previously shown that Sentinel-1 data can achieve good separability of forest and non-forest globally (Hansen et al. 2020).
A big challenge for developing and validating algorithms for deforestation detection is the scarcity of reliable reference data. Ideally, the exact time and type of change is known for a sufficiently large area and time period to be used for training and validation. However, data of this kind are infeasible to obtain because they require continuous large-scale monitoring and are therefore prohibitively expensive. For global coverage, the best available forest maps are still only updated at annual intervals, e.g. GlobalForestWatch (Hansen et al. 2013) or JRC Tropical Moist Forests (Vancutsem et al. 2021) and are themselves obtained from Machine Learning models. In order to circumvent the reliance on reference data to train deforestation detection algorithms, one could simply apply change detection algorithms that do not rely on training labels such as Bayesian Online Changepoint Detection (Adams & MacKay 2007) or PELT (Killick et al. 2012). However, this approach has the downside that the detected changes do not necessarily correspond to deforestation. Detected changes may instead reflect land cover transitions other than forest to non-forest, or indeed measurement changes that do not represent a change in the underlying land cover at all, for example:
- Seasonal changes in vegetation
- Growth and harvest cycles in agriculture
- Soil moisture changes due to rainfall
This is because such change detection algorithms are in no way specific to any particular type of change but merely pick up statistically significant changes in the raw data, whether or not these changes correspond to a change in the underlying state. To solve these problems, and to mitigate the disadvantages of both the fully supervised and the fully unsupervised methods, we have pursued a partially supervised approach: instead of requiring reference data that capture land cover changes and thus need a temporal component, our method only relies on a stationary forest map and thus classifies pixels as either being stable forest or not. Pixels that are not stable forest could include agriculture, urban areas, other forms of vegetation, etc., or pixels that undergo deforestation at some point. This reference forest map can then be used to detect pixels that deviate from the reference class, i.e., the forest prototype. This is done by computing a distance metric between the time series of the pixel and an ensemble of reference forest time series. Deforestation can then be detected as a sudden increase in distance to the reference forest class when computed over time. Preliminary results show that this method achieves a near-perfect change detection sensitivity (producer's accuracy above 99%), although false positives occasionally lead to a low user's accuracy of about 60%. The mean change detection delay amounts to about two to three months. Further work is expected to reduce the false positive rate, improve detection delay, and validate this method in different biomes. The method is particularly useful for improving existing forest maps as it is robust to noisy training data. Our results demonstrate that Sentinel-1 data has the potential to advance global deforestation monitoring.
Hansen, J. N., Mitchard, E. T., & King, S. (2020). Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar. Remote Sensing, 12(11).
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(November), 850–854.
Vancutsem, C., Achard, F., Pekel, J. F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10), 1–22.
Adams, R. P., & MacKay, D. J. C. (2007). Bayesian Online Changepoint Detection.
Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598.