Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a Bayesian deep learning approach so as to predict forest structure variables with well-calibrated uncertainty estimates. These increase the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates, such as informed decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
翻译:以知情的方式监测和管理地球森林是应对生物多样性丧失和气候变化等挑战的重要要求。传统实地或空中森林评估运动提供了准确的数据,供区域一级分析,但很难以高时间分辨率将这些数据推广到整个国家及以外。在这项工作中,我们建议采用巴伊西亚深学习方法,以10米分辨率,利用可自由获取的卫星图像作为投入,在国家范围内对森林结构变量进行密集估计,使用10米分辨率。我们的方法将Sentinel-2光学图像和Sentinel-1合成孔径雷达图像联合转化为五个不同森林结构变量的地图:95度高度、平均高度、密度、基尼系数和分层覆盖。我们从挪威各地的41个空中激光扫描任务中培训和测试我们的参考数据模型,并表明它能够向隐性测试区域推广,实现11%至15%之间的正常绝对误差,我们的工作也是第一个提出Bayesian深度学习方法,以便预测森林结构变量,并作出经充分校准的不确定性估计。这提高了模型的可信度及其在下游任务中的适度。我们提出了一个可靠的预测性模型,作为预测的准确性模型,我们提出了一个预测的全局性决定。我们提出了一个全面的预测性。我们提出了一个预测性模型,作为预测的全局性,提供了一个预测性模型的精确性。