In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.
翻译:在欧洲管理密集的森林中,森林被分为面积小的地块,并可能显示地块内的异质性,因此有理由需要高空间分辨率(10至20米),以捕捉树冠高度的差异。在这项工作中,我们开发了一个基于多流遥感测量的深学习模型,以便在法国的“GEDI波形”森林上绘制高分辨率树冠高图,这是一个庞大的海洋松树种植园,面积13 000平方公里,地势平坦,管理程度密集。该地区的特点为偶数级和单项,典型的100米长,每35至50年收获一次。我们深层次的U-Net模型使用来自Sentinel-1和Sentinel-2的多波段图像,以综合平均时间为基础,在法国的“GEDI波状”森林中绘制高树高预测投入。评价使用来自森林库存地的外部验证数据以及基于特定地点Skysat图像的立体3D重建模型进行。我们根据Sentinel-1和Sentinel-2Sentil-2 Sendorps的典型组合培训了七种不同的Unet模型,用于典型的Setty Sent-se Serop,每隔几百段,每35每35每隔35段收集。我们使用一个可使用的轨道,利用每10个轨道的SentalLlationalalLexalalalalalalalalalal livermaxmaxmax