Multitemporal analysis of the 3D structure and biomass of subarctic open canopy forests using photogrammetric point clouds

亚北极森林是开阔的林地，小树稀疏。仅在加拿大，它们的面积就超过250万平方公里(大致相当于阿根廷的大小)。这些森林分布在北纬50度和70度之间，属于气候变暖最严重的地区。如此高度的变化可能会导致森林增密，增加生态系统生产力，并改变栖息地。在这么大的面积上，密度更大、高度更高的森林可能会大幅增加大气中的碳封存数量，但仍有争议，而且可能没有记录。事实上，由于在这些偏远地区获取数据的成本很高，几乎没有资源用于测量和绘制这些森林的高度、密度和生物量。航空调查在后勤上很困难，传统的基于2D卫星图像的遥感(例如陆地卫星)缺乏准确性。需要新类型的遥感数据和分析方法，以大大增加我们对这些森林动态的了解。 因此，我们的目标是：a)使用新的甚高分辨率卫星立体图像以30厘米的像素尺寸重建亚北极森林的3D模型；b)使用自1950年代以来获得的历史立体航空照片创建代表过去这些森林结构的类似3D模型；c)从这些最近的和具有追溯性的3D重建图像中提取树高、树冠宽度、每公顷树木数量(密度)和生物量密度(每公顷木质生物量吨)的测量结果；d)测量森林结构随时间的3D变化，以及e)将这些变化与不断变化的气候联系起来。在这项工作中，我们将设计和测试一种全新的方法来评估立体森林属性测量的误差。通过使用计算机模型合成立体图像，这些模型在数学上表示传感器或照相机的光学和观察几何图形、太阳的位置、大气状态等，我们将评估这些人造图像允许我们测量森林属性的程度。这一新方法首先需要使用实地收集的数据进行验证。一旦被证实，我们将能够使用它来预测从非常高分辨率的卫星或航空相机拍摄的任何图像的误差。 拟议的研究首先将对我们绘制高精度和低模糊性的亚北极森林地图的能力产生重要影响。我们将创造一种方法来提取任何亚北极地区(加拿大、俄罗斯等)的详细森林信息，这种方法基于非常高分辨率的立体图像，而不需要昂贵的实地校准。我们的研究将对亚北极森林的森林碳含量的数量和变化率产生新的认识，将有助于改进对高纬度地区的长期气候模型预测，并将提供更准确的泰加栖息地图景。

Subarctic forests are open woodlands sparsely populated by small trees. They cover, in Canada alone, an area of more than 2.5 million km2 (roughly the size of Argentina). Spread between the 50th and 70th northern parallels, these forests fall within the regions that are experiencing some of the greatest climate warming. Such a high degree of change will likely induce forest densification, increase ecosystem productivity, and transform habitats. A sharp increase in the amount of sequestered atmospheric carbon by denser and taller forests over such huge areas is possible, but is still debated, and could go undocumented. Indeed, few resources are allocated to measuring and mapping the height, density, and biomass of these forests due to the high costs of data acquisition in such remote areas. Airborne surveys are logistically difficult, and conventional remote sensing based on 2D satellite images (e.g. Landsat) lack accuracy. New types of remote sensing data and analysis methods are needed to greatly increase our understanding of the dynamics of these forests. Our goals are therefore to a) use new very high resolution satellite stereo images to reconstruct 3D models of subarctic forests at a pixel size of 30 cm, b) use historical stereo aerial photos acquired since the 1950s to create similar 3D models representing the structure of these forests in the past, c) extract measurements of tree height, crown width, number of trees per hectare (density), and biomass density (tons of woody biomass per hectare) from these recent and retrospective 3D reconstructions, d) measure the 3D changes of the forest structure over time, and e) link these changes to the evolving climate. Throughout this endeavour, we will be designing and testing a entirely new approach for assessing the error of the stereo forest attribute measurements. By synthesizing stereo images using computer models that mathematically represent the optics and viewing geometry of the sensor or camera, the sun's position, the state of the atmosphere, etc., we will assess how well these artificial images allow us to measure forest attributes. This new approach will first need to be validated using data collected in the field. Once proven, we'll be able to use it to predict the error for any image taken from very high resolution satellites or aerial camera. The proposed research will first have an important impact on our capacity to map subarctic forests with high accuracy and low ambiguity. We will have created the means for extracting detailed forest information for any subarctic location (in Canada, Russia, etc.), based on very high resolution stereo-images without the need for costly field calibration. Our research will lead to new knowledge on the amount and rate of change of forest carbon contents of subarctic forests, will help improve long-term climate model predictions for high latitudes, and will provide a much more precise picture of the habitats of the taiga.