Deep learning algorithms to improve the management of species at risk

NCASI与安大略自然资源和森林部以及阿尔伯塔大学一起开发了大型数据集，这些数据集反映了不同类型植被对驯鹿的营养价值。下一步是分析链接到栖息地的使用，怀孕率，和身体状况的野生驯鹿在安大略这一精细尺度的信息。为了解决当前植被地图的不足，NCASI与湖首大学的Salimur Choudhury博士一起使用当前的高分辨率图像来开发具有更高精度和更高映射分辨率的新植被地图（例如，1/4公顷对~4公顷），并且比目前可用的更适合驯鹿营养。这项工作的核心是植被数据集收集在西北部和东北部安大略由NCASI的科学家，描述丰富的地衣和其他驯鹿饲料，并提供详细的措施overstory和其他网站的特点500半公顷的植被地块。该项目的目标是设计基于计算机的绘图算法，用于绘制更大比例尺（林分和景观）的营养资源图，以供各种研究和管理应用。

NCASI, with Ontario Ministry of Natural Resources and Forests, and the University of Alberta, has developed large data sets that reflect at fine-scales the nutritional value to caribou of different types of vegetation. The next step is to analytically link this fine-scale information to habitat use, pregnancy rates, and body condition of wild caribou in Ontario. To address the inadequacy of current vegetation maps, NCASI has joined with Dr. Salimur Choudhury at Lakehead University to use current, high-resolution imagery to develop new maps of vegetation with higher accuracy and greater mapping resolution (e.g., 1/4 ha versus ~4 ha) and that are tailored to caribou nutrition than are currently available. Central to this effort are vegetation data sets collected at 500 half-hectare vegetation plots in northwestern and northeastern Ontario by NCASI scientists that describe the abundance of lichens and other caribou forage, and which provide detailed measures of overstory and other site characteristics. The goal of this project is to design computer-based mapping algorithms that will be used to construct maps of nutritional resources at larger scales (stand and landscape) for a variety of research and management applications.