Deep learning algorithms to improve the management of species at risk

NCASI与安大略省自然资源和林业部以及艾伯塔大学共同开发了大型数据集，在精细尺度上反映了不同类型植被对驯鹿的营养价值。下一步是将这些精细的信息与安大略省野生驯鹿的栖息地使用、怀孕率和身体状况进行分析联系。为了解决当前植被地图的不足，NCASI与莱克黑德大学的Salimur Choudhury博士合作，利用当前的高分辨率图像，开发了新的植被地图，其精度更高，制图分辨率更高(例如，1/4公顷比~4公顷)，并且比现有的地图更适合驯鹿的营养。这项工作的核心是NCASI的科学家在安大略省西北部和东北部收集的500半公顷植被地块的植被数据集，这些数据集描述了地衣和其他驯鹿饲料的丰富程度，并提供了覆盖层和其他场地特征的详细测量。该项目的目标是设计基于计算机的绘图算法，用于构建更大比例尺(林分和景观)的营养资源地图，用于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.