An Evaluation of Various Vegetation Indexes for Predicting Wild Blueberry Yield

瓦格纳森林NS有限公司是一家森林管理公司，致力于可持续林业的实践。 瓦格纳管理着新斯科舍省约50万英亩的林地和安大略约84.5万英亩的林地。在过去的七年里，瓦格纳一直在投入时间和金钱，将砍伐的林地开发成蓝莓田。 到2019年底，该公司将开发约1000英亩土地。 瓦格纳希望与南加州大学地理科学中心（COGS）应用研究团队合作，研究利用遥感和空间分析来监测植物健康和预测野生蓝莓产量的可行性。在生长季节，将使用多光谱传感器进行几次无人机调查。从这些图像中，将计算各种植被指数，以帮助测量植物健康。 在收获后，各种植被指数将与蓝莓收获相关联，以确定预测收获产量的最佳指标。COGS的应用研究团队将领导该项目，协助调查设计和方法，进行航空图像收集，并进行图像后处理。该项目的目标是确定最佳植被指数和最佳时间来收集图像，以最好地预测野生蓝莓产量。

Wagner Forest NS Ltd. is a forest management company with a core commitment to the practice of sustainable forestry. Wagner manages approximately 500,000 acres of forest land in Nova Scotia and 845,000 acres in Ontario. For the past seven years, Wagner has been investing both time and money into developing cutover forest land into blueberry fields. The company will have approximately 1000 acres developed by the end of 2019. Wagner would like to collaborate with NSCC's Centre of Geographic Sciences (COGS) Applied Research team to investigate the feasibility of utilizing remote sensing and spatial analytics to monitor plant health and predict wild blueberry yields.Several UAV surveys will be conducted during the growing season using a multispectral sensor. From this imagery, various vegetation indices will be calculated to assist with measuring plant health. Following the harvest the various vegetation indices will be correleated with the blueberry harvest to determine the best index for predicting harvest yield.The Applied Research team at COGS will lead the project, assist in survey design and methodology, conduct aerial image collection, and perform post processing of images. The goal of the project is to determine the best vegetation index and the optimal time to collect imagery to best predict wild blueberry yield.