更新森林决策的无人机数据:从原始数据到可操作的见解1

IF 1.3 Q3 REMOTE SENSING
S. Puliti, A. Granhus
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引用次数: 3

摘要

在这项研究中,我们的目标是开发直接将原始无人机数据转化为可操作的见解的方法,从而使我们能够直接从无人机数据中做出管理决策。利用无人机摄影测量数据和数据分析对更新林的林分水平即时抚育需求和成本进行了建模。利用现场参考数据,训练并验证了用于即时抚育需求二元分类的logistic模型和用于预测抚育成本的多元线性回归模型。将无人机数据衍生的模型的性能与使用以下替代数据源的模型进行了比较:机载激光扫描数据(ALS)、森林管理计划的先验信息(prior)以及无人机+ prior和ALS + prior的组合。无人机数据和先验信息的使用在抚育需求分类方面优于其他选择,而无人机数据单独产生最准确的成本模型。我们的研究结果鼓舞了无人机在再生林运营管理中的进一步应用,并表明无人机数据和数据分析对于获得可操作的见解是有用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drone data for decision making in regeneration forests: from raw data to actionable insights1
In this study, we aim at developing ways to directly translate raw drone data into actionable insights, thus enabling us to make management decisions directly from drone data. Drone photogrammetric data and data analytics were used to model stand-level immediate tending need and cost in regeneration forests. Field reference data were used to train and validate a logistic model for the binary classification of immediate tending need and a multiple linear regression model to predict the cost to perform the tending operation. The performance of the models derived from drone data was compared to models utilizing the following alternative data sources: airborne laser scanning data (ALS), prior information from forest management plans (Prior) and the combination of drone +Prior and ALS +Prior. The use of drone data and prior information outperformed the remaining alternatives in terms of classification of tending needs, whereas drone data alone resulted in the most accurate cost models. Our results are encouraging for further use of drones in the operational management of regeneration forests and show that drone data and data analytics are useful for deriving actionable insights.
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
2
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