Abstract
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.
Highlights
Drone data have proven valuable in modeling and providing accurate data analytics on key forest biophysical variables (Puliti et al 2015; Guerra-Hernández et al 2017; Iglhaut et al 2019; Mulverhill et al 2020)
There is a need to develop methods to summarize raw drone data into data analytics and in actionable insights intended as a decision on whether to perform stand-specific silvicultural treatments (Fig. 1)
Variables derived from the predictions of biophysical variables such as the tree density (N ) and the future crop tree density (Nc) were selected in three out of the four fitted models, both for drone and airborne laser scanning data (ALS) data
Summary
Drone data have proven valuable in modeling and providing accurate data analytics on key forest biophysical variables (Puliti et al 2015; Guerra-Hernández et al 2017; Iglhaut et al 2019; Mulverhill et al 2020). There is still a gap between the quantitative knowledge of forest biophysical variables and the possibility to make silvicultural decisions. Drone data must provide actionable insights or information that allows decision-making without the need for performing a costly field visit. There is a need to develop methods to summarize raw drone data (e.g., an orthomosaic or a digital surface model) into data analytics (e.g., predictions of tree density) and in actionable insights intended as a decision on whether to perform stand-specific silvicultural treatments (Fig. 1)
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