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Open AccessArticle

Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods

1
Institute of Biometeorology, CNR, 50145 Firenze, Italy
2
Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all'Adige, Italy
3
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 949; https://doi.org/10.3390/rs11080949
Received: 1 March 2019 / Revised: 29 March 2019 / Accepted: 17 April 2019 / Published: 19 April 2019
(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)
Light detection and ranging (lidar) data are nowadays a standard data source in studies related to forest ecology and environmental mapping. Medium/high point density lidar data allow to automatically detect individual tree crowns (ITCs), and they provide useful information to predict stem diameter and aboveground biomass of each tree represented by a detected ITC. However, acquisition of field data is necessary for the construction of prediction models that relate field data to lidar data and for validation of such models. When working at ITC level, field data collection is often expensive and time-consuming as accurate tree positions are needed. Active learning (AL) can be very useful in this context as it helps to select the optimal field trees to be measured, reducing the field data collection cost. In this study, we propose a new method of AL for regression based on the minimization of the field data collection cost in terms of distance to navigate between field sample trees, and accuracy in terms of root mean square error of the predictions. The developed method is applied to the prediction of diameter at breast heights (DBH) and aboveground biomass (AGB) of individual trees by using their height and crown diameter as independent variables and support vector regression. The proposed method was tested on two boreal forest datasets, and the obtained results show the effectiveness of the proposed selecting strategy to provide substantial improvements over the different iterations compared to a random selection. The obtained RMSE of DBH/AGB for the first dataset was 5.09 cm/95.5 kg with a cost equal to 8256/6173 m by using the proposed multi-objective method of selection. However, by using a random selection, the RMSE was 5.20 cm/102.1 kg with a cost equal to 28,391/30,086 m. The proposed approach can be efficient in order to get more accurate predictions with smaller costs, especially when a large forest area with no previous field data is subject to inventory and analysis. View Full-Text
Keywords: active learning; support vector regression; aboveground biomass; diameter at breast height; lidar; forestry active learning; support vector regression; aboveground biomass; diameter at breast height; lidar; forestry
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MDPI and ACS Style

Malek, S.; Miglietta, F.; Gobakken, T.; Næsset, E.; Gianelle, D.; Dalponte, M. Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods. Remote Sens. 2019, 11, 949.

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