Abstract
Accurately estimating and mapping forest structural parameters are essential for monitoring forest resources and understanding ecological processes. The novel deep learning algorithm has the potential to be a promising approach to improve the estimation accuracy while combining with advanced remote sensing technology. Airborne light detection and ranging (LiDAR) has the preferable capability to characterize 3-D canopy structure and estimate forest structural parameters. In this study, we developed a deep learning-based algorithm (Deep-RBN) that combined the fully connected network (FCN) deep learning algorithm with the optimized radial basis neural network (RBN) algorithm for forest structural parameter estimation using airborne LiDAR data. The multiple iterations were used to constantly update the internal weights to achieve the optimized accuracy of model fitting, and the optimized RBN algorithm was developed for the limited training sets. We assessed the efficiency and capability of the Deep-RBN in the estimation of forest structural parameters in a subtropical planted forest of southern China, by comparing the traditional FCN algorithm and multiple linear regression. We found that Deep-RBN had the strongest capability in estimates of forest structural parameters (R2 = 0.67–0.86, rRMSE = 6.95%–20.34%). The sensitivity analysis of the key hyperparameters of Deep-RBN algorithm showed that the learning rate is one of the most important parameters that influence the performance of predictive models, and while its value equal is to 0.001, the predictive models had the highest accuracy (mean DBH: RMSE = 1.01, mean height: RMSE = 1.45, volume: RMSE = 26.49, stem density: RMSE = 121.06). With the increase of training samples added in Deep-RBN model, the predictive models performed better; however, no significant improvements of accuracy were observed while the number of training set is larger than 80. This study demonstrates the benefits of jointly using the Deep-RBN algorithm and airborne LiDAR data to improve the accuracy of forest structural parameter estimation and mapping, which provides a promising methodology for sustainable forest resources monitoring.
Highlights
A S THE important component of terrestrial ecosystems, forest plays a crucial role in global climate change mitigation [1], ecological services supplement [2], and biodiversity protection [3]
In order to assess the ability of deep learning regressor (Deep-radial basis neural network (RBN)), different modeling algorithms (i.e., multiple linear regression (MLR), fully connected network (FCN), and Deep-RBN) were tested and compared with the predictive accuracy in estimating four key forest structural parameters
The accuracy assessment results of three predictive methods showed that Deep-RBN had the highest accuracy (R2 = 0.67–0.86, relative RMSE (rRMSE) = 6.95%–20.34%) within the three modeling algorithms (Table IV)
Summary
A S THE important component of terrestrial ecosystems, forest plays a crucial role in global climate change mitigation [1], ecological services supplement [2], and biodiversity protection [3]. Due to intensive timber harvesting and silviculture activities, forests changed and updated rapidly [4], [5], resulting in lots of new challenges for forest sustainable management and forest inventory. The demands of increased information are urgent for assessing the composition and structure of forest biomes [6]. Accurate, effective, and timely characterization of forest structural parameters is essential for forest sustainable management [7]. Airborne light detection and ranging (LiDAR) is an active RS technology, which has the capability to measure 3-D forest canopy structure [13]–[15] and characterize vertical profiles [16]. In EFI practice, it has been proved that airborne LiDAR has the advantages of robustness and portability in estimating forest structural parameters with area-based approach (ABA) [17]–[19]
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