Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Lee, Jung‐Hee; Im, Jungho; Kim, Kyungmin; Quackenbush, Lindi J.

doi:10.3390/f9050268

Cited by 41 publications

(16 citation statements)

References 46 publications

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“…Zhou et al [11] also reached a similar conclusion, and like the results of this study, they also concluded that the GRNN model had higher accuracy than the regression models in estimating DBH in the Zhejiang province in China. Lee et al [57] used new three machine learning techniques, including support vector regression (SVR), modified regression trees (RT), and a random forest (RF) in South Korea to predict the heights of forest stands, and concluded that these three models were capable of estimating the height of stands well, and the estimates of these three models were not statistically significant. Finally, Bourque et al [4] in Kheyrud forest used genetic programming to determine the relationship between height and diameter in beech forest and select the most environmental variables.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Bayat

Bettinger

Heidari

et al. 2020

Forests

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The diameters and heights of trees are two of the most important components in a forest inventory. In some circumstances, the heights of trees need to be estimated due to the time and cost involved in measuring them in the field. Artificial intelligence models have many advantages in modeling nonlinear height–diameter relationships of trees, which sometimes make them more useful than empirical models in estimating the heights of trees. In the present study, the heights of trees in uneven-aged and mixed stands in the high elevation forests of northern Iran were estimated using an artificial neural network (ANN) model, an adaptive neuro-fuzzy inference system (ANFIS) model, and empirical models. A systematic sampling method with a 150 × 200 m network (0.1 ha area) was employed. The diameters and heights of 516 trees were measured to support the modeling effort. Using 10 nonlinear empirical models, the ANN model, and the ANFIS model, the relationship between height as a dependent variable and diameter as an independent variable was analyzed. The results show, according to R2, relative root mean square error (RMSE), and other model evaluation criteria, that there is a greater consistency between predicted height and observed height when using artificial intelligence models (R2 = 0.78; RMSE (%) = 18.49) than when using regression analysis (R2 = 0.68; RMSE (%) = 17.69). Thus, it can be said that these models may be better than empirical models for predicting the heights of common, commercially-important trees in the study area.

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Section: Discussionmentioning

confidence: 99%

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Bayat

Bettinger

Heidari

et al. 2020

Forests

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“…The results obtained for the averaged models show that RF and EM are the most robust techniques in the case of E. globulus, while EM is most robust for P. pinaster and RF for P. radiata. Both of these methods are well known and frequently used in the field of remote sensing, especially in forestry applications [2,6,66,67].…”

Section: Discussionmentioning

confidence: 99%

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

et al. 2019

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The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8-38.3%, 34.2-41.9% and 31.7-38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.

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“…In order to conduct a quantitative comparison of the prediction performance of the proposed CNN model, this study used RF, which is an ensemble-based machine-learning technique (Jang et al, 2017;Latifi et al, 2018;Lee et al, 2018;Yoo et al, 2018). The RF model was used to solve imagebased classification problems such as building extraction, land-cover classification, freeboard detection, and crop classification (Liu et al, 2018;Guo and Du, 2017;Forkuor et al, 2018;Lee et al, 2016;Park et al, 2018;Sonobe et al, 2017).…”

Section: Prediction Models: Convolutional Neural Networkmentioning

confidence: 99%

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Kim

Han

et al. 2020

The Cryosphere

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Abstract. Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error – MAE – of 2.28 %, anomaly correlation coefficient – ACC – of 0.98, root-mean-square error – RMSE – of 5.76 %, normalized RMSE – nRMSE – of 16.15 %, and NSE – Nash–Sutcliffe efficiency – of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics.

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Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Cited by 41 publications

References 46 publications

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Estimation of Tree Heights in an Uneven-Aged, Mixed Forest in Northern Iran Using Artificial Intelligence and Empirical Models

Integration of National Forest Inventory and Nationwide Airborne Laser Scanning Data to Improve Forest Yield Predictions in North-Western Spain

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

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