Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

Chiarello, Flávio; Steiner, María Teresinha Arns; Oliveira, E. B. de; Arce, Júlio Eduardo; Ferreira, Julio Cesar Batista

doi:10.1590/01047760201925022626

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…The use of ANNs for the estimation of forest variables has been growing, and several studies have been developed in Brazilian forests using this technique as an alternative to OLS. Many of these studies are mainly concerned with estimating variables such as height, volume, the shape of the trunk and tapering, and prognosis of yield and production in forests of Eucalyptus spp, Pinus spp, and Tectona grandis, as listed by [81], in Black Wattle plantations [18], in native forests for prediction of the diametric distribution [82,83] and biomass [28], both in the Amazon Forest and in the Atlantic Forest biome, aimed at estimating surviving individuals and mortality within the forest [84].…”

Section: Discussionmentioning

confidence: 99%

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

et al. 2021

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Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.

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Section: Discussionmentioning

confidence: 99%

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

et al. 2021

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“…This method maintains the precision in volume estimates and is convenient and efficient in obtaining results. It can help reduce inventory costs and time to make estimates available [ 30 , 54 , 55 ]. These results were already expected due to the several advantages shown by the ANNs, such as their massive and parallel-distributed structure (layers), the ability to learn and generalize, which enables them to solve complex problems and the fault and noise tolerance.…”

Section: Discussionmentioning

confidence: 99%

“…These results were already expected due to the several advantages shown by the ANNs, such as their massive and parallel-distributed structure (layers), the ability to learn and generalize, which enables them to solve complex problems and the fault and noise tolerance. In addition, no need to assume an underlying data distribution, as is usually done in statistical modeling; the possibility of modeling several variables and their non-linear relationships; the possibility of modeling using categorical variables, besides quantitative variables; and neurobiological analogy [ 33 – 35 , 55 ].…”

Section: Discussionmentioning

confidence: 99%

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Multi-volume modeling of Eucalyptus trees using regression and artificial neural networks

et al. 2020

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The stem volume of commercial trees is an important variable that assists in decision making and economic analysis in forest management. Wood from forest plantations can be used for several purposes, which makes estimating multi-volumes for the same tree a necessary task. Defining its exploitation and use potential, such as the total and merchantable volumes (up to a minimum diameter of interest), with or without bark, is a possible work. The goal of this study was to use different strategies to model multi-volumes of the stem of eucalyptus trees. The data came from rigorous scaling of 460 felled trees stems from four eucalyptus clones in high forest and coppice regimes. The diameters were measured at different heights, with the volume of the sections obtained by the Smalian method. Data were randomly separated into fit and validation data. The single multi-volume model, volume-specific models, and the training of artificial neural networks (ANNs) were fitted. The evaluation criteria of the models were: coefficient of determination, root mean square error, mean absolute error, mean bias error, as well as graphical analysis of observed and estimated values and distribution of residuals. Additionally, the t-test (α = 0.05) was performed between the volume obtained in the rigorous scaling and estimated by each strategy with the validation data. Results showed that the strategies used to model different tree stem volumes are efficient. The actual and estimated volumes showed no differences. The multi-volume model had the most considerable advantage in volume estimation practicality, while the volumespecific models were more efficient in the accuracy of estimates. Given the conditions of this study, the ANNs are more suitable than the regression models in the estimation of multivolumes of eucalyptus trees, revealing greater accuracy and practicality.

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“…The present study used a Multi-Layer Perceptron architecture with 21 neurons in the input layer, 2 intermediate hidden layers and 1 neuron in the output layer (Figure 2). This was the only ANN architecture used because, according to Chiarello et al (2019), in forest biometrics and modeling state of the art, regarding the use of artificial neural networks, 78% of works used Multi-layer Perceptron architecture when the second most used was Radial Basis Function network with only 12% of adoption.…”

Section: Methodsmentioning

confidence: 99%

Artificial neural networks and remote sensing for volumetric prediction in a Eucalyptus sp. plantation

Almeida

Thiersch

Bernardi

et al. 2021

RSD

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Forest inventory is an important tool for estimating the production of forest stands and normally employs traditional methods for volume estimation. However, as a result of technological advancements, artificial neural networks and remote sensing have assumed a prominent role in the forestry sector since satellite images have different components that correlate with the dendrometric variables and can be used as auxiliary variables. The objective of this work was to evaluate the performance of artificial neural networks regarding the estimation of volume in a Eucalyptus sp. plantation with the use of satellite images. Pre-cut inventory data were used with ages varying between 5.3 and 6.3 years. The variables used were volume, age, 4 bands of the satellite image with a 10 m spatial resolution from Sentinell-2 satellite, ratio between the bands, NDVI, and genetic material. All processing was performed using the free software R. The evaluation criteria for the neural network were percentage of residual standard error and graphical analysis of the residues. The best neural network configuration for volume estimation presented a residual standard error of 10.63% and 12.00% for training and validation, respectively. The methodology proposed in this work proved to be efficient in estimating the volume of the stand.

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Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

Cited by 10 publications

References 29 publications

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Multi-volume modeling of Eucalyptus trees using regression and artificial neural networks

Artificial neural networks and remote sensing for volumetric prediction in a Eucalyptus sp. plantation

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