Computational techniques applied to volume and biomass estimation of trees in Brazilian savanna

Silva, Jéferson Pereira Martins; Silva, Mayra Luiza Marques da; Silva, Evandro Ferreira da; Silva, Gilson Fernandes da; Mendonça, Adriano Ribeiro de; Cabacinha, Christian Dias; Araújo, Emanuel França; Santos, Jeangelis Silva; Vieira, Giovanni Correia; Almeida, Maria Naruna Félix de; Fernandes, Márcia Rodrigues de Moura

doi:10.1016/j.jenvman.2019.109368

Cited by 19 publications

(7 citation statements)

References 50 publications

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“…In this study, it was observed that ANN models, based on criteria for evaluating models such as RMSE and coefficient of determination, were more accurate than regression models and showed better relationships between productivity and biodiversity in the studied forest. As the results showed, they are in line with the findings of other research [32,66,67]. The ANN of the multilayer perceptron (MLP) type had good ability in prediction and estimation of productivity in our forests.…”

Section: Discussionsupporting

confidence: 91%

A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

Bayat

Bettinger

Heidari

et al. 2021

Forests

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The relative importance of different biotic and abiotic variables for estimating forest productivity remains unclear for many forest ecosystems around the world, and it is hypothesized that forest productivity could also be estimated by local biodiversity factors. Using a large dataset from 258 forest monitoring permanent sample plots distributed across uneven-aged and mixed forests in northern Iran, we tested the relationship between tree species diversity and forest productivity and examined whether several factors (solar radiation, topographic wetness index, wind velocity, seasonal air temperature, basal area, tree density, basal area in largest trees) had an effect on productivity. In our study, productivity was defined as the mean annual increment of the stem volume of a forest stand in m3 ha−1 year−1. Plot estimates of tree volume growth were based on averaged plot measurements of volume increment over a 9-year growing period. We investigated relationships between productivity and tree species diversity using parametric models and two artificial neural network models, namely the multilayer perceptron (MLP) and radial basis function networks. The artificial neural network (ANN) of the MLP type had good ability in prediction and estimation of productivity in our forests. With respect to species richness, Model 4, which had 10 inputs, 6 hidden layers and 1 output, had the highest R2 (0.94) and the lowest RMSE (0.75) and was selected as the best species richness predictor model. With respect to forest productivity, MLP Model 2 with 10 inputs, 12 hidden layers and 1 output had R2 and RMSE of 0.34 and 0.42, respectively, representing the best model. Both of these used a logistic function. According to a sensitivity analysis, diversity had significant and positive effects on productivity in species-rich broadleaved forests (approximately 31%), and the effects of biotic and abiotic factors were also important (29% and 40%, respectively). The artificial neural network based on the MLP was found to be superior for modeling productivity–diversity relationships.

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Section: Discussionsupporting

confidence: 91%

A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

Bayat

Bettinger

Heidari

et al. 2021

Forests

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“…The machine-learning techniques presented, and mixed-effect models, showed similar and highly accurate results (R 2 = from 0.97 to 0.99 and RMSE = from 12% to 13%) [58]. According to Silva et al [90], because the modelling of forest resources commonly presents complex relationships among the variables, nonlinear mixed-effects modelling (NLME) and machine-learning techniques such as RF, adaptive network-based fuzzy inference systems (ANFIS), and artificial neural networks (ANNs) may be good alternative modelling techniques. In our study, using RF, we produced an AGB map of an area in the Brazilian Cerrado with a R 2 of 0.89 and RMSE of 7.58 Mg ha −1 .…”

Section: Discussionmentioning

confidence: 99%

“…It is reported that machine-learning techniques can provide more accurate estimates than classical regression models [88]. Silva et al [90] evaluated the use of machine-learning techniques and mixed models to estimate the volume and AGB of individual trees in the Brazilian Cerrado. The machine-learning techniques presented, and mixed-effect models, showed similar and highly accurate results (R 2 = from 0.97 to 0.99 and RMSE = from 12% to 13%) [58].…”

Section: Discussionmentioning

confidence: 99%

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

et al. 2020

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The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.

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“…Saat ini belum tersedia alat ukur yang praktis, murah dan mudah yang bisa dipergunakan oleh petani skala kecil. Alat bantu yang tersedia masih berbentuk alometri dan dendrometer elektronik yang mahal harganya (Fan et al, 2020;Pereira et al, 2019).…”

Section: Pendahuluanunclassified

StandingTrees Volume Tape of Teak to Support The Management of Private Forest Budiman Achmad, Dian Diniyati

Achmad

Diniyati

2020

Jur.Agf.Ind

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Belum tersedianya alat penduga volume pohon berdiri menyebabkan pengelolaaan hutan rakyat cenderung merugikan pihak petani. Penelitian ini bertujuan untuk menyediakan alat bantu praktis penduga volume pohon jati pada transaksi kayu secara berkeadilan. Penelitian ini menggunakan sampel berupa tegakan jati unggul nusantara (JUN) di Desa Lemahbang, Kecamatan Bendo, Kabupaten Magetan Jawa Timur sebanyak 74 pohon yang mewakili kelas diameter terkecil hingga terbesar. Pengumpulan data menggunakan metode destruktif dengan menebang dan memotong batang pohon menjadi sortimen paling panjang 2 m. Volume total pohon diperoleh dengan menjumlahkan seluruh sortimen hingga diameter terkecil 7 cm. Data dianalisis menggunakan regresi linier V = a + b*DBH dan non-linear V = a*DBHb. Hasil penelitian menunjukkan bahwa pita volume pohon jati lebih baik dibangun menggunakan model non-linear V = 0,0000776 DBH2,613 dengan r = 0,94. Pita volume pohon jati ini bisa meminimalisir perbedaan penaksiran volume pohon antara petani dan tengkulak. Selain itu, pita volume ini juga bisa menekan kerusakan tanaman lainnya, meminimalisir kehilangan biodiversitas tanaman, dan menjaga mutu lingkungan karena penjualan pohon akan cenderung secara selektif, bukan tebang habis. Hal yang juga penting adalah perlunya dukungan pemerintah dalam menyediakan aturan penerapatannya untuk alat transaksi perdagangan di lapangan. Kata kunci : Berkeadilan, berkelanjutan, hutan jati rakyat pita volume

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Computational techniques applied to volume and biomass estimation of trees in Brazilian savanna

Cited by 19 publications

References 50 publications

A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

StandingTrees Volume Tape of Teak to Support The Management of Private Forest Budiman Achmad, Dian Diniyati

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