Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango, Mexico

Padilla-Martínez, Jaime Roberto; Paul, Carola; Husmann, Kai; Corral-Rivas, José Javier; von Gadow, Klaus

doi:10.1016/j.fecs.2023.100158

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…Donoso and Soto (2016) [36] confirmed that the improved quality of a site can result in increased SBA. In contrast, Padilla-Martínez et al (2024) [77] claimed that the basal area of some tree species was negatively correlated with site quality. These contrasting results can possibly be a ributed to the structure of the canopy and the efficiency of photosynthesis [16].…”

Section: Factors Affecting the Sba Modelmentioning

confidence: 93%

“…In addition, some previous studies [90,93] have asserted that sample plot size may affect the prediction accuracy and applicability of mixed forest SBA models. He et al (2021) [50] and Padilla-Martínez et al (2024) [77] constructed SBA models for natural oak forests and Mexican temperate multi-tree forests using sample plots of 0.06 ha and 0.25 ha, respectively. The present study constructed a high-precision SBA prediction model for broadleaved mixed forests using a sample plot area of 0.09 ha (30 m × 30 m), consistent with the sample plot area used by Monserud and Sterba.…”

Section: Parameter Estimation Methods Affect the Accuracy Of Sbamentioning

confidence: 99%

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Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests

Zeng,

Wang,

Zhang

et al. 2024

Plants

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Stand basal area (SBA) is an important variable in the prediction of forest growth and harvest yield. However, achieving the additivity of SBA models for multiple tree species in the complex structure of broad-leaved mixed forests is an urgent scientific issue in the study of accurately predicting the SBA of mixed forests. This study used data from 58 sample plots (30 m × 30 m) for Populus davidiana × Betula platyphylla broad-leaved mixed forests to construct the SBA basic model based on nonlinear least squares regression (NLS). Adjustment in proportion (AP) and nonlinear seemingly unrelated regression (NSUR) were used to construct a multi-species additive basal area prediction model. The results identified the Richards model (M6) and Korf model (M1) as optimal for predicting the SBA of P. davidiana and B. platyphylla, respectively. The SBA models incorporate site quality, stand density index, and age at 1.3 m above ground level, which improves the prediction accuracy of basal area. Compared to AP, NSUR is an effective method for addressing the additivity of basal area in multi-species mixed forests. The results of this study can provide a scientific basis for optimizing stand structure and accurately predicting SBA in multi-species mixed forests.

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Section: Factors Affecting the Sba Modelmentioning

confidence: 93%

Section: Parameter Estimation Methods Affect the Accuracy Of Sbamentioning

confidence: 99%

Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests

Zeng,

Wang,

Zhang

et al. 2024

Plants

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“…where W v is the weight matrix and b v is the bias. Finally, the model input of this study is the normalized data after Pearson correlation analysis and one-dimensional CNN preprocessing, which is input into the LSTM-Attention model [38]. The attention mechanism is introduced into the hidden layer to obtain the weighted average weight coefficient of the hidden layer output, and then the weight coefficient is multiplied by the output of the LSTM hidden layer to sum, and the result is input into the output layer of the LSTM for a full connection calculation.…”

Section: Remotementioning

confidence: 99%

Integrating Remote Sensing Data and CNN-LSTM-Attention Techniques for Improved Forest Stock Volume Estimation: A Comprehensive Analysis of Baishanzu Forest Park, China

Wang,

Chen,

Yan

et al. 2024

Remote Sensing

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Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China’s Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R2 = 0.8463, rMSE = 26.73 m3/ha, MAE = 16.47 m3/ha).

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Primary forest characteristics estimation through remote sensing data and machine learning: Sakhalin case study

Illarionova,

Smolina,

Shadrin

2024

E3S Web Conf.

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Currently, remote sensing techniques assist in various environmental applications and facilitate observation and spatial analysis. Machine learning algorithms allow researchers to find dependencies in satellite data and vegetation cover properties. One of the significant tasks for ecological assessment is associated with estimating forest characteristics and monitoring changes over time. In contrast to the general computer vision domain, remote sensing data and forestry measurements have their own specific requirements and necessitate tailored approaches that involve processing multispectral satellite data, creating feature spaces, and selecting training samples. In this study, we focus on extracting primary forest characteristics, including forest species groups, height, basal area, and timber stock. We utilise Sentinel-2 multispectral data to develop a machine learning-based solution for vast and remote territories. Timber stock is calculated using empirical formulas based on measurements of forest species groups, height, and basal area. These intermediate forest parameters are estimated using individually trained machine learning algorithms for each parameter. As a case study, we examine the Sakhalin region (Russia), which encompasses several forestries with varying vegetation properties. In Nevelskoye forestry, we achieved a mean absolute error (MAE) of 1.6m for height, 0.084 for basal area, and 47.8 m3/ha for timber stock. The results obtained demonstrate promise for further integrating artificial intelligencebased solutions into forestry decision-making processes and natural resources management.

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Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango, Mexico

Cited by 3 publications

References 53 publications

Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests

Developing the Additive Systems of Stand Basal Area Model for Broad-Leaved Mixed Forests

Integrating Remote Sensing Data and CNN-LSTM-Attention Techniques for Improved Forest Stock Volume Estimation: A Comprehensive Analysis of Baishanzu Forest Park, China

Primary forest characteristics estimation through remote sensing data and machine learning: Sakhalin case study

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