A Novel Strategy for Constructing Large-Scale Forest Scene: Integrating Forest Hierarchical Models and Tree Growth Models to Improve the Efficiency and Stability of Forest Polymorphism Simulation

Lei, Kexin; Zhang, Huaiqing; Qiu, Hanqing; Yang, Tingdong; Liu, Yang; Zhang, Jing; Hu, Xingtao; Cui, Zeyu

doi:10.3390/f14081595

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The study area, the Subtropical Forestry Experimental Center, Chinese Academy of Forestry, is located in the central Jiangxi Province in Southwestern China (Figure 1). The geographical coordinates are 114 [35,36]. This area has a typical south subtropical humid continental monsoon climate.…”

Section: Study Areamentioning

confidence: 99%

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Chen,

Sun,

Zhang

et al. 2023

Remote Sensing

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Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1’s VV (vertical transmit–vertical receive) and VH (vertical transmit–horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys.

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Section: Study Areamentioning

confidence: 99%

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Chen,

Sun,

Zhang

et al. 2023

Remote Sensing

Self Cite

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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network

Wang,

Zhang,

Lei

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Current visual methods of forest dynamic growth mostly focus on the plot or stand level, which cannot express the morphological and structural characteristics of individual trees, as well as their statistical linkages, and causes each tree in the stand growing at the same rate. Additionally, these visual growth models still have some space for improvement in terms of prediction accuracy and multi-relational data mining. In our study, uneven-aged Chinese fir (Cunninghamia lanceolata) plantations were chosen as our study subject and proposed a novel method of forest dynamic growth visualization modeling by incorporating spatial structure parameters and using convolutional neural network technique (FDGVM-CNN-SSP) to explore the effect of spatial structure on the morphological growth and to develop a prediction growth model of Chinese fir plantations by introducing a convolutional neural network (CNN) model. The results show that, (1) Spatial structural parameters C and U have a certain contribution to the forest growth, which C and U can explained 21.5%, 15.2%, and 9.3% of the variance in DBH, H, and CW growth models, respectively. (2) CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance. (3) Based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018-2022 and found that DBH and H' fitting performance in measured and predicted data was highly consistent with R 2 and RMSE of 86.8%, 2.06cm in DBH and 79.2%, 1.11m in H, but CW's R 2 and RMSE of 72.2%, 0.65m caused crowding (C) inconsistency.

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A Novel Strategy for Constructing Large-Scale Forest Scene: Integrating Forest Hierarchical Models and Tree Growth Models to Improve the Efficiency and Stability of Forest Polymorphism Simulation

Cited by 2 publications

References 33 publications

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network

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