Prognosis of Forest Production Using Machine Learning Techniques

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (1 June 2024) | Viewed by 5109

Special Issue Editor


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Smart Forest Research Group, Polytechnical School of Mieres, University of Oviedo, 33600 Mieres, Spain
Interests: forest management; reforestation; tree growth; forest ecology; silviculture; tree plantation; restoration; forestry; biomass production; short rotation forestry
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Special Issue Information

Dear Colleagues,

The prediction of stand structure, biomass, and carbon storage during tree growth is key to further understanding the forest's capacity for climate change mitigation. Moreover, predicting forest production, based on remote sensing data and field data, has progressed substantially in recent years through the application of different machine learning (e.g., vector regression, random forest, artificial neural networks). To strengthen forest management for climate change, this Special Issue on “Prognosis of Forest Production Using Machine Learning Techniques” mainly focuses on new methods and technologies for predicting production in forest ecosystems.

This Special Issue welcomes submissions from authors engaged in research on the forest growth model.

Potential topics include but are not limited to:

  • Machine learning and forest growth;
  • Forest growth model;
  • Forest production and forest model;
  • Forests model and climate change;
  • Model prediction and forest growth.

Dr. Asunción Cámara-Obregón
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • forest production
  • carbon storage
  • remote sensing
  • model prediction

Published Papers (3 papers)

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Research

25 pages, 8735 KiB  
Article
Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms
by Fengyun Cheng, Guanglong Ou, Meng Wang and Chang Liu
Forests 2024, 15(4), 681; https://doi.org/10.3390/f15040681 - 10 Apr 2024
Cited by 1 | Viewed by 1517
Abstract
Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and [...] Read more.
Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization. Full article
(This article belongs to the Special Issue Prognosis of Forest Production Using Machine Learning Techniques)
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30 pages, 4613 KiB  
Article
Estimating Forest Variables for Major Commercial Timber Plantations in Northern Spain Using Sentinel-2 and Ancillary Data
by Alís Novo-Fernández, Carlos A. López-Sánchez, Asunción Cámara-Obregón, Marcos Barrio-Anta and Iyán Teijido-Murias
Forests 2024, 15(1), 99; https://doi.org/10.3390/f15010099 - 4 Jan 2024
Viewed by 1568
Abstract
In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, [...] Read more.
In this study, we used Spanish National Forest Inventory (SNFI) data, Sentinel-2 imagery and ancillary data to develop models that estimate forest variables for major commercial timber plantations in northern Spain. We carried out the analysis in two stages. In the first stage, we considered plots with and without sub-meter geolocation, three pre-processing levels for the Sentinel-2 images and two machine learning algorithms. In most cases, geometrically, radiometrically, atmospherically and topographically (L2A-ATC) corrected images and the random forest algorithm provided the best results, with topographic correction producing a greater gain in model accuracy as the average slope of the plots increased. Our results did not show any clear impact of the geolocation accuracy of SNFI plots on results, suggesting that the usual geolocation accuracy of SNFI plots is adequate for developing forest models with data obtained from passive sensors. In the second stage, we used all plots together with L2A-ATC-corrected images to select five different groups of predictor variables in a cumulative process to determine the influence of each group of variables in the final RF model predictions. Yield variables produced the best fits, with R2 ranging from 0.39 to 0.46 (RMSE% ranged from 44.6% to 61.9%). Although the Sentinel-2-based estimates obtained in this research are less precise than those previously obtained with Airborne Laser Scanning (ALS) data for the same species and region, they are unbiased (Bias% was always below 1%). Therefore, accurate estimates for one hectare are expected, as they are obtained by averaging the values of 100 pixels (model resolution of 10 m pixel−1) with an expected error compensation. Moreover, the use of these models will overcome the temporal resolution problem associated with the previous ALS-based models and will enable annual updates of forest timber resource estimates to be obtained. Full article
(This article belongs to the Special Issue Prognosis of Forest Production Using Machine Learning Techniques)
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19 pages, 21855 KiB  
Article
YOLOv5-ACS: Improved Model for Apple Detection and Positioning in Apple Forests in Complex Scenes
by Jianping Liu, Chenyang Wang and Jialu Xing
Forests 2023, 14(12), 2304; https://doi.org/10.3390/f14122304 - 24 Nov 2023
Cited by 2 | Viewed by 1281
Abstract
Apple orchards, as an important center of economic activity in forestry special crops, can achieve yield prediction and automated harvesting by detecting and locating apples. Small apples, occlusion, dim lighting at night, blurriness, cluttered backgrounds, and other complex scenes significantly affect the automatic [...] Read more.
Apple orchards, as an important center of economic activity in forestry special crops, can achieve yield prediction and automated harvesting by detecting and locating apples. Small apples, occlusion, dim lighting at night, blurriness, cluttered backgrounds, and other complex scenes significantly affect the automatic harvesting and yield estimation of apples. To address these issues, this study proposes an apple detection algorithm, “YOLOv5-ACS (Apple in Complex Scenes)”, based on YOLOv5s. Firstly, the space-to-depth-conv module is introduced to avoid information loss, and a squeeze-and-excitation block is added in C3 to learn more important information. Secondly, the context augmentation module is incorporated to enrich the context information of the feature pyramid network. By combining the shallow features of the backbone P2, the low-level features of the object are retained. Finally, the addition of the context aggregation block and CoordConv aggregates the spatial context pixel by pixel, perceives the spatial information of the feature map, and enhances the semantic information and global perceptual ability of the object. We conducted comparative tests in various complex scenarios and validated the robustness of YOLOv5-ACS. The method achieved 98.3% and 74.3% for [email protected] and [email protected]:0.95, respectively, demonstrating excellent detection capabilities. This paper creates a complex scene dataset of apples on trees and designs an improved model, which can provide accurate recognition and positioning for automatic harvesting robots to improve production efficiency. Full article
(This article belongs to the Special Issue Prognosis of Forest Production Using Machine Learning Techniques)
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