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Forests
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Applications of Laser Scanning and Satellite Images in Forest Mensuration—Series...
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Applications of Laser Scanning and Satellite Images in Forest Mensuration—Series II

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: 31 October 2024 | Viewed by 4088

Special Issue Editors

Prof. Dr. Chinsu Lin

E-Mail Website
Guest Editor
Department of Forestry and Natural Resources, National Chiayi University, Chiayi 600355, Taiwan
Interests: forest management; forest ecology; forest conservation; biodiversity; remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenzhi Liao

E-Mail Website
Guest Editor
Department of Telecommunications and Information Processing, Ghent University, 9000 Ghent, Belgium
Interests: image processing; pattern recognition; remote sensing; multimodal data fusion (fusion of hyperspectral and LiDAR data for image interpretation in remote sensing)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest mensuration is the key to gathering data and information on forest resources for forest planning and adaptive management. Fully developed forest mensuration schemes and technologies help us to formulate appropriate forest rules and regulations for sustainable forest management and support forest product needs. Taking advantage of state-of-the-art remote sensing technologies, forest information including tree-level parameters, stand-level attributes and structures, and ecosystem services can be measured or retrieved through UAV, airborne, and spaceborne platforms with high-resolution optical images and lidar data. Reliable data collection and analysis enable forest societies to conduct integrity procedures involving forest measurement, reporting, and validation (the MRV processes) with global consistency. This Special Issue intends to highlight the significance of applying lidar scanning and spectral sensing data to gather accurate forest information on MRV processes in plantation forests, secondary forests, and pristine forests. Techniques for retrieving tree parameters, stand attributes, and the structure of forest ecosystems for tropical, temperate, and boreal ecoregions are encouraged. Research on the application of optical sensing data (including RGB, multispectral, and hyperspectral images) and lidar sensing data (including UAV, airborne, and spaceborne data) at variant forest scales are most welcome.

Potential topics include, but are not limited to:

  • UAV/Airborne/Spaceborne technology for forest mensuration;
  • Data processing;
  • Tree parametrization;
  • Stand attributes’ estimation;
  • Species and forest type mapping;
  • Stand dynamics;
  • Forest degradation diagnosing;
  • Plantation precision management;
  • Secondary forest management;
  • Ecosystem productivity;
  • Adaptive management of forest ecosystems.

Prof. Dr. Chinsu Lin
Prof. Dr. Wenzhi Liao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

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

  • remote sensing
  • information extraction
  • forest mapping
  • forest evaluation
  • stand structure
  • forest monitoring
  • MRV processes
  • forest sustainability
  • climate change

Published Papers (4 papers)

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Research

15 pages, 13689 KiB  
Article
Estimating the Aboveground Biomass of Robinia pseudoacacia Based on UAV LiDAR Data
by Jiaqi Cheng, Xuexia Zhang, Jianjun Zhang, Yanni Zhang, Yawei Hu, Jiongchang Zhao and Yang Li
Forests 2024, 15(3), 548; https://doi.org/10.3390/f15030548 - 17 Mar 2024
Viewed by 819
Abstract
Robinia pseudoacacia is widely planted in the Loess Plateau as a major soil and water conservation tree species because of its dense canopy, complex structure, and strong soil and water conservation ability. The precise measurement of small-scale locust forest biomass is crucial to [...] Read more.
Robinia pseudoacacia is widely planted in the Loess Plateau as a major soil and water conservation tree species because of its dense canopy, complex structure, and strong soil and water conservation ability. The precise measurement of small-scale locust forest biomass is crucial to monitoring and evaluating the carbon sequestration functions of soil and water conservation vegetation. This study focuses on an artificial locust forest planted in the early 1990s in Caijiachuan Basin, Jixian County, Shanxi Province. A drone equipped with LiDAR was used to obtain point cloud data and generate a canopy height model. A watershed segmentation algorithm was used to identify tree vertices and extract individual trees. A relationship model between tree height, diameter at breast height, and biomass, combined with sample survey data, was established to explore the spatial distribution of biomass in the artificial locust forest at the level of the entire basin. The results show the following: (1) the structural parameters of locust extracted using UAV point cloud data have a good degree of fit and accuracy, and the recall rate is 72.7%; (2) the average error rate of the extracted maximum tree height value of locust is 7%, that of the minimum tree height value is 14%, and that of the average tree height value is 18%; (3) the average error rate of the extracted maximum diameter at breast height of locust is 15%, that of the minimum diameter at breast height is 37%, and that of the average diameter at breast height is 36%; and (4) the average error rate of the biomass estimation of locust calculated using point cloud data is 16.0%. Full article
(This article belongs to the Special Issue Applications of Laser Scanning and Satellite Images in Forest Mensuration—Series II)
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33 pages, 12234 KiB  
Article
Generating Wall-to-Wall Canopy Height Information from Discrete Data Provided by Spaceborne LiDAR System
by Nova D. Doyog and Chinsu Lin
Forests 2024, 15(3), 482; https://doi.org/10.3390/f15030482 - 05 Mar 2024
Viewed by 800
Abstract
Provision of multi-temporal wall-to-wall canopy height information is one of the initiatives to combat deforestation and is necessary in strategizing forest conversion and reforestation initiatives. This study generated wall-to-wall canopy height information of the subtropical forest of Lishan, Taiwan, using discrete data provided [...] Read more.
Provision of multi-temporal wall-to-wall canopy height information is one of the initiatives to combat deforestation and is necessary in strategizing forest conversion and reforestation initiatives. This study generated wall-to-wall canopy height information of the subtropical forest of Lishan, Taiwan, using discrete data provided by spaceborne LiDARs, wall-to-wall passive and active remote sensing imageries, topographic data, and machine learning (ML) regression models such as gradient boosting (GB), k-nearest neighbor (k-NN), and random forest (RF). ICESat-2- and GEDI-based canopy height data were used as training data, and medium-resolution passive satellite image (Sentinel-2) data, active remote sensing data such as synthetic aperture radar (SAR), and topographic data were used as regressors. The ALS-based canopy height was used to validate the models’ performance using root mean square error (RMSE) and percentage RMSE (PRMSE) as validation criteria. Notably, GB displayed the highest accuracy among the regression models, followed by k-NN and then RF. Using the GEDI-based canopy height as training data, the GB model can achieve optimum accuracy with an RMSE/PRMSE of 8.00 m/31.59%, k-NN can achieve an RMSE/PRMSE of as low as 8.05 m/31.78%, and RF can achieve optimum RMSE/PRMSE of 8.16 m/32.24%. If using ICESat-2 data, GB can have an optimum RMSE/PRMSE of 13.89 m/54.86%; k-NN can have an optimum RMSE/PRMSE of 14.32 m/56.56%, while RF can achieve an RMSE/PRMSE of 14.72 m/58.14%. Additionally, integrating Sentinel-1 with Sentinel-2 data improves the accuracy of canopy height modeling. Finally, the study underlined the crucial relevance of correct canopy height estimation for sustainable forest management, as well as the potential ramifications of poor-quality projections on a variety of biological and environmental factors. Full article
(This article belongs to the Special Issue Applications of Laser Scanning and Satellite Images in Forest Mensuration—Series II)
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19 pages, 7020 KiB  
Article
Classification of Tree Species Based on Point Cloud Projection Images with Depth Information
by Zhongmou Fan, Wenxuan Zhang, Ruiyang Zhang, Jinhuang Wei, Zhanyong Wang and Yunkai Ruan
Forests 2023, 14(10), 2014; https://doi.org/10.3390/f14102014 - 07 Oct 2023
Viewed by 856
Abstract
To address the disorderliness issue of point cloud data when directly used for tree species classification, this study transformed point cloud data into projected images for classification. Building upon this foundation, the influence of incorporating multiple distinct projection perspectives, integrating depth information, and [...] Read more.
To address the disorderliness issue of point cloud data when directly used for tree species classification, this study transformed point cloud data into projected images for classification. Building upon this foundation, the influence of incorporating multiple distinct projection perspectives, integrating depth information, and utilising various classification models on the classification of tree point cloud projected images was investigated. Nine tree species in Sanjiangkou Ecological Park, Fuzhou City, were selected as samples. In the single-direction projection classification, the X-direction projection exhibited the highest average accuracy of 80.56%. In the dual-direction projection classification, the XY-direction projection exhibited the highest accuracy of 84.76%, which increased to 87.14% after adding depth information. Four classification models (convolutional neural network, CNN; visual geometry group, VGG; ResNet; and densely connected convolutional networks, DenseNet) were used to classify the datasets, with average accuracies of 73.53%, 85.83%, 87%, and 86.79%, respectively. Utilising datasets with depth and multidirectional information can enhance the accuracy and robustness of image classification. Among the models, the CNN served as a baseline model, VGG accuracy was 12.3% higher than that of CNN, DenseNet had a smaller gap between the average accuracy and the optimal result, and ResNet performed the best in classification tasks. Full article
(This article belongs to the Special Issue Applications of Laser Scanning and Satellite Images in Forest Mensuration—Series II)
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13 pages, 7856 KiB  
Article
Mapping the Spatial Distribution of Aboveground Biomass in China’s Subtropical Forests Based on UAV LiDAR Data
by Ganxing Wang, Shun Li, Chao Huang, Guowei He, Yang Li, Jiayuan Feng, Fangran Tang, Pengbin Yan and Lihong Qiu
Forests 2023, 14(8), 1560; https://doi.org/10.3390/f14081560 - 31 Jul 2023
Cited by 1 | Viewed by 1221
Abstract
Accurately estimating aboveground biomass (AGB) is crucial for assessing carbon storage in forest ecosystems. However, traditional field survey methods are time-consuming, and vegetation indices based on optical remote sensing are prone to saturation effects, potentially underestimating AGB in subtropical forests. To overcome these [...] Read more.
Accurately estimating aboveground biomass (AGB) is crucial for assessing carbon storage in forest ecosystems. However, traditional field survey methods are time-consuming, and vegetation indices based on optical remote sensing are prone to saturation effects, potentially underestimating AGB in subtropical forests. To overcome these limitations, we propose an improved approach that combines three-dimensional (3D) forest structure data collected using unmanned aerial vehicle light detection and ranging (UAV LiDAR) technology with ground measurements to apply a binary allometric growth equation for estimating and mapping the spatial distribution of AGB in subtropical forests of China. Additionally, we analyze the influence of terrain factors such as elevation and slope on the distribution of forest biomass. Our results demonstrate a high accuracy in estimating tree height and diameter at breast height (DBH) using LiDAR data, with an R2 of 0.89 for tree height and 0.92 for DBH. In the study area, AGB ranges from 0.22 to 755.19 t/ha, with an average of 121.28 t/ha. High AGB values are mainly distributed in the western and central-southern parts of the study area, while low AGB values are concentrated in the northern and northeastern regions. Furthermore, we observe that AGB in the study area exhibits an increasing trend with altitude, reaching its peak at approximately 1650 m, followed by a gradual decline with further increase in altitude. Forest AGB gradually increases with slope, reaching its peak near 30°. However, AGB decreases within the 30–80° range as the slope increases. This study confirms the effectiveness of using UAV LiDAR for estimating and mapping the spatial distribution of AGB in complex terrains. This method can be widely applied in productivity, carbon sequestration, and biodiversity studies of subtropical forests. Full article
(This article belongs to the Special Issue Applications of Laser Scanning and Satellite Images in Forest Mensuration—Series II)
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