Application of Laser Scanning and Satellite Image in Forest Mensuration

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 (28 February 2023) | Viewed by 10758

Special Issue Editors

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
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 the needs of forest products. Taking advantage of state-of-the-art remote sensing technologies, forest information regarding 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

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Keywords

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

Published Papers (7 papers)

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Research

24 pages, 32546 KiB  
Article
An Individual Tree Segmentation Method That Combines LiDAR Data and Spectral Imagery
Forests 2023, 14(5), 1009; https://doi.org/10.3390/f14051009 - 13 May 2023
Cited by 1 | Viewed by 1570
Abstract
The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity [...] Read more.
The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications. Full article
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13 pages, 2861 KiB  
Article
Site Index Estimation Using Airborne Laser Scanner Data in Eucalyptus dunnii Maide Stands in Uruguay
Forests 2023, 14(5), 933; https://doi.org/10.3390/f14050933 - 01 May 2023
Viewed by 1277
Abstract
Intensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps [...] Read more.
Intensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps of the site index (SI) using ALS data in the intensive silviculture of Eucalyptus dunnii Maide plantations in Uruguay. Forty-three plots (314.16 m3) were established in intensive E. dunnii plantations in the departments of Río Negro and Paysandú (Uruguay). ALS data were obtained for an area of 1995 ha. Linear and Random Forest models were fitted to estimate the height and site index, and OrpheoToolBox (OTB) software was used for stand segmentation. Linear models for dominant height (DH) estimation had a better fit (R2 = 0.84, RMSE = 0.94 m, MAPE = 0.04, Bias = 0.002) than the Random Forest (R2 = 0.85, RMSE = 1.27 m, MAPE = 7.20, Bias=−0.173) model when including only the 99th percentile metric. The coefficient between RMSE values of the cross-validation and RMSE of the model had a higher value for the linear model (0.93) than the Random Forest (0.75). The SI was estimated by applying the RF model, which included the ALS metrics corresponding to the 99th height percentile and the 80th height bicentile (R2 = 0.65; RMSE = 1.62 m). OTB segmentation made it possible to define a minimum segment size of 2.03 ha (spatial radius = 30, range radius = 1 and minimum region size = 64). This study provides a new tool for better forest management and promotes the need for further progress in the application of ALS data in the intensive silviculture of Eucalyptus spp. plantations in Uruguay. Full article
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18 pages, 45371 KiB  
Article
Challenges of Retrieving LULC Information in Rural-Forest Mosaic Landscapes Using Random Forest Technique
Forests 2023, 14(4), 816; https://doi.org/10.3390/f14040816 - 15 Apr 2023
Cited by 3 | Viewed by 1289
Abstract
Land use and land cover (LULC) information plays a crucial role in determining the trend of the global carbon cycle in various fields, such as urban land planning, agriculture, rural management, and sustainable development, and serves as an up-to-date indicator of forest changes. [...] Read more.
Land use and land cover (LULC) information plays a crucial role in determining the trend of the global carbon cycle in various fields, such as urban land planning, agriculture, rural management, and sustainable development, and serves as an up-to-date indicator of forest changes. Accurate and reliable LULC information is needed to address the detailed changes in conservation-based and development-based classes. This study integrates Sentinel-2 multispectral surface reflectance and vegetation indices, and lidar-based canopy height and slope to generate a random forest model for 3-level LULC classification. The challenges for LULC classification by RF approach are discussed by comparing it with the SVM model. To summarize, the RF model achieved an overall accuracy (OA) of 0.79 and a macro F1-score of 0.72 for the Level-III classification. In contrast, the SVM model outperformed the RF model by 0.04 and 0.09 in OA and macro F1-score, respectively. The accuracy difference increased to 0.89 vs. 0.96 for OA and 0.79 vs. 0.91 for macro F1-score for the Level-I classification. The mapping reliability of the RF model for different classes with nearly identical features was challenging with regard to precision and recall measures which are both inconsistent in the RF model. Therefore, further research is needed to close the knowledge gap associated with reliable and high thematic LULC mapping using the RF classifier. Full article
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15 pages, 2306 KiB  
Article
Effects of Plot Size on Airborne LiDAR-Derived Metrics and Predicted Model Performances of Subtropical Planted Forest Attributes
Forests 2022, 13(12), 2124; https://doi.org/10.3390/f13122124 - 11 Dec 2022
Viewed by 1006
Abstract
Investigating the impact of field plot size on the performance of estimation models for forest inventory attributes could help optimize the technical schemes for an operational airborne LiDAR-assisted forest resource inventory. However, few studies on the topic have focused on subtropical forests. In [...] Read more.
Investigating the impact of field plot size on the performance of estimation models for forest inventory attributes could help optimize the technical schemes for an operational airborne LiDAR-assisted forest resource inventory. However, few studies on the topic have focused on subtropical forests. In this study, 104 rectangular plots of 900 m2 (subdivided into nine quadrats with an area of 10 × 10 m) in subtropical planted forests (Chinese fir, pine, eucalyptus, and broad-leaved forest, 2–56 years old) were used to establish four datasets with six different plot sizes (100, 200, 300, 400, 600, and 900 m2) by combining quadrats. The differences in the LiDAR-derived metrics and forest attributes between plots of different sizes were statistically analyzed. Based on the multivariate power models with stable structures, the differences in estimation accuracies of the stand volume (VOL) and basal area (BA) using plot data of different sizes were compared. The results indicated that: (1) the mean differences in LiDAR-derived metrics of the plots of different sizes in all forest types were small, and most of them had no statistically significant differences (α = 0.05) between the plots of different sizes and the 900 m2 plots; however, the standard deviation of the difference increased rapidly with decreasing plot size; (2) except for the maximal tree height of the plots, the other forest attributes, including the mean tree height, diameter at breast height, BA, and VOL of all forest types, showed no statistically significant differences between the plots of different sizes and the 900 m2 plots; and (3) with increasing plot size, the accuracies of VOL and BA estimations improved markedly, and the effects of plot size on the estimation accuracies of the different forest attributes and different forest types were essentially the same. Spatial averaging resulted in the variations in the independent variables (LiDAR variables) and dependent variables (forest attributes) decreasing gradually with the increasing plot size, which was the main reason for the model’s accuracy improving. In applying airborne LiDAR to a large-scale subtropical planted forest inventory, the plot size should be at least 600 m2 for all forest types. Full article
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22 pages, 5811 KiB  
Article
Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters
Forests 2022, 13(12), 2115; https://doi.org/10.3390/f13122115 - 10 Dec 2022
Cited by 1 | Viewed by 1548
Abstract
In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the [...] Read more.
In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (≈136 points·m−2) of the initial point cloud density (≈37,240.86 points·m−2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs. Full article
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14 pages, 2402 KiB  
Article
GCPs-Free Photogrammetry for Estimating Tree Height and Crown Diameter in Arizona Cypress Plantation Using UAV-Mounted GNSS RTK
Forests 2022, 13(11), 1905; https://doi.org/10.3390/f13111905 - 12 Nov 2022
Cited by 5 | Viewed by 1706
Abstract
One of the main challenges of using unmanned aerial vehicles (UAVs) in forest data acquisition is the implementation of Ground Control Points (GCPs) as a mandatory step, which is sometimes impossible for inaccessible areas or within canopy closures. This study aimed to test [...] Read more.
One of the main challenges of using unmanned aerial vehicles (UAVs) in forest data acquisition is the implementation of Ground Control Points (GCPs) as a mandatory step, which is sometimes impossible for inaccessible areas or within canopy closures. This study aimed to test the accuracy of a UAV-mounted GNSS RTK (real-time kinematic) system for calculating tree height and crown height without any GCPs. The study was conducted on a Cupressus arizonica (Greene., Arizona cypress) plantation on the Razi University Campus in Kermanshah, Iran. Arizona cypress is commonly planted as an ornamental tree. As it can tolerate harsh conditions, this species is highly appropriate for afforestation and reforestation projects. A total of 107 trees were subjected to field-measured dendrometric measurements (height and crown diameter). UAV data acquisition was performed at three altitudes of 25, 50, and 100 m using a local network RTK system (NRTK). The crown height model (CHM), derived from a digital surface model (DSM), was used to estimate tree height, and an inverse watershed segmentation (IWS) algorithm was used to estimate crown diameter. The results indicated that the means of tree height obtained from field measurements and UAV estimation were not significantly different, except for the mean values calculated at 100 m flight altitude. Additionally, the means of crown diameter reported from field measurements and UAV estimation at all flight altitudes were not statistically different. Root mean square error (RMSE < 11%) indicated a reliable estimation at all the flight altitudes for trees height and crown diameter. According to the findings of this study, it was concluded that UAV-RTK imagery can be considered a promising solution, but more work is needed before concluding its effectiveness in inaccessible areas. Full article
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22 pages, 11012 KiB  
Article
Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
Forests 2022, 13(10), 1591; https://doi.org/10.3390/f13101591 - 29 Sep 2022
Cited by 2 | Viewed by 1583
Abstract
This study focuses on evaluating the positioning accuracy of smartphones in a deciduous forest environment compared to various levels of Global Navigation Satellite System (GNSS) devices. In a mixed coniferous forest with 90% broad-leaved forest (deciduous season), the accuracy of 57 test points [...] Read more.
This study focuses on evaluating the positioning accuracy of smartphones in a deciduous forest environment compared to various levels of Global Navigation Satellite System (GNSS) devices. In a mixed coniferous forest with 90% broad-leaved forest (deciduous season), the accuracy of 57 test points was evaluated according to different openness levels under the forest. Taking the coordinates obtained by survey-grade GNSS devices in RTK (Real-time Kinematic) mode as standard, the accuracy of the single-point positioning (SPP) mode and precise-point positioning (PPP) mode obtained by three smartphones (one single frequency and two dual frequency), one survey-grade receiver and one recreational-grade receiver are compared. It can be found that there was a significant positive correlation between canopy openness and carrier-to-noise density(C/N0) (p < 0.05). Meanwhile, the C/N0 of survey-grade devices is significantly higher than that of smartphones. The results show that the positioning accuracy of dual-frequency smartphones under forests is better than that of single-frequency smartphones. Furthermore, the positioning accuracy of the smartphone corrected by PPP mode is better than that of the recreational-grade GNSS receiver and can achieve an accuracy of about 2.5 m in the horizontal direction, which can be used for forestry stakeout, reset and determination of forest area boundaries in environments with high openness (R > 0.7). However, in an environment with low openness (R < 0.7) and relatively complex forest area positioning, survey-grade GNSS devices are still required to cooperate with the PPP or real-time differential positioning method to obtain accurate sub-meter-level positioning data. Full article
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