Forestry Inventory Research Articles

Unravelling the impact of soil data quality on species distribution models of temperate forest woody plants

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an ‘extreme’ niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning.

Evaluation of Tree-Growth Rate in the Laurentides Wildlife Reserve Using GEDI and Airborne-LiDAR Data

Loss of forest cover and derived effects on forest ecosystems services has led to the establishment of land management policies and forest monitoring systems, and consequently to the demand for accurate and multitemporal data on forest extent and structure. In recent years, spaceborne Light Detection and Ranging (LiDAR) missions, such as the Global Ecosystem Dynamics Investigation (GEDI) instrument, have facilitated the repeated acquisition of data on the vertical structure of vegetation. In this study, we designed an approach incorporating GEDI and airborne LiDAR data, in addition to detailed forestry inventory data, for estimating tree-growth dynamics for the Laurentides wildlife reserve in Canada. We estimated an average tree-growth rate of 0.32 ± 0.23 (SD) m/year for the study site and evaluated our results against field data and a time series of NDVI from Landsat images. The results are in agreement with expected patterns in tree-growth rates related to tree species and forest stand age, and the produced dataset is able to track disturbance events resulting in the loss of canopy height. Our study demonstrates the benefits of using spaceborne-LiDAR data for extending the temporal coverage of forestry inventories and highlights the ability of GEDI data for detecting changes in forests’ vertical structure.

Volume Estimation of Stem Segments Based on a Tetrahedron Model Using Terrestrial Laser Scanning Data

Stem volume is a very important parameter in forestry inventory and carbon storage. The stem volume estimated by most existing methods deviates from its true value because the irregularity of the stem is usually overlooked. In this study, we propose a stem segment volume estimation based on the tetrahedron model using TLS data. First, the initial stem segment surface model, including the lower, upper, and outer triangular surface models, was gradually reconstructed. Next, the outer surface model was subdivided based on the edge subdivision. Then, a closed triangular surface model without self-intersection was obtained. Afterward, a tetrahedron model of the stem segment was generated using TetGen software (Version 1.6.0) for the triangular surface model. Finally, the stem segment volume was calculated by summing the volumes of all the tetrahedrons in the tetrahedron model. An experiment with 76 stem segments from different tree species with different parameters showed that the reconstructed stem segment surface model effectively reflected the geometrical features of the stem segment surface. Compared to the volume based on the simulated sectional measurement, the MAPE of the volume based on the tetrahedron model was 2.12%. The results demonstrated the validity of the presented method for stem surface reconstruction and stem volume estimation, and the volume based on the tetrahedron model was closer to the true value than that based on the sectional measurement.

Remarks on Geomatics Measurement Methods Focused on Forestry Inventory.

This contribution focuses on a comparison of modern geomatics technologies for the derivation of growth parameters in forest management. The present text summarizes the results of our measurements over the last five years. As a case project, a mountain spruce forest with planned forest logging was selected. In this locality, terrestrial laser scanning (TLS) and terrestrial and drone close-range photogrammetry were experimentally used, as was the use of PLS mobile technology (personal laser scanning) and ALS (aerial laser scanning). Results from the data joining, usability, and economics of all technologies for forest management and ecology were discussed. ALS is expensive for small areas and the results were not suitable for a detailed parameter derivation. The RPAS (remotely piloted aircraft systems, known as "drones") method of data acquisition combines the benefits of close-range and aerial photogrammetry. If the approximate height and number of the trees are known, one can approximately calculate the extracted cubage of wood mass before forest logging. The use of conventional terrestrial close-range photogrammetry and TLS proved to be inappropriate and practically unusable in our case, and also in standard forestry practice after consultation with forestry workers. On the other hand, the use of PLS is very simple and allows you to quickly define ordered parameters and further calculate, for example, the cubic volume of wood stockpiles. The results from our research into forestry show that drones can be used to estimate quantities (wood cubature) and inspect the health status of spruce forests, However, PLS seems, nowadays, to be the best solution in forest management for deriving forest parameters. Our results are mainly oriented to practice and in no way diminish the general research in this area.

Проблемы управления лесными ресурсами в Арктической зоне Российской Федерации

The study exposes the issues with managing land and forest resources in the Arctic region. Among them are the inability to estimate the cost of forest resources, the conversion of forestry lands into industrial lands, the consequences of renting forest lands, an insufficient forestry inventory, a small number of employees of the State Forest Protection Service, and underdeveloped infrastructure. The main statements of the Arctic Hectare Program are reviewed, which give the possibility of granting land plots primarily located in forest areas. The program is discussed in comparison with the analogous Far Eastern Hectare Program. The development of the Arctic Hectare Program is expected to preserve and increase the popularity of the Arctic region in the future. However, the efficient use of the land requires the improvement of transport infrastructure. The internet site, Geoportal, is used for an analysis of the primary disadvantages of geoinformation systems for selecting land plots in the Arkhangelsk region, which is a part of the Arctic zone of the Russian Federation. The website was established within the Arctic Hectare Program. The article provides suggestions for additional functions of the public cadastral map. It also reveals the reasons for the absence of a methodology for estimating forest resources, including Arctic tundra vegetation. Furthermore, the difficulties with the forest land delegation are observed in relation to the implementation of investment projects in mining, fuel, and energy areas, the development of transportation infrastructure, and the formation of environmental protection zones. The cases of renting out forest lands are discussed. For citation: Kovyazin V.F., Lepikhina O.Yu, Demidova P.M., Kolesnik O.A., Shobairi S.O.R., Nguyen A.T. Problems of Forest Resource Management in the Arctic Zone of the Russian Federation. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 3, pp. 185–194. (In Russ.). https://doi.org/10.37482/0536-1036-2023-3-185-194

Development of a multispectral fluorescence LiDAR for point cloud segmentation of plants.

The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g., unmanned aerial vehicles (UAV) and poles. Towards the aims above, what we believe to be, a novel multispectral fluorescence LiDAR, featuring compact volume, light weight, and low cost, has been proposed and designed. A 405 nm laser diode was employed to excite the fluorescence of plants, and the point cloud attached with both the elastic and inelastic signal intensities that was obtained through the R-, G-, B-channels of a color image sensor. A new position retrieval method has been developed to evaluate far field echo signals, from which the spectral point cloud can be obtained. Experiments were designed to validate the spectral/spatial accuracy and the segmentation performance. It has been found out that the values obtained through the R-, G-, B-channels are consistent with the emission spectrum measured by a spectrometer, achieving a maximum R2 of 0.97. The theoretical spatial resolution can reach up to 47 mm and 0.7 mm in the x- and y-direction at a distance of around 30 m, respectively. The values of recall, precision, and F score for the segmentation of the fluorescence point cloud were all beyond 0.97. Besides, a field test has been carried out on plants at a distance of about 26 m, which further demonstrated that the multispectral fluorescence data can significantly facilitate the segmentation process in a complex scene. These promising results prove that the proposed multispectral fluorescence LiDAR has great potential in applications of digital forestry inventory and intelligent agriculture.

Regeneration Status and Population Structure in Terai Community Forest: Evidence from Kalyankot Community Forest, Kapilvastu District, Nepal

The regeneration status of a forest is an essential metrics to assess the regeneration potential and population structure of forests. In emerging nations like Nepal, however, human dependency on forests has had a negative influence on forest diversity and sustainability. This paper analyzes the regeneration status and its link with bio-physical aspects and human disturbances. The data were collected using a systematic random sampling method and sample plots were established using the fishnet tool in ArcGIS. An inventory survey of 96 plots was carried out with nested circular sample plots with a main radius of 1261 cm. The overall regeneration condition of the forest was found to be in good condition according to Community Forestry Inventory Guideline, 2004. The majority of the tree species were determined to have a sound quality and medium (II) grades in this study. In terms of the diameter class distribution, lower diameter classes (21-60 cm) comprised more adults than the upper diameter classes (61-120 cm). This study found no significant variations in the effects of biophysical factors, such as slope and aspect, on species regeneration. The study concludes the inadequate silvicultural management interventions in the forest. This information can be useful to devise systematic plans to promote good-quality regeneration and manage the factors that are likely to affect the overall regeneration. Further research focusing on other biophysical factors as well as social factors and their influence on regeneration including its management techniques is recommended.

An Analysis on the Long-term Management of Urban Tree Carbon Reduction ‘Forestry Inventory Analysis’ towards Climate Change Adaptation

Background and objective: Climate change and its impact on carbon storage in urban trees is a topic that has received increasing attention. Related studies focusing on data collection and analysis-based programs, such as the Forestry Inventory Analysis (FIA) programme in the US, have presented remarkable approaches to obtaining integrated analysis estimates and its management structure from a long-term perspective. This study explored the FIA programme in the context of long-term management in relation to tree carbon-related data collection and analysis.Methods: For the analysis, this study employed bibliometric methods (network using VOSviewer and coordinated analysis using NVivo) and an analytical framework. The case study is based on FIA-related driver changes of the keywords 'carbon' and 'tree' as well as the FIA management structure, using place-keeping theory as an integrated analytical framework and as the approach to long-term management.Results: Analysing FIA shows long-term management which has run since 2010, revealing key issues and significant terms in six dimensions of place-keeping analytical frameworks: public-private partnership-based data collection and political support in policy, central and local government-integrated fundraising from income generation, active governance-formed community activities in governance, alliance-structured networks in partnerships, integrated or unified estimated structures in evaluation, and maintenance. The case analysis reveals the necessity of a long-term management approach that incorporates a carbon storage estimate-focused policy, integrated income-partnerships, expanded active governance, Private Public Community Partnership (PPCP) multi-sector partnerships, and data platform settings.Conclusion: Newly emerging urban tree management structures should be reflected first on establishing an integrated carbon neutrality-based estimating system and secondly, on building long-term management approaches to the system. This will ultimately allow for climate change adaptation to approach carbon neutrality.

Forest stand-by-environment interaction invalidates the use of space-for-time substitution for site index modeling under climate change

Quantification and prediction of forest site productivity potential under climate changes are core issues of growth and yield modelling and crucial for the effective management of forest stands. Climatic factors typically enter these models as long-term baseline data of mean air temperature and sum of precipitation. Such models frequently are parameterized using spatially distributed data, an approach that can be considered as indirect space-for-time substitution (SFTS), which is based on the assumption that the growth response across spatial environmental gradients is equivalent to the dynamic growth response in time when site factors shift in situ due to environmental changes over time. In site index modelling this assumption has never been thoroughly tested. We applied procedures to test the validity of the SFTS approach using altitudinal transect analysis of data from long-term experimental plots with Norway spruce (Picea abies) in southwest Germany. The following procedures were applied 1. Partitioning of spatial and temporal variance components of site index and air temperature; 2. Test of the equivalence of spatial and temporal variation in models predicting site index from air temperature; 3. Bias analysis of the SFTS- and the altitudinal range-specific individual models. The results show that the response of site index to changes in growing season air temperature as estimated by the SFTS-based approach is significantly different from the observed temporal dynamics within the plots. The estimated sensitivity of site index to changes in growing season air temperature is significantly larger in the SFTS-model. Bias trends increase with increasing air temperature in all altitudinal ranges. We found significant effects of stand-by-environment interaction on site index responses to changing growing season air temperature. Possible reasons for the found differences are discussed. Furthermore, based on an independent data set from the German National Forestry Inventory (NFI), a SFTS-based climate-sensitive site index model was compared to a state-space modelling approach (SSM) which considers the dynamics of environmental changes over time. The results show that the SSM is statistically superior to the SFTS approach, corroborating the findings of the altitudinal transect analysis. Finally, future site index dynamics of Norway spruce were simulated on the basis of climate change scenarios using the SSM approach. We recommend for all climate sensitive forest growth and yield models based on spatially and temporally distributed data to properly account for within- and between-group effects to avoid erroneous model predictions under climate change.

Conservation of Tree Species Richness in a Traditional Agroforestry Landscape in the Vhembe Biosphere Reserve, South Africa

Tree species richness is a critical element concerning trees on farms, on communal land and in protected areas to support biodiversity and socio-economic livelihoods in traditional agroforestry landscapes. Tree species richness is directly linked to the use of provisioning ecosystem services and to management practices in traditional agroforestry landscapes. The study aimed to investigate the link between socio-ecological and conservation strategies regarding tree species richness in traditional agroforestry landscapes. The study was conducted in the Damani, Thenzheni, Tshiombo and Tshipako villages located in Thulamela Municipality of the Vhembe Biosphere Reserve, South Africa. The data were collected using a mixed method approach combining forestry inventory and focus group discussion. The study recorded a total number of 126 tree species: 83 communal-land-hosted species, 68 species of trees on farms and 81 species in the protected areas. The indigenous species Englerophytum magalismontanum (Sond.) T.D.Penn. was the most cited (62%) by interviewees, with a primary use for wild fruits, followed by Pteleopsis myrtifolia (M.A. Lawson) Engl. & Diels. (57%) for fuelwood, Combretum molle R.Br. ex G.Don (36%) for traditional medicine and Albizia adianthifolia (Schumach.) W.F.Wight (12%) for fodder. Species richness was found to be commonly driven by provisioning ecosystem services with trees on farms and on communal land. Distance was found to be major driving factor of species richness in protected areas. This study found that the local people have no conservation strategy and practices targeting the enhancement of tree species richness in the traditional agroforestry landscape. This study advocates for the establishment of a conservation strategic framework for restoring tree species richness by targeting traditional agroforestry landscapes.

Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR

Forest land plays a vital role in global climate, ecosystems, farming and human living environments. Therefore, forest biomass estimation methods are necessary to monitor changes in the forest structure and function, which are key data in natural resources research. Although accurate forest biomass measurements are important in forest inventory and assessment, high-density measurements that involve airborne light detection and ranging (LiDAR) at a low flight height in large mountainous areas are expensive. The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve using a system that synergistically combines an unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to leverage their complementary advantages. In this study, we utilized digital aerial photogrammetry (DAP), which has the unique advantages of speed, high spatial resolution, and low cost, to compensate for the deficiency of forestry inventory using UAS-based LiDAR that requires terrain-following flight for high-resolution data acquisition. Combined with the sparse LiDAR points acquired by using a high-altitude and high-speed UAS for terrain extraction, dense normalized DAP point clouds can be obtained to produce an accurate and high-resolution canopy height model (CHM). Based on the CHM and spectral attributes obtained from multispectral images, we estimated and mapped the AGB of the region of interest with considerable cost efficiency. It is proved that sparse LiDAR point cloud could serve as important sources for terrain estimation. The accuracy of the AGB estimates could be improved by 9% and 5% in terms of the RMSE and R2, respectively, by adding spectral metrics to point cloud structural metrics. Compared with results obtained using only spectral metrics, the results obtained using the combination of spectral and point cloud metrics were better by 37% in terms of the RMSE and 55% in R2. Our study supports the development of predictive models for large-scale wall-to-wall AGB mapping by leveraging the complementarity between DAP and LiDAR measurements. This work also reveals the potential of utilizing a UAS-based digital camera and LiDAR synergistically in a plateau mountainous forest area. In this future, we will further investigate different establishing strategies for combining different sensors in a synergistic system.

Diametric distribution of forestry species in riparian forest in the southern part of the Amazon

The expansion and changes in land use and land cover in the Southwest Amazon are mainly related to the activities of logging without management rules, agriculture and cattle production, which resulted in the conversion of natural forests, especially along water courses. This study aimed to verify the diametric distribution of forestry species with higher importance value index in the riparian forest of the Acre River (Acre, Brazil). The forestry inventory was performed at eight municipalities crossed by the Acre River, using two stage sample units (conglomerates) and applying stratified random sampling techniques at the river bank. Twenty-seven primary plots were installed, within which another four secondary plots were implanted. It was fitted Weibull’s probability density functions with 2 and 3 parameters to species diametric distribution, provided by the maximum likelihood method. Graphic analysis verified that 86% of the species analyzed presented a distribution trend with positive asymmetry. The distribution of the Weibull function with two parameters presented better the best estimative of the frequency of species by diameter class of the natural forest evaluated. Considering the heterogeneity of the species, further studies to verify whether the distribution behavior follows the same trend is recommended.

Measuring the Tree Height of Picea crassifolia in Alpine Mountain Forests in Northwest China Based on UAV-LiDAR

Forests in alpine mountainous regions are sensitive to global climate change. Accurate measurement of tree height is essential for forest aboveground biomass estimation. Unmanned aerial vehicle light detection and ranging (UAV-LiDAR) in tree height estimation has been extensively used in forestry inventories. This study investigated the influence of varying flight heights and point cloud densities on the extraction of tree height, and four flight heights (i.e., 85, 115, 145, and 175 m) were set in three Picea crassifolia plots in the Qilian Mountains. After point cloud data were classified, tree height was extracted from a canopy height model (CHM) on the basis of the individual tree segmentation. Through comparison with ground measurements, the tree height estimations of different flight heights and point cloud densities were analyzed. The results indicated that (1) with a flight height of 85 m, the tree height estimation achieved the highest accuracy (R2 = 0.75, RMSE = 2.65), and the lowest accuracy occurred at a height of 175 m (R2 = 0.65, RMSE = 3.00). (2) The accuracy of the tree height estimation decreased as the point cloud density decreased. The accuracies of tree height estimation from low-point cloud density (R2 = 0.70, RMSE = 2.75) and medium density (R2 = 0.69, RMSE = 2.80) were comparable. (3) Tree height was slightly underestimated in most cases when CHM-based segmentation methods were used. Consequently, a flight height of 145 m was more applicable for maintaining tree height estimation accuracy and assuring the safety of UAVs flying in alpine mountain regions. A point cloud density of 125–185 pts/m2 can guarantee tree height estimation accuracy. The results of this study could potentially improve tree height estimation and provide available UAV-LiDAR flight parameters in alpine mountainous regions in Northwest China.

Pioneering easy-to-use forestry data with Forest Explorer

Forest Explorer is a web tool that can be used to easily browse the contents of the Cross-Forest dataset, a Linked Open Data resource containing the forestry inventory and land cover map from Spain. The tool is purposed for domain experts and lay users to facilitate the exploration of forestry data. Since these two groups are not knowledgable on Semantic Web, the user interface is designed to hide the complexity of RDF, OWL or SPARQL. An interactive map is provided for this purpose, allowing users to navigate to the area of interest and presenting forestry data with different levels of detail according to the zoom level. Forest Explorer offers different filter controls and is localized to English and Spanish. All the data is retrieved from the Cross-Forest and DBpedia endpoints through the Data manager. This component feeds the different Feature managers with the data needed to be displayed in the map. The Data manager uses a reduced set of SPARQL templates to accommodate any data request of the Feature managers. Caching and smart geographic querying are employed to limit data exchanges with the endpoint. A live version of the tool is freely available for everybody that wants to try it – any device with a modern browser should be sufficient to test it. Since December 2019, more than 3,200 users have employed Forest Explorer and it has appeared 12 times in the Spanish media. Results from a user study with 28 participants (mainly domain experts) show that Forest Explorer can be used to easily navigate the contents of the Cross-Forest dataset. No important limitations were found, only feature requests such as the integration of new datasets from other countries that are part of our future work.

Certification of a\xe7a\xed agroforestry increases the conservation potential of the Amazonian tree flora

The harvesting of açaí berries (palm fruits from the genus Euterpe) in Amazonia has increased over the last 20 years due to a high local and global market demand and triggered by their widely acclaimed health benefits as a ‘superfood’. Although such increase represents a financial boom for local communities, unregulated extraction in Amazonia risks negative environmental effects including biodiversity loss through açai intensification and deforestation. Alternatively, the introduction of certified sustainable agroforestry production programs of açaí has been strategically applied to reduce the exploitation of Amazonian forests. Local açaí producers are required to follow principles of defined sustainable management practices, environmental guidelines, and social behaviors, paying specific attention to fair trade and human rights. In this study we investigate whether sustainable agroforestry and certification effectively promotes biodiversity conservation in Amazonia. To address this question, we conducted a forestry inventory in two hectares of long-term certified açai harvesting areas to gain further knowledge on the plant diversity and forest structure in açaí managed forests and to understand the contribution of certification towards sustainable forest management. On average, we found that certified managed forests harbor 50% more tree species than non-certified açaí groves. Trees in certified areas also have significantly higher mean basal area, meaning larger and hence older individuals are more likely to be protected. Certified harvesting sites also harbor dense populations of threatened species as classified by the International Union for Conservation of Nature (e.g. Virola surinamensis, classified as ‘endangered’). Besides increasing the knowledge of plant diversity in açaí managed areas, we present baseline information for monitoring the impact of harvesting activities in natural ecosystems in Amazonia.

FOREST COVER MAPPING AND PINUS SPECIES CLASSIFICATION USING VERY HIGH-RESOLUTION SATELLITE IMAGES AND RANDOM FOREST

Abstract. Advances in remote sensing technologies are generating new perspectives concerning the role of and methods used for National Forestry Inventories (NFIs). The increase in computation capabilities over the last several decades and the development of new statistical techniques have allowed for the automation of forest resource map generation through image analysis techniques and machine learning algorithms. The availability of large-scale data and the high temporal resolution that satellite platforms provide mean that it is possible to obtain updated information about forest resources at the stand level, thus increasing the quality of the spatial information. However, photointerpretation of satellite and aerial images is still the most common way that remote sensing information is used for NFIs or forest management purposes. This study describes a methodology that automatically maps the main forest covers in Galicia (Eucalyptus spp., conifers and broadleaves) using Worldview-2 and the random forest classifier. Furthermore, the method also evaluates the separate mapping of the three most abundant Pinus tree species in Galicia (Pinus pinaster, Pinus radiata and Pinus sylvestris). According to the results, Worldview-2 multispectral images allow for the efficient differentiation between the main forest classes that are present in Galicia with a very high degree of accuracy (91%) and ample spatial detail. Pinus species can also be efficiently differentiated (83%).

Comparison of Numerical Calculation Methods for Stem Diameter Retrieval Using Terrestrial Laser Data

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bézier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy.

Reduced-Impact Logging Maintain High Moss Diversity in Temperate Forests

Forestry harvesting represents an important economic activity around the world. Habitat degradation due to forest harvesting contributes to biodiversity loss; therefore, it is necessary to implement logging management aimed at reducing its impact. Forest management by reduce-impact logging (RIL) involves cutting trees following regulations focused on diminishing the impact on biodiversity by following harvesting plans based on forestry inventories and participation of trained workers. In Mexico, RIL is applied mainly in temperate habitats and its effectiveness has been assessed based on vascular plants. In this study, we analyzed the diversity and community structure of terrestrial and epiphytic mosses in managed (sites number = 3) and conserved (sites number = 3) sites in the temperate forest of Sierra Juárez, Oaxaca, Mexico. Likewise, we evaluated the potential function of mosses as indicators of habitat degradation. Environmental variables were also quantified at local (canopy coverage, altitude, daily temperature, and light) and regional (total annual rainfall, orientation, and slope) scales to evaluate potential relationships with the community and species diversity. We documented 70 mosses species with a diversity (alfa, beta) and community structure similar between managed and conserved sites. For terrestrial mosses, we found marginal differences in their communities, likely related to species coverture variation in managed sites. The diversity and community structure epiphytic mosses were not statistically different in managed and conserved sites. Only the daily variation in light intensity was positively related to the variation of alpha diversity of epiphytic mosses. The species Dicranum sumichrastii Duby and Leptodontium viticulosoides (P. Beauv.) Wijk & Margad. can be considered as ecological indicators for conserved and managed sites, respectively, likely due to their relationship with light and humidity conditions. Our results suggest that that forest management by RIL could be considered as a promising tool to balance timber production and moss diversity.

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

A Convex Hull-Based Feature Descriptor for Learning Tree Species Classification From ALS Point Clouds

Classifying tree species from point clouds acquired by light detection and ranging (LiDAR) scanning systems is important in many applications, including remote sensing, virtual reality, and forestry inventory. Compared with terrestrial laser scanning systems, airborne laser scanning (ALS) systems can acquire large-scale tree point clouds from only a single scan. However, ALS point clouds have the disadvantages of low density, uneven distribution, and unclear branch structure, making the classification of tree species from ALS point clouds a challenging task. Recently, deep learning-based classification approaches, such as PointNet++, which can operate directly on 3-D point sets, have been intensively studied in scene classification. However, the classification precision of learning-based approaches for point clouds relies on point coordinates and features, such as normals. Unlike the face normals of regular objects, trees have complex branch structures and detailed leaves, which are difficult to capture using ALS systems. Hence, it might be inappropriate to use the normals of ALS tree points for classification. In this letter, we propose a novel convex hull-based feature descriptor for tree species classification using the deep learning network PointNet++. To evaluate the effectiveness of our approach, three additional feature descriptors (normal descriptor, alpha shape-based descriptor, and covariance descriptor) are also investigated with PointNet++. The results show that the convex hull-based feature descriptor can achieve 86.6% overall accuracy in tree species classification, which is notably higher than the other three descriptors.