Plot Volume Research Articles

Evaluating the performance of farmer-managed fruit trees in participatory homestead trials of semi-arid Ethiopia: the case of East Shoa Zone, Oromia

ABSTRACT The growth of farmer-managed tree seedlings (FMTS) is pramount to impact agroforestry technology adoption. This paper presents the performances of FMTS of 3 avocado varieties (Hass, Ettinger and Fuerte) and one papaya variety (Solo) in three districts (Dugda, Adamitulu, and Lome). Thirty-six trials were established (12 trials per district) in 36 homsteads selected purposively. Each homestead was considered as a block in RCBD. Watering frequency was found higher in Dugda than the remaining districts. Seedling survival exhibited significant differences (α<0.05) among varieties with in and across districts. Survival of Solo papaya was significantly higher (α<0.05) at Dugda (91%) than in Adamitulu and Lome. Hass avocado reveled significantlly higher survival (91%) at Dugda than Ettinger and Fuerte across districs. Significant differences in Plot Volume Index (PVI, an integrative growth measurement) among varieties within and across districts indicated that seedling growth was influenced by variety, planting site and, watering fequency. PVI of Hass at Dugda was significantly higher (by 35% and 110%) than that of Ettinger and Fuerte, respectively. PVI of Solo Papaya at Dugda was significantly higher (α<0.05). Hass was found promising avocado variety for planting locations considered, and frequent watering was found crucial for good growth of the tested plant varieties.

TLS2trees: A scalable tree segmentation pipeline for TLS data

Abstract Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming. Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest. A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step. The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open‐source software.

Planting methods influence revegetation success of native species in an arid environment

In Kuwait, declining native vegetation cover due to prevailing arid conditions and severe anthropogenic disturbances necessitated undertaking revegetation programs. Seed sowing and outplanting of nursery-grown seedlings are the two major plant establishment methods commonly used all around the world. However, the success of plant establishment methods may vary between the species as well as between the methods. Therefore, we compared the effect of sowing of primed or non-primed seeds and outplanting of nursery-grown seedlings on field performance of four dominant native desert species of Kuwait: Vachellia pachyceras, Rhanterium epapposum, Farsetia aegyptia, and Haloxylon salicornicum for 22 months to find the best method for revegetation in arid conditions. A significant species and treatment interaction effect was observed in all plant parameters except plot volume index. Direct sowing of hydro-primed seeds appeared to be the most effective method for establishment and growth of V. pachyceras. In contrast, outplanting of nursery-grown seedlings showed better performance under the field conditions in H. salicornicum and F. aegyptia. These results suggest that direct sowing of primed seeds can also be used effectively along with seedling outplanting for the revegetation of arid lands.

UAV Image-Based Crop Growth Analysis of 3D-Reconstructed Crop Canopies.

Plant growth rate is an essential phenotypic parameter for quantifying potential crop productivity. Under field conditions, manual measurement of plant growth rate is less accurate in most cases. Image-based high-throughput platforms offer great potential for rapid, non-destructive, and objective estimation of plant growth parameters. The aim of this study was to assess the potential for quantifying plant growth rate using UAV-based (unoccupied aerial vehicle) imagery collected multiple times throughout the growing season. In this study, six diverse lines of lentils were grown in three replicates of 1 m2 microplots with six biomass collection time-points throughout the growing season over five site-years. Aerial imagery was collected simultaneously with each manual measurement of the above-ground biomass time-point and was used to produce two-dimensional orthomosaics and three-dimensional point clouds. Non-linear logistic models were fit to multiple data collection points throughout the growing season. Overall, remotely detected vegetation area and crop volume were found to produce trends comparable to the accumulation of dry weight biomass throughout the growing season. The growth rate and G50 (days to 50% of maximum growth) parameters of the model effectively quantified lentil growth rate indicating significant potential for image-based tools to be used in plant breeding programs. Comparing image-based groundcover and vegetation volume estimates with manually measured above-ground biomass suggested strong correlations. Vegetation area measured from a UAV has utility in quantifying lentil biomass and is indicative of leaf area early in the growing season. For mid- to late-season biomass estimation, plot volume was determined to be a better estimator. Apart from traditional traits, the estimation and analysis of plant parameters not typically collected in traditional breeding programs are possible with image-based methods, and this can create new opportunities to improve breeding efficiency mainly by offering new phenotypes and affecting selection intensity.

Study on the Estimation of Forest Volume Based on Multi-Source Data.

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.

Modelos para estimativas de ataque de &lt;i&gt;Sirex noctilio&lt;/i&gt; (Fabricius) em povoamentos de &lt;i&gt;Pinus taeda&lt;/i&gt; L.

Uma das principais pragas dos plantios florestais de Pinus spp. é Sirex noctilio (Fabricius), conhecida popularmente como vespa-da-madeira. Este trabalho tem por objetivo desenvolver um modelo matemático para estimar o número de árvores atacadas por Sirex noctilio em povoamentos de Pinus taeda L. baseado nos métodos de monitoramento e nas características dendrométricas do reflorestamento. Selecionaram-se, para a coleta de dados, cinco áreas localizadas no município de Palmas - PR. Foram utilizadas como variáveis independentes do modelo a serem selecionadas pelo método Stepwise a amostragem sequencial, número de indivíduos da parcela, árvores-armadilha, diâmetro a altura do peito (DAP) médio, altura média, volume médio individual, volume da parcela, idade, espaçamento, sítio, área basal da parcela, área transversal média, diâmetro médio quadrático e o DAP estratificado em três classes de diâmetro. A maior influência sobre a estimativa de ataque (Y) foi exercida pelas variáveis número de árvores atacadas detectadas na amostragem sequencial (AS) (p-valor 0,001) e o número de indivíduos da parcela (n) (p-valor 0,05). O modelo mais adequado para a obtenção da variável resposta foi δ = β0 + β1AS+β2n + ε, o qual apresentou coeficiente de determinação ajustado (R²adj) de 0,965 e erro padrão de estimativa (Syx%) de 18,65% obtendo o melhor desempenho na análise gráfica dos resíduos. O modelo pode ser utilizado pelos gestores florestais como uma ferramenta na tomada de decisões quanto ao manejo do povoamento.

Potential Lidar Height, Intensity, and Ratio Parameters for Plot Dominant Species Discrimination and Volume Estimation

Precise stand species classification and volume estimation are key research topics for automated forest inventory. This study aims to explore the feasibility of light detection and ranging (lidar) height, intensity, and ratio parameters for discriminating dominant species (Pinus densiflora, Larix kaempferi, and Quercus spp.) and estimating volume at plot scale. To achieve these objectives, multiple linear discriminant and regression analyses were utilized after a separate selection of explanatory variables from extracted 38 lidar height, intensity, and ratio parameters. A kappa accuracy of 0.75 was achieved in discriminating the plot-dominant species from three different species by adopting a combination of nine selected explanatory variables. Further investigation found that dispersion and mean of lidar intensity within a plot are key classifiers of identifying three species. Species-specific optimal plot volume models for Pinus densiflora, Larix kaempferi, and Quercus spp. were evaluated by coefficients of determination of 0.71, 0.74, and 0.56, respectively. Compared to species classification, height-related lidar variables play a key role in modeling forest plot volume. Several explanatory variables for each modeling practice were correlated to canopy vertical and horizontal structures and were enough to represent species-specific characteristics in both approaches for species classification and plot volume estimation. Additionally, observed different variable combinations for two important applications imply that future studies should use proper variable combinations for each purpose.

An efficient method of evaluating large scale urban residential skylight environment and an empirical study of Beijing main area

A good skylight environment in urban residential areas is an important component of a healthy city, and has always been highly valued. With the rapid development of new-type urbanization, the density of buildings continues to increase, and megacities have entered the stage of stock transformation. An effective method for evaluating the skylight environment of large-scale urban residential areas is urgently needed. However, there is still a lack of empirical research methods and cases of large-scale residential skylight environment. In this regard, this article takes the megacity Beijing as the research object, and proposes an efficient analysis method of residential skylight environment that integrates multiple real-world data at city scale. In terms of data, it collects and integrates 3D data of urban-scale building space and residential boundary data; in terms of algorithm, Sky View Factor (SVF) is used as the evaluation index of residential skylight environment, and an efficient analysis method of urban-scale skylight environment based on cloud parallel simulation is realized. Through analysis, it is found that: (1) the average SVF value of Beijing residential area is 61%, which means that its skylight quality is in general level; (2) the skylight environment of Beijing residential area is distributed in a circle, and there are 4 types of skylight environment quality residential areas; (3) The skylight environment of Beijing residential area is relatively weakly related to the distance from the residential area to the city center and the average height of the residential buildings, and is closely related to the plot volume ratio, the residential building density and the shading from surrounding buildings. The highlight of this study lies in the empirical research on the skylight environment of mega-city residential areas that incorporates multiple real data for the first time, which can promote the study of skylight environment on a city scale and provide a reference for the updating of Beijing’s residential daylight environment.

Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation

The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

Modeling the scenic beauty of autumnal tree color at the landscape scale: A case study of Purple Mountain, Nanjing, China

Scenic forests are a basic component of forest parks. Evaluating the beauty of the color of scenic forests is an important consideration in scientific management measures. The Purple Mountain National Forest Park in Nanjing was considered the case study area. Using the autumn scenic forest typical sample survey in 2017 and color beauty questionnaire surveys of tourists as the main information sources, the beauty of the autumn color in the study area was quantitatively analyzed by using multiple linear regression and random forest models. The results reveal the following: (1) of the two evaluation models, the fitting accuracy of the random forest model is higher than that of the multiple linear regression model with a correlation coefficient of 0.91; (2) the main affecting factors include the canopy density (CD), average diameter at breast height (DBH), average tree height (H), plot volume (PV), quantification of orange (O), number of seasonal changing colors (SC), average hue value (AH), number of color categories (CC), distance to residence (DRE), distance to road (DRO), slope (SL) and aspect (AS); and (3) the distribution of the color scenic beauty value gradually decreased from the southwest to northeast. The results of this study show that the color scenic beauty of an urban forest park in autumn can be evaluated using a quantitative model.

Eucalyptus spp. volume determined through geospatial interpolation

Planning and managing forest resources is fundamental for increasing the productivity of forest products and by­products. This study evaluates the use of the ordinary kriging interpolator in the estimation of standing wood vol­ume in Eucalyptus spp. plantations in Paragominas city, Para State, Brazil. For this, we used data from the precut forest inventory, processed by the ordinary kriging method. Measurements took place in 27 georeferenced fields, with plots of 504 m², where trees were rigorously cubed. Geostatistical modeling was performed using ordinary kriging (spherical semivariogram model) with isotropic and anisotropic interpolators, seeking to map the volume of the area. To this end, experimental semivariograms were obtained through the Geostatistical Analyst tool of ArcGIS 10.1 software. Interpolation presented satisfactory results regarding plot volume mapping, in which the average volume fitted by ordinary kriging was statistically similar to the average volume obtained by the forest inventory, 11.3517 m³ and 11.3842 m³, respectively, showing efficiency with an adjusted coefficient of determina­tion of 0.94. The use of ordinary kriging interpolation proved to be effective in plot volume estimation in the forest stands under study.

The extent to which an unforeseen biotic disturbance can challenge timber expectations

Forest pathogens are one of the key drivers of the dynamics and diversity of forested landscapes, contributing to structural and compositional complexity at multiple scales in space and time. Growth and yield models help guide forest management decisions but the roles of abiotic and biotic disturbance agents including pathogens are often overlooked. In our study of mid-rotation spruce plantations in northwest British Columbia (BC), Canada, we examine the influence of tomentosus root disease on the oldest managed stands in a previously unmanaged landscape allowing us to witness pathogen expression in a first rotation setting over the longest duration available. Our 10 one-hectare stem-mapped plots capture the spatial variability of root disease expression and their large size, coupled with their random selection without any prior knowledge of the pathogen, make our study unique. We found tomentosus root disease accounted for nearly 90% of both the number and the volume of spruce trees that died and that the incidence of the disease more than doubled over a 16-year period from stand age ~30–46. The volume growth of trees that were initially healthy but became infected was 17% less than that of the trees that remained healthy, while the volume growth of trees that were infected since plot establishment was 28% less than that of trees that remained healthy. We found a weak relationship between infected stumps from the previous stand and the condition of trees surrounding those stumps at age 30, but more critically we found a similarly weak relationship between infected trees at age 30 and the condition of the neighbouring trees 16 years later. Using the growth and yield model TASS II v2.7.75, we found model projections of spruce volume exceeded measured plot volume by an average of 30.5%. TASS is based on the same foundational tenets that are the dominant paradigm in forestry where the prediction of forest growth is based on the assessment of site index (dominant stand height and age), Eichhorn’s rule (which implies that when the dominant tree height is known, stand yield can be easily estimated now and then projected into the future), and the thinning response hypothesis. Our results reflect the influential role of a forest pathogen that is operating on a different scale and following a different set of rules. This, in combination with the unexpected behaviour of the forest pathogen itself, represents a multifaceted challenge in forecasting forest growth.

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.).

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.

RegisTree: a registration algorithm to enhance forest inventory plot georeferencing

The accuracy of remote sensing-based models of forest attributes could be improved by controlling the spatial registration of field and remote sensing data. We have demonstrated the potential of an algorithm matching plot-level field tree positions with lidar canopy height models and derived local maxima to achieve a precise registration automatically. The accuracy of remote sensing-based estimates of forest parameters depends on the quality of the spatial registration of the training data. This study introduces an algorithm called RegisTree to correct field plot positions by matching a spatialized field tree height map with lidar canopy height models (CHMs). RegisTree is based on a point (field positions) to surface (CHM) adjustment approach modified to ensure that at least one field tree position corresponds to CHM local maxima. RegisTree has been validated with respect to positioning errors and the performance of lidar-derived estimation of plot volume. Overall, RegisTree enabled to register field plots surveyed in a range of forest conditions with a precision of 1.5 m (± 1.23 m), but a higher performance for conifer plots, and a limited efficiency in homogeneous stands, having similar heights. Improved plot positions were found to have a limited impact on volume predictions under the range of tested conditions, with a gain up to 1.3%. RegisTree could be used to improve the forest plot position from field surveys collected with low-grade GPS and to contribute to the development of processing chains of 3D remote sensing-based models of forest parameters.

Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning

Modern cut-to-length harvesters are recording information about each harvested tree, and with accurate positioning, this information can be used as field reference data, replacing manually measured reference data. In the present study, models developed from accurately positioned harvester data were compared with a reference model. A set of ∼55 000 accurately positioned trees was used as the basis for a division into 792 reference plots of 400 m2 each. A set of manually measured field plots was used for validation. Regression models were developed based on the relationship between airborne laser scanning data and the reference plot volumes. Separate models were developed for two strata: medium and high site productivity. Several modelling methods were compared, including nonparametric models; at the plot level, predictions for the validation dataset yielded RMSEs of 32%–60% for the medium productivity stratum and 19%–22% for the high productivity stratum. A reference model was fitted to the manually measured validation data in each stratum, and RMSEs of 45% and 25% were obtained for the medium and high productivity strata, respectively. The results show that the models based on the harvester data yield prediction errors at the same level as the reference model.

How much can airborne laser scanning based forest inventory by tree species benefit from auxiliary optical data?

The objective of this study was to investigate the benefit of three different optical data sources (Sentinel-2, Landsat 8 and aerial images) to support airborne laser scanning (ALS) data in species-specific forest inventory. The data covered 633 sample plots in eastern Finland. We used nearest neighbor imputation for simultaneous prediction of Scots pine, Norway spruce and broadleaved species’ volume by species group. The variable selection was performed by means of simulated annealing of different data combinations. The results showed that, on average, all optical data sources improved the species-specific plot volume predictions. The improvement was always greatest for broadleaves. The species-specific root mean square errors were 64.3%, 61.5%, 58.1% and 57.9% for ALS, ALS+Landsat 8, ALS+Sentinel-2 and ALS+aerial image data combinations, respectively, and 54.2% for ALS with the channels of both aerial images and Sentinel-2. Compared to using just ALS and aerial images, adding the Sentinel’s second red edge and narrow near-infrared bands improved the separation of pine and spruce. Sentinel-2 outperformed Landsat 8 and was almost as good an option as aerial images. The results suggest that all optical data, from airborne or spaceborne sources, are useful when combined with ALS data in species-specific forest inventory.

Photogrammetry for the estimation of wheat biomass and harvest index

Field-based next generation phenotyping has become of great interest to plant breeders and agricultural researchers in recent years, particularly for circumventing destructive or impractical phenotyping methods commonly used for certain traits. The non-destructive estimation of one such trait, above ground biomass (AGB), has been investigated repeatedly using 2D imagery, though little research has been conducted on 3D methods. The aims of the current study were to (i) investigate the use of readily-available consumer level digital cameras and software to estimate AGB, canopy height (CH) and harvest index (HI) of wheat plots, (ii) investigate the suitability of this data as a replacement for destructive sampling methods within a wheat breeding programme, and (iii) identify the point cloud density required for accurate estimation of AGB. To achieve this, a small plot trial of a single wheat cultivar was conducted in an irrigated nursery, at Roseworthy, South Australia. At physiological maturity plots were measured for CH and whole plots were harvested to attain AGB and threshed to measure grain yield and calculate HI. Prior to harvesting each plot was imaged using a digital camera, with these images being processed into 3D point clouds, which were subsequently used to estimate plot volume and CH. Strong correlations were observed between actual measurements of AGB, CH and HI to those estimated from point clouds. Images were processed in subset batches to determine an optimal number of images for processing. Stronger correlations between AGB and plot volume were observed when more images were processed, though as few as 48 images provided sufficiently accurate estimates of AGB. These methods were shown to be effective at estimating AGB, CH and HI and could be adopted by small scale research programmes. This study shows that a higher-throughput adaptation of this photogrammetry method could be used in phenotype intensive research such as plant breeding programmes.

Effect of flying altitude, scanning angle and scanning mode on the accuracy of ALS based forest inventory

Airborne laser scanning (ALS) is a widely used technology in the mapping of environment and forests. Data acquisition costs and the accuracy of the forest inventory are closely dependent on some extrinsic parameters of the ALS survey. These parameters have been assessed in numerous studies about a decade ago, but since then ALS devices have developed and it is possible that previous findings do not hold true with newer technology. That is why, the effect of flying altitudes (2000, 2500 or 3000m), scanning angles (±15° and ±20° off nadir) and scanning modes (single- and multiple pulses in air) with the area-based approach using a Leica ALS70HA-laser scanner was studied here. The study was conducted in a managed pine-dominated forest area in Finland, where eight separate discrete-return ALS data were acquired. The comparison of datasets was based on the bootstrap approach with 5-fold cross validation. Results indicated that the narrower scanning angle (±15° i.e. 30°) led to slightly more accurate estimates of plot volume (RMSE%: 21–24 vs. 22.5–25) and mean height (RMSE%: 8.5–11 vs. 9–12). We also tested the use case where the models are constructed using one data and then applied to other data gathered with different parameters. The most accurate models were identified using the bootstrap approach and applied to different datasets with and without refitting. The bias increased without refitting the models (bias%: volume 0±10, mean height 0±3), but in most cases the results did not differ much in terms of RMSE%. This confirms previous observations that models should only be used for datasets collected under similar data acquisition conditions. We also calculated the proportions of echoes as a function of height for different echo categories. This indicated that the accuracy of the inventory is affected more by the height distribution than the proportions of echo categories.

Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs) for both supervised (linear regression) and unsupervised (k-Means clustering) analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R2) of up to 0.63–0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data.

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [&lt;i&gt;Picea abies&lt;/i&gt; (L.) Karst.] Test Site in Romania

The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m3).