Terrestrial LiDAR-derived non-destructive woody biomass estimates for 10 hardwood species in Virginia.
Ontology highlight
ABSTRACT:
SUBMITTER: Stovall AEL
PROVIDER: S-EPMC6141268 | biostudies-literature | 2018 Aug
REPOSITORIES: biostudies-literature
Publications
Terrestrial LiDAR-derived non-destructive woody biomass estimates for 10 hardwood species in Virginia.
Stovall Atticus E L AEL Anderson-Teixeira Kristina J KJ Shugart Herman H HH
Data in brief 20180622
This article contains data related to the research article entitled "Assessing terrestrial laser scanning for developing non-destructive biomass allometry" (Stovall et al., 2018 [1]) and presents 258 terrestrial LiDAR-derived estimates of tree volume and biomass. The terrestrial LiDAR acquisitions were completed in the Center for Tropical Forest Science - Forest Global Earth Observatory (CTFS-ForestGEO) plot in Front Royal, Virginia, USA. The data includes tree diameter at breast height (DBH), t ...[more]
Similar Datasets
Project description:Background:Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results:In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2?=?0.97, RMSE?=?2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion:We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.
| S-EPMC7222476 | biostudies-literature
Project description:In recent years, LIght Detection And Ranging (LiDAR) and especially Terrestrial Laser Scanning (TLS) systems have shown the potential to revolutionise forest structural characterisation by providing unprecedented 3D data. However, manned Airborne Laser Scanning (ALS) requires costly campaigns and produces relatively low point density, while TLS is labour intense and time demanding. Unmanned Aerial Vehicle (UAV)-borne laser scanning can be the way in between. In this study, we present first results and experiences with the RIEGL RiCOPTER with VUX ® -1UAV ALS system and compare it with the well tested RIEGL VZ-400 TLS system. We scanned the same forest plots with both systems over the course of two days. We derived Digital Terrain Model (DTMs), Digital Surface Model (DSMs) and finally Canopy Height Model (CHMs) from the resulting point clouds. ALS CHMs were on average 11.5 c m higher in five plots with different canopy conditions. This showed that TLS could not always detect the top of canopy. Moreover, we extracted trunk segments of 58 trees for ALS and TLS simultaneously, of which 39 could be used to model Diameter at Breast Height (DBH). ALS DBH showed a high agreement with TLS DBH with a correlation coefficient of 0.98 and root mean square error of 4.24 c m . We conclude that RiCOPTER has the potential to perform comparable to TLS for estimating forest canopy height and DBH under the studied forest conditions. Further research should be directed to testing UAV-borne LiDAR for explicit 3D modelling of whole trees to estimate tree volume and subsequently Above-Ground Biomass (AGB).
| S-EPMC5677400 | biostudies-literature
Project description:Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Square Based Columns (SBCs) are considered for their ability to estimate the volume of temperate and tropical trees, as well as geomorphological features such as bluffs and saltmarsh creeks. For temperate trees, supplementary geometric models are evaluated for their ability to bound the uncertainty in cylinder-based reconstructions, finding that coarser volumetric methods do not currently constrain volume meaningfully, but may be helpful with further refinement, or in hybridized models. Three-dimensional convex hull polygons consistently overestimate object volume, and SBCs consistently underestimate volume. Voxel estimations vary in their bias, due to the point density of the TLS data, and occlusion, particularly in trees. The response of the models to parametrization is analysed, observing unexpected trends in the SBC estimates for the drumlin dataset. Establishing that this result is due to the resolution of the TLS observations being insufficient to support the resolution of the geometric model, it is suggested that geometric models with predictable outcomes can also highlight data quality issues when they produce illogical results.
| S-EPMC5829184 | biostudies-literature
Project description:Above-ground biomass (AGB) is a trait with much potential for exploitation within wheat breeding programs and is linked closely to canopy height (CH). However, collecting phenotypic data for AGB and CH within breeding programs is labor intensive, and in the case of AGB, destructive and prone to assessment error. As a result, measuring these traits is seldom a priority for breeders, especially at the early stages of a selection program. LiDAR has been demonstrated as a sensor capable of collecting three-dimensional data from wheat field trials, and potentially suitable for providing objective, non-destructive, high-throughput estimates of AGB and CH for use by wheat breeders. The current study investigates the deployment of a LiDAR system on a ground-based high-throughput phenotyping platform in eight wheat field trials across southern Australia, for the non-destructive estimate of AGB and CH. LiDAR-derived measurements were compared to manual measurements of AGB and CH collected at each site and assessed for their suitability of application within a breeding program. Correlations between AGB and LiDAR Projected Volume (LPV) were generally strong (up to r = 0.86), as were correlations between CH and LiDAR Canopy Height (LCH) (up to r = 0.94). Heritability (H2) of LPV (H2 = 0.32-0.90) was observed to be greater than, or similar to, the heritability of AGB (H2 = 0.12-0.78) for the majority of measurements. A similar level of heritability was observed for LCH (H2 = 0.41-0.98) and CH (H2 = 0.49-0.98). Further to this, measurements of LPV and LCH were shown to be highly repeatable when collected from either the same or opposite direction of travel. LiDAR scans were collected at a rate of 2,400 plots per hour, with the potential to further increase throughput to 7,400 plots per hour. This research demonstrates the capability of LiDAR sensors to collect high-quality, non-destructive, repeatable measurements of AGB and CH suitable for use within both breeding and research programs.
| S-EPMC6775483 | biostudies-literature
Project description:BACKGROUND AND AIMS:To meet the increasing need for rapid and non-destructive extraction of canopy traits, two methods were used and compared with regard to their accuracy in estimating 2-D and 3-D parameters of a hybrid poplar sapling. METHODS:The first method consisted of the analysis of high definition photographs in Tree Analyser (TA) software (PIAF-INRA/Kasetsart University). TA allowed the extraction of individual traits using a space carving approach. The second method utilized 3-D point clouds acquired from terrestrial light detection and ranging (T-LiDAR) scans. T-LiDAR scans were performed on trees without leaves to reconstruct the lignified structure of the sapling. From this skeleton, foliage was added using simple modelling rules extrapolated from field measurements. Validation of the estimated dimension and the accuracy of reconstruction was then achieved by comparison with an empirical data set. KEY RESULTS:TA was found to be slightly less precise than T-LiDAR for estimating tree height, canopy height and mean canopy diameter, but for 2-D traits both methods were, however, fully satisfactory. TA tended to over-estimate total leaf area (error up to 50 %), but better estimates were obtained by reducing the size of the voxels used for calculations. In contrast, T-LiDAR estimated total leaf area with an error of <6 %. Finally, both methods led to an over-estimation of canopy volume. With respect to this trait, T-LiDAR (14·5 % deviation) greatly surpassed the accuracy of TA (up to 50 % deviation), even if the voxels used were reduced in size. CONCLUSIONS:Taking into account their magnitude of data acquisition and analysis and their accuracy in trait estimations, both methods showed contrasting potential future uses. Specifically, T-LiDAR is a particularly promising tool for investigating the development of large perennial plants, by itself or in association with plant modelling.
| S-EPMC3189831 | biostudies-other
Project description:Root to shoot ratio (RS) is commonly used to describe the biomass allocation between below- and aboveground parts of plants. Determining the key factors influencing RS and interpreting the relationship between RS and environmental factors is important for biological and ecological research. In this study, we compiled 2088 pairs of root and shoot biomass data across China's terrestrial biomes to examine variations in the RS and its responses to biotic and abiotic factors including vegetation type, soil texture, climatic variables, and stand age. The median value of RS (RSm) for grasslands, shrublands, and forests was 6.0, 0.73, and 0.23, respectively. The range of RS was considerably wide for each vegetation type. RS values for all three major vegetation types were found to be significantly correlated to mean annual precipitation (MAP) and potential water deficit index (PWDI). Mean annual temperature (MAT) also significantly affect the RS for forests and grasslands. Soil texture and forest origin altered the response of RS to climatic factors as well. An allometric formula could be used to well quantify the relationship between aboveground and belowground biomass, although each vegetation type had its own inherent allometric relationship.
| S-EPMC3977935 | biostudies-literature
Project description:Resprouting multi-stemmed woody plants form an important component of the woody vegetation in many ecosystems, but a clear methodology for reliable measurement of their size and quick, non-destructive estimation of their woody biomass and carbon stock is lacking. Our goal was to find a minimum number of sprouts, i.e., the most easily obtainable, and sprout parameters that should be measured for accurate sprout biomass and carbon stock estimates. Using data for 5 common temperate woody species, we modelled carbon stock and sprout biomass as a function of an increasing number of sprouts in an interaction with different sprout parameters. The mean basal diameter of only two to five of the thickest sprouts and the basal diameter and DBH of the thickest sprouts per stump proved to be accurate estimators for the total sprout biomass of the individual resprouters and the populations of resprouters, respectively. Carbon stock estimates were strongly correlated with biomass estimates, but relative carbon content varied among species. Our study demonstrated that the size of the resprouters can be easily measured, and their biomass and carbon stock estimated; therefore, resprouters can be simply incorporated into studies of woody vegetation.
| S-EPMC4342014 | biostudies-literature
Project description:Being the only sustainable source of organic carbon, biomass is playing an ever-increasingly important role in our energy landscape. The conversion of renewable lignocellulosic biomass into liquid fuels is particularly attractive but extremely challenging due to the inertness and complexity of lignocellulose. Here we describe the direct hydrodeoxygenation of raw woods into liquid alkanes with mass yields up to 28.1 wt% over a multifunctional Pt/NbOPO4 catalyst in cyclohexane. The superior performance of this catalyst allows simultaneous conversion of cellulose, hemicellulose and, more significantly, lignin fractions in the wood sawdust into hexane, pentane and alkylcyclohexanes, respectively. Investigation on the molecular mechanism reveals that a synergistic effect between Pt, NbOx species and acidic sites promotes this highly efficient hydrodeoxygenation of bulk lignocellulose. No chemical pretreatment of the raw woody biomass or separation is required for this one-pot process, which opens a general and energy-efficient route for converting raw lignocellulose into valuable alkanes.
| S-EPMC4820995 | biostudies-literature
Project description:For the first time quantitative data on the abundance of Bacteria, Archaea, and Eukarya in deep terrestrial sediments are provided using multiple methods (total cell counting, quantitative real-time PCR, Q-PCR and catalyzed reporter deposition-fluorescence in situ hybridization, CARD-FISH). The oligotrophic (organic carbon content of ?0.2%) deep terrestrial sediments in the Chesapeake Bay area at Eyreville, Virginia, USA, were drilled and sampled up to a depth of 140?m in 2006. The possibility of contamination during drilling was checked using fluorescent microspheres. Total cell counts decreased from 10(9) to 10(6) cells/g dry weight within the uppermost 20?m, and did not further decrease with depth below. Within the top 7?m, a significant proportion of the total cell counts could be detected with CARD-FISH. The CARD-FISH numbers for Bacteria were about an order of magnitude higher than those for Archaea. The dominance of Bacteria over Archaea was confirmed by Q-PCR. The down core quantitative distribution of prokaryotic and eukaryotic small subunit ribosomal RNA genes as well as functional genes involved in different biogeochemical processes was revealed by Q-PCR for the uppermost 10?m and for 80-140?m depth. Eukarya and the Fe(III)- and Mn(IV)-reducing bacterial group Geobacteriaceae were almost exclusively found in the uppermost meter (arable soil), where reactive iron was detected in higher amounts. The bacterial candidate division JS-1 and the classes Anaerolineae and Caldilineae of the phylum Chloroflexi, highly abundant in marine sediments, were found up to the maximum sampling depth in high copy numbers at this terrestrial site as well. A similar high abundance of the functional gene cbbL encoding for the large subunit of RubisCO suggests that autotrophic microorganisms could be relevant in addition to heterotrophs. The functional gene aprA of sulfate reducing bacteria was found within distinct layers up to ca. 100?m depth in low copy numbers. The gene mcrA of methanogens was not detectable. Cloning and sequencing data of 16S rRNA genes revealed sequences of typical soil Bacteria. The closest relatives of the archaeal sequences were Archaea recovered from terrestrial and marine environments. Phylogenetic analysis of the Crenarchaeota and Euryarchaeota revealed new members of the uncultured South African Gold Mine Group, Deep Sea Hydrothermal Vent Euryarchaeotal Group 6, and Miscellaneous Crenarcheotic Group clusters.
| S-EPMC3141351 | biostudies-literature
Project description:Lignocellulosic biomass, being ubiquitous and easily accessible, bears a huge potential for sustainable energy and other products. Fractionation, delignification, and subsequent utilization of hardwood biomass has been ever challenging for a bio-based refinery. Acacia nilotica (kikar in local language) is a hardwood tree commonly found in Pakistan, where its abundance owes to favorable and benign environmental conditions. Ionic liquids based on superbase "tetramethylguanidine (TMG)" are green solvents that are found to be good cellulose processing agents. Previously, TMG-based protic ionic liquids (PILs) have been used for cellulose processing and transformation into value-added products. In this study, an ionic liquid comprising a tetramethylguanidinium cation and a hydrogen sulfate anion was employed for the evaluation of the potential of acacia for fermentable sugars and lignin. Pretreatment was carried out at 100, 120, and 140 °C for 0.5, 1, 2 and 4 hours. The results indicate that [TMG][HSO4] is an efficient delignifying agent affording 81% lignin removal and 76% sugar yield by subsequent enzymatic hydrolysis. The evaluation of the efficiency of IL and the biomass was verified by compositional analysis, FT-IR, HSQC, and SEM analyses.
| S-EPMC9033503 | biostudies-literature