Aboveground Tree Biomass Research Articles

Radial variations in wood functional traits in a rain forest from eastern Amazonia

Key messagePositive radial trends in WSG were common among light-demanding species, and were mainly explained by radial shift in fiber and parenchyma traits.Trees can modify their wood structure in response to changes in mechanical, hydraulic and storage demands during their life-cycles. Thus, examining radial variations in wood traits is important to expand our knowledge of tree functioning and species ecological strategies. Yet, several aspects of radial changes in wood functional traits are still poorly understood, especially in angiosperm trees from tropical humid forests. Here, we examined radial shifts in wood traits in trunks of tropical forest species and explored their potential ecological implications. We first examined radial variations in wood specific gravity (WSG). Then, we asked what anatomical traits drove radial variations in WSG, and whether WSG, vessel fraction and specific hydraulic conductivity vary independently from each other along the radius gradients. We measured WSG and eight wood anatomical traits, at different radial positions along the trunks, in 19 tree species with contrasting shade-tolerance from a lowland tropical forest in eastern Amazonia. Most species had significant radials shifts in WSG. Positive radial gradients in WSG (i.e., increments from pith to bark) were common among shade-intolerant species and were explained by different combinations of fiber and parenchyma traits, while negative radial shifts in WSG (e.g., decreases towards the bark) were present in shade-tolerants, but were generally weakly related to anatomical traits. We also found that, in general, WSG was unrelated to vessel fraction and specific hydraulic conductivity in any radial position. This study illustrates the contrasting radial variations in wood functional traits that occur in tree species from a humid lowland tropical forest. In particular, our results provide valuable insights into the anatomical traits driving WSG variations during tree development. These insights are important to expand our knowledge on tree ecological strategies by providing evidence on how wood allocation varies as trees grow, which in turn can be useful in studying trait-demography associations, and in estimating tree above-ground biomass.

Comparing an exponential respiration model to alternative models for soil respiration components in a Canadian wildfire chronosequence (FireResp v1.0)

Abstract. Forest fires modify soil organic carbon and suppress soil respiration for many decades after the initial disturbance. The associated changes in soil autotrophic and heterotrophic respiration from the time of the forest fire, however, are less well characterized. The FireResp model predicts soil autotrophic and heterotrophic respiration parameterized with a novel dataset across a fire chronosequence in the Yukon and Northwest Territories of Canada. The dataset consisted of soil incubation experiments and field measurements of soil respiration and soil carbon stocks. The FireResp model contains submodels that consider a Q10 (exponential) model of respiration compared to models of heterotrophic respiration using Michaelis–Menten kinetics parameterized with soil microbial carbon. For model evaluation we applied the Akaike information criterion and compared predicted patterns in components of soil respiration across the chronosequence. Parameters estimated with data from the 5 cm soil depth had better model–data comparisons than parameters estimated with data from the 10 cm soil depth. The model–data fit was improved by including parameters estimated from soil incubation experiments. Models that incorporated microbial carbon with Michaelis–Menten kinetics reproduced patterns in autotrophic and heterotrophic soil respiration components across the chronosequence. Autotrophic respiration was associated with aboveground tree biomass at more recently burned sites, but this association was less robust at older sites in the chronosequence. Our results provide support for more structured soil respiration models than standard Q10 exponential models.

Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression

Most of the allometric models used to estimate tree aboveground biomass rely on tree diameter at breast height (DBH). However, it is difficult to measure DBH from airborne remote sensors, and is common to draw upon traditional least squares linear regression models to relate DBH with dendrometric variables measured from airborne sensors, such as tree height (H) and crown diameter (CD). This study explores the usefulness of ensemble-type supervised machine learning regression algorithms, such as random forest regression (RFR), categorical boosting (CatBoost), gradient boosting (GBoost), or AdaBoost regression (AdaBoost), as an alternative to linear regression (LR) for modelling the allometric relationships DBH = Φ(H) and DBH = Ψ(H, CD). The original dataset was made up of 2272 teak trees (Tectona grandis Linn. F.) belonging to three different plantations located in Ecuador. All teak trees were digitally reconstructed from terrestrial laser scanning point clouds. The results showed that allometric models involving both H and CD to estimate DBH performed better than those based solely on H. Furthermore, boosting machine learning regression algorithms (CatBoost and GBoost) outperformed RFR (bagging) and LR (traditional linear regression) models, both in terms of goodness-of-fit (R2) and stability (variations in training and testing samples).

Allometric models for aboveground biomass of six common subtropical shrubs and small trees

The aboveground biomass (AGB) of shrubs and small trees is the main component for the productivity and carbon storage of understory vegetation in subtropical secondary forests. However, few allometric models exist to accurately evaluate understory biomass. To estimate the AGB of five common shrub (diameter at base < 5 cm, < 5 m high) and one small tree species (< 8 m high, trees’s seedling), 206 individuals were harvested and species-specific and multi-species allometric models developed based on four predictors, height (H), stem diameter (D), crown area (Ca), and wood density (ρ). As expected, the six species possessed greater biomass in their stems compared with branches, with the lowest biomass in the leaves. Species-specific allometric models that employed stem diameter and the combined variables of D2H and ρDH as predictors accurately estimated the components and total AGB, with R2 values from 0.602 and 0.971. A multi-species shrub allometric model revealed that wood density × diameter × height (ρDH) was the best predictor, with R2 values ranging from between 0.81 and 0.89 for the components and total AGB, respectively. These results indicated that height (H) and diameter (D) were effective predictors for the models to estimate the AGB of the six species, and the introduction of wood density (ρ) improved their accuracy. The optimal models selected in this study could be applied to estimate the biomass of shrubs and small trees in subtropical regions.

Unlocking the potential of hyperspectral and LiDAR for above-ground biomass (AGB) and tree species classification in tropical forests

Tree species classification using a combination of airborne hyperspectral and Light Detection and Ranging (LiDAR) can provide valuable and effective methods for forest management, such as planning and monitoring purposes. However, only a few studies have applied tree species classification using both combinations in the tropical forest. The research takes a comparative classification approach to examine several classifiers using airborne hyperspectral in the tropical forest. In addition, Object-Based Image Analysis (OBIA) method was applied on hyperspectral data to extract the crown of individual tree species for classification and estimation purposes. Minimum Noise Fraction Transform (MNF) was applied to reduce the data dimensionality and different training samples from the various species used in this study. The result shows that Support Vector Machine (SVM) and Random Forest (RF) achieved the highest overall accuracy above 50% compared to other classifiers in the tropical forest. Besides, LiDAR data was also used to estimate individual trees' height for all species in the study area. The multiple coefficients of determination (R2) test result between LiDAR and field observation data in eight years gaps is 0.754. Therefore, Above-Ground Biomass (AGB) and carbon stock will estimate using a combination of LiDAR, hyperspectral, and field observation data for individual tree species. This method has proven to provide the required information for forest planning and generation in a short time, especially in tree species identification, AGB, and carbon stock estimation in tropical forests.

Assessing Pathways of Climate Change Effects in SpaDES: An Application to Boreal Landbirds of Northwest Territories Canada

Distributions of landbirds in Canadian northern forests are expected to be affected by climate change, but it remains unclear which pathways are responsible for projected climate effects. Determining whether climate change acts indirectly through changing fire regimes and/or vegetation dynamics, or directly through changes in climatic suitability may allow land managers to address negative trajectories via forest management. We used SpaDES, a novel toolkit built in R that facilitates the implementation of simulation models from different areas of knowledge to develop a simulation experiment for a study area comprising 50 million ha in the Northwest Territories, Canada. Our factorial experiment was designed to contrast climate effects pathways on 64 landbird species using climate-sensitive and non-climate sensitive models for tree growth and mortality, wildfire, and landbirds. Climate-change effects were predicted to increase suitable habitat for 73% of species, resulting in average net gain of 7.49 million ha across species. We observed higher species turnover in the northeastern, south-central (species loss), and western regions (species gain). Importantly, we found that most of the predicted differences in net area of occupancy across models were attributed to direct climate effects rather than simulated vegetation change, despite a similar relative importance of vegetation and climate variables in landbird models. Even with close to a doubling of annual area burned by 2100, and a 600 kg/ha increase in aboveground tree biomass predicted in this region, differences in landbird net occupancy across models attributed to climate-driven forest growth were very small, likely resulting from differences in the pace of vegetation and climate changes, or vegetation lags. The effect of vegetation lags (i.e., differences from climatic equilibrium) varied across species, resulting in a wide range of changes in landbird distribution, and consequently predicted occupancy, due to climate effects. These findings suggest that hybrid approaches using statistical models and landscape simulation tools could improve wildlife forecasts when future uncoupling of vegetation and climate is anticipated. This study lays some of the methodological groundwork for ecological adaptive management using the new platform SpaDES, which allows for iterative forecasting, mixing of modeling paradigms, and tightening connections between data, parameterization, and simulation.

Ontogenetic niche shifts in a locally endangered tree species (Olea europaea subsp. cuspidata) in a disturbed forest in Northern Ethiopia: Implications for conservation.

Understanding the responses of different ontogenetic stages to environmental and human disturbance factors is essential for developing efficient conservation strategies for endangered plant species. We examined how three ontogenetic stages of a locally endangered tree species, Olea europaea subsp. cuspidata, responded to environmental factors and human disturbance in Hugumburda dry Afromontane forest in Ethiopia. We counted individual seedlings, saplings and adults of O. europaea in 70 20 × 20 m quadrats over ca. 2.8 ha, and measured biotic (woody species richness, canopy cover, aboveground tree biomass, herbaceous cover), abiotic (soil and topographic variables), and human disturbance factors (logging and tracks). To detect ontogenetic niche shifts, we compared observed vs. simulated locations of trees in the three life stages and how they related to the environmental and human disturbance factors. We found that the population structure of O. europaea showed generally low recruitment, with few seedlings per hectare compared with the abundance of saplings and adults. The probability of finding O. europaea individuals was influenced by biotic (woody species richness) and abiotic (soil depth, slope) environmental conditions and human disturbance (logging intensity), but the direction, strength and shape of the relationships differed between seedling, sapling and adult life stages, indicating ontogenetic niche shifts. All life stages showed a positive relationship with elevation. The observed environmental niches of the different lifestages of O. europaea, and their association with human disturbance levels, should be considered when conservation strategies are developed for this species. Human disturbance in terms of logging decreases the abundance of saplings, but may facilitate emerging seedlings through creation of gaps with improved light conditions. Recruitment is, however, very low in the study area, and seedlings should be protected from browsing to enhance survival. Woody species richness in general should be conserved to optimize conditions also for O. europaea saplings.

Effects of topographic variability and forest attributes on fine-scale soil fertility in late-secondary succession of Atlantic Forest

BackgroundUnderstanding how soil fertility changes due to topographical conditions and forest attributes is an essential premise for local-scale forest management practices. We evaluated the effects of topographic variables and forest attributes on soil fertility along a local topographical gradient in a Brazilian Atlantic Forest. We hypothesised that soil fertility is positively affected by topographic variability and forest attributes (structure and diversity). We used tree species richness, composition, abundance, and aboveground biomass as forest attributes. We analysed two 1-ha forest patches with contrasting topographical conditions. We used different linear mixed effects models (LMMs) to test the main effects of different forest attributes and topography variables on soil fertility.ResultsThe results showed that higher topographic variability determines soil fertility along a fine-scale gradient. The first two axes of the PCA explained 66.8% of the variation in soil data, with the first axis (PCA1) explaining 49.6% of the variation in soil data and positively correlating with fertility-related soil properties. The second axis (PCA2) explained 17.2% of the variation in topographical data and positively correlated with convexity (the elevation of a plot minus the average elevation of all immediate neighbour plots) and elevation. Our best models showed that topographic variables (elevation and convexity) are the main predictors that affect fine-scale soil fertility.ConclusionsOur study demonstrates that the topographic variability, mainly elevation and convexity, determines fine-scale soil fertility in an Atlantic Forest. These results advance our understanding that context-dependent conditions based on topography and soil properties have a high variability at a fine scale, which can influence variations in forest attributes (i.e., species distribution, diversity and structure of tree communities). In addition, the information generated in this research may be important for planning forest restoration activities (passive and active) based on the high variability of environmental variables at a fine scale.

Mapping Canopy Heights in Dense Tropical Forests Using Low-Cost UAV-Derived Photogrammetric Point Clouds and Machine Learning Approaches

Tropical forests are a key component of the global carbon cycle and climate change mitigation. Field- or LiDAR-based approaches enable reliable measurements of the structure and above-ground biomass (AGB) of tropical forests. Data derived from digital aerial photogrammetry (DAP) on the unmanned aerial vehicle (UAV) platform offer several advantages over field- and LiDAR-based approaches in terms of scale and efficiency, and DAP has been presented as a viable and economical alternative in boreal or deciduous forests. However, detecting with DAP the ground in dense tropical forests, which is required for the estimation of canopy height, is currently considered highly challenging. To address this issue, we present a generally applicable method that is based on machine learning methods to identify the forest floor in DAP-derived point clouds of dense tropical forests. We capitalize on the DAP-derived high-resolution vertical forest structure to inform ground detection. We conducted UAV-DAP surveys combined with field inventories in the tropical forest of the Congo Basin. Using airborne LiDAR (ALS) for ground truthing, we present a canopy height model (CHM) generation workflow that constitutes the detection, classification and interpolation of ground points using a combination of local minima filters, supervised machine learning algorithms and TIN densification for classifying ground points using spectral and geometrical features from the UAV-based 3D data. We demonstrate that our DAP-based method provides estimates of tree heights that are identical to LiDAR-based approaches (conservatively estimated NSE = 0.88, RMSE = 1.6 m). An external validation shows that our method is capable of providing accurate and precise estimates of tree heights and AGB in dense tropical forests (DAP vs. field inventories of old forest: r2 = 0.913, RMSE = 31.93 Mg ha−1). Overall, this study demonstrates that the application of cheap and easily deployable UAV-DAP platforms can be deployed without expert knowledge to generate biophysical information and advance the study and monitoring of dense tropical forests.

Radial variations in wood density, and their implications for above-ground biomass estimations, in a tropical high-andean forest

Wood density (WD) is a central trait to explain tree functioning, and is also an important predic­tor of tree above-ground biomass (AGB). Therefore, radial trends in WD (i.e., from pith to bark) may have important implications in understanding tree life-history variations, as well as in forest biomass and carbon estimations. The occurrence of these radial trends in WD is thought to vary among forests, particularly with canopy openness and stratification. Yet, most of the studies on this topic in tropical forests have been conducted on lowland closed-canopy forests, while very little is known about the prevalence and magnitude of these trends among trees from open-canopy forests, such as high-mountain forests. I examined radial gradients in WD and explored their implications for AGB estimations. Radial wood cores were taken with increment borers from 69 trees belonging to 18 species from a high-An­dean forest. Each wood core was cut every 1-cm, and WD was measured for every 1-cm segment. Errors in AGB estimations that resulted from not considering radial trends in WD were estimated for each tree and species. Eight out of eighteen species had significant radial trends in WD. Among these species, two species showed decreases of WD towards the bark, one species showed increments of WD from pith to bark, and five species showed U-shaped gradients (i.e., high WD near the pith and bark, and relatively low WD at inter­mediate diameters). The prevalence of U-shaped radial trends in WD may be related to the relatively open and less stratified canopy of the study forest. Not taking into account radial trends in WD led in general to under-estimations of AGB (averaging −7.66 % when using mean WD, and −5.56 % for outer WD) in most of the study species, suggesting that tropical high-Andean forests may possibly store more biomass carbon than has been previously estimated. These findings are important to expand our knowledge on wood allocation patterns during tree ontogeny, and also to improve the accuracy of biomass and carbon estimations in tropical high-Andean forests.

Assessment of tree species diversity and above-ground biomass in two disturbed tropical dry evergreen forests of Coromandel coast of India

The tree diversity and carbon stock of all woody plants were investigated in two-hectare square plots (100 m x 100 m) were established in Suryanpet (SP) and Velleripet (VP) which is tropical dry evergreen forest (TDEF) sites on the Coromandel Coast of peninsular India. All trees ? 10 cm girth at breast height measured at 1.3 m from the rooting point were enumerated. A total of 35 tree species (? 10 cm gbh) belonging to 34 genera and 23 families were recorded in tropical dry evergreen forests. Tree species richness in 27 (dominant species Dimorphocalyx glabellus Thw.) and 18 (dominant species Strychnos nux-vomica L.) in SP and VP respectively. A total density of woody plants 671 and 1154 individuals in SP and VP respectively. The basal area of trees in the two study sites SP (40.70 m2 ha-1) and VP (45.46 m2 ha-1). Most abundant families are Loganiaceae, Euphorbiaceae and Rutaceae and its family index value (FIV) is 56.14, 39.12 and 21.40 respectively. The aboveground biomass (AGB) of trees totaled in site SP (405.3 Mg ha-1) and VP (721.3 Mg ha-1). The extent of tree species diversity and estimated carbon stock of the TDEF sites, which provides the baseline data on the floristic structure and diversity of this forest for better management and conservation.

Temporal dynamics of above ground biomass of Kaimoor Wildlife Sanctuary, Uttar Pradesh, India: conjunctive use of field and Landsat data

The present study assesses the temporal dynamics of above ground biomass (AGB) in Kaimoor Wildlife Sanctuary (KWS), Uttar Pradesh, India using series of Landsat data for the years 1989, 2000, 2010 and 2018. The satellite images were preprocessed for surface reflectance, and subsequently we computed Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI). A forest density map was generated by slicing the computed NDVI and this was used as a basis for sampling strategy. A total of 30 sampling locations were randomly identified. Plot size of 30 m × 30 m was established. Tree species were enumerated at each plot and the girth at breast height and tree height measurements were recorded for the year 2018. Tree and plot level AGB (i.e. AGBF, t ha−1) was computed by multiplying the tree volume and specific gravity of wood. AGB (i.e. AGBP) prediction models were developed as linear regression equations for the year 2018 by assessing the vegetation indices and the AGBF. The significant AGBP models (R2 = 0.94; P = 0.0001) were applied for all study years after the data correction among the Landsat sensor series. The AGBP was over estimated (22.67 t ha−1; 7.75%) compared to AGBF (t ha−1). Moreover, EVI (R2 = 0.90) was found to be a better predictor for AGB compared to NDVI (R2 = 0.69) or SAVI (R2 = 0.77). The AGBP of KWS ranged between 289 ± 36 (in 1989) and 292 ± 40 (in 2018) with an average decadal positive change of 1.06%.

Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia

Abstract. Setiahadi R. 2021. Comparison of individual tree aboveground biomass estimation in community forests using allometric equation and expansion factor in Magetan, East Java, Indonesia. Biodiversitas 22: 3899-3909. The use of allometric equation and biomass expansion factor can facilitate more efficient tree biomass estimation. This study evaluates the accuracy of the allometric equation and expansion factor for quantifying the individual tree aboveground biomass in community forest tree species. Destructive sampling n on 120 trees from four different species: Falcataria moluccana, Melia azedarach, Swietenia macrophylla, and Tectona grandis. For each tree sample, aboveground biomass measured at every tree component, i.e., stem, branches, and leaves. The allometric equation developed using regression analysis with several predictor variables, such as diameter at breast height (D), squared diameter at breast height combined with tree height (D2H), and D and H separately. On another side, the biomass expansion factor was calculated based on the total aboveground biomass and stem biomass ratio. The results found the highest mean aboveground biomass for all species are M. azedarach (326.36±88.40 kg tree-1), S. macrophylla (244.47±98.73 kg tree-1), T. grandis (173.31±80.97 kg tree-1), and F. moluccana (56.56±23.10 kg tree-1). The most significant average biomass expansion factor observed in M. azedarach (1.78±0.03), adhered by T. grandis (1.66±0.09), S. macrophylla (1.61±0.04), and F. moluccana (1.59±0.06). The equation ln? = lna + b x ln (D) was best for estimating aboveground biomass in each tree component and a total of four species with an accuracy of more than 90%.

High aboveground carbon stock of African tropical montane forests.

Tropical forests store 40-50per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4megagrams of carbon per hectare (95% confidence interval 137.1-164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70per cent and 32per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to helpto guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems.

Permafrost condition determines plant community composition and community-level foliar functional traits in a boreal peatland.

Boreal peatlands are critical ecosystems globally because they house 30%–40% of terrestrial carbon (C), much of which is stored in permafrost soil vulnerable to climate warming‐induced thaw. Permafrost thaw leads to thickening of the active (seasonally thawed) layer and alters nutrient and light availability. These physical changes may influence community‐level plant functional traits through intraspecific trait variation and/or species turnover. As permafrost thaw is expected to cause an efflux of carbon dioxide (CO2) and methane (CH4) from the soil to the atmosphere, it is important to understand thaw‐induced changes in plant community productivity to evaluate whether these changes may offset some of the anticipated increases in C emissions. To this end, we collected vascular plant community composition and foliar functional trait data along gradients in aboveground tree biomass and active layer thickness (ALT) in a rapidly thawing boreal peatland, with the expectation that changes in above‐ and belowground conditions are indicative of altered resource availability. We aimed to determine whether community‐level traits vary across these gradients, and whether these changes are dominated by intraspecific trait variation, species turnover, or both. Our results highlight that variability in community‐level traits was largely attributable to species turnover and that both community composition and traits were predominantly driven by ALT. Specifically, thicker active layers associated with permafrost‐free peatlands (i.e., bogs and fens) shifted community composition from slower‐growing evergreen shrubs to faster‐growing graminoids and forbs with a corresponding shift toward more productive trait values. The results from this rapidly thawing peatland suggest that continued warming‐induced permafrost thaw and thermokarst development alter plant community composition and community‐level traits and thus ecosystem productivity. Increased productivity may help to mitigate anticipated CO2 efflux from thawing permafrost, at least in the short term, though this response may be swamped by increase CH4 release.

Tree Diversity, Site Index, and Carbon Storage Decrease With Aridity in Douglas-Fir Forests in Western Canada

Forests are important for biodiversity, timber production and carbon accumulation, but these ecosystem services may be impacted by climate change. Field data collected from individual forest types occurring across a climatic gradient can contribute to forecasting these consequences. We examined how changes in temperature, precipitation and aridity affect ecosystem services in 23 mature Douglas-fir (Pseudotsuga menziesii) forests in nine climatic regions across a 900 km gradient in British Columbia, Canada. Using Canadian National Forest Inventory methodology, we assessed richness and diversity of plant functional groups, site index, and above- and below-ground carbon stocks. As aridity increased, ecosystem-level tree species richness declined on average from four to one species, Douglas-fir site index declined from 30 to 15 m, and ecosystem carbon storage decreased from 565 to 222 Mg ha–1. Tree species richness was positively and herb species richness negatively correlated with carbon storage. Carbon storage by ecosystem compartment was largest in aboveground live tree biomass, declining in the following order: mineral soils &amp;gt; coarse woody debris and dead standing trees &amp;gt; forest floor &amp;gt; small and fine woody debris &amp;gt; understory plants. Mineral soil carbon at depths of 0-15 cm, 15-35 cm, and 35-55 cm increased with increasing mean annual precipitation and decreasing aridity. Our results indicate that as aridity increases and precipitation decreases, tree species richness, site index and carbon storage in existing Douglas-fir forests declines. However, assisted or natural migration of Douglas-fir into more humid regions could be associated with more diverse, productive, carbon-rich forests. This study informs carbon stock vulnerability and provides empirical data essential for carbon stock forecasts.

Effect of scan angle on ALS metrics and area-based predictions of forest attributes for balsam fir dominated stands

Abstract In this study, we assessed the effect of airborne laser scanning (ALS) scan angle on point cloud metrics and the estimation of forest attributes in balsam fir (Abies balsamea (L.) Mill.) dominated forests of western Newfoundland, Canada. We collected calibration data from ground plot locations representing varying scan angles from two flight lines: within 4° of nadir in one flight line, and either 11–20° from nadir (low scan angle plots: L), or 21–30° from nadir (high scan angle plots: H) in an adjacent flight line. We computed three sets of ALS point cloud metrics for each ground plot using ALS data from: individual flight lines (near-nadir and off-nadir) and data from all available flight lines (up to 4) combined (aggregated, as commonly used in an operational inventory context). We generated three sets of models for each of the L and H plots using the ALS metric sets, and applied the models to independent validation data. We analysed the effect of scan angle on both the ALS metrics and performance statistics for area-based models generated using the L and H datasets. Our results demonstrate that off-nadir scan angles significantly affected (P &amp;lt; 0.05) specific metrics from both L (i.e. coefficient of variation (COVAR)) and H (i.e. maximum height, 95th percentile of height, mean height) plots, although the effects were trivial (mean absolute differences were ≤ 0.01 for COVAR and &amp;lt; 0.3 m for the height metrics). Forest attribute predictions using these and other metrics were also significantly affected (P &amp;lt; 0.05), namely gross merchantable volume (GMV), total volume (TVOL) and aboveground tree biomass (AGB) from L; and Lorey’s mean height (HGT), mean diameter at breast height (DBH), and GMV from H. We further demonstrated that combining ALS data from all available flight lines significantly increased errors for the predictions of HGT, GMV, and TVOL using L, and significantly reduced errors of HGT using H when compared to errors resulting from models developed with near-nadir data. While the differences in prediction errors were significant, they were small, with differences in mean absolute prediction errors all &amp;lt;1.3 per cent. Based on our results, we concluded that the effects of large scan angles, up to 30° off-nadir, on area-based forest attribute predictions were minimal in this study, which used ALS metrics calculated from ALS returns with a height above ground &amp;gt;2 m for balsam fir-dominated forests. This result may provide for operational efficiencies in implementing enhanced forest inventories in this particular forest environment.

Estimating tree aboveground biomass using multispectral satellite-based data in Mediterranean agroforestry system using random forest algorithm

Forest aboveground biomass (AGB) is a key biophysical variable to assess and monitor the spatio-temporal changes of forest ecosystems. AGB should be accurately and timely estimated through remote sensing to provide valuable information to better support sustainable forest management strategies. QuickBird and WorldView-2 satellites data and Random Forest (RF) regression model were used to estimate tree AGB in Mediterranean agroforestry systems. Spectral bands, vegetation indices and Grey-Level Co-occurrence Matrix (GLCM) texture features of 140 plots with and without vegetation mask were used as independent variables, while total of AGB per plot was used as dependent variable. A model with good performance was obtained for a complex agroforestry system, with an R2 of 82.0% and RMSE of 10.5 t/ha (22.6%). The top 11 most important variables have 80.3% of total relative importance, with 59.6% of GLCM textural features, 12.3% of vegetation indices and 8.4% of spectral bands. The results highlight the importance of the variable GLCM texture, and the use of vegetation mask and RF regression model to collect accurate spatial information on key crown cover attributes, by excluding the spectral contribution of understory vegetation and soil characteristic, of Mediterranean agroforestry systems.

Modelling Tree Growth in Monospecific Forests from Forest Inventory Data

The prediction of tree growth is key to further understand the carbon sink role of forests and the short-term forest capacity on climate change mitigation. In this work, we used large-scale data available from three consecutive forest inventories in a Euro-Mediterranean region and the Bertalanffy–Chapman–Richards equation to model up to a decade’s tree size variation in monospecific forests in the growing stages. We showed that a tree-level fitting with ordinary differential equations can be used to forecast tree diameter growth across time and space as function of environmental characteristics and initial size. This modelling approximation was applied at different aggregation levels to monospecific regions with forest inventories to predict trends in aboveground tree biomass stocks. Furthermore, we showed that this model accurately forecasts tree growth temporal dynamics as a function of size and environmental conditions. Further research to provide longer term prediction forest stock dynamics in a wide variety of forests should model regeneration and mortality processes and biotic interactions.

Compost applications increase bacterial community diversity in the apple rhizosphere

AbstractSustainable practices are key to the improvement of soil fertility and quality in apple (Malus × domestica Borkh.) orchards. Rootstock genotype and fertilizer inputs can alter soil biology, as well as aboveground traits including nutrient acquisition. In this study, a factorial design was used to assess the interaction between two apple rootstocks, ‘Geneva 41’ (‘G.41’) and ‘Malling 9’ (‘M.9’) with four fertilizer treatments [chicken‐litter compost, yardwaste compost, fertigation using Ca(NO3)2, and an unamended control]. The bacterial community in the rhizosphere was assessed for its impact on both plant and soil properties for each rootstock × fertilizer treatment combination. The bacterial community was dominated by Acidobacteria, Proteobacteria, and Planctomycetes, but Verrucomicrobia and Chloroflexi were the most responsive to the fertilizer treatments. The chicken litter and yardwaste treatments had a greater effect on bacterial community structure than the control. Yardwaste, in particular, was associated with increased relative abundance of Chloroflexi, which was correlated with soil nutrient concentrations. Malling 9 had a greater bacterial diversity than G.41, but the rootstock treatment had no independent effect on the rhizosphere community structure. There was, however, a strong interaction between the rootstock and fertilizer treatments. Carbon cycling was the most prominent functional change associated with the soil bacterial community. These results suggest that compost amendments have a more positive effect on soil bacterial activity and nutrient availability than Ca(NO3)2. Our work shows that waste‐stream amendments can lead to multiple positive responses, such as increasing aboveground tree biomass, thus potentially improving orchard productivity.