Mixed Canopy Research Articles

An evaluation of Goudriaan’s summary model for light interception in strip canopies, using functional-structural plant models

Abstract Dealing with heterogeneity in leaf canopies when calculating light interception per species in a mixed canopy is a challenge. Goudriaan developed a computationally simple, though conceptually sophisticated, model for light interception in strip canopies, which can be reasonably represented as ‘blocks’, such as vineyards and crop rows. This model is widely used, but there is no independent verification of the model. Hence, we developed a comparison of light interception calculations with Goudriaan’s model and with detailed spatially explicit three-dimensional functional–structural plant models (FSPM) of maize in which plant architecture can be represented explicitly. Two models were developed, one with small randomly oriented leaves in blocks, similar to Goudriaan’s assumption, which we refer to as the intermediate model (IM), and another with a realistic representation of individual plants with stems and leaves having shape, orientation and so on, referred as FSPM. In IM and FSPM, light interception was calculated using ray tracing. In Goudriaan’s model, the light extinction coefficient (k), including both its daily and seasonal average values, was generated using the FSPM. Correspondence between the three models was excellent in terms of light capture for different levels of crop height, leaf area and uniformity, with the difference less than 3.3 %. The results are strong support for the use of Goudriaan’s summary model for calculating light interception in strip canopies.

The early impact of mixed canopies with Norway spruce, European beech and silver fir on a new forest floor

The character of pure or mixed forest canopies and their litterfalls contribute to different forest-floor properties. These organic layers and also subjacent topsoil were studied at three study sites covered by mixed treatments such as beech–spruce, beech–fir, spruce–fir and two monospecific beech and spruce treatments. The age of the forest stands ranged from 11 to 15 years when sampled. All study sites were used as meadows when afforested; therefore, the forest floors were new, and the A-horizon topsoil properties were not influenced by older humus inherited from previous forest generations. The mineral soil was likely affected by different levels of former fertilization, which resulted in differences among the study sites. The early-developed forest floors showed differences between the treatments with beech and the others. The topsoil below beech with spruce had more nitrogen, oxidizable carbon and cations of exchangeable hydrogen as well as pH showing more acidic conditions and lower contents and saturation of base cations. Pure beech had more phosphorus. The nutrient pools did not differ among the treatments; significantly more matter was found below the oldest stands on the first afforested site, which also increased nutrient pools.

The effect of Crataegus and Berberis canopy types on bioindicators of soil quality in a semi-arid climate

Crataegus and Berberis shrublands are natively distributed in Asia, Europe, North America and Africa. Despite the widespread distribution of these two species, the effect of pure and mixed canopy composition of these species on bioindicators of soil quality has received little attention. In the current research, the effect of pure [Crataegus (PC) and Berberis (PB)] and mixed [Crataegus and Berberis with almost equal canopy cover (50C:50B), Crataegus-dominated stand (70C:30B) and Berberis-dominated stand (30C:70B)] canopy types on litter properties, soil fauna and flora communities in different depths (0–10, 10–20 and 20–30 cm) was studied in the Mazandaran province, Iran. A total of 75 litter and 225 soil samples were collected and analysed. The results of this research showed that soil micro-climate and fertility are the main drivers of the population, composition and diversity of soil biota under the studied canopy covers. Increased soil temperature commonly had adverse effects on the populations of soil biota in the study area. Accordingly, the 50C:50B canopy type with a more appropriate micro-climate (i.e., higher soil moisture and lower soil temperature) and fertile soils showed higher populations of soil organisms, especially in the topsoil (0–10 cm). It is expected that results can improve scientific approaches to better comprehend of soil functions mechanism in the different canopy covers and also help to optimize land cover structures and enhancing of ecosystem services in semi-arid regions.

Smallholder Coffee in the Global Economy—A Framework to Explore Transformation Alternatives of Traditional Agroforestry for Greater Economic, Ecological, and Livelihood Viability

Sixty percent of global coffee is produced from farms of <5 ha. Studies show that returns from such farms do not generate a living income for producers or workers threatening supplies. Smallholders use agroforestry to reduce coffee production costs, diversify income and address livelihood needs. We undertook a three-phase analysis to test the following hypothesis. Current coffee agroforestry must shift from a low labor, low risk-stable return, slowly-changing matrix to more active management of species and stem turnover in system renovation cycles targeted to sustaining, reorienting and intensifying ecosystem-based benefits to coffee production, diversified income and household food. First, we conducted a document survey of current traditional tree diversity, research trends, and market drivers for more benefits-oriented agroforestry. Second, we proposed a framework for multiple benefits quantification converting tree use characteristics and density into five categories of benefits, each with sub-categories which we tested using previously collected data of stem density by species from coffee agroforestry in northern Nicaragua. Third, we modeled radiation in mixed canopy scenarios using the program SExI- FS based on modifications of species and density to target food and income diversification and tested our framework by quantifying benefits. We found that smallholder coffee faces farms decreasing coffee margins, labor scarcity, new pests and climate variability best addressed with targeted and adaptive shifts in coffee varieties and associated trees. Increasing data demands from certification and regulations provide a basis more data-driven coffee farm management. Our data bases of stem density by species of established agroforestry systems were sufficient to identify gaps in food and income benefits which were addressed in the scenarios thereby verifying the hypothesis. The benefits ranking both of current systems and three scenarios also provided insights into data collection specifications for a more rigorous academic test of the hypothesis and data-driven grower strategies for agroforestry transformation.

Remote Sensing, Yield, Physical Characteristics, and Fruit Composition Variability in Cabernet Sauvignon Vineyards

Soil texture, topographical data, fruit zone light measurements, yield components, and fruit composition data were taken from 125 locations in each of four <i>Vitis vinifera</i> L. cv. Cabernet Sauvignon vineyards in the Lodi region of California during the 2017, 2018, and 2019 seasons. Data were compared against three sources of normalized difference vegetation index (NDVI) with different spatial resolutions: Landsat 8 (LS8_NDVI; 30 m), Sentinel-2 (S2_NDVI; 10 m), and manned aircraft (at high resolution, HR) with the interrow removed (HR_NDVI; 20 cm). The manned aircraft also captured canopy temperature (CT) derived from infrared (thermal) wavelengths (HR_CT; 40 cm) for additional comparisons. HR_NDVI was inversely related to HR_CT, as well as to several chemical components of fruit composition including tannins and anthocyanins. While some constituents of fruit composition such as anthocyanins may be related to NDVI, canopy temperature, and/or indirect measurements collected in the field, results presented here suggest that yield and fruit composition have a strong seasonal response and therefore environmental conditions should be considered if more accurate predictions are desired. Furthermore, freely available public satellite data sources with mixed canopy and interrow pixels, such as Sentinel-2 and Landsat 8, provided similar information related to predicting specific fruit composition parameters compared to higher resolution imagery from contracted manned aircraft, from which the interrow signal was removed. Growers and wineries interested in predicting fruit composition that accounts for spatial variability may be able to conserve resources by using publicly available imagery sources and small numbers of targeted samples to achieve this goal.

Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data

BackgroundNational estimates of canopy bulk density (CBD; kg m−3) for fire behavior modeling are generated and supported by the LANDFIRE program. However, locally derived estimates of CBD at finer scales are preferred over national estimates if they exist, as the absolute accuracy of the LANDFIRE CBD product is low and varies regionally. Active sensors (e.g., lidar or radar) are better suited for this task, as passive sensors are ill equipped to detect differences among key vertical fuel structures, such as coniferous surface fuels (≤2 m high) and canopy fuels above this threshold—a key categorical fuel distinction in fire behavior modeling. However, previous efforts to map CBD using lidar sensor data in the Superior National Forest (SNF) of Minnesota, USA, yielded substandard results. Therefore, we use a combination of dormant-season synthetic aperture radar (SAR) and optical satellite sensor data to (1) expand detectability of coniferous fuels among mixed forest canopies to improve the accuracy of CBD modeling and (2) better understand the influence of surface fuels in this regard. Response variables included FuelCalc output and indirect estimates of maximum burnable fuel based on canopy gap fraction (CGF) measured at ground level and 2 m above ground level.ResultsSAR variables were important predictors of CBD and total fuel density (TFD) in all independent model calibrations with ground data, in which we define TFD as the sum of CBD and primarily live coniferous surface fuel density (SFD) 0 to 2 m above ground. Exploratory estimates of TFD appeared biased to the presence of sapling-stage conifer fuel on measures of CGF at the ground level. Thus, modeling efforts to calibrate SFD with satellite sensor data failed. Both CGF-based and FuelCalc-based field estimates of CBD yielded close unity with satellite-calibrated estimates, although substantial differences in data distributions existed. Estimates of CBD from the widest CGF zenith angle range (0 to 38°) correlated best with FuelCalc-based CBD estimates, while both resulted in maximum biomass values that exceeded those considered typical for the SNF. Model results from the narrowest zenith angle range (0 to 7°) produced estimates of CBD that were more in line with values considered typical. LANDFIRE’s estimates of CBD were weakly, but significantly (P = 0.05), correlated to both narrow- and wide-angle CGF-based estimates of CBD, but not with FuelCalc-based estimates.ConclusionsThe combined use of field estimates of CBD, based on indirect measures of CGF according to Keane et al. (Canadian Journal of Forest Research 35:724–739, 2005), with SAR and optical satellite sensor data demonstrates the potential of this method for mapping CBD in the Upper Midwest, USA. Results suggested that the presence of live, coniferous surface fuels neither confounds remote detection nor precludes mapping of CBD in this region using SAR satellite sensor data, as C- and L-band idiosyncrasies likely limit the visibility of these smaller understory fuels from space. Nevertheless, research using direct measures of burnable SFD for calibrations with SAR satellite sensor data should be conducted to more definitively answer this remote detection question, as we suspect substantial bias among measures of CGF from ground level when estimating SFD as the difference between TFD and CBD.

Linear mixed model analysis of NDVI-based canopy coverage, extracted from sequential UAV multispectral imagery of an open field tomato irrigation experiment

This study presents the use of a linear mixed model to accurately estimate the dynamics of canopy cover (CC) during an irrigation experiment on a tomato crop, performed in the Peruvian coastal desert. Initially, the normalized difference vegetation index (NDVI)-based CC was computed using multispectral sensors over consecutive weekly UAV flights, during the growing season of the crop. The drone high-resolution sequential imagery, combined with image segmentation on plant level, results in repeated measures growth curves of the coverage per plant. To analyze these repeated measures data per plant, cubic polynomials without intercept showed the best goodness-of-fit of the CC dynamics per plant. As a consequence, this polynomial was incorporated in a linear mixed model (LMM) as a random coefficient model to fit the plant-specific time evolution of CC as deviations from the mean time effects per irrigation treatment. The mixed model approach is capable to estimate the coverage curves per plant with high accuracy and a very limited number of model parameters. The results and statistical analysis demonstrate the potential benefits of the linear mixed model for incorporating plant-specific random components in addition to classical fixed effects models. Thus, the proposed linear mixed canopy model is a very efficient way to model plant-specific growth curves together with the treatment and design structure of the experiment.

Soil function can sensitively respond to different canopy composition of Crataegus and Berberis

Mountainous sites can only grow a limited variety of vegetation types due to their fragile soil conditions. In the north of Iran, mountainous sites are commonly occupied by Crataegus and Berberis shrubs, which are natively distributed in Asia, Europe, North America and Africa. Despite the widespread distribution of these two species, the effect of pure and mixed canopy composition of these species on soil fertility and microbial activities (as soil function indicators) has not been considered. In this paper, five types of canopy composition have been studied for their effect on soil function; including pure Crataegus (PC), and pure Berberis (PB), as well as mixed Crataegus and Berberis with almost equal canopy cover (50C:50B), Crataegus-dominated stand (70C:30B), and Berberis-dominated stand (30C:70B). A total of 225 soil samples (5 plant types × 15 samples × 3 soil depths including 0–10 cm, 10–20 cm and 20–30 cm belowground) were taken from the field, transferred to the laboratory and analyzed. PCA results showed a difference in the soil characteristics between the canopy composition types, with the first and second axes justifying 46.67% and 14.12% of the total variance, respectively. The 50C:50B mixed canopy composition resulted in soils with higher values of soil organic matter fractions (i.e. particulate and dissolved C and N), C and nutrient (N, P, K, Ca and Mg) pools, and soil microbial (i.e. respiration and biomass) and enzyme (urease, acid phosphatase, arylsulfatase and invertase) activities, especially in the topsoil (0–10 cm). Soil function indicators decreased in ranked order of 70C:30B > 30C:70B > PC > PB plant types. As a conclusion, it can be stated that the equal ratio of Crataegus and Berberis can enhance soil function and create fertile islands in the high altitudes of natural ecosystems.

Effects of initial spacing and species mix on stand developmental patterns in two field trials in central Oregon

Productivity, stand dynamics, and ecosystem processes characterizing mixed-species forests have been inferred from a mix of inventory data, observational studies, and designed experiments. Designed experiments are expensive to implement and maintain for a sufficiently long time in most temperate species, but associated treatment replication and randomization lower the risk of confounding from spatial correlations among species composition, environmental conditions, and productivity. Results from replicated mixed-species spacing trials in 45-yr-old Pinus ponderosa/Abies grandis plots and 53-yr-old P. ponderosa/P. contorta plots demonstrated main and interaction effects of spacing and species composition on growth and stand structure, as well as the change in these effects over time. Initial spacing ranged from 1.8 m to 5.5 m on a square grid. Mixtures of P. ponderosa and A. grandis at wide spacings offered A. grandis the opportunity to achieve cumulative height growth nearly equal to P. ponderosa and thereby form a uniform mixed canopy but lower cumulative stem volume growth. The same initial species mixture at closer spacing resulted in increasingly stratified mixtures with P. ponderosa dominating the overstory due largely to its more rapid juvenile height growth and wider crown, relegating shade tolerant A. grandis to the understory. A similar pattern emerged in the mixtures of P. ponderosa and P. contorta, but with more subtle gradients in vertical structure attributable to less extreme differences in juvenile height growth and shade tolerance. By the end of 2019 (plantation age 53), no significant overyielding or underyielding occurred in the mixed-species plots of P. ponderosa/P. contorta. In contrast, by the end of 2018 the mixed-species plots of P. ponderosa/A. grandis (plantation age 45 years) achieved some overyielding that was marginally significant (net volume production) or very significant (gross volume production) only at the narrowest (1.8-m) spacing. The timing and intensity of mortality shifted mixing effects on periodic annual increment slightly over time. Evidence for consistent overyielding among spacings was weak in these P. ponderosa/P. contorta and P. ponderosa/A. grandis spacing trials at total plantation age of 53 and 45 years, respectively. However, statistical power for detecting mixing effects was limited by the relatively small number of replications, and size differentiation continues to emerge at successively wider spacings on mixed-species plots.

SenSCOPE: Modeling mixed canopies combining green and brown senesced leaves. Evaluation in a Mediterranean Grassland

The coupling of radiative transfer, energy balance, and photosynthesis models has brought new opportunities to characterize vegetation functional properties from space. However, these models do not accurately represent processes in ecosystems characterized by mixtures of green vegetation and senescent plant material (SPM), in particular grasslands. These inaccuracies limit the retrieval of vegetation biophysical and functional properties. Green and senesced plants feature contrasting spectral properties and carry out different functions that current coupled models do not represent separately. Besides, senescent pigments' absorption features change as SPM decomposes, and neither is this process well parameterized in radiative transfer models. This manuscript aims at overcoming these limitations. On the one hand, we have developed senSCOPE, a version of the Soil-Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) that separately represents light interaction and physiology of green and senesced leaves. On the other, we have characterized new specific absorption coefficients of senescent pigments (Ks) from optical measurements of SPM from a Mediterranean grassland. Sensitivity analyses revealed that compared to SCOPE, senSCOPE 1) predicts variables that respond more linearly to the faction of green leaf area; and 2) keeps high levels of absorbed photosynthetically active radiation in the green leaves, which leads to significant differences in leaf photosynthesis, non-photochemical quenching, and transpiration. Moreover, we compared SCOPE vs. senSCOPE's capability to provide estimates of functional and biophysical parameters of vegetation. We assimilated different combinations of reflectance factors (R), chlorophyll sun-induced fluorescence radiance in the O2-A band (F760), gross primary production (GPP), and thermal radiance (Lt) measured in a Mediterranean grassland. Besides, we compared the role of three different sets of Ks coefficients in the inversion of senSCOPE, two estimated from SPM. The performance of the inversions was assessed using field data and a pattern-oriented model evaluation approach. Unlike SCOPE, senSCOPE provided unbiased estimates of chlorophyll content (Cab) during the dry season. The use of SPM-specific Ks improved the representation of R in the near-infrared wavelengths; and, consequently, the estimation of leaf area index (LAI). Compared with field LAI, the coefficient of determination R2 increased from ~0.4 to ~0.6, depending on the inversion constraints. Compared with SCOPE, the new model and coefficients together reduced the root mean squared error between observed and modeled R (~40%), F760 (~30%), and GPP (~5%). Both models failed to represent Lt; in this case, senSCOPE featured larger uncertainties. The modeling approach we propose improves the simulation and retrieval of vegetation properties and function in grasslands. Further work is needed to test the applicability of senSCOPE in different ecosystems, improve the simulation of the thermal spectral domain, and better characterize the optical parameters of SPM. To do so, new databases of SPM optical and biophysical properties should be produced.

Investigating different versions of PROSPECT and PROSAIL for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands

ABSTRACT The PROSPECT and PROSAIL family of radiative transfer models (RTMs) are among the most popular for simulating vegetation spectra and estimating vegetation properties at the leaf and canopy levels. However, the main limitation of the radiative transfer model approach is that model performance depends on the exhaustiveness of the calibration database(s). The PROSPECT model was calibrated mainly with photosynthetic leaves, and thus does not contain specific absorption coefficients of decay pigments responsible for the spectral behavior of non-photosynthetic vegetation. Hence, PROSPECT and PROSAIL may be ill-suited to mixed ecosystems (e.g., grasslands and wetlands), especially in the late growing season when the non-photosynthetic vegetation is likely to obfuscate the quantification of green vegetation. This study investigates the performance of different versions of PROSPECT/PROSAIL models for simulating spectra and estimating biophysical properties of photosynthetic and non-photosynthetic vegetation, and aims to better understand the limitations of these RTMs and identify possible ways to improve their performance. Results show that the PROSPECT-5 and PROSPECT-D had challenges in simulating spectra of non-photosynthetic leaves in a mixed grassland area, while a modified version (PROSPECT-5M) that considered the absorption effects of decay pigments achieved higher accuracy (e.g., mean Root Mean Square Error (RMSE) of 0.014 compared to 0.026 for the PROSPECT-5). In comparison, there is minimal difference in RMSE between models for simulating green photosynthetic leaves. At the canopy level, the original PROSAIL model simulated the spectra well for homogeneous green canopies (with mean RMSE around 0.012), while a modified PROSAIL model simulated the spectra more accurately for mixed canopies that have photosynthetic and non-photosynthetic leaves (e.g., with a mean RMSE of 0.010 compared to 0.020 of original PROSAIL). The original and modified PROSAIL were then inverted using helicopter-based high-spatial resolution hyperspectral imagery to estimate vegetation properties, and achieved higher accuracies for green and mixed canopies, respectively (e.g., estimating canopy chlorophyll with R2 values over 0.75). Overall, different versions of PROSPECT/PROSAIL models have a varied performance for photosynthetic and non-photosynthetic vegetation. Understanding the limitations of the models and adopting corresponding measures to improve their performance is critical for successful applications of RTMs in the estimation of vegetation spectral and biophysical properties.

Close-range, heavy-duty hyperspectral imaging for tracking drought impacts using the PROCOSINE model

Leaf response to stressors is heterogeneously distributed within the canopy, creating complex light-matter interactions that hinder translation of spectra into meaningful plant traits at larger scales. A deeper understanding of the linkages between leaf traits and canopy spectra has been achieved by a variety of close-range hyperspectral imaging systems. Yet the majority are scaled for small plants in pots, and thereby face numerous challenges in replicating field conditions for temporal drought monitoring. In monocots, drought alters foliar biochemistry and canopy structure, leading to the eventual senescence and decay of individual leaves, yet few radiative transfer models have been applied to track drought stress at the submillimeter scale in mixed canopies of photosynthetic and non-photosynthetic vegetation. Our objective was to compare the retrieved parameter set for PROCOSINE and PROSPECT radiative transfer model inversions utilizing two decay pigments. Although the average inversion values for both models indicated an increase in initial decay pigments after drought, the per-pixel inversion results for the PROCOSINE and PROSPECT inversions were unique (Mann-Whitney U-test p<0.05). Model retrievals for PROCOSINE were consistently higher for N, and lower for chlorophyll and carotenoids. In PROCOSINE, the Thatai and BSpec trended towards smaller values due to the increasing percentage of non-photosynthetic leaves, suggesting that mixed canopies are less prone to bidirectional reflectance. Regardless, the spatial-temporal trend of the inversion estimated parameter set was similar between both models, demonstrating the efficacy of the close-range imaging platform to monitor drought effects over time.

Coarse Woody Debris’ Invertebrate Community is Affected Directly by Canopy Type and Indirectly by Thinning in Mixed Scots Pine—European Beech Forests

Research Highlights: Thinning and tree species alter the forest floor microclimate by modifying canopy cover, radiation, wind, and humidity. Thus, forest management can directly influence the edaphic mesofauna responsible for decomposing coarse woody debris (CWD). Background and Objectives: This research was carried out in the Southwestern Pyrenees Mountains (Northern Spain) and aimed to determine the influence of forest thinning and canopy type (pure Pinus sylvestris L. or a mix of P. sylvestris and Fagus sylvatica L.) on CWD colonization by edaphic fauna. Materials and Methods: CWD samples were collected belonging to intermediate and advanced decomposition stages, approximately 10 cm long and 5 cm in diameter. Using a design of three thinning intensities (0%, 20%, and 40% of basal area removed), with three replications per treatment (nine plots in total), four samples were taken per plot (two per canopy type) to reach 36 samples in total. Meso- and macrofauna were extracted from CWD samples with Berlese–Tullgren funnels, and individuals were counted and identified. Results: 19 taxonomic groups were recorded, the most abundant being the mesofauna (mites and Collembola). Mixed canopy type had a significant positive influence on richness, whereas advanced decay class had a positive significant influence on total abundance and richness. In addition, there were non-significant decreasing trends in richness and abundance with increasing thinning intensity. However, interactions among thinning intensity, canopy type, and decay class significantly affected mesofauna. Furthermore, some taxonomic groups showed differential responses to canopy type. CWD water content was positively correlated with total invertebrate abundance and some taxonomic groups. Our results suggest that stand composition has the potential to directly affect invertebrate communities in CWD, whereas stand density influence is indirect and mostly realized through changes in CWD moisture. As mesofauna is related to CWD decomposition rates, these effects should be accounted for when planning forest management transition from pure to mixed forests.

Comparison of TanDEM-X InSAR data and high-density ALS for the prediction of forest inventory attributes in plantation forests with steep terrain

Satellite interferometric synthetic aperture radar (InSAR) is emerging as a viable low-cost alternative method to airborne laser scanning (ALS) for forest inventory though little research has examined its efficacy for plantation forests located in temperate regions on steep terrain. InSAR and ALS data were collected from Geraldine Forest which is located on rolling to very steep topography in the southeast of New Zealand. These data were combined with an extensive set of plot measurements from which mean top height (H), basal area (G), stem density (N), and total stem volume (TSV) were calculated. InSAR and ALS-based Random Forest models of each variable were developed and compared.Using the ALS data as a reference, the mean RMSE of the InSAR DSM and DTM surfaces were, respectively, 4.58 and 8.09 m and these errors increased to mean values of, respectively, 6.02 and 10.17 m for slopes of 40–50°.ALS-based models were substantially more precise than those developed from InSAR for H (R2 = 0.86 vs. 0.60; RMSE% = 5.47 vs. 10.8%), G (R2 = 0.56 vs. 0.32; RMSE% = 21.5 vs. 30.4%), N (R2 = 0.47 vs. 0.09; RMSE% = 32.3 vs. 43.2%), and TSV (R2 = 0.70 vs. 0.41; RMSE% = 19.4 vs. 30.7%). The base metrics (i.e. ALS height and canopy cover variables) accounted for most of the variance in the ALS models with addition of further metrics providing ≤ 1% reduction in the RMSE%. Base metrics (i.e. InSAR observables) also accounted for most of the variation in InSAR models. Addition of metrics from a mixed Canopy Height Model (CHM), derived from InSAR Digital Surface Model (DSM) and ALS Digital Terrain Model (DTM), resulted in reductions in RMSE% of 3.1–5.4% for H, G, and TSV models with addition of textural metrics providing further reductions of 0.2–0.3% for H and G models. Addition of metrics from the radar CHM, derived from the InSAR DTM and DSM, and texture metrics reduced the RMSE% of the base model for N by 2.3% and 0.5%, respectively.The results were generated using a SAR image pair with a height of ambiguity (HOA) that was higher than ideal, which reduced the sensitivity of results to changes in terrain. Despite this limitation, and the steep slopes throughout the forest, the InSAR models described here had comparable precision to developed InSAR models for key inventory metrics from previous studies.

Population Dynamics in Mixed Canopies Composed of Kikuyu-Grass and Tall Fescue

The current work was based on the central hypothesis that grazing management strategies modulate tillering dynamics of mixed canopies composed of kikuyu-grass (C4 perennial grass) and tall fescue (C3 perennial grass). Among the five grazing management conditions evaluated, three (7, 12, and 17 cm) represented the heights up to which the grasses were kept by mimicking a continuous stocking method throughout the experimental period (from May 2016 to October 2017), and the other two consisted of the heights 12 and 17 cm with a single grazing to 7 cm in mid-autumn. Nitrogen fertilization was applied only during winter–spring. The results showed that under severe grazing (7 cm), kikuyu-grass predominated in the area and that tall fescue population predominated in the moderately lenient (12 cm) and lenient (17 cm) grazed pastures, regardless of an occasional autumn grazing to 7 cm. After two years of evaluation, kikuyu-grass tillers were reduced to 6% of the total population in those pastures managed at the heights of 12 and 17 cm. Grazing management strategies modulate species proportions in a mixed canopy composed of kikuyu and tall fescue. Moreover, our data also suggest that it seems unlikely that there is a stable association of tall fescue and kikuyu-grass when no nitrogen fertilization is applied during summer.

Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data

In forests with dense mixed canopies, laser scanning is often the only effective technique to acquire forest inventory attributes, rather than structure-from-motion optical methods. This study investigates the potential of laser scanner data collected with a low-cost unmanned aerial vehicle laser scanner (UAV-LS), for individual tree crown (ITC) delineation to derive forest biometric parameters, over two-layered dense mixed forest stands in central Italy. A raster-based local maxima region growing algorithm (itcLiDAR) and a point cloud-based algorithm (li2012) were applied to isolate individual tree crowns, compute height and crown area, estimate the diameter at breast height (DBH) and the above ground biomass (AGB) of individual trees. To maximize the level of detection rate, the ITC algorithm parameters were tuned varying 1350 setting combinations and matching the segmented trees with field measured trees. For each setting, the delineation accuracy was assessed by computing the detection rate, the omission and commission errors over three forest plots. Segmentation using itcLiDAR showed detection rates between 40% and 57%, while ITC delineation was successful at segmenting trees with DBH larger than 10 cm (detection rate ~78%), while failed to detect trees with smaller DBH (detection rate ~37%). The performance of li2012 was quite lower with the higher detection rate equal to 27%. Errors and goodness-of-fit between field-surveyed and flight-derived biometric parameters (AGB and tree height) were species-dependent, with higher error and lower r2 for shorter species that constitute the lowermost layer of the forest. Overall, while the application of UAV-LS to delineate tree crowns and estimate biometric parameters is satisfactory, its accuracy is affected by the presence of a multilayered and multispecies canopy that will require specific approaches and algorithms to better deal with the added complexity.

Canopy structure of mixed kikuyugrass–tall fescue pastures in response to grazing management

ABSTRACTThe effects of different management strategies on improving growth and modifying the botanical composition of C3 and C4 perennial grasses grown in a mixed canopy are underexplored. We aimed to evaluate the botanical composition and vegetation dynamics of a mixed canopy containing kikuyugrass (Pennisetum clandestinum Hochst. Ex Chiov) and tall fescue (Festuca arundinacea Schreber cv. Rizomat) subjected to four grazing management strategies over 22 mo. The experimental design was a randomized complete block in a factorial arrangement (two maintenance canopy heights: grazed to 7 cm in the autumn, or uninterrupted and maintained at 12 or 17 cm) with three replicates. The plots were sampled every month throughout the experimental period; the forage mass data were used to determine the botanical composition and leaf area index of the canopy. Although autumn grazing to 7‐cm height promoted an increase in pasture leaf proportion, it did not represent an effective augment in pasture leaf mass in the following winter–spring season. Canopies heights managed at 17 cm presented a greater proportion of tall fescue in herbage mass (81%) compared with those maintained at 12 cm (73%). Severe defoliation in the autumn delayed the regrowth to 12‐ or 17‐cm canopy heights by approximately 90–120 d in both evaluation years. From a farm‐level perspective, this result would restrict the recommendation of this grazing management strategy because this time lapse would demand that the pastures not be grazed for the 25–33% period of the year compared with the pastures that were not grazed in the autumn.

Optimal resolution selection for monitoring forest vegetation using remote sensing images.

Image resolution directly affects precision, cost, and efficiency of forest vegetation extraction. To determine the best spatial resolution of remote sensing images for forest vegetation extraction, a method was proposed for optimal spatial resolution of remote sensing images when monitoring forest vegetation. Based on a panchromatic image of GF-2, a step length variation function was used to analyze forest vegetation of three distribution types considering monitoring precision, cost, and efficiency in Yangquan Town, Changning City, Hunan Province. A preliminary minimum resolution of the image suitable for forest vegetation extraction was determined. Then, through resampling of GF-2 multi-spectral images after fusion, middle and low resolution images of different scales were made to extract forest vegetation using the supervised classification method. Quantitative and qualitative analyses of results were carried out for accuracy of forest vegetation extraction, image cost, and data processing time. Results showed that the optimal spatial resolution for remote sensing monitoring images was different for different types of forest vegetation, with forest vegetation of small canopies being 3.2 m, for big canopies being 16.0 m, and for mixed canopies being 8.0 m. The optimal spatial resolution of remote sensing images to monitor forest vegetation could be used as a reference in other areas.

Temporal interactions among throughfall, type of canopy and thinning drive radial growth in an Iberian mixed pine-beech forest

Many factors can influence tree growth over time such as different forest management practices, climate or tree-to-tree interactions, especially in mixed forests. We show in this work how the temporal growth patterns for Scots pine and European beech depend on thinning intensity (0%, 20% and 40% extraction of basal area), canopy type (pine–beech vs. pine patches), throughfall and their interactions. To fulfill this objective we monitored radial growth of both species using band dendrometers during a 6-year long period including two very dry years. Temporal growth patterns differed between both species. Whereas Scots pine showed two main peaks of growth in May–June and October, European beech mainly grew from May to early September even when throughfall was very limited. Effects of thinning on growth generally increased for both species during dry periods both at the seasonal and annual scales. The treatment with 20% of thinning intensity was the most effective at the annual scale for enhancing growth of both species. However, increases in growth due to thinning were much higher in beech than in pine and lasted longer. Thinning effects on pine were higher in mixed canopy than in pure canopy and appeared to be modulated by throughfall. Global differences in pine growth between canopy types as a function of throughfall increased during the main growing season as beech canopy developed. Growth of Scots pine, but not that of European beech, generally increased with throughfall which suggests that pine might be more dependent for its growth on water from the soil surface layer while beech would depend more on water from deeper soil layers. Our findings have implications to select the most convenient thinning treatments and canopy type under a potential climate change scenario characterized by warmer conditions, more severe droughts and less throughfall.

Cultivar architecture modulates spore dispersal by rain splash: A new perspective to reduce disease progression in cultivar mixtures.

Cultivar mixtures can be used to improve the sustainability of disease management within farming systems by growing cultivars that differ in their disease resistance level in the same field. The impact of canopy aerial architecture on rain-splash dispersal could amplify disease reduction within mixtures. We designed a controlled conditions experiment to study single splash-dispersal events and their consequences for disease. We quantified this impact through the spore interception capacities of the component cultivars of a mixture. Two wheat cultivars, differing in their aerial architecture (mainly leaf area density) and resistance to Septoria tritici blotch, were used to constitute pure stands and mixtures with 75% of resistant plants that accounted for 80% of the canopy leaf area. Canopies composed of 3 rows of plants were exposed to standardized spore fluxes produced by splashing calibrated rain drops on a linear source of inoculum. Disease propagation was measured through spore fluxes and several disease indicators. Leaf susceptibility was higher for upper than for lower leaves. Dense canopies intercepted more spores and mainly limited horizontal spore transfer to the first two rows. The presence of the resistant and dense cultivar made the mixed canopy denser than the susceptible pure stand. No disease symptoms were observed on susceptible plants of the second and third rows in the cultivar mixture, suggesting that the number of spores intercepted by these plants was too low to cause disease symptoms. Both lesion area and disease conditional severity were significantly reduced on susceptible plants within mixtures on the first row beside the inoculum source. Those reductions on one single-splash dispersal event, should be amplified after several cycle over the full epidemic season. Control of splash-dispersed diseases within mixtures could therefore be improved by a careful choice of cultivars taking into consideration both resistance and architecture.