Gap Fraction Research Articles

Variation of Clumping Index With Zenith Angle for Forest Canopies

Canopy clumping index (CI) characterizes the extent of the nonrandom spatial distribution of foliage elements within a canopy and is critical for determining the radiative transfer, photosynthesis, and transpiration processes in the canopy. It is widely perceived that CI increases with zenith angle (θ), because between-crown gaps decrease in size and number with increasing θ. In this study, we demonstrate that this is not always true. Analytical equations between CI and θ are first developed based on widely-used forest canopy gap fraction theories. The results show that the zenith angular variation of CI is closely related to crown projected area or crown shapes (i.e., the ratio of the crown height to its diameter, RHD): CI increases with θ for canopies with “tower” crowns (RHD > 1), but decreases with θ for “umbrella” crowns (RHD < 1) and does not vary much with θ for “sphere” crowns (RHD = 1). These results are validated in a LargE-Scale remote sensing data and image Simulation framework (LESS) platform, and published datasets including the measurements in field and RAMI forest stands. The findings are essential for the derivation of angular integrated (hemispherical) CI from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> measurements and multi-angular remote sensing.

Estimation of canopy attributes of wild cacao trees using digital cover photography and machine learning algorithms

Surveying canopy attributes while conducting fieldwork in the rain forest is time-consuming. Low-cost imagery such as digital cover photography is a potential source of information to speed up the process of vegetation assessments and reduce costs during expeditions. This study presents an image-based non-destructive method to estimate canopy attributes of wild cacao trees in two regions of the rain forest in Colombia, using digital cover photography and machine learning algorithms. Upward-looking photography at the base of each cacao tree and machine learning algorithms were used to estimate gap fraction (GF), foliage cover (FC), crown cover (CC), crown porosity (CP), clumping index (Ω), and leaf area index (LAI) of the canopy cover. Here we used the cacao wild trees found on forestry plots as a case study to test the application of low-cost imagery on the extraction and analysis of canopy attributes. Canopy attributes were successfully extracted from the canopy cover imagery and provided 92% of classification accuracy for the structural attributes of the canopy. Canopy cover attributes allowed us to differentiate between canopy structures of the Amazon and Pacific rainforests sites suggesting that wild cacao trees are associated with different vegetation types. We also compare classification results for the computer extraction of canopy attributes with a digital canopy cover benchmark. We conclude that our approach was effective to quickly survey canopy features of vegetation associated with and of crop wild relatives of cacao. This study allows highly reproducible estimates of canopy attributes using cover photography and state-of-the-art machine learning algorithms such as deep learning Convolutional Neural Networks.

Global quasi-daily fractional vegetation cover estimated from the DSCOVR EPIC directional hotspot dataset

Fractional Vegetation Cover (FVC) represents the planar fraction of the land-surface covered by green foliage, and its dynamics are important for an enhanced understanding of ecosystems especially how they respond to climate change. The lack of global near-real-time satellite-based products restricts the application of FVC in ecosystem modeling, climate change, and vegetation phenology studies. Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR) spacecraft provides daily spectral reflectance of the entire sunlit Earth in the near Hotspot directions. Hotspot observations (i.e., observation in Hotspot direction which has the peak backscattering reflected radiation) with only sunlit vegetation and sunlit soil components are more suitable for FVC estimation with a two-endmember mixture model as such observations exclude contributions from shaded vegetation and soil components. In this study, an algorithm for retrieving quasi-daily FVC from EPIC based on two-endmember mixture and gap fraction models is developed. Analyses of its performance predict that the average Root-Mean-Square Deviations (RMSDs) of retrievals in FVC units is below 0.050 when compared with reference values. The RMSD is 0.043 when compared to field-based Landsat reference FVC, which confirms lower retrieval uncertainty than FVC retrieved from Low-Earth-Orbit (LEO) satellite products such as MODIS, VIIRS, and GEOV2 with RMSDs 0.049– 0.087. The comparison analyses suggest a good consistency between EPIC FVC and FVC products from LEO and geostationary (GEO) satellites sensor, SEVIRI, with RMSD values less than 0.129. EPIC allows for quasi-daily FVC estimation across the global terrestrial surface at 10 km resolution, which is an important development for numerous biophysical applications.

To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests

AbstractTropical forests are complex multi‐layered systems, with the height and three‐dimensional (3D) structure of trees influencing the carbon and biodiversity they contain. Fine‐resolution 3D data on forest structure can be collected reliably with Light Detection and Ranging (LiDAR) sensors mounted on aircraft or Unoccupied Aerial Vehicles (UAVs), however, they remain expensive to collect and process. Structure‐from‐Motion (SfM) Digital Aerial Photogrammetry (SfM‐DAP), which relies on photographs taken of the same area from multiple angles, is a lower‐cost alternative to LiDAR for generating 3D data on forest structure. Here, we evaluate how SfM‐DAP compares to LiDAR data acquired concurrently using a fixed‐wing UAV, over two contrasting tropical forests in Gabon and Peru. We show that SfM‐DAP data cannot be used in isolation to measure key aspects of forest structure, including canopy height (%Bias: 40%–50%), fractional cover, and gap fraction, due to difficulties measuring ground elevation, even under low tree cover. However, we find even in complex forests, SfM‐DAP is an effective means of measuring top‐of‐canopy structure, including surface heterogeneity, and is capable of producing similar measurements of vertical structure as LiDAR. Thus, in areas where ground height is known, SfM‐DAP is an effective method for measuring important aspects of forest structure, including canopy height, and gaps, however, without ground data, SfM‐DAP is of more limited utility. Our results support the growing evidence base pointing to photogrammetry as a viable complement, or alternative, to LiDAR, capable of providing much needed information to support the mapping and monitoring of biomass and biodiversity.

Plant interactions control the carbon distribution of Dodonaea viscosa in karst regions.

Biomass and carbon (C) distribution are suggested as strategies of plant responses to resource stress. Understanding the distribution patterns of biomass and C is the key to vegetation restoration in fragile ecosystems, however, there is limited understanding of the intraspecific biomass and C distributions of shrubs resulting from plant interactions in karst areas. In this study, three vegetation restoration types, a Dodonaea viscosa monoculture (DM), a Eucalyptus maideni and D. viscosa mixed-species plantation (EDP) and a Pinus massoniana and D. viscosa mixed-species plantation (PDP), were selected to determine the effects of plant interactions on the variations in the C distributions of D. viscosa among the three vegetation restoration types following 7 years of restoration. The results showed that: (1) plant interactions decreased the leaf biomass fraction. The interaction of P. massoniana and D. viscosa decreased the branch biomass fraction and increased the stem and root biomass fraction, but not the interaction of E. maideni and D. viscosa. Plant interactions changed the C concentrations of stems and roots rather than those of leaves and branches. (2) Plant interactions affected the soil nutrients and forest characteristics significantly. Meanwhile, the biomass distribution was affected by soil total nitrogen, clumping index and gap fraction; the C concentrations were influenced by the leaf area index and soil total phosphorus. (3) The C storage proportions of all the components correlated significantly with the proportion of biomass. Our results suggested that both the biomass distribution and C concentration of D. viscosa were affected by plant interactions, however, the biomass fraction not the C concentration determines the C storage fraction characteristics for D. viscosa.

Estimation of leaf area index using inclined smartphone camera

Measurements of leaf area index (LAI) are important for modeling microclimate in vegetation research. Among the instruments for measuring the LAI, smartphone cameras are becoming an attractive alternative to special LAI instruments. However, the narrow full field of view (FOV) of the common smartphones offer only an effective viewing zenith angle (VZA) of less than 35° when the camera is pointing straight up. To overcome this limitation, we propose a method to estimate LAI from an inclined smartphone camera that can enlarge the range of the sensor’s effective VZA. With the directional gap fractions extracted from the images taken by the inclined smartphone camera, a curve matching algorithm is used to iteratively search for the simulated G functions, i.e. functions of mean tilt angle (MTA) and VZA. The MTAs corresponding to the matched G functions are selected as ancillary parameters to help calculate the LAI. The proposed method is validated using data collected over crops and trees by a LAI-2200 instrument and a Huawei Honor 7 smartphone. The results reveal that an inclination angle of 30° from zenith is superior to other angles of 0, 45 and 60°. A good agreement between the LAI measurements from the proposed method and those from the LAI-2200 supports the accurate estimation of MTAs. The success of the MTA estimates and thus LAI measurements is attributed to the enlarged VZA ranging from 4° to 60°, and this VZA is comparable with that of the LAI-2200 instrument. The attraction of the proposed method is that it does not rely on the empiric al G value or MTA, providing an affordable alternative to traditional commercial instruments. Future efforts can be directed to automatically capture images when the smartphone is inclined to the desired angle.

Complementary airborne LiDAR and satellite indices are reliable predictors of disturbance-induced structural diversity in mixed old-growth forest landscapes

In old-growth forests, natural disturbances form a complex mosaic of structures, providing a wide diversity of habitats and functions of great importance. Old-growth forests are still often seen as a homogeneous whole and few remote-sensing approaches have been tested to identify their structural diversity, especially in boreal forests. The aim of this study is to use a combination of airborne LiDAR and satellite imagery to identify and discriminate old-growth forest structures resulting from different disturbance histories. The study area, which was located in the mixed boreal forest of Quebec (Canada), is Monts Valin National Park and adjacent managed territories. Balsam fir (Abies balsamea (L.) Mill) is the dominant species in the study area, but hardwood species such as white birch (Betula papyrifera Marsh.) and trembling aspen (Populus tremuloides Michx.) can also be abundant in the early succession stages. Four forest classes were studied: second-growth (logged between 1970 and 1980); transition old-growth (burned in 1920); undisturbed old-growth (unburned for at least 125 years); and disturbed old-growth forest (unburned for at least 125 years, but severely disturbed by an insect outbreak around 1980). A multivariate Random Forest model was used to discriminate the classes on 6466 1 ha tiles, based on 11 complementary LiDAR and satellite-derived indices describing stand vertical and horizontal structure, together with “greenness” and disturbance history over the last 30 years. This model had high predictive efficiency (AUC = 94.2), with 81.8% of the tiles accurately classified. Interestingly, undisturbed old-growth forests exhibited intermediate characteristics compared to transition and disturbed old-growth forests. This emphasizes that some structural attributes recognized as important for the classification of temperate and tropical old-growth forests, such as high vertical complexity, are of lesser relevance for boreal old-growth forests. In comparison to undisturbed old-growth forests, transition old-growth forests had a taller canopy of high “greenness” due to a greater hardwood abundance; disturbed old-growth forests had a higher gap fraction and heterogeneity in tree size; second-growth forests exhibited a lower and more even canopy. Misclassified tiles were explained by spatial variation in disturbance severity or different levels of forest resistance and resilience to disturbance. These misclassifications are also of ecological interest, as they highlight the nuances in structural diversity that are rarely identified by disturbance mapping. A reasonable combination of LiDAR and satellite indices was effective not only in discriminating old-growth forests from second-growth forests, but also identifying their different structures, which result from specific disturbance histories. This method could contribute to effective monitoring of changes in the areas and characteristics old-growth forest that are caused by anthropogenic and natural disturbances.

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.

Natural Advance Regeneration of Native Tree Species in Pinus radiata Plantations of South-Central Chile Suggests Potential for a Passive Restoration Approach

Restoration of natural forests previously replaced by plantations is a widespread challenge for forestry in Chile and elsewhere. However, there is little documented evidence for successful restoration, either through active or passive approaches. In this study, we aimed at (1) determining the potential for passive restoration in first-rotation Pinus radiata plantations through natural regeneration of native tree species and (2) identifying drivers of this advance regeneration. Across different regions in south-central Chile, we established nearly 260 plots to assess regeneration and environmental conditions along 26 transects running from plantations into adjacent natural forests. The regeneration was exclusively composed by native species, except for 7 individuals of P. radiata. Mean density and diversity of seedlings were significantly higher in natural forests than in plantations, but this was not the case for sapling density, and no differences in sapling diversity were supported. Additionally, significant differences in regeneration composition between plantations and natural forests were found only at two of the eight study sites. Compared to climatic and soil chemical variables, which varied mostly at regional scales, local environmental conditions showed little influence on regeneration, possibly due to the structural homogeneity of plantations. Yet, the significantly higher basal area, litter thickness and gap fraction of plantations compared to natural forests suggest that these factors may explain differences at the seedling stage. Our study indicates that the use of appropriate harvesting methods that maintain advance regeneration may facilitate the transition from plantations to native forests through passive restoration. The use this approach should be further investigated through analyzing regeneration’s response to different forms of plantation harvesting.

Sensitivity Analysis of Canopy Structural and Radiative Transfer Parameters to Reconstructed Maize Structures Based on Terrestrial LiDAR Data

The maturity and affordability of light detection and ranging (LiDAR) sensors have made possible the quick acquisition of 3D point cloud data to monitor phenotypic traits of vegetation canopies. However, while the majority of studies focused on the retrieval of macro scale parameters of vegetation, there are few studies addressing the reconstruction of explicit 3D structures from terrestrial LiDAR data and the retrieval of fine scale parameters from such structures. A challenging problem that arises from the latter studies is the need for a large amount of data to represent the various components in the actual canopy, which can be time consuming and resource intensive for processing and for further applications. In this study, we present a pipeline to reconstruct the 3D maize structures composed of triangle primitives based on multi-view terrestrial LiDAR measurements. We then study the sensitivity of the details with which the canopy architecture was represented for the computation of leaf angle distribution (LAD), leaf area index (LAI), gap fraction, and directional reflectance factors (DRF). Based on point clouds of a maize field in three stages of growth, we reconstructed the reference structures, which have the maximum number of triangles. To get a compromise between the details of the structure and accuracy reserved for later applications, we carried out a simplified process to have multiple configurations of details based on the decimation rate and the Hausdorff distance. Results show that LAD is not highly sensitive to the details of the structure (or the number of triangles). However, LAI, gap fraction, and DRF are more sensitive, and require a relatively high number of triangles. A choice of 100−500 triangles per leaf while maintaining the overall shapes of the leaves and a low Hausdorff distance is suggested as a good compromise to represent the canopy and give an overall accuracy of 98% for the computation of the various parameters.

Niche differentiation between Malus sylvestris and its hybrid with Malus domestica indicated by plant community, soil and light

AbstractQuestionMalus sylvestris is considered an endangered tree species in Central Europe. Hybridization with Malus domestica poses a serious threat to the genetic integrity of the wild species. Here we investigate whether M. sylvestris and the hybrid M. domestica × sylvestris occur in the same habitat or have different ecological niches and whether M. sylvestris is threatened by displacement by the hybrid.LocationNorthern Bavaria.MethodsTaxon delimitation was accomplished using six genetic microsatellite markers and 613 Germany‐wide references of M. sylvestris and 75 cultivars. To determine differences in the ecological niches between M. sylvestris and hybrids, light availability for the trees was estimated via gap fractions in hemispherical photographs. Soil particle size fractions and pH values were determined for each horizon. Vegetation relevé data were collected, and mean Ellenberg indicator values calculated. For habitat differences, means in combination with frequency patterns of the parameters were compared, and logistic models and detrended correspondence analysis (DCA) of community data were calculated.ResultsGenetic markers identified 22 M. sylvestris and 11 hybrid specimens, meaning that in the study area the wild taxon is much more frequent than the hybrid. Ecological site differences between M. sylvestris and its hybrid with M. domestica were best explained by light availability, pH and mean Ellenberg moisture value. In contrast to the ecological demands of the hybrid, Malus sylvestris tolerated wet soil and flooding and even somewhat shadier conditions in the later successional stages. DCA revealed that differences in the composition of the plant communities in which the taxa were found were primarily driven by soil moisture.ConclusionsOur data suggested different ecological niches, which are appropriate to reduce the risk of replacement of M. sylvestris by the hybrid M. domestica × sylvestris. Hence, these findings provide important implications for a more targeted planning of in‐situ conservation strategies of M. sylvestris genomes with low levels of admixture and help to protect plant communities suitable for the threatened wild apple.

Canopy Gap Structure as an Indicator of Intact, Old-Growth Temperate Rainforests in the Valdivian Ecoregion

Forest degradation continues to increase globally, threatening biodiversity and the survival of species. In this context, identifying intact, old-growth forest stands is both urgent and vital to ensure their existence and multiple contributions to society. Despite the global ecological importance of the Valdivian temperate rainforests, they are threatened by forest degradation resulting from constant and intense human use in the region. Identification of remnant intact forests in this region is urgent to global forest protection efforts. In this paper, we analyzed whether forests-canopy alterations due to logging produce a distinctive canopy gap structure (e.g., a gap area and a fraction of canopy gaps in the forest) that can be used to remotely distinguish intact from altered forests. We tested this question by comparing the canopy gap structure of 12 old-growth temperate rainforests in south-central Chile (39–40° S), with different levels of canopy alterations due to logging. At each stand, we obtained aerial or satellite very high spatial-resolution images that were automatically segmented using the Mean-Shift segmentation algorithm. We validated the results obtained remotely with ground data on the canopy gap structure. We found that the variables, canopy gap fraction, gap area frequency distribution, and mean gap area could be measured remotely with a high level of accuracy. Intact forests have a distinct canopy gap structure in comparison to forests with canopy alterations due to logging. Our results provided a fast, low-cost, and reliable method to obtain canopy gap structure indicators for mapping and monitoring intact forests in the Valdivian ecoregion. The method provided valuable information for managers interested in maintaining and restoring old-growth forest structures in these southern-temperate rainforests.

Comparative Evaluation of Algorithms for Leaf Area Index Estimation from Digital Hemispherical Photography through Virtual Forests

The in situ leaf area index (LAI) measurement plays a vital role in calibrating and validating satellite LAI products. Digital hemispherical photography (DHP) is a widely used in situ forest LAI measurement method. There have been many software programs encompassing a variety of algorithms to estimate LAI from DHP. However, there is no conclusive study for an accuracy comparison among them, due to the difficulty in acquiring forest LAI reference values. In this study, we aim to use virtual (i.e., computer-simulated) broadleaf forests for the accuracy assessment of LAI algorithms in commonly used LAI software programs. Three commonly used DHP programs, including Can_Eye, CIMES, and Hemisfer, were selected since they provide estimates of both effective LAI and true LAI. Individual tree models with and without leaves were first reconstructed based on terrestrial LiDAR point clouds. Various stands were then created from these models. A ray-tracing technique was combined with the virtual forests to model synthetic DHP, for both leaf-on and leaf-off conditions. Afterward, three programs were applied to estimate PAI from leaf-on DHP and the woody area index (WAI) from leaf-off DHP. Finally, by subtracting WAI from PAI, true LAI estimates from 37 different algorithms were achieved for evaluation. The performance of these algorithms was compared with pre-defined LAI and PAI values in the virtual forests. The results demonstrated that without correcting for the vegetation clumping effect, Can_Eye, CIMES, and Hemisfer could estimate effective PAI and effective LAI consistent with each other (R2 &gt; 0.8, RMSD &lt; 0.2). After correcting for the vegetation clumping effect, there was a large inconsistency. In general, Can_Eye more accurately estimated true LAI than CIMES and Hemisfer (with R2 = 0.88 &gt; 0.72, 0.49; RMSE = 0.45 &lt; 0.7, 0.94; nRMSE = 15.7% &lt; 24.21%, 32.81%). There was a systematic underestimation of PAI and LAI using Hemisfer. The most accurate algorithm for estimating LAI was identified as the P57 algorithm in Can_Eye which used the 57.5° gap fraction inversion combined with the finite-length averaging clumping correction. These results demonstrated the inconsistency of LAI estimates from DHP using different algorithms. It highlights the importance and provides a reference for standardizing the algorithm protocol for in situ forest LAI measurement using DHP.

Assessing Forest Level Response to the Death of a Dominant Tree within a Premontane Tropical Rainforest

Small-scale treefall gaps are among the most important forms of forest disturbance in tropical forests. These gaps expose surrounding trees to more light, promoting rapid growth of understory plants. However, the effects of such small-scale disturbances on the distribution of plant water use across tree canopy levels are less known. To address this, we explored plant transpiration response to the death of a large emergent tree, Mortoniodendron anisophyllum Standl. &amp; Steyerm (DBH &gt; 220 cm; height ~40 m). Three suppressed, four mid-story, and two subdominant trees were selected within a 50 × 44 m premontane tropical forest plot at the Texas A&amp;M Soltis Center for Research and Education located in Costa Rica. We compared water use rates of the selected trees before (2015) and after (2019) the tree gap using thermal dissipation sap flow sensors. Hemispherical photography indicated a 40% increase in gap fraction as a result of changes in canopy structure after the treefall gap. Micrometeorological differences (e.g., air temperature, relative humidity, and vapor pressure deficit (VPD)) could not explain the observed trends. Rather, light penetration, as measured by sensors within the canopy, increased significantly in 2019. One year after the tree fell, the water usage of trees across all canopy levels increased modestly (15%). Moreover, average water usage by understory trees increased by 36%, possibly as a result of the treefall gap, exceeding even that of overstory trees. These observations suggest the possible reallocation of water use between overstory and understory trees in response to the emergent tree death. With increasing global temperatures and shifting rainfall patterns increasing the likelihood of tree mortality in tropical forests, there is a greater need to enhance our understanding of treefall disturbances that have the potential to redistribute resources within forests.

Long-term estimation of the canopy photosynthesis of a leafy vegetable based on greenhouse climate conditions and nadir photographs

In horticultural crop production, accurately estimating the canopy photosynthetic rate (Ac) based on greenhouse climate conditions is of practical value for predicting and controlling crop growth and yield. We developed a method for continuously estimating Ac in a greenhouse from readily obtainable information (i.e., climatic data and canopy photographs) by combining a canopy photosynthesis model with image analysis. The canopy photosynthesis model was based on a sun/shade representation of the crop canopy combined with models of single-leaf photosynthesis, stomatal conductance, mass transfer and leaf energy balance. This combination allowed 1) the incorporation of all major climatic variables (i.e., radiation, CO2 concentration, air temperature, humidity, and wind velocity) into the Ac estimation and 2) the simultaneous estimation of Ac, canopy transpiration rate (Ec), and leaf temperature (TL). The leaf area index (Lc), which changes considerably throughout the growth period of a crop canopy, was evaluated nondestructively from the gap fractions of nadir digital photographs (i.e., the fractions of nonleaf area). The canopy photosynthesis model and image analysis results were combined and used to estimate the Ac, Ec, and TL of spinach canopies grown at ambient and elevated CO2 concentrations (c. 400 and 800 µmol mol−1, respectively). Regardless of the CO2 concentration, the estimates of Ac, Ec, and TL were in good agreement with measurements obtained with the open chamber method throughout the growth period. Analyzing the sensitivity of Ac to input variables revealed that the effects of climatic variables on Ac can vary considerably depending on the Lc and incoming radiation. The proposed method enables not only the estimation of Ac from readily obtainable information but also the quantitative evaluation of the effect of climate control on Ac in horticultural greenhouses.

Estimation of forest canopy structure and understory light using spherical panorama images from smartphone photography

Abstract Accurate estimates of forest canopy structure are central for a wide range of ecological studies. Hemispherical photography (HP) is a popular tool to estimate canopy attributes. However, traditional HP methods require expensive equipment, are sensitive to exposure settings, and produce limited resolution which dramatically affects the accuracy of gap fraction estimates. As an alternative, hemispherical images can be extracted from spherical panoramas produced by many smartphone camera applications. I compared hemispherical photos captured with a digital single lens reflex camera and 180° lens to those extracted from smartphone spherical panoramas (SSP). The SSP HP method leverages built-in features of current generation smartphones to produce sharper images of higher resolution, resulting in more definition of fine canopy structure. Canopy openness and global site factor from SSP HP are highly correlated with traditional methods (R2 &amp;gt; 0.9), while leaf area index estimates are lower, especially in more closed canopies where traditional methods fail to capture fine gaps.

Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.

Effect of Forest Canopy Structure on Wintertime Land Surface Albedo: Evaluating CLM5 Simulations With In‐Situ Measurements

AbstractLand Surface Albedo (LSA) of forested environments continues to be a source of uncertainty in land surface modeling, especially across seasonally snow covered domains. Assessment and improvement of global scale model performance has been hampered by the contrasting spatial scales of model resolution and in‐situ LSA measurements. In this study, point‐scale simulations of the Community Land Model 5.0 (CLM5) were evaluated across a large range of forest structures and solar angles at two climatically different locations. LSA measurements, using an uncrewed aerial vehicle with up and down‐looking shortwave radiation sensors, showed canopy structural shading of the snow surface exerted a primary control on LSA. Diurnal patterns of measured LSA revealed strong effects of both azimuth and zenith angles, neither of which were adequately represented in simulations. In sparse forest environments, LSA were overestimated by up to 66%. Further analysis revealed a lack of correlation between Plant Area Index (PAI), the primary canopy descriptor in CLM5, and measured LSA. Instead, measured LSA showed considerable correlation with the fraction of snow visible in the sensor's field of view, a correlation which increased further when only considering the sunlit fraction of visible snow. The use of effective PAI values as a simple first‐order correction for the discrepancy between measured and simulated LSA in sparse forest environments substantially improved model results (64%–76% RMSE reduction). However, the large biases suggest the need for a more generic solution, for example, by introducing a canopy metric that represents canopy gap fraction rather than assuming a spatially homogeneous canopy.

Canopy gap dynamics, disturbances, and natural regeneration patterns in a beech-dominated Hyrcanian old-growth forest

Canopy gaps play a crucial role in forest dynamic processes and help preserve biodiversity, influence nutrient cycles, and maintain the complex structure of the forests. This study aimed to quantify the gap dynamics, regeneration establishment, and gap closure in a natural old-growth Hyrcanian forest in the north of Iran. We used a repeated inventory of gap size-frequency and fraction in beech (Fagus orientalis) dominant forest over a 9-year interval (2010–2019). The total gap area documented in 2010, 2016, and 2019 was 2,487, 6,890, and 8,864 m2, respectively. The gap area ranged from the smallest sizes of 139, 83, and 153 m2 to the largest sizes 906, 1,668, and 871 m2 in 2010, 2016, and 2019, respectively. Gap fraction significantly increased from 0.52%, 1.93%, and 3.7 in 2010, 2016, and 2019, respectively. The size distribution of gaps was strongly skewed to the medium class (200-500 m2), with approximately 60% of the gaps. Results revealed that total regenerations are not in correlation with gap size. Small gaps were closed within a few years through rapid horizontal canopy expansion of neighboring beech trees. The gap closure rate decreased by increasing the gap size (70% in 71 m2 to 10% in 1,600 m2). The highest density and greatest regeneration growth occurred mostly along the eastern part of gaps. The spatial distributions of regeneration density demonstrated differences in different gap size classes, which probably resulted from heterogeneity in the microenvironment within the gap and the differences in the regeneration responses to these variations. This investigation provided useful data for managing natural regenerations based on forest sustainability. The changes in gap patterns observed between 2010 and 2019 highlight the high value of repeated gap inventories for better comprehending the disturbance regeneration and dynamics of natural gaps. Keywords: Gap size, Gap development, Special distribution, Regeneration density, Gap closure

Gap and stand structural characteristics in a managed and an unmanaged old-growth oriental beech (Fagus orientalisLipsky) forest

AbstractSimplified forest structures following even-age management have been associated with the loss of biodiversity, which may be avoided through disturbance-inspired silviculture. Here, we ask how much do gap characteristics in a managed old-growth differ from those in unmanaged old-growth subject only to natural dynamics? In this study, we compared important characteristics of gaps (e.g. canopy gap fraction, distribution of gap sizes) and gapmakers (e.g. size classes, frequency, decay classes) between a managed and an adjacent unmanaged old-growth Oriental beech (Fagus orientalis Lipsky) compartment in the Keladarsht region of northern Iran 10 years after a single harvest entry using single-tree selection. Canopy openings &amp;gt;100 m2 with visible remnants of gapmakers (i.e. stumps) were included in this study. Gap characteristics of both compartments were within typical ranges for old-growth beech. Nonetheless, small but potentially important differences between the two areas were observed. In the managed compartment, harvesting poor quality trees with structural defects and typical diameters at breast height &amp;gt;52.5 cm plus natural mortality resulted in 102 canopy gaps (1–6 gapmakers, averaging 3.5 gaps/ha, gap fraction 9.8 per cent) compared with 59 natural canopy gaps (1–7 gapmakers, averaging 2.6 gaps/ha, gap fraction 13.7 per cent) in the unmanaged compartment. In both compartments, medium-sized gaps (200–500 m2) were most prevalent. In the managed compartment, 60 per cent of gapmakers were large or very large (typically cut) compared with 39 per cent in the unmanaged compartment where large trees typically snapped and became snags. Uprooting, particularly of small and medium sized gapmakers, was less common in the managed than the unmanaged compartment. Our results indicate that even one single-tree selection harvest may lead to a short-term divergence in stand structure compared with the unmanaged forest. While such managed forests may no longer be considered as old-growth, divergences in canopy gap characteristics indicate that a more nuanced harvesting scheme that includes cutting some larger gaps may more closely mimic the canopy dynamics of this old-growth forest.