Estimation Of Gap Fraction Research Articles

A universal canopy gap fraction model for forests with various tree distributions based on Nilson's models considering directional overlaps among crowns

An accurate estimation of forest canopy gap fraction (GF) is a prerequisite for remote sensing retrievals of several associated canopy parameters e.g. leaf area index, fAPAR (fraction of photosynthetically active radiation absorbed by the canopy), clumping index, etc. A universal canopy GF model for forests is required for various tree distribution patterns on flat and slopping terrains, yet existing models so far do not possess all these abilities simultaneously. For example, the Neyman type-A model, the Poisson model, and the hypergeometric model are suitable only to forests with clumped, random, and regular tree distributions, respectively. In this regard, the suite of Nilson's canopy GF models developed for forests with various tree distributions have the potential to become universal models. For this purpose, this study attempts to overcome the following limitations of these models to make them be true universally applicable: (1) canopy GF calculated by the Nilson's models is mostly accurate for various tree distributions at nadir; whereas at it is either underestimated for clumped tree distributions or overestimated for regular tree distributions at off-nadir; and (2) Nilson's models were developed for flat terrains and untested for sloping terrains. Herein, two Nilson's models are modified by expanding the calculation of overlaps among crowns (OAC) at nadir to the whole hemispherical space. The results show that all canopy GF simulations in the new models (New91 and New99) show higher consistency with ray-tracing results than those in original Nilson's models in all directions, highlighting the need to consider directional OAC. The modified models (especially for New99) are validated in two RAMI forest stands and a Saihanba forest stand: the relative error in canopy GF between the modified models and a ray-tracing model is less than 10 % in all forest stands. The modified models (especially New99) are suitable for various tree distributions on flat and slopping terrains and therefore increased the universality of the original Nilson's models suitable for a wide-range of remote sensing applications. New99 can be deemed as a universal canopy GF model for forests with various tree distributions.

CoveR: an R package for processing digital cover photography images to retrieve forest canopy attributes

Key messagecoveR is an R package for estimating canopy attributes from digital cover photography (DCP) images. The simplicity of the method and the open-accessibility of coveR can effectively extend the accessibility and applicability of DCP to a wider audience.Digital cover photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for hemispherical photography, whereas open-access tools for DCP are lacking. We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline while ensuring quality-checking of each processing step. A wrapper function ‘coveR()’ is also created to perform all the image processing workflow in a single function. A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. By providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the possibility to implement DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts.

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 > 0.9), while leaf area index estimates are lower, especially in more closed canopies where traditional methods fail to capture fine gaps.

Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

Estimation of Canopy Gap Fraction from Terrestrial Laser Scanner Using an Angular Grid to Take Advantage of the Full Data Spatial Resolution

This paper develops an algorithm to estimate vegetation canopy gap fraction (GF), taking advantage of the full Terrestrial Laser Scanner (TLS) resolution. After calculating the TLS angular resolution, the algorithm identifies the missing laser hits (gaps) within an angular grid in the azimuthal and zenithal directions. The algorithm was first tested on angular data simulations with random (R), cluster (C) and random and cluster together (RC) gap pattern distributions. Noise introduced in the simulations as a percentage of the resolution accounted for the effect of TLS angular uncertainty. The algorithm performs accurately if angular noise is <6% of the angular resolution. To assess the impact of the change in projection, this study compared these GF estimates from angular grid simulations to their transformation into simulated hemispherical images (SHI). SHI with C patterns perform accurately, but R and RC patterns underestimate GF, especially for GF values below 0.6. The SHI performance to estimate GF was always far below the algorithm developed here with the angular grid simulations. When applied to actual TLS data acquired over individual Quercus ilex L. trees, the algorithm rendered a GF between 0.26 and 0.40. TLS had an angular noise <6%. Converting the angular grid into simulated HI (TLS-SHI) provided a better agreement with actual HI acquired in the same location as the TLS data, since they are in the same projection. The TLS-SHI underestimated GF by an average of 4% compared to HI. HI and TLS-SHI presented 14% and 17% lower values than the GF calculated from the angular grids, respectively. Nevertheless, the results from the simulations indicate that the algorithm based on the angular grid should be closer to the actual GF of the tree canopy.

Estimation of Gap Fraction and Foliage Clumping in Forest Canopies

The gap fractions of three mature hemi-boreal forest stands in Estonia were estimated using the LAI-2000 plant canopy analyzer ( LI-COR Biosciences, Lincoln, NE, USA), the TRAC instrument (Edgewall, Miami, FL, USA), Cajanus’ tube, hemispherical photos, as well as terrestrial (TLS) and airborne (ALS) laser scanners. ALS measurements with an 8-year interval confirmed that changes in the structure of mature forest stands are slow, and that measurements in the same season of different years should be well comparable. Gap fraction estimates varied considerably depending on the instruments and methods used. None of the methods considered for the estimation of gap fraction of forest canopies proved superior to others. The increasing spatial resolution of new ALS devices allows the canopy structure to be analyzed in more detail than was possible before. The high vertical resolution of point clouds improves the possibility of estimating the stand height, crown length, and clumping of foliage in the canopy. The clumping/regularity of the foliage in a forest canopy is correlated with tree height, crown length, and basal area. The method suggested herein for the estimation of foliage clumping allows the leaf area estimates of forest canopies to be improved.

Digital photography for tracking the phenology of an evergreen conifer stand

Gap fraction measurements with digital a hemispherical camera were carried out in 2007–2016 in a Scots pine (Pinus sylvestris L.) stand growing on transitional Sphagnum bog in Järvselja, Estonia. The measurements were done at the time when (1) the pines had maximum foliage, or (2) after autumn needle fall or before bud burst. Data for gap fraction calculation were extracted from camera raw files. For data recording, the sensor output signal maximum was kept according to the image brightness histogram within the interquartile range of the sensor dynamic range. A linear conversion method (LinearRatio) was used for image processing. The relative needle loss in autumn (20–30%) increased gap fraction by 0.02–0.05 at view zenith angles from 10° to 60° and decreased the mean plant area index from 1.86 to 1.68 after autumn litter fall. The measurement results were in good agreement with a theoretical gap fraction model simulation and with gap fraction estimates made from cover photos. The measurement and data processing protocol is appropriate for long-term monitoring in permanent sample plots.

Continental Estimates of Canopy Gap Fraction by Active Remote Sensing

ABSTRACTThis study estimates canopy gap fraction (GF) from Geoscience Laser Altimeter System (GLAS) waveform data retrieved from a cross section of the Australian ecosystem, represented by 4 sites. However, as the literature supports, waveform profiles from systems such as GLAS require scaling in order to retrieve more accurate estimates of GF; the degree of scaling required is not a priori known, which has hindered large-scale estimates of GF by such systems to date. We employ spatially coincident GLAS footprints and airborne laser scanning (ALS) point cloud data from each site to scale initial estimates of GLAS GF to match ALS estimates, which allows the inference of scale factors to all footprints across the Australian forest landscape via the Random Forest (RF) technique. These refined estimates of GF are submitted to a second RF model to yield regional predictions of GF across the forest landscape. These predictions are validated against ALS data from 20 sites throughout Australia (R2 = 0.56, RMSE = 25.95%) and compared to MODIS, and also compared to ALS data (R2 = 0.13, RMSE = 66.46%). The developed method for producing large-scale estimates of GF shows promise for future studies and refinement, and is particularly pertinent as future spaceborne waveform systems come online (ICESat-2 and the Global Ecosystem Dynamics Investigation LiDAR).

Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

Spaceborne laser altimetry waveform estimates of canopy Gap Fraction (GF) vary with respect to discrete return airborne equivalents due to their greater sensitivity to reflectance differences between canopy and ground surfaces resulting from differences in footprint size, energy thresholding, noise characteristics and sampling geometry. Applying scaling factors to either the ground or canopy portions of waveforms has successfully circumvented this issue, but not at large scales. This study develops a method to scale spaceborne altimeter waveforms by identifying which remotely-sensed vegetation, terrain and environmental attributes are best suited to predicting scaling factors based on an independent measure of importance. The most important attributes were identified as: soil phosphorus and nitrogen contents, vegetation height, MODIS vegetation continuous fields product and terrain slope. Unscaled and scaled estimates of GF are compared to corresponding ALS data for all available data and an optimized subset, where the latter produced most encouraging results (R2 = 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions.

Retrieving Directional Gap Fraction, Extinction Coefficient, and Effective Leaf Area Index by Incorporating Scan Angle Information From Discrete Aerial Lidar Data

The scan angle information implicitly contained within the 3-D point cloud data (PCD) generated from light detection and ranging strongly affects the retrieval accuracy of the forest canopy structural parameters. Using information generated from overlapping aerial laser scanning (ALS) flight paths with multiple scan angles over a forest canopy can help to remove the occlusion effects between foliage elements, ultimately creating a relative comprehensive PCD. In this paper, we develop a novel physically based scan angle correction algorithm to retrieve the effective leaf area index (LAIe) of a forest canopy using ALS. Furthermore, we investigate the effects of scan angle and the number of ALS overpass lines from adjacent flight paths over a forest canopy on directional gap fraction (DGF) estimates. Our results suggest that ALS-based LAIe estimates capture 71.35% of the variations in LAIe derived from digital hemispherical photography. A forest canopy point cloud created using PCD from multiple overlapping ALS flight paths was sufficient to quantitatively reveal the anisotropy characteristics of DGF variations. These results suggest that scan angle information should not be neglected in retrieving forest canopy structural parameters, especially when using ALS data collected with a wide scan angle (i.e., −30° to 30° in this paper). Finally, this paper provides a solid foundation to characterize the 3-D spatial distribution of a forest radiation regime using ALS-based forest PCD.

EVALUATION OF FOREST CANOPY AND UNDERSTORY GAP FRACTION DERIVED FROM TERRESTRIAL LASER SCANNING

The quantification of forest carbon sequestration is helpful to understand the carbon storage on the Earth. The estimation of forest carbon sequestration can be achieved by the use of leaf area index (LAI), which is derived from forest gap fraction. The hemispherical image-based technique is the most popular non-destructive means for obtaining such information. However, only the gap fraction of the top canopy is derived due to the limitation of imaging technique. The gap fraction information of understory is thus neglected. In this study, we evaluate the use of a terrestrial laser scanner (TLS) to obtain the forest canopy and understory gap fraction. The forest TLS data were manually classified as the top canopy and understory layers to facilitate the estimation of top canopy and understory gap fraction, respectively.

EVALUATION OF FOREST CANOPY AND UNDERSTORY GAP FRACTION DERIVED FROM TERRESTRIAL LASER SCANNING

Abstract. The quantification of forest carbon sequestration is helpful to understand the carbon storage on the Earth. The estimation of forest carbon sequestration can be achieved by the use of leaf area index (LAI), which is derived from forest gap fraction. The hemispherical image-based technique is the most popular non-destructive means for obtaining such information. However, only the gap fraction of the top canopy is derived due to the limitation of imaging technique. The gap fraction information of understory is thus neglected. In this study, we evaluate the use of a terrestrial laser scanner (TLS) to obtain the forest canopy and understory gap fraction. The forest TLS data were manually classified as the top canopy and understory layers to facilitate the estimation of top canopy and understory gap fraction, respectively.

Correction for light scattering combined with sub-pixel classification improves estimation of gap fraction from digital cover photography

Abstract Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products.

Comparison of optical LAI measurements under diffuse and clear skies after correcting for scattered radiation

Abstract Leaf area index (LAI) is a forest canopy variable that is closely related to forest growth and health. The LAI-2000 Plant Canopy Analyzer is widely used for indirect measurement of LAI; however, use of the instrument is limited to diffuse sky conditions, greatly restricting sampling. A new bidirectional transmission model allows measurements to be obtained under clear sky conditions. This is accomplished by calculating a correction factor to reduce the impact of scattered light from canopy elements on gap fraction estimates. In this study we evaluate this technique by contrasting LAI measurements taken under diffuse and clear skies in a Pinus radiata D. Don plantation. We also evaluate the importance of obtaining accurate needle optical properties to parameterise the scattering correction model. Clear sky LAI estimates calculated with (a) measured optical values of P. radiata needles, (b) default values from the instrument software, and (c) maximum published values were compared to diffuse sky LAI estimates. Agreement was strongest where measured optical properties were used ( R 2 = 0.87), with the relationship weakening under the default ( R 2 = 0.78), and maximum value ( R 2 = 0.67) scenarios. Under these three scenarios average clear sky LAI exceeded diffuse sky LAI by 16%, 17%, and 22%, respectively. The disagreement was due in part to erroneous measurements from the outer sensor ring under diffuse skies. With these data removed agreement improved markedly under all scenarios ( R 2 = 0.94, 0.89, and 0.75, respectively), and the mean differences under each scenario declined to 8%, 9%, and 15%, respectively. Measurement of LAI under clear skies appeared to reduce error in the outer ring, greatly reduced logistical constraints, and reduced errors associated with sky variability.

Influences of crown size on the estimation of gap fraction and clumping index

Influences of crown size on the estimation of gap fraction and clumping index

Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems

Leaf area index (LAI) is a primary descriptor of vegetation structure, function, and condition. It is a vegetation product commonly derived from earth observation data. Independently obtained ground-based LAI estimates are vital for global satellite product validation. Acceptable uncertainties of these estimates are guided by satellite product accuracy thresholds stipulated by the World Meteorological Organisation (WMO) and the Global Climate Observing System (GCOS). This study compared canopy openness, gap fraction and LAI estimates derived from ground-based instruments; the primary focus was to compare high- and low-resolution (HR and LR) digital hemispherical photography (DHP) to a terrestrial laser scanner (TLS), augmented with measurements using the LAI-2200 plant canopy analyser in a subset of plots. Additionally, three common DHP classification methods were evaluated including a manual supervised (S) classification, a global (G) binary automated threshold, and a two-corner (TC) automated threshold applied to mixed pixels only. Coincident measurements were collected across five diverse forest systems in Eastern Australia with LAI values ranging from 0.5 to 5.5. Canopy openness, gap fraction and LAI were estimated following standard operational field data collection and data processing protocols. A total of 75 method-to-method pairwise comparisons were conducted, out of which 37 had an RMSD≥0.5 LAI and 26 were significantly different (p<0.05). HR-DHP (S) and two-corner (TC) methods were in close agreement with LAI-2200 (LAI RMSD 0.18 and 0.19, respectively). Additionally, the supervised (S) and two-corner (TC) methods were in close agreement over all canopy openness and LAI levels, matching to within 6% (openness: RMSD 0.04, LAI: RMSD 0.19). The automated classification method (TC) demonstrated the potential to be used as a substitute for the manual (S) classification (openness and LAI not significantly different, p>0.75). Although TLS produced on average 55% higher openness and LAI than the HR-DHP (S) and (TC) classification methods, the strong coefficient of determination indicated the potential to calibrate these methods (R2=0.88 and 0.79, respectively). Overall, results demonstrate a level of variability typically above the targeted uncertainty levels stipulated by the WMO and GCOS for satellite product validation. Further instrument calibration of TLS and improved DHP image capture and processing methods are expected to reduce these uncertainties.

Estimation of leaf area index in isolated trees with digital photography and its application to urban forestry

Accurate estimates of leaf area index (L) are strongly required for modelling ecophysiological processes within urban forests. The majority of methods available for estimating L is ideally applicable at stand scale and is therefore poorly suitable in urban settings, where trees are typically sparse and isolated. In addition, accurate measurements in urban settings are hindered by proximity of trees to infrastructure elements, which can strongly affect the accuracy of tree canopy analysis.In this study we tested whether digital photography can be used to obtain indirect estimate of L of isolated trees. The sampled species were Platanus orientalis, Liquidambar styraciflua and Juglans regia. Upward-facing photography was used to estimate gap fraction and foliage clumping from images collected in unobstructed (open areas) and obstructed (nearby buildings) settings; two image classification methods provided accurate estimates of gap fraction, based on comparison with measurements obtained from a high quality quantum sensor (LAI-2000). Leveled photography was used to characterize the leaf angle distribution of the examined tree species. L estimates obtained combining the two photographic methods agreed well with direct L measurements obtained from harvesting. We conclude that digital photography is suitable for estimating leaf area in isolated urban trees, due to its simple, fast and cost-effective procedures. Use of vegetation indices allows extending significantly the applicability of the photographic method in urban settings, including green roofs and vertical greenery systems.

Effects of voxel size and sampling setup on the estimation of forest canopy gap fraction from terrestrial laser scanning data

Assessments of forest canopy structure remain a challenge and are most often conducted using indirect techniques limited to a two-dimensional perspective. Using terrestrial laser scanner (TLS) technology, a three-dimensional (3D) approach to study canopy structure was conducted by modeling forest scenes from three broadleaved forest stands with different canopy features. Field TLS data were collected from each stand using a phase based FARO® LS880 laser scanner on four sampling setups. The capability of TLS-derived data to represent canopy structure was evaluated by comparing gap fraction estimates from the 3D models with gap fraction values from digital hemispherical photographs (DHP). Firstly, the collected 3D point clouds were processed to obtain fully representative voxel-based models of the forest canopy. Secondly, ray tracing algorithms were applied on these models to simulate hemispherical views and estimate gap fraction. Finally, a sensitivity analysis was done using different voxel sizes and the four sampling setups on the simulations, in order to assess their impact on the gap fraction estimates derived from TLS data. Results of TLS-derived gap fraction showed that combining nine scans produced better results in all forest stand. Similarly, the dimension of voxels have a marked influence on these results. Voxel sizes of 1cm, 2cm and 4cm were found to have less error when compared with real gap fraction values derived from DHP for young, intermediate and mature forest stands, respectively (RMSE ranging from 9% to 16%). However, substantial differences in gap fraction were observed and described at different zenith angles. These results suggest that specific TLS sampling setup and processing are required depending upon the forest type under analysis. Overall, this research indicates that phase based TLS data can be used for objective calculation of gap fraction.

Characterising forest gap fraction with terrestrial lidar and photography: An examination of relative limitations

Previous studies have shown that terrestrial lidar is capable of characterising forest canopies but suggest that lidar underestimates gap fraction compared to hemispherical camera photography. This paper performs a detailed comparison of lidar to camera-derived gap fractions over a range of forest structures (in snow affected areas) and reasons for any disagreements are analysed.A terrestrial laser scanner (Leica C10 first return system) was taken to Abisko in Northern Sweden (sparse birch forests) and Sodankylä in Finland (spruce and pine forests) where five plots of varying density were scanned at each (though one Abisko plot was rejected due to geolocation issues). Traditional hemispherical photographs were taken and gap fraction estimates compared.It is concluded that, for the sites tested, the reported underestimates in gap fraction can be removed by taking partial hits into account using the return intensity. The scan density used (5–8 scans per 20m by 20m plot) was sufficient to ensure that occlusion of the laser beam was not significant. The choice of sampling density of the lidar data is important, but over a certain sampling density the gap fraction estimates become insensitive to further change. The lidar gap fractions altered by around 3–8% when all subjective parameters were adjusted over their complete range.The choice of manual threshold for the hemispherical photographs is found to have a large effect (up to 17% range in gap fraction between three operators). Therefore we propose that, as long as a site has been covered by sufficient scan positions and the data sampled at high enough resolution, the lidar gap fraction estimates are more stable than those derived from a camera and avoid issues with variable illumination. In addition the lidar allows the determination of gap fraction at every point within a plot rather than just where hemispherical photographs were taken, giving a much fuller picture of the canopy. The relative difference between TLS (taking intensity into account) and camera derived gap fraction was 0.7% for Abisko and −2.8% for Sodankylä with relative root mean square errors (RMSEs) of 6.9% and 9.8% respectively, less than the variation within TLS and camera estimates and so bias has been removed.

Gap Fraction Estimates over Selectively Logged Forests in Western Amazon

Gap fraction is a biophysical variable related to energy balance, forest fauna, micro-climate and regeneration, and is an important indicator of forest management quality. The objective of this study was to compare gap fraction estimates from undisturbed forests and different environments or strata of selectively logged areas. Moreover, gap fraction measurements were collected with two distinct instruments (optical canopy analyzer LAI-2000 and hemispherical photographs). Field data were collected from two sustainable forest management sites at Jamari National Forest, Rondonia State, Brazilian Amazon. Our results indicated significant differences between data acquired using these two instruments. For instance, the LAI-2000 data showed greater variation for each environment compared to hemispherical photographics data, and the data were also more sensitive to the increase in gap fraction. Small variations were found in the gap fraction means for the two study areas, and only data for the undisturbed area were significantly different. A gradient of increasing gap fraction that ranged from primary forests to log decks was observed. Furthermore, a multiple linear regression analysis determined the contribution of the selectively logged environments to decreased forest cover, confirming the observed gradient.