Forest Canopy Characteristics Research Articles

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43–90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1–2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.

Development of a Pixel-Wise Forest Transmissivity Model at Frequencies of 19 GHz and 37 GHz for Snow Depth Inversion in Northeast China

Satellite passive microwave remote sensing has been extensively used to estimate snow depth (SD) and snow water equivalent (SWE) across both regional and continental scales. However, the presence of forests causes significant uncertainties in the estimations of snow parameters. Forest transmissivity is one of the most important parameters for describing the microwave radiation and scattering characteristics of forest canopies. Although many researchers have constructed models for the functional relationship between forest transmissivity and forest vegetation parameters (e.g., stand growth and accumulation), such relationships are strongly limited by the inversion accuracy of vegetation parameters, forest distribution types, and scale-transformation effects in terms of regional or global scale applications. In this research, we propose a pixel-wise forest transmissivity estimation model (Pixel-wise γ Model) based on long-term series satellite brightness temperature (TB) data for the satellite remote sensing inversion of snow parameters. The model performance is evaluated and applied in SD inversion. The results show that the SD inversion errors RMSE and Bias are 9.8 cm and −1.5 cm, respectively; the SD inversion results are improved by 41% and 84% after using the Pixel-wise γ Model, compared with the forest transmissivity model applied in the GlobSnow v3.0 product. The proposed forest transmissivity model does not depend on forest cover parameters and other ground measurement parameters, which greatly improves its application scope and simplicity.

Satellite data track spatial and temporal declines in European beech forest canopy characteristics associated with intense drought events in the Rhön Biosphere Reserve, central Germany.

The increasing intensity and frequency of droughts under climate change demands effective ways to monitor drought impacts. We sought to determine how different satellite remote sensing sources influence our ability to identify temporal and spatial impacts on European beech forest canopy health during intense drought events. Imagery from three satellite series (MODIS, Landsat and Sentinel-2) was used to observe changes in canopy health during the intense droughts of 2003 and 2018 in the Rhön Biosphere Reserve, central Germany. Monthly normalized difference vegetation index (NDVI) anomalies were calculated for each satellite between 2000-2020 and compared against temperature, precipitation and the standardized precipitation evapotranspiration index (SPEI). Severe canopy impacts in 2003 and 2018 were associated with low NDVI in August and September. At the stand-scale, Sentinel-2 data allowed a spatially detailed understanding of canopy-level impacts, while MODIS provided the clearest temporal progression of the drought's impacts on the forest canopy. Low NDVI values were not exclusively associated with extremes of either temperature and precipitation individually; however, low canopy NDVI in August was associated with SPEI values below -1.5. Although the intense drought of 2018, as defined by meteorological parameters, peaked in July, canopy NDVI did not decline until August, highlighting that our ability to detect canopy impact during drought events is sensitive to the timing of image acquisition. No single satellite sensor affords a full picture of the temporal or spatial progression of drought impacts. Consequently, using sensors in tandem provides the best possible representation of canopy health during intense drought events.

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest.

Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun-sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate-phenology relationships in the tropics.

Human bipedal instability in tree canopy environments is reduced by \u201clight touch\u201d fingertip support

Whether tree canopy habitats played a sustained role in the ecology of ancestral bipedal hominins is unresolved. Some argue that arboreal bipedalism was prohibitively risky for hominins whose increasingly modern anatomy prevented them from gripping branches with their feet. Balancing on two legs is indeed challenging for humans under optimal conditions let alone in forest canopy, which is physically and visually highly dynamic. Here we quantify the impact of forest canopy characteristics on postural stability in humans. Viewing a movie of swaying branches while standing on a branch-like bouncy springboard destabilised the participants as much as wearing a blindfold. However “light touch”, a sensorimotor strategy based on light fingertip support, significantly enhanced their balance and lowered their thigh muscle activity by up to 30%. This demonstrates how a light touch strategy could have been central to our ancestor’s ability to avoid falls and reduce the mechanical and metabolic cost of arboreal feeding and movement. Our results may also indicate that some adaptations in the hand that facilitated continued access to forest canopy may have complemented, rather than opposed, adaptations that facilitated precise manipulation and tool use.

Airborne S-Band SAR for Forest Biophysical Retrieval in Temperate Mixed Forests of the UK

Radar backscatter from forest canopies is related to forest cover, canopy structure and aboveground biomass (AGB). The S-band frequency (3.1–3.3 GHz) lies between the longer L-band (1–2 GHz) and the shorter C-band (5–6 GHz) and has been insufficiently studied for forest applications due to limited data availability. In anticipation of the British built NovaSAR-S satellite mission, this study evaluates the benefits of polarimetric S-band SAR for forest biophysical properties. To understand the scattering mechanisms in forest canopies at S-band the Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model was used. S-band backscatter was found to have high sensitivity to the forest canopy characteristics across all polarisations and incidence angles. This sensitivity originates from ground/trunk interaction as the dominant scattering mechanism related to broadleaved species for co-polarised mode and specific incidence angles. The study was carried out in the temperate mixed forest at Savernake Forest and Wytham Woods in southern England, where airborne S-band SAR imagery and field data are available from the recent AirSAR campaign. Field data from the test sites revealed wide ranges of forest parameters, including average canopy height (6–23 m), diameter at breast-height (7–42 cm), basal area (0.2–56 m2/ha), stem density (20–350 trees/ha) and woody biomass density (31–520 t/ha). S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest AGB with least error between 90.63 and 99.39 t/ha and coefficient of determination (r2) between 0.42 and 0.47 for the co-polarised channel at 0.25 ha resolution. The conclusion is that S-band SAR data such as from NovaSAR-S is suitable for monitoring forest aboveground biomass less than 100 t/ha at 25 m resolution in low to medium incidence angle range.

Convergent elevation trends in canopy chemical traits of tropical forests.

The functional biogeography of tropical forests is expressed in foliar chemicals that are key physiologically based predictors of plant adaptation to changing environmental conditions including climate. However, understanding the degree to which environmental filters sort the canopy chemical characteristics of forest canopies remains a challenge. Here, we report on the elevation and soil-type dependence of forest canopy chemistry among 75 compositionally and environmentally distinct forests in nine regions, with a total of 7819 individual trees representing 3246 species collected, identified and assayed for foliar traits. We assessed whether there are consistent relationships between canopy chemical traits and both elevation and soil type, and evaluated the general role of phylogeny in mediating patterns of canopy traits within and across communities. Chemical trait variation and partitioning suggested a general model based on four interconnected findings. First, geographic variation at the soil-Order level, expressing broad changes in fertility, underpins major shifts in foliar phosphorus (P) and calcium (Ca). Second, elevation-dependent shifts in average community leaf dry mass per area (LMA), chlorophyll, and carbon allocation (including nonstructural carbohydrates) are most strongly correlated with changes in foliar Ca. Third, chemical diversity within communities is driven by differences between species rather than by plasticity within species. Finally, elevation- and soil-dependent changes in N, LMA and leaf carbon allocation are mediated by canopy compositional turnover, whereas foliar P and Ca are driven more by changes in site conditions than by phylogeny. Our findings have broad implications for understanding the global ecology of humid tropical forests, and their functional responses to changing climate.

Semi-empirical modeling of the scene reflectance of snow-covered boreal forest: Validation with airborne spectrometer and LIDAR observations

This work aims at the development and validation of a zeroth order radiative transfer (RT) approach to describe the visible band (555nm) reflectance of conifer-dominated boreal forest for the needs of remote sensing of snow. This is accomplished by applying airborne and mast-borne spectrometer data sets together with high-resolution information on forest canopy characteristics. In case of aerial spectrometer observations, tree characteristics determined from airborne LIDAR observations are applied to quantify the effect of forest canopy on scene reflectance. The results indicate that a simple RT model is feasible to describe extinction and reflectance properties of both homogeneous and heterogeneous forest scenes (corresponding to varying scales of satellite data footprints and varying structures of forest canopies). The obtained results also justify the application of apparent forest canopy transmissivity to describe the influence of forest to reflectance, as is done e.g. in the SCAmod method for the continental scale monitoring of fractional snow cover (FSC) from optical satellite data. Additionally, the feasibility of the zeroth order RT approach is compared with the use of linear mixing model of scene reflectance. Results suggest that the non-linear RT approach describes the scene reflectance of a snow-covered boreal forest more realistically than the linear mixing model (in case when shadows on tree crowns and surface are not modeled separately, which is a relevant suggestion when considering the use of models for large scale snow mapping applications).

Developing a dual-wavelength full-waveform terrestrial laser scanner to characterize forest canopy structure

The development of a dual-wavelength full-waveform terrestrial laser scanner to measure the three-dimensional structure of forest canopies is described, and field measurements used to evaluate and test the instrument measurement characteristics. The Salford Advanced Laser Canopy Analyser (SALCA) measures the full-waveform of backscattered radiation at two laser wavelengths, one in the near-infrared (1063nm) and one in the shortwave infrared (1545nm). The instrument is field-portable and measures up to nine million waveforms, at the two wavelengths, across a complete hemisphere above the instrument. SALCA was purpose-built to measure structural characteristics of forest canopies and this paper reports the first results of field-based data collection using the instrument. Characteristics of the waveforms, and waveform data processing are outlined, applications of dual wavelength measurements are evaluated, and field deployment of the instrument at a forest test site described. Preliminary instrument calibration results are presented and challenges in extracting useful information on forest structure are highlighted. Full-waveform multiple-wavelength terrestrial laser scanners are likely to provide more detailed and more accurate forest structural measurement in the future. This research demonstrates how SALCA provides a key step to develop, test and apply this new technology in a range of forest-related problems.

Positive feedbacks between phosphorus deposition and forest canopy trapping, evidence from Southern Mexico

AbstractFor some phosphorus (P)‐limited ecosystems, vegetation can be sustained by atmospheric P inputs. The ability of the canopy to trap airborne particles influences atmospheric P deposition. This dependence suggests a positive feedback, which could impact forest regeneration following deforestation. We examine how the amount of P deposited atmospherically varies as a function of forest canopy characteristics. We quantify the amount of P in bulk deposition (i.e., rainfall and dry deposition) and throughfall from a mature forest and 6 year old successional vegetation stand. To rule out the possibility that P enrichment in throughfall is due to canopy leaching, we construct an artificial forest made of P‐free plastic materials. We then compare throughfall samples collected beneath the artificial forest with those collected beneath the successional vegetation due to similarities in forest characteristics such as height and stem density. Over 1 year, 0.6 ± 0.1 kg P ha−1 yr−1 were deposited in the open area, 0.8 ± 0.0 kg P ha−1 yr−1 beneath the successional vegetation, 0.5 ± 0.0 kg P ha−1 yr−1 beneath the artificial forest, and 1.9 ± 0.0 kg P ha−1 yr−1 beneath the mature forest. Results also showed an enrichment of P concentration beneath the artificial forest relative to the open area. Atmospheric P sources sustain 37% of the annual P demand in the mature forest, but only 13% in the successional vegetation. Thus, following deforestation, more of the P demand would have to be met from other sources that if unavailable, could lead to conditions where the forest does not recover.

The effect of boreal forest canopy to reflectance of snow covered terrain based on airborne imaging spectrometer observations

Optical remote sensing methods for mapping of the seasonal snow cover are often obstructed by the masking effect of forest canopy. Therefore, optical algorithms tend to underestimate the amount of snow cover in forested regions. In this paper, we investigate the influence of boreal forest stand characteristics on the observed scene reflectance under full dry snow cover conditions by applying an advantageous experimental setup combining airborne hyperspectral imaging and LIDAR data sets from a test region in Sodankylä, northern Finland. This is particularly useful to the understanding of the composition of the mixed satellite scene reflectance behavior and it is relation to the natural ground targets’ spectral signatures.At first, we demonstrate the effects of varying forest stand characteristics, including Canopy Cover (CC), Tree Height (TH) and the product of the these parameters referred to as CCxTH, on the reflectance measured by airborne imaging spectrometer AisaDUAL. Then, we analyze the effects of the presence of snow on forest canopy on the observed AisaDUAL data. The analysis of the effects of canopy was enabled by the high resolution LIDAR measurements which provide reference information on forest canopy characteristics. According to the results the change in Canopy Cover, as well as in CCxTH, is related to the observed change in reflectance, as well as to changes in such spectral indices as Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI). Additionally, NDSI was found to vary extensively particularly in dense forests (CC>85%), where the relative variation was over 100%. This should be considered when applying NDSI-based snow mapping methods in the case of forested areas. One notable finding was that the relation between the forest characteristics and reflectance was nearly exponential, while with reflectance indices it was linear. Besides, the results show that NDSI was a more effective parameter in detecting snow on canopy (values deviated 0.3 on average) than NDVI (values deviated 0.3 on average) in all Canopy Cover classes. The difference in NDSI between these two cases, snow-covered and snow-free canopy, increased when the canopy coverage increased.

Forest mapping by geoinformatics for landscape fire behaviour modelling in coastal forests, Greece

This study aims at quantifying and mapping fire-related characteristics of forest structure through field inventories, statistics, remote sensing, and geographical information systems in the island of Lesvos, northeast Aegean Sea, Greece. Simulation of fire behaviour requires forest biomass inputs that describe surface fuel types/models along with canopy fuel properties, such as canopy cover, stand height, crown base height, and crown bulk density, to accurately predict surface and crown fire spread and spotting potential. Forest canopy characteristics and other vegetation attributes were sampled and derived in over 100 field plots, the majority of which were located in coastal pine forest stands. Regression models involving four dependent forest stand variables (stand height, canopy cover, crown base height, and crown bulk density) were developed using generalized additive models. The values of adjusted R 2 were 0.72 for stand height, 0.68 for canopy cover, 0.51 for crown base height, and 0.33 for crown bulk density. These regression models were used to create forest fuel characteristics layers, which can be used as inputs to fire management applications and state-of-the-art landscape-scale fire behaviour models.

Feature analysis of LIDAR waveforms from forest canopies

Airborne light detection and ranging (LIDAR) can detect the three-dimensional structure of forest canopies by transmitting laser pulses and receiving returned waveforms which contain backscatter from branches and leaves at different heights. We established a solid scatterer model to explain the widened durations found in analyzing the relationship between laser pulses and forest canopies, and obtained the corresponding rule between laser pulse duration and scatterer depth. Based on returned waveform characteristics, scatterers were classified into three types: simple, solid and complex. We developed single-peak derivative and multiple-peak derivative analysis methods to retrieve waveform features and discriminate between scatterer types. Solid scatterer simulations showed that the returned waveforms were widened as scatterer depth increased, and as space between sub-scatterers increased the returned waveforms developed two peaks which subsequently developed into two separate sub-waveforms. There were slight differences between the durations of simulated and measured waveforms. LIDAR waveform data are able to describe the backscatter characteristics of forest canopies, and have potential to improve the estimation accuracy of forest parameters.

Habitat selection by a rare forest antelope: A multi-scale approach combining field data and imagery from three sensors

It can be difficult to further scientific understanding of rare or endangered species that live in inaccessible habitat using traditional methods, such as probabilistic modeling based on field data collection. Remote sensing (RS) can be an important source of information for the study of these animals. A key advantage of RS is its ability to provide information over an animal's complete range, but models incorporating RS data are limited by RS's ability to detect important habitat features. In this study, we focus on the rare, poorly-understood mountain bongo antelope (Tragelaphus euryceros isaaci) which survives in the wild in isolated pockets of montane forest in Kenya. We hypothesize that mountain bongo habitat is multi-scaled. We analyzed field and RS data (derived from SPOT, ASTER, and MODIS) ranging in scale from 0.02–85.93 ha to test our hypothesis. Important microhabitat features were identified through logistic regression models of vegetation structure data collected in plots (0.04 ha) of bongo presence (n=36) and absence (n=90). Models were selected using an information theoretic approach. We analyzed the correlations between microhabitat (four canopy and four understorey structure measures) and RS variables derived using spectral mixture (SMA) and texture analysis; most ASTER and SPOT variables were significantly related with canopy structure variables (max|r|=0.56), but correlations between understorey structure and all but two RS variables were insignificant. Further logistic regression modeling showed that combining field microhabitat (primarily understorey structure variables) and larger-scaled RS measures (ASTER spectral mixture analysis variables aggregated to 450 m (20.25 ha)) provided superior models of bongo habitat selection than those based on field or RS data only. The results demonstrate that: 1) forest canopy characteristics at scales of ~20 ha and understorey structural conditions at the micro-scale of 0.04 ha were the most important features influencing bongo habitat selection; 2) models for predicting bongo habitat distribution must incorporate both micro- and macro-habitat variables; 3) optical RS data may characterize important micro-scale canopy variables with reasonable accuracy, but are ineffective for detecting understorey features (unless alternative techniques such as forest structural indices can be successfully applied); 4) RS and field data are both essential for understanding bongo habitat selection. The technique employed here for understanding this rare antelope's habitat selection may also be applied in studies of other large herbivores.

Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization

A comprehensive canopy characterization of forests is derived from the combined remote sensing signal of imaging spectrometry and large footprint LIDAR. The inversion of two linked physically based Radiative Transfer Models (RTM) provided the platform for synergistically exploiting the specific and independent information dimensions obtained by the two earth observation systems. Due to its measurement principle, LIght Detection And Ranging (LIDAR) is particularly suited to assess the horizontal and vertical canopy structure of forests, while the spectral measurements of imaging spectrometry are specifically rich on information for biophysical and -chemical canopy properties. In the presented approach, the specific information content inherent to the observations of the respective sensor was not only able to complement the canopy characterization, but also helped to solve the ill-posed problem of the RTM inversion. The theoretical feasibility of the proposed earth observation concept has been tested on a synthetic data set generated by a forest growth model for a wide range of forest stands. Robust estimates on forest canopy characteristics were achieved, ranging from maximal tree height, fractional cover (fcover), Leaf Area Index (LAI) to the foliage chlorophyll and water content. The introduction of prior information on the canopy structure derived from large footprint LIDAR observations significantly improved the retrieval performance relative to estimates based solely on spectral information.

Near infrared model development for pond-cypress subjected to chronic water stress and Botryosphaeria rhodina

Visible and near infrared (NIR) reflectance correlate with chemical and physical characteristics of forest canopies. Laboratory analysis of spectral reflectance in the NIR region has advantages over wet-chemical analysis. Spectral analysis of pond-cypress (Taxodium ascendens Brong.) under controlled water stress and Botryosphaeria rhodina (Cooke) Aux. inoculation has not been reported previously. Eight models were selected for preliminary multivariate evaluation of the relationship between reflectance (NIR 1100‐2500 nm) and soluble carbohydrate constituents identified in a previous study. “Best fit models” for half of the constituents were first derivative, modified partial least squares (Mod PLS) models. The remainder were second derivative, Mod PLS models. The R2 values from the best fit models for branch-tip carbohydrates were (from greatest to least): arabinose (0.93), xylose (0.93), rhamnose (0.92), pentose:hexose ratio (0.91), total pentoses (0.87), total carbohydrates (0.83), galacturonic acid (0.81), total hexoses (0.81), mannose (0.78), glucose (0.67), galactose (0.47). Evaluation of dry, milled branch tips from natural stands of pond-cypress trees may provide better understanding of physiological impacts on trees associated with anthropogenic hydroperiod perturbations (e.g., from unsustainable aquifer yield, aquifer storage and recovery, and mining solid materials).

Canopy tree development and undergrowth bamboo dynamics in old-growth Abies– Betula forests in southwestern China: a 12-year study

Interactions between forest canopy characteristics and plants in the forest understory are important determinants of forest community structure and dynamics. In the highlands of southwestern, China the dwarf bamboo Bashania fangiana Yi is an understory dominant beneath a mixed canopy of the evergreen Abies faxoniana (Rheder & Wilson) and the deciduous Betula utilis (D. Don). The goal of this study was to better understand the role of bamboo dominance, canopy characteristics, and periodic bamboo dieback on forest development. To achieve this goal, we measured tree seedling, tree saplings, and trees, forest canopy characteristics, and bamboo cover in permanent forest ( n = 4) and gap plots ( n = 31) in a mixed A. faxoniana and B. utilis forest in Sichuan, China. Dwarf bamboos died off in 1983 in the gap plots, and in three of the four forest plots. Forest development was assessed for the period 1984–1996. The seedling bank in forest and gap plots increased after bamboo die-off. A. faxoniana seedlings increased more than B. utilis in forest plots; the opposite pattern characterized gap plots. The proportion of seedlings on raised micro-sites on the forest floor also changed and new seedling were more abundant on the forest floor. By 1996, bamboo seedling cover and biomass had recovered to ca. 45% or their pre-flowering values. Rates of bamboo seedling recovery were faster beneath canopy gaps and deciduous trees than beneath forest or evergreen trees. Tree mortality exceeded recruitment in plots with dense bamboo; the opposite pattern was found in the plot with little bamboo. The mortality rate for B. utilis trees (2.4% year −1) was higher than that for A. faxoniana (0.8% year −1) and forests with dense bamboos became more open over the census period. Tree mortality was size-dependent and intermediate sized trees had the lowest rates of mortality. Stand basal area increased mainly due to greater basal area gain than loss for A. faxoniana. Interactions between tree species life history, canopy type, and bamboo life-cycles create heterogeneous conditions that influence tree and bamboo regeneration and contribute to the coexistence of A. faxoniana and B. utilis in old-growth forests in southwestern China.

Measurement of forest canopy structure by a laser plane range-finding method: Improvement of radiative resolution and examples of its application

In studies of flux in forests, the measurement and description of forest canopy characteristics are important but difficult tasks. In 1998, we assessed a laser plane range-finding method as a new, non-destructive way of measuring the three-dimensional (3-D) structure of forest canopies. Our ultimate goal is to develop the method and the instrument in such a way that we can accurately measure the 3-D architecture of forest canopies and automatically distinguish foliage and stems. In the current study we improved the system concerning the radiative resolution. This improvement was conducted by introducing a highly sensitive digital charge-coupled device (CCD) camera whose exposure time could be controlled by a personal computer and a proposed algorithm of plural exposure time. We expect the principle of using two wavelengths of laser light to be valid for distinguishing foliage from stems. Distinguishing each element of the canopy was conducted successfully by the regional segmentation method. With this system, we could acquire a hemispherical range-finding image in which each pixel had its own range-finding information. As an example of the application of this system, we proposed a new method to calibrate Leaf Area Index (LAI) value using the hemispherical range-finding image as follows: (1) normalizing the canopy into a horizontal unit thickness (1 m), (2) compensating radiative intensity for normalized canopy by using Beer’s Equations, (3) calculating LAI of the normalized canopy, and (4) reconstructing “Real canopy” and estimating “Real LAI” by multiplying by the canopy thickness. Practical examples executed in a real forest have proved that this new method is efficient.

An Empirical Relation Describing Leaf-Area Density inside the Forest for Environmental Modeling

Abstract The spatial distribution of the leaf-area density is a key parameter in describing the forest canopy characteristics, which has a strong impact on its radiation balance and the mass and energy exchange with the atmosphere as well. The objective of this short study is to define an empirical relation describing the vertical distribution of leaf-area density that can be used for an improved estimation of the turbulent transfer coefficient inside the forest as well as above it. To check the validity of the method proposed, the calculated values are compared with the observations using datasets from eight observational sites located in four different types of forest, covering a broad range of mean values of leaf-area indices between 2 and 18.

Recovery of forest canopy characteristics through inversion of a complex 3D model

Radiative transfer models for vegetation serve as a basis for extracting vegetation variables using directional/spectral data from modern-borne sensors (e.g., MODIS, MISR, POLDER, SeaWiFS). Only recently have significant efforts been made to provide operational algorithms to invert these models. These efforts have exposed a need to significantly improve the efficiency and accuracy of traditional methods for inverting these physically based models. In an effort to overcome the limitations of traditional inversion methods, a neural network method was designed and tested. In this study, a complex 3D model (Discrete Anisotropic Radiative Transfer, DART) was inverted for a wide range of simulated forest canopies using POLDER-like data. The model was inverted to recover three forest canopy variables: forest cover, leaf area index, and a soil reflectance parameter. The ranges of these variables were 0.4–1.0, 0.8–9.3, and 0.0–1.0, respectively. Two inversion methods were used — a traditional inversion technique using a modified simplex method, and a neural network method in combination with an exhaustive variable selection technique. A comparison of the methods' efficiency, accuracy, and stability was made. The neural network method gave relatively accurate solutions to the inversion problem given a small subset of directional/spectral data using only one to five view angles. Using only nadir data, the root mean squared error (RMSE) for the forest cover, leaf area index, and the soil reflectance parameter were 0.025, 0.23, and 0.15, respectively, and using the “best” view directions (2–5) were 0.021, 0.21, and 0.11, respectively. In general, the neural network method was more accurate than the simplex method. The results from both methods showed that the addition of directional view angles, as opposed to only a nadir view, can significantly improve the accuracy of recovering forest canopy characteristics. The traditional simplex method is computationally intensive and may not be appropriate for many operational applications on a per-pixel basis for regional and global data. The neural network method was computationally efficient and can be applied on a per-pixel basis. In general, the neural network technique had significantly lower RMSE values at the low noise levels. However, at moderate noise levels, the simplex method was equal to the neural network method in RMSE values. At high noise levels, the simplex method had significantly lower RMSE values than the neural network method. The neural network approach can provide an accurate, efficient, and stable inversion method for radiative transfer models using directional/spectral data from modern-borne sensors.