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The regional ecological environment has become a significant subject of study due to the dynamics of the ecosystem, which is represented by vegetation, under the influence of human activities. The objective of this research is to demonstrate the implementation and effectiveness of the forest canopy density (FCD) model in generating a map that illustrates changes in forest canopy density using multitemporal remote sensing data in Tabunio watershed. The methodology relies on vegetation index, including the normalized difference vegetation index (NDVI), shadow index (SI), and bare soil index (BI), to generate a composite vegetation index (CVI). FCD uses multitemporal remote sensing data from Landsat TM images from 2005 to 2020, which have been utilized to accomplish multisource categorization. The findings indicated that the vegetation coverage of the Tabunio watershed presented a predominant pattern of high coverage in the northeastern and eastern regions, whereas most areas of the western region had low coverage; (2) vegetation cover from 2005 to 2020 is dominated by sparse to very dense vegetation cover classes; (3) changes in vegetation cover over two decades are very significant. The expansion of plantation land in 2005 caused a lot of non-vegetated land, which gradually changed in the following year period along with plant growth. At the end of 2020, the percentage of very dense vegetation became increasingly dominant, which was around 42 percent. The results of the study indicate the three biophysical index (NDVI, SI, and BI) used in this model approach were appropriate for precisely discriminating across all canopy density classes, as seen by the overall producer's accuracy of 81.3%. FCD model in multitemporal data can helps in the early identification of deforestation or forest degradation activities. Furthermore, the FCD model may have certain constraints, as it requires an understanding of ground conditions to establish threshold values for each class. [ABSTRACT FROM AUTHOR]

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Land 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. Key Points: Shading by trees was shown to exert a key control on Land Surface Albedo (LSA) across seasonally snow covered domainsDiurnal patterns of measured LSA revealed strong effects of azimuth and zenith angles, neither of which were represented in Community Land Model 5.0 (CLM5) simulationsIn sparse forests, CLM5 overestimated LSA by up to 66%; the use of effective Plant Area Index values reduced this bias substantially [ABSTRACT FROM AUTHOR]

ContentsSummaryI.IntroductionII.Terrestrial laser scanningIII.Turning points into treesIV.Current and future applications of TLSV.ConclusionsAcknowledgementsReferences Summary: Terrestrial laser scanning (TLS) is providing new, very detailed three‐dimensional (3D) measurements of forest canopy structure. The information that TLS measurements can provide in describing detailed, accurate 3D canopy architecture offers fascinating new insights into the variety of tree form, environmental drivers and constraints, and the relationship between form and function, particularly for tall, hard‐to‐measure trees. TLS measurements are helping to test fundamental ecological theories and enabling new and better exploitation of other measurements and models that depend on 3D structural information. This Tansley insight introduces the background and capabilities of TLS in forest ecology, discusses some of the barriers to progress, and identifies some of the directions for new work. [ABSTRACT FROM AUTHOR]

This paper is based on two initial hypotheses, firstly, it is proposed that the vegetation volume obtained with a LIDAR-based system or tree row LIDAR volume (TRLV) has a high correlation with the leaf area (LA). Secondly, it is proposed that the projected outer surface or projected tree row surface (PTRS), also LIDAR-based, is linearly related with the LA. The verification of these two hypotheses corresponds to the first two objectives of this work. The third objective is to propose an alternative method, without using LIDAR sensors, simpler and more economical, for in situ LA evaluation. To achieve these objectives a total of 17 blocks of pear, 14 of apple and 26 of vine, in different phenological states, were LIDAR scanned and subsequently manually defoliated. After the field and calculation work, the TRLV and LA were compared. The logarithmic regressions obtained had high correlations. For apple and pear trees the equations are practically the same with R 2 of 0.85 and 0.84, respectively. The equation corresponding to vines is somewhat different and has an R 2 of 0.86. The regression without species differentiation is 3.66ln(x)+9.65 with R 2  = 0.90. Based on the TRLV, the front and top projected surface areas of each block were then obtained and, using these variables, the PTRS. The linear regressions obtained between PTRS and LA have high correlations with R 2 of 0.88, 0.85 and 0.80 for apple trees, pear trees, and vineyard respectively. The three crops show very similar behavior. The straight lines are very close, with very similar slopes. With no species differentiation the linear regression model is y = 1.47x – 1.18 with R 2  = 0.93. The starting point of the third objective is to obtain the projected surfaces, frontal and top, without using a LIDAR sensor. These surfaces are not as precise as those obtained with LIDAR and for this reason they are referred to as “estimated” projected surfaces. To calculate the estimated PTRS without a LIDAR sensor, the height and depth of the vegetation are measured with a tape measure. It is also necessary to make a visual estimation of the frontal gap-fraction. For this, a training method with known gap-fraction pictograms is proposed. The final results with this non-LIDAR method are very similar to those obtained with LIDAR. This method, although it needs human intervention, is simple, easy, economical and precise for in situ LA estimation. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (N mass ) and phosphorus concentrations (P mass ) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of N mass and P mass . We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for N mass (R 2 cv 0.31 vs. 0.41, % RSME cv 3.3 vs. 3.0) and P mass (R 2 cv 0.45 vs. 0.63, % RSME cv 15.3 vs. 12.5). The predictive performances of N mass models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R 2 cv values ranged from 0.26 for N mass to 0.54 for P mass indicating considerable covariation between biochemical traits and forest structural properties. N mass was negatively related to the spatial heterogeneity of canopy density, whereas P mass was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents. [ABSTRACT FROM AUTHOR]

Remote sensing estimates of forest canopy cover have frequently been used to support a variety of applications including wildlife habitat modeling, monitoring of watershed health, change detection, and are also correlated to various aspects of forest structure and ecosystem function. Although data from the long running Landsat earth observation program (1972–present) have been previously utilized to characterize forest canopy cover, the variability in spatial and spectral resolutions between the Landsat sensors has generally limited analyses to readily comparable imagery from the TM and ETM+ sensors, which omits large portions of the full temporal record. In this study, we present an R package, LandsatLinkr, which automates the processes for harmonizing Landsat MSS and OLI imagery to the spatial and spectral qualities of TM and ETM+ imagery, allowing for the generation of annual cloud-free composites of tasseled cap spectral indices across the entire Landsat archive. We demonstrate the utility of LandsatLinkr products, further enhanced through the LandTrendr segmentation algorithm, for characterizing forest attributes through time by developing annual forest masks and maps of estimated canopy cover for the state of Minnesota from 1973 to 2015. The forest mask model had an overall accuracy of 87%, with omission and commission errors for the forest class of 17% and 10%, respectively, and 9% and 16% for non-forest classification. Our resulting maps depicted a significant positive trend in forest cover across all ecological provinces of Minnesota during the study period. A random forest model used to predict continuous canopy cover had a pseudo R 2 of 0.75, with a cross validation RMSE of 5%. Our results are comparable to previous Landsat-based canopy cover mapping efforts, but expand the evaluation time period as we were able to utilize the entire Landsat archive for assessment. [ABSTRACT FROM AUTHOR]

Spatially-explicit information on forest structure is paramount to estimating aboveground carbon stocks for designing sustainable forest management strategies and mitigating greenhouse gas emissions from deforestation and forest degradation. LiDAR measurements provide samples of forest structure that must be integrated with satellite imagery to predict and to map landscape scale variations of forest structure. Here we evaluate the capability of existing satellite synthetic aperture radar (SAR) with multispectral data to estimate forest canopy height over five study sites across two biomes in North America, namely temperate broadleaf and mixed forests and temperate coniferous forests. Pixel size affected the modelling results, with an improvement in model performance as pixel resolution coarsened from 25 m to 100 m. Likewise, the sample size was an important factor in the uncertainty of height prediction using the Support Vector Machine modelling approach. Larger sample size yielded better results but the improvement stabilised when the sample size reached approximately 10% of the study area. We also evaluated the impact of surface moisture (soil and vegetation moisture) on the modelling approach. Whereas the impact of surface moisture had a moderate effect on the proportion of the variance explained by the model (up to 14%), its impact was more evident in the bias of the models with bias reaching values up to 4 m. Averaging the incidence angle corrected radar backscatter coefficient (γ°) reduced the impact of surface moisture on the models and improved their performance at all study sites, with R 2 ranging between 0.61 and 0.82, RMSE between 2.02 and 5.64 and bias between 0.02 and −0.06, respectively, at 100 m spatial resolution. An evaluation of the relative importance of the variables in the model performance showed that for the study sites located within the temperate broadleaf and mixed forests biome ALOS-PALSAR HV polarised backscatter was the most important variable, with Landsat Tasselled Cap Transformation components barely contributing to the models for two of the study sites whereas it had a significant contribution at the third one. Over the temperate conifer forests, Landsat Tasselled Cap variables contributed more than the ALOS-PALSAR HV band to predict the landscape height variability. In all cases, incorporation of multispectral data improved the retrieval of forest canopy height and reduced the estimation uncertainty for tall forests. Finally, we concluded that models trained at one study site had higher uncertainty when applied to other sites, but a model developed from multiple sites performed equally to site-specific models to predict forest canopy height. This result suggest that a biome level model developed from several study sites can be used as a reliable estimator of biome-level forest structure from existing satellite imagery. [ABSTRACT FROM AUTHOR]

Urban green spaces have been shown to benefit residents’ physical and mental health as well as strengthen social ties. Some studies have found that presence of vegetation also might reduce crime. We examined whether an association exists between two forms of green space–(1) tree canopy and (2) public parks–and crime rates in the city of Chicago. Using publicly available data, we calculated percent tree canopy, percent acreage parks, and crime rate for assault, battery, burglary, homicide, narcotics, and robbery between years 2009–2013 for each of Chicago’s 801 census tracts. We used general linear modeling to determine whether tree canopy and crime rates as well as park area and crime rates were correlated after accounting for socioeconomic variables and spatial autocorrelation. An inverse association was found between percent tree canopy and crime rates for assault, battery, robbery, and narcotics. No significant association was found between crime types analyzed, with the exception of burglary, and percent park acreage. [ABSTRACT FROM AUTHOR]

Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the natural regeneration dynamics of Mora paraensis in floodplain forests in three regions of the eastern Amazon, and specifically, to explore the relationship between the abundance of regenerating trees and canopy openness. Tree height and circumference of all individuals of height >50 cm and diameter at breast height (DBH) <5 cm were measured in 2012, 2013 and 2015. A matrix of transitions was created and the natural regeneration (RR), demographic and dynamic rates were calculated. Hemispherical photos were used to measure canopy openness. There was no difference in the density of regenerating trees between the three study regions (mean = 2.957 ind. ha-1). The abundance of regenerating trees was highest in 2013, when 5614 individuals were inventoried, and as such the RR 2012-2013 was positive in all three regions (mean = 7.9%). Between 2013 and 2015, the RR was negative and above 50% in all regions. Canopy openness had no significant effect on the abundance of regenerating trees. The abundance of regenerating trees of M. paraensis was high in estuarine floodplain forests of the Amazon River, but the regeneration dynamics varied between years with more favourable years alternating with periods of high mortality and negative regeneration. [ABSTRACT FROM AUTHOR]

A thorough evaluation of the capability of modeling vegetation lidar returns is a critical aspect of deriving vegetation structure from lidar measurements. This study assesses the performance of the Analytical Clumped Two-Stream (ACTS) canopy radiative transfer model to simulate large-footprint lidar waveforms. Modeled lidar waveforms were compared to airborne Laser Vegetation Imaging Sensor (LVIS) data collected in deciduous and conifer forests: Harvard Forest, MA; Bartlett Experimental Forest, NH; and Howland Experimental Forest, ME. The simulated and LVIS lidar waveforms have coefficients of determination R 2 > 0.9 and RMSE ~ 0.01 at both plot and stand level for most sites. The ACTS model also produces realistic multi-peak returns from vegetation for the multi-layer and multi-species canopies with R 2 ~ 0.79–0.86 and RMSE ~ 0.01 between the simulated and LVIS waveforms. This validation work lays the foundation to retrieve vegetation structure and above-ground biomass directly from lidar waveforms through model inversion with the ACTS model. [ABSTRACT FROM AUTHOR]

Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessments that use them. LAI estimates for minimally managed or natural forest stands can be particularly difficult to develop as a result of interspecies competition, age and spatial distribution. Satellite-based LAI estimates hold promise for retrospective analyses, but we must continue to rely on numerical models for alternative management analysis. Our objective for this study is to calculate and validate LAI estimates generated from the USDA Environmental Policy Impact Climate (EPIC) model (a widely used, field-scale, biogeochemical model) on four forest complexes spanning three physiographic provinces in Virginia and North Carolina. Measurements of forest composition (species and number), LAI, tree diameter, basal area, and canopy height were recorded at each site during the 2002 field season. Calibrated EPIC results show stand-level temporally resolved LAI estimates with R2 values ranging from 0.69 to 0.96, and stand maximum height estimates within 20% of observation. This relatively high level of performance is attributable to EPIC’s approach to the characterization of forest stand biogeochemical budgets, stand history, interspecies competition and species-specific response to local weather conditions. We close by illustrating the extension of this site-level approach to scales that could support regional air quality model simulations. [ABSTRACT FROM AUTHOR]

Rapid urbanization has resulted in temperature differences between the urban area and its surrounding areas. Academics have called this as the urban heat island phenomenon. Among the places that have seen rapid urbanization is the City of Cebu. The Philippine's oldest street, Colon, was chosen as the study area due to the near absence of vegetation and closely spaced buildings. Buildings that are spaced more closely as well as multiple absorptions and reflections produce higher and more viable street temperatures. This study tries to systematically understand the urban heat island effect between Colon and Lawaan, the rural area defined in this study. In order to quantify the urban heat island between two given locations, the mobile traverse method during the summer time, for a 10-day period in May 2016. A digital thermometer measuring platform was mounted on top of a vehicle to measure the different temperatures of Colon Street. Urban temperatures were also gathered in the Lawaan area using the same device. Preliminary results showed the presence of the urban heat island phenomenon between the two areas (ΔT =1.17 °C). The provision of green spaces and proper urban planning are essential in mitigating future urban heat stress due to anthropogenic changes of existing cities. [ABSTRACT FROM AUTHOR]

Aspen is a keystone species that is relatively rare in the Sierra Nevada Mountains and is undergoing succession to conifers in the absence of disturbance. In the presence of more conifers, recruitment of young aspen to replace aging overstory aspen is failing and diverse understory plant communities are being lost. We studied vegetation attributes and understory light in nine aspen-conifer stands in the Lake Tahoe Basin in California and Nevada, USA. Within each stand we measured and mapped trees in a one-hectare study area and collected 27–30 hemispherical canopy images on a grid that were used to estimate forest canopy variables and the amount of light reaching the understory. We also measured percent cover of herbaceous vegetation on the same sample grid. After restoration treatments cut smaller conifer trees at six of the nine sites, we repeated canopy images and began monitoring the growth of young aspen adjacent to sample grid points. Less light was reaching the understory beneath areas of forest canopy dominated by true firs. Understory light was enhanced by removal of conifers (pine and fir species). Stand density and species composition of trees surrounding a grid point where each hemispherical image was taken were useful predictors of understory light and forest canopy variables, herbaceous vegetation cover, and the morphology and growth of young aspen. Less herbaceous vegetation was found in the vicinity of true firs and pines, particularly in areas with less light being transmitted to the forest floor. In areas where the overstory was pure aspen, herbaceous vegetation cover remained high across the range of understory light levels measured. Greater conifer removal treatment intensity, in terms of proportion of stand density cut, enhanced crown ratio and growth of young aspen. Height and diameter increment of young aspen was negatively impacted by the presence of true fir trees in the vicinity. If the objectives of forest management include promoting understory vegetation cover or vigorous aspen regeneration, then removing conifers (especially true firs) from aspen-conifer stands appears to be a viable management tool; using such an approach should focus on removing as many conifers as possible within approximately 8–11 m of areas where an enhancement of understory light, herbaceous vegetation, and the growth and vigor of young aspen is desired. [ABSTRACT FROM AUTHOR]

Integration of remote sensing (RS) data in forest inventories for enhancing plot-based forest variable prediction is a widely researched topic. Geometric consistency between forest inventory plots and areas for extraction of RS-based predictive metrics is considered a crucial factor for accurate modelling of forest variables. Achieving geometric consistency is particularly difficult with regard to angle-count sampling (ACS) plots, which have neither distinct shape nor distinct extent. This initial study considers a new approach for integrating ACS and RS data, where the concept of ACS is transferred to RS-based metrics extraction. By using the relationship between tree height and diameter at breast height (DBH), pixels of a RS-based canopy height model are extracted if their value suggests a DBH that would lead to inclusion in an angle-count sample at the given distance to the plot centre. Different variations of this approach are tested by modelling timber volume in national forest inventory plots in Germany. The results are compared to those achieved using fixed-radius plots. A root mean square error of approximately 42% is achieved by both the new and fixed-radius approaches. Therefore, the new approach is not yet considered sufficient for overcoming all difficulties concerning the integration of ACS plot and RS data. However, possibilities for improvement are discussed and will be the subject of further research. [ABSTRACT FROM AUTHOR]

Urban tree canopy cover (UTC) is a simple, and common, measure of urban forest resource. Urban infill development is likely to lead to losses in UTC under private tenure, at a time when local governments are setting ambitious targets to increase UTC overall. Simple, statistically rigorous methods are required to benchmark and track change in UTC, whilst identifying which land-use types or tenures experience change. We estimated UTC in six Melbourne suburbs in 2010 and 2015 by randomly sampling 2000 points across public land, public streetscapes and private land. We were able to detect a net change in UTC of <2% over five years to a 95% level of confidence. A significant net decrease in UTC (−2.4%) was only detected in one of the six suburbs. Two suburbs had a net increase in UTC by +2.7% over five years. On private land, there was often areas of UTC loss, but this was generally offset by canopy gain in other areas of the private realm as well as in streetscapes and public land. Losses in UTC on private land were mainly due to tree removal, with or without subsequent construction works. This study describes a simple, but statistically rigorous, method to quantify UTC change and the drivers of change in different land-use types and tenure. Despite studying two suburbs will high rates of infill development, only one suburb showed evidence of net UTC decrease. The ‘dynamic equilibrium’ in UTC, whereby canopy losses area approximately offset by concurrent canopy gain, means that ambitious targets being set by local governments to increase UTC may be difficult to achieve without changes in tree protection and infill development policy and planning. [ABSTRACT FROM AUTHOR]

Motivated by the desire to gain insight into the details of conventional airborne synthetic aperture radar (SAR) imaging of trees, a ground-based SAR system designed for short-range three-dimensional (3D) radar imaging is developed using a two-dimensional (2D) synthetic aperture. The heart of the system is a compact linear frequency modulation-continuous wave (LFM-CW) radar, a custom two-dimensional scan mechanism, and a three-dimensional time-domain backprojection algorithm that generates three-dimensional backscatter images at an over-sampled resolution of 10 cm by 10 cm by 10 cm. The backprojection algorithm is formulated directly in spatial coordinates. A new method for estimating and compensating for signal attenuation within the canopy is used that exploits the backprojection image formation approach. Several three-dimensional C-band backscatter images of different individual trees of multiple species are generated from data collected for trees both in isolation and near buildings. The trees imaged in this study are about 10 m in height. The transformation of the three-dimensional images to airborne SAR images is described and a sample result provided. [ABSTRACT FROM AUTHOR]

Canopy height estimates are widely used in forest biomass and carbon assessment modeling applications with the goal of mitigating climate change through the modification of forest sustainability strategies. As a result, large-scale accurate estimates of contemporary forest conditions are required. The current study utilizes Random Forest (RF) algorithms to integrate land cover, vegetation, soil, and other supplementary data with Geoscience Laser Altimeter System (GLAS) data to predict a wall-to-wall canopy height model (CHM) across Australia. Multiple CHMs are predicted from RF models trained from unique permutations of 6 predictor variables. Each 250 m resolution CHM is independently validated against airborne laser scanning (ALS) heights from 18 countrywide sites; the best CHM yielding R2= 0.72, and RMSE = 7.43 m. The best countrywide CHM is compared against 2 similar products from the literature, both of which are subject to intersecting ALS performance assessment also. The developed CHM product utilizes up-to-date data, and is tailored to Australia, complementing the National Ecosystem Surveillance Monitoring project mandated to the Terrestrial Ecosystem Research Network (TERN) by the Australian Department of Environment. Furthermore, with future altimetry-based Earth observation missions due for launch, the developed CHM will act as a baseline from which monitoring investigations can be executed. [ABSTRACT FROM PUBLISHER]

Background: Mapping tropical forest structure is a critical requirement for accurate estimation of emissions and removals from land use activities. With the availability of a wide range of remote sensing imagery of vegetation characteristics from space, development of finer resolution and more accurate maps has advanced in recent years. However, the mapping accuracy relies heavily on the quality of input layers, the algorithm chosen, and the size and quality of inventory samples for calibration and validation. Results: By using airborne lidar data as the 'truth' and focusing on the mean canopy height (MCH) as a key structural parameter, we test two commonly-used non-parametric techniques of maximum entropy (ME) and random forest (RF) for developing maps over a study site in Central Gabon. Results of mapping show that both approaches have improved accuracy with more input layers in mapping canopy height at 100 m (1-ha) pixels. The bias-corrected spatial models further improve estimates for small and large trees across the tails of height distributions with a trade-off in increasing overall mean squared error that can be readily compensated by increasing the sample size. Conclusions: A significant improvement in tropical forest mapping can be achieved by weighting the number of inventory samples against the choice of image layers and the non-parametric algorithms. Without future satellite observations with better sensitivity to forest biomass, the maps based on existing data will remain slightly biased towards the mean of the distribution and under and over estimating the upper and lower tails of the distribution. [ABSTRACT FROM AUTHOR]

Canopy gaps are small-scale openings in forest canopies which offer suitable micro-climatic conditions for tree regeneration. Field mapping of gaps is complex and time-consuming. Several studies have used Canopy Height Models (CHM) derived from airborne laser scanning (ALS) to delineate gaps but limited accuracy assessment has been carried out, especially regarding the gap geometry. In this study, we investigate three mapping methods based on raster layers produced from ALS leaf-off and leaf-on datasets: thresholding, per-pixel and per-object supervised classifications with Random Forest. In addition to the CHM, other metrics related to the canopy porosity are tested. The gap detection is good, with a global accuracy up to 82% and consumer's accuracy often exceeding 90%. The Geometric Accuracy (GAc) was analyzed with the gap area, main orientation, gap shape-complexity index and a quantitative assessment index of the matching with reference gaps polygons. The GAc assessment shows difficulties in identifying a method which properly delineates gaps. The performance of CHM-based thresholding was exceeded by that of other methods, especially thresholding of canopy porosity rasters and the per-pixel supervised classification. Beyond assessing the methods performance, we argue the critical need for future ALS-based gap studies to consider the geometric accuracy of results. [ABSTRACT FROM AUTHOR]

In this study, a method for estimating the stand diameter at breast height (DBH) classes in a South Korea forest using airborne lidar and field data was proposed. First, a digital surface model (DSM) and digital terrain model (DTM) were generated from the lidar data that have a point density of 4.3 points/m2, then a tree canopy model (TCM) was created by subtracting the DTM from the DSM. The tree height and crown diameter were estimated from the rasterized TCM using local maximum points, minimum points and a circle fitting algorithm. Individual tree heights and crown diameters were converted into DBH using the allometric equations obtained from the field survey data. We calculated the proportion of the total number of individual trees belonging to each DBH class in each stand to determine the stand DBH class according to the standard guidelines. More than 60% of the stand DBH classes were correctly estimated by the proposed method, and their area occupied over 80% of the total forest area. The proposed method generated more accurate results compared to the digital forest type map provided by the government. [ABSTRACT FROM AUTHOR]

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 (1063 nm) and one in the shortwave infrared (1545 nm). 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. [ABSTRACT FROM AUTHOR]

This study was designed to estimate the canopy biophysical characteristics of semi-arid grassland ecosystems by using in situ field spectrometry measurements to identify important spectral information for predictions at broader spatial scales. Spectral vegetation indices (VIs), reflectance spectra, continuum removal spectra, and the amplitude of the red edge peak (drre) based on 61 well-replicated field measurements across a large area in Inner Mongolia were used to develop empirical models for estimating four key canopy biophysical features: percent green coverage (PGC), canopy height (H), green aboveground biomass (GBM), and total aboveground biomass (TBM). The results showed that NDVI, EVI, NDSVI, and LSWI were useful for estimating canopy biophysical features, with NDSVI being the most significant variable. The PGC was accurately estimated with spectral reflectance at 441 nm and 2220 nm (R2 = 0.71), while the maximum depth of band (Dc), absorption area (Darea) in the red domain and drre were selected for estimating TBM and GBM (R2 = 0.51 and 0.44). Among the four canopy features, PGC received the highest confidence from all of the models (R2 = 0.81), while H was the most difficult to estimate (R2 = 0.49). Finally, the degree of disturbances and ecosystem types appeared to be a significant variable for model development. [ABSTRACT FROM AUTHOR]

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints. Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies. [ABSTRACT FROM AUTHOR]

Quantifying tree canopy cover in a spatially explicit fashion is important for broad-scale monitoring of ecosystems and for management of natural resources. Researchers have developed empirical models of tree canopy cover to produce geospatial products. For subpixel models, percent tree canopy cover estimates (derived from fine-scale imagery) serve as the response variable. The explanatory variables are developed from reflectance values and derivatives, elevation and derivatives, and other ancillary data. However, there is a lack of guidance in the literature regarding the use of leaf-on only imagery versus multi-season imagery for the explanatory variables. We compared models developed from leaf-on only Landsat imagery with models developed from multi-season imagery for a study area in Georgia. There was no statistical difference among models. We suggest that leaf-on imagery is adequate for the development of empirical models of percent tree canopy cover in the Piedmont of the Southeastern United States. [ABSTRACT FROM AUTHOR]

In this work we describe how ultrasonic sensors can be used to detect tree canopies and their density, which correspond to the leaf quantity. In order to test the approach and estimate the best distance to be used for measurement, a test field was constructed and used in three test cases. The results show that the sensors are able to detect tree canopies in all examples, where the distance of 45 cm proved to be the best choice to measure the density of the tree canopy. [ABSTRACT FROM AUTHOR]

This work focuses on the accuracy estimation of canopy height models (CHMs) derived from image matching of Pléiades stereo imagery over forested mountain areas. To determine the height above ground and hence canopy height in forest areas, we use normalised digital surface models (nDSMs), computed as the differences between external high-resolution digital terrain models (DTMs) and digital surface models (DSMs) from Pléiades image matching. With the overall goal of testing the operational feasibility of Pléiades images for forest monitoring over mountain areas, two questions guide this work whose answers can help in identifying the optimal acquisition planning to derive CHMs. Specifically, we want to assess (1) the benefit of using tri-stereo images instead of stereo pairs, and (2) the impact of different viewing angles and topography. To answer the first question, we acquired new Pléiades data over a study site in Canton Ticino (Switzerland), and we compare the accuracies of CHMs from Pléiades tri-stereo and from each stereo pair combination. We perform the investigation on different viewing angles over a study area near Ljubljana (Slovenia), where three stereo pairs were acquired at one-day offsets. We focus the analyses on open stable and on tree covered areas. To evaluate the accuracy of Pléiades CHMs, we use CHMs from aerial image matching and airborne laser scanning as reference for the Ticino and Ljubljana study areas, respectively. For the two study areas, the statistics of the nDSMs in stable areas show median values close to the expected value of zero. The smallest standard deviation based on the median of absolute differences (σMAD) was 0.80 m for the forward-backward image pair in Ticino and 0.29 m in Ljubljana for the stereo images with the smallest absolute across-track angle (−5.3°). The differences between the highest accuracy Pléiades CHMs and their reference CHMs show a median of 0.02 m in Ticino with a σMAD of 1.90 m and in Ljubljana a median of 0.32 m with a σMAD of 3.79 m. The discrepancies between these results are most likely attributed to differences in forest structure, particularly tree height, density, and forest gaps. Furthermore, it should be taken into account that temporal vegetational changes between the Pléiades and reference data acquisitions introduce additional, spurious CHM differences. Overall, for narrow forward–backward angle of convergence (12°) and based on the used software and workflow to generate the nDSMs from Pléiades images, the results show that the differences between tri-stereo and stereo matching are rather small in terms of accuracy and completeness of the CHM/nDSMs. Therefore, a small angle of convergence does not constitute a major limiting factor. More relevant is the impact of a large across-track angle (19°), which considerably reduces the quality of Pléiades CHMs/nDSMs. [ABSTRACT FROM AUTHOR]

A total of 138 Dahurian larch (Larix gmelinii Rupr.) trees and 108 white birch (Betula platyphylla Suk.) trees were harvested in the eastern Daxing’an Mountains, northeast China. We developed four additive systems of biomass equations as follows: the first additive model system (MS-1) used the best combination of tree variables as the predictors; the second additive model system (MS-2) included tree diameter at breast height (D) as the sole predictor; the third additive model system (MS-3) included both D and tree height (H) as the predictors; and the fourth additive model system (MS-4) included D, H, and crown attributes (crown width (CW) and crown length (CL)) as the predictors. The model coefficients were simultaneously estimated using seemingly unrelated regression (SUR). The heteroscedasticity in model residuals was addressed by applying a unique weight function to each equation. The results indicated that: (1) the stem biomass accounted for the largest proportion of the total tree biomass, while the foliage biomass had the smallest proportion for the two species; (2) the four additive systems of biomass equations exhibited good model fitting and prediction performance, of which the model Ra2 > 0.81, the mean prediction error (MPE) was close to 0, and the mean absolute error (MAE) was relatively small (<9 kg); (3) MS-1 and MS-4 significantly improved the model fitting and performance; the ranking of the four additive systems followed the order of MS-1 > MS-4 > MS-3 > MS-2. Overall, the four additive systems can be applied to estimate individual tree biomass of both species in the Chinese National Forest Inventory. [ABSTRACT FROM AUTHOR]

A review of the article "Estimating Canopy Cover from Standard Forest Inventory Measurements in Western Oregon," by Anne C.S. McIntosh, Andrew N. Gray and Steven L. Garman, which appeared in the journal "Forest Science" on April 2012, is presented.

The forest canopy is one of the chief determinants of the microhabitat within the forest. It affects plant growth and survival, hence determining the nature of the vegetation, and wildlife habitat. A plethora of different techniques have been devised to measure the canopy. Evaluation of the literature reveals confusion over what is actually being measured. This paper distinguishes two basic types of measurement: canopy cover is the area of ground covered by a vertical projection of the canopy, while canopy closure is the proportion of the sky hemisphere obscured by vegetation when viewed from a single point. The principal techniques used to measure canopy cover, canopy closure, and a number of related measures are described and discussed. The advantages and limitations are outlined and some sampling guidelines are provided. The authors hope to clarify the nature of the measurements and to provide foresters with sufficient information to select techniques suitable for their needs. [ABSTRACT FROM AUTHOR]