Estimates Of Forest Structure Research Articles

Lidar-derived estimates of forest structure in response to fire frequency

BackgroundLongleaf pine (Pinus palustris) ecosystems are recognized as biodiversity hotspots, and their sustainability is tightly coupled to a complex nexus of feedbacks between fire, composition, and structure. While previous research has demonstrated that frequent fire is often associated with higher levels of biodiversity, relationships between fire frequency and forest structure are more nuanced because structure can be difficult to measure and characterize. We expanded on this body of research by using lidar to characterize vegetation structure in response to fire frequency at a long-term prescribed-fire experiment. We asked (1) how does prescribed fire frequency affect structure and (2) how do structural metrics vary in the strength of their relationships with fire frequency.ResultsOur results indicated that forest structure varied significantly in response to fire frequency, with more frequent fire reducing vegetation structural complexity. Metrics that characterized the central tendency of vegetation and/or the variance of canopy-related properties were weakly to moderately correlated with prescribed fire frequency, while metrics that captured the vertical dispersion or variability of vegetation throughout the forest strata were moderately to strongly correlated with fire frequency. Of all the metrics evaluated, the understory complexity index had the strongest correlation with fire frequency and explained 88% of the structural variation in response to prescribed fire treatments.ConclusionsThe findings presented in this study highlight the usefulness of lidar technology for characterizing forest structure and that structural complexity cannot be fully characterized by a single metric. Instead, a range of diverse metrics is required to refine scientific understanding of the feedbacks between fire, composition, and structure in support of longleaf pine sustainability. Furthermore, there is a need for further research to broaden structural assessments beyond the overstory and incorporate more understory components, particularly within the realm of prescribed fire science and land management.

Connecting spaceborne lidar with NFI networks: A method for improved estimation of forest structure and biomass

Spaceborne lidar provides a unique opportunity to supplement the field plot measurements of national forest inventories (NFIs) by providing dense measurements of vertical canopy structure. For full waveform instruments such as the Global Ecosystem Dynamics Investigation (GEDI), measurements take the form of reflected energy as a function of height within an observed footprint. Many forest attributes cannot be directly computed from the waveforms, and thus statistical models relating the measurements to target attributes must be trained using field plot data. Because the discrete footprint samples produced by spaceborne lidar instruments have little chance of collocation with pre-existing field plots, model calibration remains a primary challenge. We leveraged the density and spatial correlation of GEDI observations to make predictions of the cumulative waveform over 326,787 NFI plots distributed across the contiguous United States. The predictions yield probability distributions, giving not only a predicted waveform but quantification of prediction uncertainty. The product of this work is a data set comprised of the NFI plots paired with the predictive waveform distributions. The predicted waveforms and their uncertainties can be used to train models with “error-in-variables” techniques that account for and filter the uncertainty in the predicted waveforms. This data set and the corresponding techniques allow statistically rigorous model training without requirements of collocation, yielding downstream forest attribute predictions that are consistent with NFI measurements and estimates. We demonstrate the data set by training a model for forest above ground biomass density (AGBD) and then use the model to produce a spatially complete 1 km map of AGBD for the contiguous United States. We further compare our AGBD predictions to US NFI estimates at a 64,000 hectare scale, showing the increase in precision to be proportional to the collection of almost 500,000 additional NFI plots across forested regions of CONUS.

Modeling forest canopy surface retrievals using very high-resolution spaceborne stereogrammetry: (I) methods and comparisons with actual data

The capability of spaceborne stereogrammetry using very high-resolution (VHR, <2 m) imagery with various environmental, experimental, and sensor configurations for characterizing forest canopy surfaces has not been completely explored. Existing archives of VHR imagery include a limited subset of potential stereo image acquisition configurations and may therefore exclude optimal configurations for capturing critical structural features of forest canopy surface. By contrast, simulated VHR imagery from 3-D radiative transfer models (RTM) can explore the full range of spatial, spectral, and sun-sensor configurations to identify factors that contribute to uncertainties in stereo-derived estimates of forest canopy structure. We developed a novel method to simulate VHR stereopairs using the discrete anisotropic radiative transfer (DART) model and then derive surface elevations from the simulated images. We reconstructed one open-canopy and one closed-canopy forest scene and created a reference digital surface model/digital terrain model (DSM/DTM) using airborne small-footprint lidar points over the study sites. The VHR simulations were configured to match three independent WorldView stereopairs. The results showed that, compared to the reference DSM, the surface elevations derived using simulated and WorldView image data were consistent, with differences of <1.6 m in vertical bias, < 1 m in root mean square error (RMSE), and < 0.07 in correlation coefficient (R). We demonstrated that realistic 3-D RTM simulations could be georeferenced with a camera model for DSM generation from simulated stereopairs. This work will support a follow-up investigation that examines stereo-derived DSM quality over a broad range of surface types and acquisition parameters to suggest optimal configurations for actual VHR stereo data acquisition of vegetation canopy surfaces.

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments.

Forest Structure Measurement in Tropical Rainforest using Laser Scanning Technology

Numerous measurements can be made to extract the forest structure. Forest structure is one of the main aspects of forest management. The precise estimation of forest structure is vital for some forestry applications. Thus, this study presents a novel non-destructive approach for the measurement of forest structure using laser scanning technologies of airborne LiDAR and Terrestrial Laser Scanning (TLS). The study area was located at the forest campus of the Forest Research Institute of Malaysia (FRIM), Kepong, Selangor. The elements of forest structure that were measured: were canopy height; plant density and basal area. The field survey was conducted over 3 plots of 25 m radius circular shape with a total of 60 trees with diameter at breast height (DBH) of 10 cm and above. The tree canopy height was estimated based on canopy height model (CHM) of LiDAR data. For plant density, it was estimated based on crown delineation created from CHM. While, for TLS data, the extraction of individual trees was done using Cyclone algorithm. The forest structure measurement obtained from laser scanning technologies is proven to be reliable with the root mean square error (RMSE) of 5.415, t-test of 3.011, and p-value of 0.004 for canopy height. For basal area, the mean of RMSE, t-test and p-value was 0.22589, 0.620 and 0.324 for the overall 3 plots, respectively. The result obtained for plant density was one tree per meter². The final outputs were presented as the map of CHM, plant density and basal area map. In conclusion, laser scanning measurement improves and provides a precise technique for forest structure measurement.

Evaluating Threatened Bird Occurrence in the Tropics by Using L-Band SAR Remote Sensing Data

The biodiversity loss in Southeast Asia indicates an urgent need for long-term monitoring, which is lacking. Much attention is being directed toward bird diversity monitoring using remote sensing, based on relation to forest structure. However, few studies have utilized space-borne active microwave remote sensing, which has considerable advantages in terms of repetitive observations over tropical areas. Here, we evaluate threatened bird occurrence from L-band satellite data explaining forest structure in Sumatra, Indonesia. First, we identified L-band parameters with strong correlations with the forest layer structure, defined as forest floor, understory, and canopy layers. Then, we analyzed the correlation between threatened bird occurrence and L-band parameters identified as explaining forest structure. The results reveal that several parameters can represent the layers of forest floor, understory, and canopy. Subsequent statistical analysis elucidated that forest-dependent and threatened bird species exhibit significant positive correlations with the selected L-band parameters explaining forest floor and understory. Our results highlight the potential of applying microwave satellite remote sensing to evaluate bird diversity through forest structure estimation, although a more comprehensive study is needed to strengthen our findings.

Mega-disturbances cause rapid decline of mature conifer forest habitat in California.

Mature forests provide important wildlife habitat and support critical ecosystem functions globally. Within the dry conifer forests of the western United States, past management and fire exclusion have contributed to forest conditions that are susceptible to increasingly severe wildfire and drought. We evaluated declines in conifer forest cover in the southern Sierra Nevada of California during a decade of record disturbance by using spatially comprehensive forest structure estimates, wildfire perimeter data, and the eDaRT forest disturbance tracking algorithm. Primarily due to the combination of wildfires, drought, and drought-associated beetle epidemics, 30% of the region's conifer forest extent transitioned to nonforest vegetation during 2011-2020. In total, 50% of mature forest habitat and 85% of high density mature forests either transitioned to lower density forest or nonforest vegetation types. California spotted owl protected activity centers (PAC) experienced greater canopy cover decline (49% of 2011 cover) than non-PAC areas (42% decline). Areas with high initial canopy cover and without tall trees were most vulnerable to canopy cover declines, likely explaining the disproportionate declines of mature forest habitat and within PACs. Drought and beetle attack caused greater cumulative declines than areas where drought and wildfire mortality overlapped, and both types of natural disturbance far outpaced declines attributable to mechanical activities. Drought mortality that disproportionately affects large conifers is particularly problematic to mature forest specialist species reliant on large trees. However, patches of degraded forests within wildfire perimeters were larger with greater core area than those outside burned areas, and remnant forest habitats were more fragmented within burned perimeters than those affected by drought and beetle mortality alone. The percentage of mature forest that survived and potentially benefited from lower severity wildfire increased over time as the total extent of mature forest declined. These areas provide some opportunity for improved resilience to future disturbances, but strategic management interventions are likely also necessary to mitigate worsening mega-disturbances. Remaining dry mature forest habitat in California may be susceptible to complete loss in the coming decades without a rapid transition from a conservation paradigm that attempts to maintain static conditions to one that manages for sustainable disturbance dynamics.

Impact of plot size on tropical forest structure and diversity estimation

Introduction: Inventories are essential for forest management, but, in the Amazon region, the absence of standardization produces information loss, low accuracy, and inconsistent measurements. This prevents valid comparisons and compromises the use of information in networks and software. Sampling unit size is of key importance in the inventory of native forests, particularly regarding accuracy and costs. Objective: To identify a plot size that provides adequate precision for dendrometric parameters in the Amazon. Methods: In Cotriguaçu, Mato Grosso, Brazil, we tested four plot sizes with six repetitions each: 2 500, 5 000, 7 500, and 10 000 m². We measured diameter at breast height, population density, basal area, and biomass. We applied Shannon and Jaccard indexes; Weibull 2P and Gamma functions to fit the diametric distribution; and the Akaike Information Criterion for the best model. Results: There was a directly proportional relationship between plot area and population similarity, but diversity did not indicate significant alterations. Plot size did not affect dendrometric attributes and diametric distribution. Larger plot areas led to lower coefficients of variation and smaller confidence intervals. The Gamma function was the best model to represent the distributions of different plot sizes. Conclusions: For similar forests, we recommend the 2 500 m² plot to evaluate diameter at breast height, population density, basal area, and biomass.

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

AbstractAimRapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).LocationEastern USA.Time period2017–2018.Taxa studiedTrees.MethodsWith a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.ResultsModels using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusionsWe found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

Comprehensive comparison of airborne and spaceborne SAR and LiDAR estimates of forest structure in the tallest mangrove forest on earth

A recent suite of new global-scale satellite sensors and regional-scale airborne campaigns are providing a wealth of remote sensing data capable of dramatically advancing our current understanding of the spatial distribution of forest structure and carbon stocks. However, a baseline for forest stature and biomass estimates has yet to be established for the wide array of available remote sensing products. At present, it remains unclear how the estimates from these sensors compare to one another in terrestrial forests, with a clear dearth of studies in high carbon density mangrove ecosystems. In the tallest mangrove forest on Earth (Pongara National Park, Gabon), we leverage the data collected during the AfriSAR campaign to evaluate 17 state-of-the-art sensor data products across the full range of height and biomass known to exist globally in mangrove forest ecosystems, providing a much-needed baseline for sensor performance. Our major findings are: (Houghton, Hall, Goetz) height estimates are not consistent across products, with opposing trends in relative and absolute errors, highlighting the need for an adaptive approach to constraining height estimates (Panet al., 2011); radar height estimates had the lowest calibration error and bias, with further improvements using LiDAR fusion (Bonan, 2008); biomass variability and uncertainty strongly depends on forest stature, with variation across products increasing with canopy height, while relative biomass variation was highest in low-stature stands (Le Quéréet al., 2017); a remote sensing product's sensitivity to variations in canopy structure is more important than the absolute accuracy of height estimates (Mitchardet al., 2014); locally-calibrated area-wide totals are more representative than generalized global biomass models for high-precision biomass estimates. The findings presented here provide critical baseline expectations for height and biomass predictions across the full range of mangrove forest stature, which can be directly applied to current (TanDEM-X, GEDI, ICESat-2) and future (NISAR, BIOMASS) global-scale forest monitoring missions.

Climate mediates the relationship between plant biodiversity and forest structure across the United States

AbstractAimEmpirical biodiversity – forest structure relationships (BSRs) underlie the use of forest structure as a remotely sensible proxy of biodiversity. However, little is known about how BSRs generalize to continental scales or how climate interacts with structure to drive local patterns in plant diversity. Resolving these research gaps in macrosystems ecology will strengthen our understanding of the biogeography of plant diversity, with implications for global‐scale biodiversity mapping.LocationUSA.Time periodContemporary.Major taxa studiedVascular plants and trees.MethodsWe combined climate variables with field measurements and airborne lidar from all forest and woodland plots in the National Ecological Observatory Network (NEON) to characterize the role of climate in constraining BSRs across the United States. Spatial generalized linear mixed models were used to quantify the individual and joint effects of structure and climate on vascular plant and tree diversity.ResultsThese findings provide evidence for broad‐scale BSRs across the USA; namely, between plant/tree diversity and forest structural metrics in the vertical and horizontal planes. Vascular plant diversity was positively related to horizontally heterogeneous and bottom‐skewed canopies, whereas tree diversity was positively associated with canopy cover and structural heterogeneity. In addition, climate variables related to stress (negatively), energy (positively) and seasonality (negatively) affected broad‐scale patterns in diversity, with water availability (but not temperature) exerting significant effects on structural conditions. Importantly, climate and structure interact to explain variance in BSRs along coldness, mean temperature and evapotranspiration gradients.Main conclusionsOur findings reinforce the importance of local context dependence in assessing non‐stationary biogeographical patterns in forest biodiversity, as well as the unique ways that forest structure and climate interact to constrain plant diversity. Beyond these theoretical insights, this study provides an empirical foundation for generalizing remotely sensed estimates of climate and forest structure to model biodiversity over vast extents.

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

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

Comparing airborne and spaceborne photon-counting LiDAR canopy structural estimates across different boreal forest types

The monitoring of forested ecosystems relies on an accurate description of forest structure. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2), launched in September 2018, carries the Advanced Topographic Laser Altimeter System (ATLAS), a Light Detection and Ranging (LiDAR) instrument capable of detecting individual photons reflected back from vegetation canopy. ICESat-2 data is delivering global estimates of forest structure; however, analysis of the performance of ICESat-2 on-orbit data across a range of forest conditions remains limited. This study derives structural estimates of (i) canopy height, (ii) canopy cover and (iii) canopy height variability from ICESat-2 data acquired in snow-free and low atmospheric scattering conditions over different boreal forest structural types in Ontario, Canada. ICESat-2 structural estimates were derived from the Global Geolocated Photon Data (ATL03) and Land and Vegetation Height (ATL08) data products and compared against single-photon detection airborne LiDAR (Leica SPL100). An extensive network of ground plots were used to stratify the study area into three distinct forest structural groups, each resulting from different stand development stages. ICESat-2 and SPL100 estimates of canopy height were compared at the ATL03 photon level, whereas estimates of height variability and canopy cover were compared for spatial analysis units (AU; mean size = 1287 m2). ICESat-2 photons returned from the top of the canopy underestimated canopy height relative to SPL100 by an average of 2.3 m overall and corresponded most strongly to the 90th percentile (P90) of coincident airborne SPL100 returns (root mean square difference (RMSD) = 2.9 m and correlation coefficient (r) = 0.84). The lowest average underestimation of SPL P90 was observed in homogeneous stands that were relatively simple, and single-layered with a single dominant species (RMSD = 2.5 m, r = 0.84). We observed the least agreement of ICESat-2 and SPL forest structural metrics in over-mature stands with complex structure and greater variability in canopy heights (RMSD = 3.5 m, r = 0.64). For the AUs, the strength of the relationship between SPL100 and ICESat-2 canopy height percentiles increased with increasing height percentiles (e.g. P25 RMSD% = 77.8%; P95 RMSD% = 23.7%). ICESat-2 generally underestimated canopy height variability relative to the SPL100 data, with both data having similar absolute variability (standard deviation of canopy heights RMSD% = 26.8%, r = 0.75), but lower agreement in relative variability (coefficient of variation of canopy heights RMSD% = 33.9%, r = 0.45). Herein we propose the use of the vegetation fill index as a method to estimate canopy cover with ICESat-2. Comparison of SPL100 and ICESat-2 vegetation fill indices at the AU level resulted in strong agreement overall (RMSD% = 19.7%; r = 0.57). These observations and results contribute to the overall objective of building a comprehensive understanding of the performance of ICESat-2 for characterizing vegetation structure in boreal forest environments.

Remote-sensing estimates of forest structure and dynamics as indicators of habitat quality for Magellanic woodpeckers

Bridging remote-sensing ecosystem indices with biodiversity conservation implies converting these indices into habitat quality indicators for species playing an important role in communities and ecosystems, such as woodpeckers. However, an ecologically reliable estimation of habitat quality necessarily involves an assessment of fitness components ultimately responsible for population persistence. Here, we assessed the relationship between remote-sensing indices of forest structure, dynamics and composition with the breeding performance of Magellanic woodpeckers in conservation areas of southern Chile. We used a Bayesian-spatial model based on age and sex information from woodpecker’s social group data collected seasonally in seven years. The probability of a young remaining in the group decreased with the mean group size during the previous year, with group size fluctuating temporally in all conservation areas. Tree senescence had a positive effect on the pairing probability of woodpeckers, but this effect became more marked in sites of higher altitude. Paired woodpeckers were more likely to be observed in sites supporting more than 69% of forest cover. The probability of the young remaining in the territory was positively affected by the interaction between forest canopy continuity and altitude, with retention of the young being more likely in sites located higher than 1000 masl. Social groups were larger in sites where the continuity of canopy increased over time. Those findings suggest that remote sensing indices representing the structure and dynamics of forest ecosystems are important indicators of the habitat quality for woodpeckers. Thus, forest biodiversity that depends on the woodpecker's engineering function may be conserved through the retention of senescent trees and the maintaining of critical levels of forest cover ensuring high habitat-quality for breeding woodpeckers.

A Modified Capon Method for SAR Tomography Over Forest

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

Assessing the 3-D Structure of Bamboo Forests Using an Advanced Pseudo-Vertical Waveform Approach Based on Airborne Full-Waveform LiDAR Data

Remote-sensing-assisted estimates of Moso bamboo forest structure are imperative for supporting sustainable forest management (SFM). Passive sensors have limited usages in characterizing continuous vertical structures of Moso bamboos since they have difficulty in penetrating dense upper canopies. Airborne full-waveform (FWF) Light Detection and Ranging (LiDAR) technology has shown significant potentials in providing an improved representation of 3-D structures in much denser canopies, while the capability of FWF to assess the structure of the unique Moso bamboo forests is less known. Many previous studies commonly assume perfectly vertical waveforms and do not account for slanted penetrations of waveforms in canopies. This study presented a framework for investigating the availability of FWF to assess subtropical Moso bamboo forest structures. We used a physical-based approach for radiometric calibration and an advanced voxel-based pseudo-vertical waveform approach to assign waveforms into voxels and reconstruct composite waveforms for extracting FWF metrics. Second, the differences in FWF-derived metrics in response to canopy structures across various management strategies were compared. Finally, the capability of FWF metrics for estimating bamboo structural parameters was evaluated by parametric and nonparametric models. The results showed that the FWF metrics varied significantly (p-values < 0.05) in response to different management strategies. In general, nonparametric approaches (locally weighted linear regression and k-nearest neighbor) outperformed parametric approaches (multiple linear regression). This study demonstrated that the pseudo-vertical waveform approach could improve the performances of FWF metrics for characterizing 3-D Bamboo forest structure and provide new insights on accurately estimating bamboo structural parameters across different management strategies.

The influence of sampling grid resolution and understory on forest structure estimation from terrestrial laser scanning

The influence of sampling grid resolution and understory on forest structure estimation from terrestrial laser scanning

Estimating Forest Productivity Using Site Characteristics, Multipoint Measures, and a Nonparametric Approach

Abstract Understanding the productivity of forestland is essential in sustainable management of forest ecosystems. The most common measure of site productivity is breast height–age site index (BHASI). BHASI has limitations as a productivity measure and can compound error in predictive models. We explored the accuracy of productivity predictions using an alternative productivity measure (10-meter site index) and a nonparametric approach. An orthogonal sampling design ensured samples were collected across the range of conditions known to influence Douglas-fir (Pseudotsuga menziesii var. glauca) height-growth rates. Using climate, soil, and topographic data along with 10-meter site index measurements, we compared five possible models to estimate forest productivity. Model parameters, performance, and predictions were compared. Twelve validation sites were used to test the accuracy of model predictions. Model performance was significantly improved when smoothing span values were optimized and elevation was added as a predictor. A four-predictor nonparametric model with a bias-corrected Akaike information criterion–optimized smoothing span value produced the most accurate results and was used to produce forest productivity maps for the study area. The low resolution of currently available climatic data and the complex nature of the study area landscape necessitate a topographic variable for accurate productivity predictions. Study Implications Defining and understanding forest productivity is of interest to a wide variety of natural resource professionals including ecologists, climate change experts, forest biometricians, and forest managers. A new method of defining forest productivity using multipoint height-age pairs at 10 and 20 meters and calculated growth rates combined with an appropriate landscape-scale stratification and a nonparametric approach provides accurate productivity estimates. This method is more widely applicable and more precise for specific locations than previous productivity estimation methods. Better productivity and tree growth information will provide more accurate estimates of future forest condition and structure.

Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes

Abstract There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.

Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.