Lidar Sampling Research Articles

Properties of tropospheric aerosols observed over southwest Slovenia

From August to October 2010 lidar measurements of aerosols in the troposphere were performed at Otlica observatory, Slovenia, using a vertical scanning elastic lidar. The lidar data sample, which contains 38 nighttime vertical profiles of the mean aerosol extinction, was combined with continuous ozone concentration (O3), particulate matter concentrations (PM) and daily radiosonde data. The obtained radiosonde- and lidar-derived heights of the atmospheric boundary layer (ABL), which varied considerably from day to day, were found to be in good agreement. The mean values of the aerosol optical depth (AOD) at 355 nm, were calculated separately for the ABL and for the free troposphere (FT). A ten-fold increase of the FT AOD was observed during the days with predicted presence of Saharan dust above the lidar site. To correlate AOD values with the type and origin of aerosols, backward trajectories of air-masses above Otlica were modeled using the HYSPLIT model and clustered. High ABL AOD values were found to be correlated with local circulations and slowly approaching air masses from the Balkans and low values with northwestern flows. The highest values correlated with southwestern flows originating in northern Africa.

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

Lidar provides critical information on the three-dimensional structure of forests. However, collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As a result, studies employing lidar for large area estimation typically collect data via strip sampling, leaving large swaths of the forest unmeasured by the instrument. The goal of this research was to develop and examine the performance of a coregionalization modeling approach for combining field measurements, strip samples of airborne lidar and Landsat-based remote sensing products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates mapping of AGB density across the domain. Additionally, the coregionalization framework allows for estimation of total AGB for arbitrary areal units within the study area—a key advance to support diverse management objectives in interior Alaska. This research focuses on characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from point-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total (mean) AGB precision. Additionally, including a Landsat-derived forest cover covariate further improved precision in regions with lower lidar sampling intensity. Findings also demonstrate that model-based approaches not explicitly accounting for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. The inferential capabilities of AGB posterior predictive distribution (PPD) products extend beyond simply mapping AGB density. We show how PPD products can provide insight regarding drivers of AGB heterogeneity in boreal forests, including permafrost and fire, highlighting the range of potential applications for Bayesian geostatistical methods to integrate field, airborne and satellite data.

Predicting Volume and Biomass Change from Multi-Temporal Lidar Sampling and Remeasured Field Inventory Data in Panther Creek Watershed, Oregon, USA

Using lidar for large-scale forest management can improve operational and management decisions. Using multi-temporal lidar sampling and remeasured field inventory data collected from 78 plots in the Panther Creek Watershed, Oregon, USA, we evaluated the performance of different fixed and mixed models in estimating change in aboveground biomass ( ∆ AGB ) and cubic volume including top and stump ( ∆ CVTS ) over a five-year period. Actual values of CVTS and AGB were obtained using newly fitted volume and biomass equations or the equations used by the Pacific Northwest unit of the Forest Inventory and Analysis program. Estimates of change based on fixed and mixed-effect linear models were more accurate than change estimates based on differences in LIDAR-based estimates. This may have been due to the compounding of errors in LIDAR-based estimates over the two time periods. Models used to predict volume and biomass at a given time were, however, more precise than the models used to predict change. Models used to estimate ∆ CVTS were not as accurate as the models employed to estimate ∆ AGB . Final models had cross-validation root mean squared errors as low as 40.90% for ∆ AGB and 54.36% for ∆ CVTS .

Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery

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 25m to 100m. 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 4m. 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 R2 ranging between 0.61 and 0.82, RMSE between 2.02 and 5.64 and bias between 0.02 and −0.06, respectively, at 100m 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.

A practical method for creating a digital topographic surface for ecological plots using ground-based measurements

Digital elevation models (DEMs) are widely used in landscape ecology to link topographic features with biotic and abiotic factors. However, to date, high-resolution, affordable, and easy to process elevation data are not available for many regions. Here we propose a field-based method for efficiently and inexpensively collecting or analysing already existing slope data. We compare the field approach to two commonly used remote sensing techniques to test the similarly of the DEMs using different methods. To provide an ecological example of the method, we selected four 1-ha forest plots and compared the DEM generated by using our field method with those derived from: (i) coarse (~ 30 m pixel) data from the Shuttle Radar Topography Mission and (ii) high-resolution (~ 1 m) data from Light Detection and Ranging devices (LiDAR). Field- and LiDAR-based DEMs showed strong concordance in two of the four sites. The sites where field-based and LiDAR DEMs substantially differed, suffered from relatively few LiDAR sampling points. Diagnostic tests suggested that the field–LiDAR discrepancy was due to dense over-storey vegetation, which reduced LiDAR’s accuracy due to a failure to penetrate the forest canopy adequately in some areas. Our method has the advantage of being quick and cheap to collect yet able to produce small-scale (plot-scale) DEMs of high quality. By using the R-code we have provided, ecologists will be able to use slope data (collected using any means) to generate a DEM without the need of specific skills in spatial sciences.

Spatial Distribution of Carbon Stored in Forests of the\xa0Democratic Republic of Congo

National forest inventories in tropical regions are sparse and have large uncertainty in capturing the physiographical variations of forest carbon across landscapes. Here, we produce for the first time the spatial patterns of carbon stored in forests of Democratic Republic of Congo (DRC) by using airborne LiDAR inventory of more than 432,000 ha of forests based on a designed probability sampling methodology. The LiDAR mean top canopy height measurements were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed across key forest types in DRC. LiDAR samples provided estimates of mean and uncertainty of aboveground carbon density at provincial scales and were combined with optical and radar satellite imagery in a machine learning algorithm to map forest height and carbon density over the entire country. By using the forest definition of DRC, we found a total of 23.3 ± 1.6 GtC carbon with a mean carbon density of 140 ± 9 MgC ha−1 in the aboveground and belowground live trees. The probability based LiDAR samples capture variations of structure and carbon across edaphic and climate conditions, and provide an alternative approach to national ground inventory for efficient and precise assessment of forest carbon resources for emission reduction (ER) programs.

Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources

Emissions from strong point sources, primarily large power plants, are a major portion of the total CO2 emissions. International climate agreements will increasingly require their independent monitoring. A satellite-based, double-pulse, direct detection Integrated Path Differential Absorption (IPDA) Lidar with the capability to actively target point sources has the potential to usefully complement the current and future GHG observing system. This initial study uses simple approaches to determine the required Lidar characteristics and the expected skill of spaceborne Lidar plume detection and emission quantification. A Gaussian plume model simulates the CO2 or CH4 distribution downstream of the sources. A Lidar simulator provides the instrument characteristics and dimensions required to retrieve the emission rates, assuming an ideal detector configuration. The Lidar sampling frequency, the footprint distance to the emitting source and the error of an individual measurement are of great importance. If wind speed and direction are known and environmental conditions are ideal, an IPDA Lidar on a 500-km orbit with 2 W average power in the 1.6 µm CO2 absorption band, 500 Hz pulse repetition frequency, 50 m footprint at sea level and 0.7 m telescope diameter can be expected to measure CO2 emission rates of 20 Mt/a with an average accuracy better than 3% up to a distance of 3 km away from the source. CH4 point source emission rates can be quantified with comparable skill if they are larger than 10 kt/a, or if the Lidar pulse repetition frequency is augmented.

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data

BackgroundLiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m−2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m.ResultsThe results show that LiDAR pulse density of 5 pulses m−2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m−2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system.ConclusionLiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m−2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

Surveying the distribution and abundance of flying fishes and other epipelagics in the northern Gulf of Mexico using airborne lidar

Flying fishes (family Exocoetidae) are important components of epipelagic ecosystems and are targeted by fishing fleets in the Caribbean Sea and elsewhere. However, owing to their anti-predator behavior and habitats, their ecology, abundance, and distributions are only partially known. From September 20 to October 6, 2011, we conducted a series of surveys over a large area (approximately 75,000 km2) of the northern Gulf of Mexico (87°W–90.5°W, 28°N–30°N). The surveys used an airborne lidar and vessel-based sampling, supported by near real time satellite observations of oceanic conditions. The aerial survey was conducted from a fixed wing aircraft that flew repeated surveys day and night, enabling data collection that was both broad-scale and synoptic. Vessel-based sampling included quantitative visual observations, trawl sampling, and qualitative dip-netting for species identifications. The combined surveys identified large aggregations of epipelagic organisms dominated by flying fishes. Large numbers of jellyfish (Aurelia sp.) and low numbers of numerous other species were also observed. The putative flying fish aggregations had an average length scale of 6.1 km and an average population estimated at 10,000 individuals. While always near the surface, flying fish aggregations were slightly deeper at night than during the day and found most often off the continental shelf in warm water with low chlorophyll concentrations. At least three species were present: Hirundichthys rondeletii (Valenciennes, 1847), Cheilopogon melanurus (Valenciennes, 1847), and Prognichthys occidentalis Parin, 1999. This combination of aerial and surface surveys afforded repeated synoptic, ground-truthed data collection over a large area and indicates that this method could be useful for surveying such mobile epipelagic fishes.

Significant impacts of heterogeneous reactions on the chemical composition and mixing state of dust particles: A case study during dust events over northern China

The impact of heterogeneous reactions on the chemical components and mixing state of dust particles are investigated by observations and an air quality model over northern China between March 27, 2015 and April 2, 2015. Synergetic observations were conducted using a polarization optical particle counter (POPC), a depolarized two-wavelength Lidar and filter samples in Beijing. During this period, dust plume passed through Beijing on March 28, and flew back on March 29 because of synoptic weather changes. Mineral dust mixed with anthropogenic pollutants was simulated using the Nested Air Quality Prediction Modeling System (NAQPMS) to examine the role of heterogeneous processes on the dust. A comparison of observations shows that the NAQPMS successfully reproduces the time series of the vertical profile, particulate matter concentration, and chemical components of fine mode (diameter ≤ 2.5 μm) and coarse mode (2.5 μm < diameter ≤ 10 μm) particles. After considering the heterogeneous reactions, the simulated nitrate, ammonium, and sulfate are in better agreement with the observed values during this period. The modeling results with observations show that heterogeneous reactions are the major mechanisms producing nitrate reaching 19 μg/m3, and sulfate reaching 7 μg/m3, on coarse mode dust particles, which were almost 100% of the coarse mode nitrate and sulfate. The heterogeneous reactions are also important for fine mode secondary aerosols, for producing 17% of nitrate and 11% of sulfate on fine mode dust particles, with maximum mass concentrations of 6 μg/m3 and 4 μg/m3. In contrast, due to uptake of acid gases (e.g. HNO3 and SO2) by dust particles, the fine mode anthropogenic ammonium nitrate and ammonium sulfate decreased. As a result, the total fine mode nitrate decreased with a maximum of 14 μg/m3, while the total fine mode sulfate increased with a maximum of 2 μg/m3. Because of heterogeneous reactions, 15% of fine mode secondary inorganic aerosols and the entire coarse mode nitrate and sulfate were internally mixed with dust particles. The significant alterations of the chemical composition and mixing state of particles due to heterogeneous reactions are important for the direct and indirect climate effects of dust and anthropogenic aerosols.

LiDAR-Assisted Multi-Source Program (LAMP) for Measuring Above Ground Biomass and Forest Carbon

Forest measurement for purposes like harvesting planning, biomass estimation and mitigating climate change through carbon capture by forests call for increasingly frequent forest measurement campaigns that need to balance cost with accuracy and precision. Often this implies the use of remote sensing based measurement methods. For any remote-sensing based methods to be accurate, they must be validated against field data. We present a method that combines field measurements with two layers of remote sensing data: sampling of forests by airborne laser scanning (LiDAR) and Landsat imagery. The Bayesian model-based framework presented here is called Lidar-Assisted Multi-source Programme—or LAMP—for Above Ground Biomass estimation. The method has two variants: LAMP2 which splits the biomass estimation task into two separate stages: forest type stratification from Landsat imagery and mean biomass density estimation of each forest type by LiDAR models calibrated on field plots. LAMP3, on the other hand, estimates first the biomass on a LiDAR sample using models calibrated with field plots and then uses these LiDAR-based models to generate biomass density estimates on thousands of surrogate plots, with which a satellite image based model is calibrated and subsequently used to estimate biomass density on the entire forest area. Both LAMP methods have been applied to a 2 million hectare area in Southern Nepal, the Terai Arc Landscape or TAL to calculate the emission Reference Levels (RLs) that are required for the UN REDD+ program that was accepted as part of the Paris Climate Agreement. The uncertainty of these estimates is studied with error variance estimation, cross-validation and Monte Carlo simulation. The relative accuracy of activity data at pixel level was found to be 14 per cent at 95 per cent confidence level and the root mean squared error of biomass estimates to be between 35 and 39 per cent at 1 ha resolution.

In Memorium: Thomas Hilker

Dr. Thomas Hilker left us on 4 September 2016 following a sudden cardiac arrest. Thomas was adevoted husband to Yhasmin, and a brother and son to his family in Germany to whom we expressour deepest sympathies. Friends and colleagues of Thomas in the remote sensing and ecologicalcommunities lament this tragic loss. During his short but stellar science career, Thomas becamea world leader in the field of carbon, water and energy exchange from the land. He pioneeredstudies in the Amazonian forests, using anisotropy information acquired from satellites to describethree-dimensional structures that linked these ecosystems functionally to climatic variation.Thomas had an extreme range of interests—from the engineering of advanced spectrometers toproviding new theories and innovative methods to process remotely sensed data. Dr. Piers Sellers,Acting Director of the Earth Sciences Division at NASA/GSFC, and Deputy Director of the Sciences andExploration Directorate wrote: “Thomas Hilker was something of a renaissance man in Earth Science.He could climb towers, measure tall trees, and calculate spectral indices in his head. Working with himwas like collaborating with two or three normal people. He had some of the best and most originalideas in remote sensing that I’ve come across, but unlike most of us, he could go get the data to provehis point. And he was always the best fun. I remember him coming to a couple of parties of ours—hewas always relaxed, humorous, charming. He could make people laugh and everyone felt so goodaround him.”Thomas obtained a Bachelor of Science degree in Forestry from the University of Applied Sciences,in Goettingen, Germany in 2000, a Master in Photogrammetry and Geoinformatics from the Universityof Applied Sciences in Stuttgart in 2002 and a PhD from the University of British Columbia (UBC) inForestry in 2008. After a three year postdoctoral position at UBC (2008–2011), he worked as a ResearchAssociate at NASA’s Goddard Space Flight Center (2011–2012). From 2012 to 2016, Thomas held aposition as Assistant Professor at Oregon State University’s College of Forestry, leading the RemoteSensing Laboratory and teaching classes in Remote Sensing and Spatial Data Analysis. In 2015 and2016 he was a visiting researcher at the National Institute for Space Studies in Brazil (Instituto Nacionalde Pesquisas Espaciais, INPE). He looked forward to starting a position as an Associate Professor ofEarth System Science and Remote Sensing at the University of Southampton, UK.Thomas became fascinated by the global carbon cycle following receipt of his Master’s degree andwas keen to utilize his geospatial skills to unlock the details of the cycle. Inspired by the linksbetween canopy reflectance and photosynthesis, Thomas designed an Automated MultiangularSpectroradiometer for Estimation of Canopy reflectance [1] and improved it in subsequentiterations [2,3] to be able to continuously monitor subtle changes in the reflected spectra from

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms – Part 1: Vertical resolution

Abstract. A standardized approach for the definition and reporting of vertical resolution of the ozone and temperature lidar profiles contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. Two standardized definitions homogeneously and unequivocally describing the impact of vertical filtering are recommended. The first proposed definition is based on the width of the response to a finite-impulse-type perturbation. The response is computed by convolving the filter coefficients with an impulse function, namely, a Kronecker delta function for smoothing filters, and a Heaviside step function for derivative filters. Once the response has been computed, the proposed standardized definition of vertical resolution is given by Δz = δz × HFWHM, where δz is the lidar's sampling resolution and HFWHM is the full width at half maximum (FWHM) of the response, measured in sampling intervals. The second proposed definition relates to digital filtering theory. After applying a Laplace transform to a set of filter coefficients, the filter's gain characterizing the effect of the filter on the signal in the frequency domain is computed, from which the cut-off frequency fC, defined as the frequency at which the gain equals 0.5, is computed. Vertical resolution is then defined by Δz = δz∕(2fC). Unlike common practice in the field of spectral analysis, a factor 2fC instead of fC is used here to yield vertical resolution values nearly equal to the values obtained with the impulse response definition using the same filter coefficients. When using either of the proposed definitions, unsmoothed signals yield the best possible vertical resolution Δz = δz (one sampling bin). Numerical tools were developed to support the implementation of these definitions across all NDACC lidar groups. The tools consist of ready-to-use “plug-in” routines written in several programming languages that can be inserted into any lidar data processing software and called each time a filtering operation occurs in the data processing chain. When data processing implies multiple smoothing operations, the filtering information is analytically propagated through the multiple calls to the routines in order for the standardized values of vertical resolution to remain theoretically and numerically exact at the very end of data processing.

Impacts of LiDAR Sampling Methods and Point Spacing Density on DEM Generation

ABSTRACTLight Detection and Ranging (LiDAR) technology is gradually being adopted as the primary technique for surface structure derivation. There is a tendency for users of LiDAR data to want the highest density data possible, but such dense data sets increase overall costs including financial, processing, and storage without significant value added to the derived outputs, particularly in the case of digital elevation models (DEMs). In this study, we employed three sampling methods (systematic sampling, simple random sampling, and stratified random sampling) to select points from the original LiDAR data set that represented six different sparse densities. Using the sampled data sets, DEMs were generated and sensitivity analyses were conducted to explore whether an optimum LiDAR point sampling method and density could be identified. Despite the fact that significant differences in descriptive statistics were not observed for the three sampling strategies or point densities, there are evident differences among the derived DEMs, variograms, and viewsheds for very sparse samples (e.g., less than 2 percent of the original data set). In particular, the systematic and random sampling methods resulted in noticeable differences compared to the stratified random sampling approach. This research contributes to a better understanding of how LiDAR point sampling can be used to reduce data volume without compromising the final DEM quality.

Estimation of Above Ground Forest Biomass and Carbon Stock by Integrating Lidar, Satellite Image and Field Measurement in Nepal

For the first time in South Asia, the model-based Lidar Assisted Multisource Program (LAMP) was tested in 23500 km2 TAL area of Nepal by integrating 5% LiDAR sampling, wall-to-wall Rapid Eye satellite image and a representative field inventory to estimate Above Ground Biomass (AGB) and carbon stock. The average 1.26/m2LiDAR point density recorded by the scanner was used to measure canopy height and build a model using LiDAR variables and model coefficients. The developed LAMP model successfully estimated the AGB of the study area. The research tells that the study area comprises almost 50% forest cover with an average 211.63 t/ha AGB.Standing carbon stock was converted from AGB by multiplying the 0.47 which is default carbon fraction. Average standing carbon stock is 99.47 t/ha in the study area. The LAMP method found that the standing total AGB was 214.85-208.41 t/ha at a 95% confidence level and the FRA field-plot AGB estimate is 210.09/ha. This correspondence at this level of confidence means that the LAMP estimates are as accurate as those of the field-based inventory.J. Nat. Hist. Mus. Vol. 28, 2014: 160-170

Integrating lidar with Landsat data for subalpine temperate forest aboveground carbon estimation

Accurate forest carbon accounting forms a basis for promoting the development of ecosystem service markets including forest carbon sinks. However, carbon assessments over large forest areas are challenging. Difficulties are compounded by the lack of adequate field observations especially in mountainous regions. In this study, we describe the development of a two-phase sampling framework to evaluate regional aboveground carbon density (ACD) of subalpine temperate forests in northwestern China that includes integrating ground plots, airborne lidar metrics, and Landsat images. During the first phase, an accurate, lidar-derived, ACD inventory network of a representative forested zone (Dayekou Basin) was established on the basis of a modified allometric model by adding crown coverage (CC) as a supplementary variable; cross-validated R2 was 0.88 and root mean square error (RMSE) was 14.7 Mg C ha−1. The outcomes of this step enabled the extension of quasi-field plots required for the representative carbon evaluations and the amplification of the range of observed values. Further integration of lidar measures and optical Landsat data by using the partial least squares regression (PLSR) method was conducted in the subsequent phase. The final model developed for broad-scale estimates explained 76% of the variance in forest ACD and produced a mean bias error of 27.9 Mg C ha−1. Aboveground carbon stocks for the whole ecoregion averaged 77.2 Mg ha−1, which generated an uncertainty of 13%. Visual patterns revealed a systematic overestimation for low ACD values and an underestimation in those regions with high carbon density. Potential errors in our carbon estimates could be associated with the saturation of optical signals, accuracy of land-cover map, and effects of topographic conditions. Overall, the double-sampling method demonstrated promising means for carbon accounting over large areas in a spatially-explicit manner and provided a good first approximation of carbon quantities for the forests in the ecoregion. Our study illustrated the potential for the use of lidar sampling in facilitating scaling of field surveys to a larger spatial extent than ground-based practices by supplying accurate biophysical measurements (e.g. heights).

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics

Abstract. Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors. To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s−1 at the turbine hub-height of 80 m. This slightly stable case results in 15° of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30% of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: cross-stream component errors are on the order of 15% of the hub-height inflow wind speed (1.0 m s−1) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s−1 (or 15% of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8% while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.

Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+

BackgroundCarbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing.ResultsWe compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (~20 returns m−2) data was highly accurate (mean signed error of 0.19 ± 0.97 m), while those derived from reduced-density datasets (8 m−2, 4 m−2, 2 m−2 and 1 m−2) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4 m−2, the bias in height estimates translated into errors of 80–125 Mg ha−1 in predicted aboveground biomass.ConclusionsGiven the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.Electronic supplementary materialThe online version of this article (doi:10.1186/s13021-015-0013-x) contains supplementary material, which is available to authorized users.

Improving pantropical forest carbon maps with airborne LiDAR sampling

Background: Countries interested in monitoring and quantifying the carbon stock of their tropical forests need cost-effective methodologies to map aboveground carbon density (ACD) at regional and national project levels, and with measurable precision and accuracy. This study reports on improvements made possible by the use of airborne high resolution LiDAR samples to regionally fine-tune freely available moderate resolution remote sensing data, and generate maps of ACD with greater detail and improved accuracy than existing pantropical data sets. Results: Regions in the Peruvian and Colombian Amazon indicate that, although existing pantropical data sets of ACD explain approximately 70% of the variance in ACD relative to high resolution LiDAR estimates, by fine calibration with airborne LiDAR samples, it is possible to reduce the relative root mean squared error from 25.2 and 31.4 MgC ha-1, to 15.7 and 17.6 MgC ha-1, respectively, in Colombia and Peru. Conclusion: Airborne LiDAR data can successfully be used for fine-tuning freely available moderate resolution remote sensing image data and significantly improving existing aboveground carbon density maps, to better meet the requirements for national and subnationl carbon density mapping.

Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables

Using lidar in an area-based model-assisted approach to forest inventory has the potential to increase estimation precision for some forest inventory variables. This study documents the bias and precision of a model-assisted (regression estimation) approach to forest inventory with lidar-derived auxiliary variables relative to lidar pulse density and the number of sample plots. For managed forests on the Lewis portion of the Lewis-McChord Joint Base (35025 ha, 23290 forested) in western Washington state, we evaluated a regression estimator for combinations of pulse density (.05–3 pulses/m2) and sample size (15–105 plots) to estimate five forest yield variables: basal area, volume, biomass, number of stems, and Lorey's height. The results indicate that there is almost no loss in precision in using as few as .05 pulses/m2 relative to 3 pulses/m2. We demonstrate that estimation precision declined quickly for reduced sample sizes (as expected from sampling theory); but of more importance we demonstrate that sample size has a dramatic effect on the validity of inferences. Our investigations indicate that for our test dataset that central limit theorem based confidence intervals were too small on average for sample sizes smaller than 55. The results from this study can aid in identifying design components for forest inventory with lidar which satisfy users’ objectives.