Fusion of LiDAR and imagery for estimating forest canopy fuels

Due to increased fuel loading as a result of fire suppression, land managers in the American west are in need of precise information about the fuels they manage, including canopy fuels. Canopy fuel metrics such as canopy height (CH), canopy base height (CBH), canopy bulk density (CBD) and available canopy fuel (ACF) are specific inputs for wildfire behavior models such as FARSITE and emission models such as FOFEM. With finer spatial resolution data, accurate quantification of these metrics with detailed spatial heterogeneity can be accomplished. Light Detection and Ranging (LiDAR) and color near-infrared imagery are active and passive systems, respectively, that have been utilized for measuring a range of forest structure characteristics at high resolution. The objective of this research was to determine which remote sensing dataset can estimate canopy fuels more accurately and whether a fusion of these datasets produces more accurate estimates. Regression models were developed for ponderosa pine (Pinus ponderosa) stand representative of eastern Washington State using field data collected in the Ahtanum State Forest and metrics derived from LiDAR and imagery. Strong relationships were found with LiDAR alone and LiDAR was found to increase canopy fuel accuracy compared to imagery. Fusing LiDAR with imagery and/or LiDAR intensity led to small increases in estimation accuracy over LiDAR alone. By improving the ability to estimate canopy fuels at higher spatial resolutions, spatially explicit fuel layers can be created and used in wildfire behavior and smoke emission models leading to more accurate estimations of crown fire risk and smoke related emissions.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

Assessment of forest structure with airborne LiDAR and the effects of platform altitude

Airborne scanning LiDAR is a spatial technology increasingly used for forestry and environmental applications. However, the accuracy and coverage of LiDAR observations is highly dependent on both the extrinsic specifications of the LiDAR survey as well as the intrinsic effects such as the underlying forest structure. Extrinsic parameters which are set as part of the LiDAR survey include platform altitude, scan angle (half max. angle off nadir), and beam cross sectional diameter at the reflecting surface (referred to as footprint size). In this paper we investigate the effect of a number of these extrinsic parameters, including three different platform altitudes (1000, 2000, and 3000 m), two scan angles at 1000 m (10° and 15° half max. angle off nadir), and three footprint sizes (0.2, 0.4, and 0.6 m). The comparison was undertaken in eucalypt forests at three sites, varying in vegetation structure and topography within the Wedding Bells State Forest, Coffs Harbour, Australia. Results at the plot scale (40 × 90 m areas) indicate that tree heights computed from the 1000 m LiDAR data set (10° half max. angle off nadir) are well correlated with maximum plot heights (difference < 3 m) and field measured canopy volume (r² > 0.75, p < 0.001). Using normalised canopy height profiles (CHP) derived for sites, from data recorded at each altitude, we observed no significant difference between the relative distribution of LiDAR returns, indicating that platform altitude and footprint size have not had a major influence on CHP estimation. Interestingly, comparisons of first and last returns for individual pulses at increasing altitudes identified progressively fewer discrete first/last pulse combinations with more than 70% of pulses recorded as a single return at the highest altitude (3000 m). A possible hypothesis is that greater platform altitude and footprint size reduces the intensity of laser beam incident on a given surface area thus decreasing the probability of recording a last return above the noise threshold. Furthermore, tree scale analysis found a positive relationship between platform altitude and the underestimation of crown area and crown volume. The implications of this work for forest management are: (i) platform altitudes as high as 3000 m can be used to quantify the vertical distribution of phyto-elements, (ii) higher platform altitudes record a lower proportion of first/last return combinations that will further reduce the number of points available for forest structural assessment and development of digital elevation models, and (iii) for discrete LiDAR data, increasing platform altitude will record a lower frequency of returns per crown, resulting in larger underestimates of individual tree crown area and volume if standard algorithms are applied.     

Development of a simulation model to predict LiDAR interception in forested environments

Airborne scanning LiDAR systems are used to predict a range of forest attributes. However, the accuracy with which this can be achieved is highly dependent on the sensor configuration and the structural characteristics of the forest examined. As a result, there is a need to understand laser light interactions with forest canopies so that LiDAR sensor configurations can be optimised to assess particular forest types. Such optimisation will not only ensure the targeted forest attributes can be accurately and consistently quantified, but may also minimise the cost of data acquisition and indicate when a survey configuration will not deliver information needs.In this paper, we detail the development and application of a model to simulate laser interactions within forested environments. The developed model, known as the LiDAR Interception and Tree Environment (LITE) model, utilises a range of structural configurations to simulate trees with variable heights, crown dimensions and foliage clumping. We developed and validated the LITE model using field data obtained from three forested sites covering a range of structural classes. Model simulations were then compared to coincident airborne LiDAR data collected over the same sites. Results indicate that the LITE model can be used to produce comparable estimates of maximum height of trees within plots (differences < 2.42 m), mean heights of first return data (differences < 2.27 m), and canopy height percentiles (r² = 0.94, p < 0.001) when compared to airborne LiDAR data. In addition, the distribution of airborne LiDAR hits through the canopy profile was closely matched by model predictions across the range of sites. Importantly, this demonstrates that the structural differences between forest stands can be characterised by LITE. Models that are capable of interpreting the response of small-footprint LiDAR waveforms can facilitate algorithm development, the generation of corrections for actual LiDAR data, and the optimisation of sensor configurations for differing forest types, benefiting a range of experimental and commercial LiDAR applications. As a result, we also performed a scenario analysis to demonstrate how differences in forest structure, terrain, and sensor configuration can influence the interception of LiDAR beams.     

Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors

Tropical forests are an important component of the global carbon balance, yet there is considerable uncertainty in estimates of their carbon stocks and fluxes, which are typically estimated through analysis of aboveground biomass in field plots. Remote sensing technology is critical for assessing fine-scale spatial variability of tropical forest biomass over broad spatial extents. The goal of our study was to evaluate relatively new technology, small-footprint, discrete-return lidar and hyperspectral sensors, for the estimation of aboveground biomass in a Costa Rican tropical rain forest landscape. We derived a suite of predictive metrics for field plots: lidar metrics were calculated from plot vertical height profiles and hyperspectral metrics included fraction of spectral mixing endmembers and narrowband indices that respond to photosynthetic vegetation, structure, senescence, health and water and lignin content. We used single- and two-variable linear regression analyses to relate lidar and hyperspectral metrics to aboveground biomass of plantation, managed parkland and old-growth forest plots. The best model using all 83 biomass plots included two lidar metrics, plot-level mean height and maximum height, with an r² of 0.90 and root-mean-square error (RMSE) of 38.3 Mg/ha. When the analysis was constrained to plantation plots, which had the most accurate field data, the r² of the model increased to 0.96, with RMSE of 10.8 Mg/ha (n = 32). Hyperspectral metrics provided lower accuracy in estimating biomass than lidar metrics, and models with a single lidar and hyperspectral metric were no better than the best model using two lidar metrics. These results should be viewed as an initial assessment of using these combined sensors to estimate tropical forest biomass; hyperspectral data were reduced to nine indices and three spectral mixture fractions, lidar data were limited to first-return canopy height, sensors were flown only once at different seasons, and we explored only linear regression for modeling. However, this study does support conclusions from studies at this and other climate zones that lidar is a premier instrument for mapping biomass (i.e., carbon stocks) across broad spatial scales.     

Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories

According to the IPCC GPG (Intergovernmental Panel on Climate Change, Good Practice Guidance), remote sensing methods are especially suitable for independent verification of the national LULUCF (Land Use, Land-Use Change, and Forestry) carbon pool estimates, particularly the aboveground biomass. In the present study, we demonstrate the potential of standwise (forest stand is a homogenous forest unit with average size of 1-3 ha) forest inventory data, and ASTER and MODIS satellite data for estimating stand volume (m³ ha^− 1) and aboveground biomass (t ha^− 1) over a large area of boreal forests in southern Finland. The regression models, developed using standwise forest inventory data and standwise averages of moderate spatial resolution ASTER data (15 m × 15 m), were utilized to estimate stand volume for coarse resolution MODIS pixels (250 m × 250 m). The MODIS datasets for three 8-day periods produced slightly different predictions, but the averaged MODIS data produced the most accurate estimates. The inaccuracy in radiometric calibration between the datasets, the effect of gridding and compositing artifacts and phenological variability are the most probable reasons for this variability. Averaging of the several MODIS datasets seems to be one possibility to reduce bias. The estimates obtained were significantly close to the district-level mean values provided by the Finnish National Forest Inventory; the relative RMSE was 9.9%. The use of finer spatial resolution data is an essential step to integrate ground measurements with coarse spatial resolution data. Furthermore, the use of standwise forest inventory data reduces co-registration errors and helps in solving the scaling problem between the datasets. The approach employed here can be used for estimating the stand volume and biomass, and as required independent verification data.     

Quantifying forest above ground carbon content using LiDAR remote sensing

The UNFCCC and interest in the source of the missing terrestrial carbon sink are prompting research and development into methods for carbon accounting in forest ecosystems. Here we present a canopy height quantile-based approach for quantifying above ground carbon content (AGCC) in a temperate deciduous woodland, by means of a discrete-return, small-footprint airborne LiDAR. Fieldwork was conducted in Monks Wood National Nature Reserve UK to estimate the AGCC of five stands from forest mensuration and allometric relations. In parallel, a digital canopy height model (DCHM) and a digital terrain model (DTM) were derived from elevation measurements obtained by means of an Optech Airborne Laser Terrain Mapper 1210. A quantile-based approach was adopted to select a representative statistic of height distributions per plot. A forestry yield model was selected as a basis to estimate stemwood volume per plot from these heights metrics. Agreement of r=0.74 at the plot level was achieved between ground-based AGCC estimates and those derived from the DCHM. Using a 20×20 m grids superposed to the DCHM, the AGCC was estimated at the stand level and at the woodland level. At the stand level, the agreement between the plot data upscaled in proportion to area and the LiDAR estimates was r=0.85. At the woodland level, LiDAR estimates were nearly 24% lower than those from the upscaled plot data. This suggests that field-based approaches alone may not be adequate for carbon accounting in heterogeneous forests. Conversely, the LiDAR 20×20 m grid approach has an enhanced capability of monitoring the natural variability of AGCC across the woodland.     

Strengths and limitations of assessing forest density and spatial configuration with aerial LiDAR

Changes in the structural state of forests of the semi-arid U.S.A., such as an increase in tree density, are widely believed to be leading to an ecological crisis, but accurate methods of quantifying forest density and configuration are lacking at landscape scales. An individual tree canopy (ITC) method based on aerial LiDAR has been developed to assess forest structure by estimating the density and spatial configuration of trees in four different height classes. The method has been tested against field measured forest inventory data from two geographically distinct forests with independent LiDAR acquisitions. The results show two distinct patterns: accurate, unbiased density estimates for trees taller than 20 m, and underestimation of density in trees less than 20 m tall. The underestimation of smaller trees is suggested to be a limitation of LiDAR remote sensing. Ecological applications of the method are demonstrated through landscape metrics analysis of density and configuration rasters.     

Fusion of airborne LiDAR and satellite multispectral data for the estimation of timber volume in the Southern Alps

Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km² of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 21.3% considering altitude and slope sets.     

Computer-based synthetic data to assess the tree delineation algorithm from airborne LiDAR survey

Small Footprint LiDAR (Light Detection And Ranging) has been proposed as an effective tool for measuring detailed biophysical characteristics of forests over broad spatial scales. However, by itself LiDAR yields only a sample of the true 3D structure of a forest. In order to extract useful forestry relevant information, this data must be interpreted using mathematical models and computer algorithms that infer or estimate specific forest metrics. For these outputs to be useful, algorithms must be validated and/or calibrated using a sub-sample of ‘known’ metrics measured using more detailed, reliable methods such as field sampling. In this paper we describe a novel method for delineating and deriving metrics of individual trees from LiDAR data based on watershed segmentation. Because of the costs involved with collecting both LiDAR data and field samples for validation, we use synthetic LiDAR data to validate and assess the accuracy of our algorithm. This synthetic LiDAR data is generated using a simple geometric model of Loblolly pine (Pinus taeda) trees and a simulation of LiDAR sampling. Our results suggest that point densities greater than 2 and preferably greater than 4 points per m2 are necessary to obtain accurate forest inventory data from Loblolly pine stands. However the results also demonstrate that the detection errors (i.e. the accuracy and biases of the algorithm) are intrinsically related to the structural characteristics of the forest being measured. We argue that experiments with synthetic data are directly useful to forest managers to guide the design of operational forest inventory studies. In addition, we argue that the development of LiDAR simulation models and experiments with the data they generate represents a fundamental and useful approach to designing, improving and exploring the accuracy and efficiency of LiDAR algorithms.     

Modeling forest songbird species richness using LiDAR-derived measures of forest structure

Conservation of biodiversity requires information at many spatial scales in order to detect and preserve habitat for many species, often simultaneously. Vegetation structure information is particularly important for avian habitat models and has largely been unavailable for large areas at the desired resolution. Airborne LiDAR, with its combination of relatively broad coverage and fine resolution provides existing new opportunities to map vegetation structure and hence avian habitat. Our goal was to model the richness of forest songbirds using forest structure information obtained from LiDAR data. In deciduous forests of southern Wisconsin, USA, we used discrete-return airborne LiDAR to derive forest structure metrics related to the height and density of vegetation returns, as well as composite variables that captured major forest structural elements. We conducted point counts to determine total forest songbird richness and the richness of foraging, nesting, and forest edge-related habitat guilds. A suite of 35 LiDAR variables were used to model bird species richness using best-subsets regression and we used hierarchical partitioning analysis to quantify the explanatory power of each variable in the multivariate models. Songbird species richness was correlated most strongly with LiDAR variables related to canopy and midstory height and midstory density (R² = 0.204, p < 0.001). Richness of species that nest in the midstory was best explained by canopy height variables (R² = 0.197, p < 0.001). Species that forage on the ground responded to mean canopy height and the height of the lower canopy (R² = 0.149, p < 0.005) while aerial foragers had higher richness where the canopy was tall and dense and the midstory more sparse (R² = 0.216, p < 0.001). Richness of edge-preferring species was greater where there were fewer vegetation returns but higher density in the understory (R² = 0.153, p < 0.005). Forest interior specialists responded positively to a tall canopy, developed midstory, and a higher proportion of vegetation returns (R² = 0.195, p < 0.001). LiDAR forest structure metrics explained between 15 and 20% of the variability in richness within deciduous forest songbird communities. This variability was associated with vertical structure alone and shows how LiDAR can provide a source of complementary predictive data that can be incorporated in models of wildlife habitat associations across broad geographical extents.     

Evaluating uncertainty in mapping forest carbon with airborne LiDAR

Airborne LiDAR is increasingly used to map carbon stocks in tropical forests, but our understanding of mapping errors is constrained by the spatial resolution (i.e., plot size) used to calibrate LiDAR with field data (typically 0.1-0.36 ha). Reported LiDAR errors range from 17 to 40 Mg C ha^− 1, but should be lower at coarser resolutions because relative errors are expected to scale with (plot area)^-1/2. We tested this prediction empirically using a 50-ha plot with mapped trees, allowing an assessment of LiDAR prediction errors at multiple spatial resolutions. We found that errors scaled approximately as expected, declining by 38% (compared to 40% predicted from theory) from 0.36- to 1-ha resolution. We further reduced errors at all spatial resolutions by accounting for tree crowns that are bisected by plot edges (not typically done in forestry), and collectively show that airborne LiDAR can map carbon stocks with 10% error at 1-ha resolution — a level comparable to the use of field plots alone.     

Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information

A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the conterminous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (FIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes serving as the predictor variables. The plot attributes came from intersecting plot coordinates with geospatial datasets. Consequently, these models serve as mapping models. The geospatial predictor variables included Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; land cover proportions and other data from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the mapping models for the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. First, we developed a forest mask by modeling the forest vs. nonforest assignment of field plots as functions of the predictor layers using classification trees in See5©. Secondly, forest biomass models were built within the predicted forest areas using tree-based algorithms in Cubist©. To validate the models, we compared field-measured with model-predicted forest/nonforest classification and biomass from an independent test set, randomly selected from available plot data for each mapping zone. The estimated proportion of correctly classified pixels for the forest mask ranged from 0.79 in Puerto Rico to 0.94 in Alaska. For biomass, model correlation coefficients ranged from a high of 0.73 in the Pacific Northwest, to a low of 0.31 in the Southern region. There was a tendency in all regions for these models to over-predict areas of small biomass and under-predict areas of large biomass, not capturing the full range in variability. Map-based estimates of forest area and forest biomass compared well with traditional plot-based estimates for individual states and for four scales of spatial aggregation. Variable importance analyses revealed that MODIS-derived information could contribute more predictive power than other classes of information when used in isolation. However, the true contribution of each variable is confounded by high correlations. Consequently, excluding any one class of variables resulted in only small effects on overall map accuracy. An estimate of total C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, also compared well with previously published estimates.     

Exploring LiDAR-RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Scanning Light Detecting and Ranging (LiDAR), Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) were analyzed to determine (1) which of the three sensor systems most accurately predicted forest biomass, and (2) if LiDAR and SAR/InSAR data sets, jointly considered, produced more accurate, precise results relative to those same data sets considered separately. LiDAR ranging measurements, VHF-SAR cross-sectional returns, and X- and P-band cross-sectional returns and interferometric ranges were regressed with ground-estimated (from dbh) forest biomass in ponderosa pine forests in the southwestern United States. All models were cross-validated. Results indicated that the average canopy height measured by the scanning LiDAR produced the best predictive equation. The simple linear LiDAR equation explained 83% of the biomass variability (n = 52 plots) with a cross-validated root mean square error of 26.0 t/ha. Additional LiDAR metrics were not significant to the model. The GeoSAR P-band (λ = 86 cm) cross-sectional return and the GeoSAR/InSAR canopy height (X-P) captured 30% of the forest biomass variation with an average predictive error of 52.5 t/ha. A second RaDAR-FOPEN collected VHF (λ ∼ 7.8 m) and cross-polarized P-band (λ = 88 cm) cross-sectional returns, none of which proved useful for forest biomass estimation (cross-validated R² = 0.09, RMSE = 63.7 t/ha). Joint consideration of LiDAR and RaDAR measurements produced a statistically significant, albeit small improvement in biomass estimation precision. The cross-validated R² increased from 83% to 84% and the prediction error decreased from 26.0 t/ha to 24.9 t/ha when the GeoSAR X-P interferometric height is considered along with the average LiDAR canopy height. Inclusion of a third LiDAR metric, the 60th decile height, further increased the R² to 85% and decreased the RMSE to 24.1 t/ha. On this 11 km² ponderosa pine study area, LiDAR data proved most useful for predicting forest biomass. RaDAR ranging measurements did not improve the LiDAR estimates.     

结合LiDAR单木分割和高光谱特征提取的城市森林树种分类

基于多源遥感数据的城市森林树种分类对城市森林资源调查、森林健康状况评价及科学化管理具有重要意义。以江苏省常熟市虞山国家森林公园内的典型城市森林树种为研究对象,利用同期获取的机载激光雷达(LiDAR)和高光谱数据,针对5个典型城市森林树种进行了树种分类的研究。首先,基于点云距离判断单木分割方法进行单木位置和冠幅提取,并借助实测数据和目视解译结果进行精度验证;然后,在冠幅内提取4组高光谱特征变量,并借助随机森林模型对特征变量进行重要性分析;最后,筛选出重要性高的特征变量进行2个级别的树种分类并借助混淆矩阵进行验证评价。结果表明:基于点云距离判断分割方法的单木位置提取精度较高(探测率为85.7%,准确率为96%,总体精度为90.9%);利用全部特征变量(n=36)对5个树种进行分类,分类的总体精度达到了84%,Kappa系数为0.80;利用优选特征变量(n=9)进行分类,总体精度达83%,Kappa系数为0.79;利用全部特征变量(n=36)对两种森林类型进行分类,分类的总体精度达91.3%,Kappa系数为0.82,其中阔叶树种分类精度为95.6%,针叶树种分类精度为85%;利用优选特征变量(n=9)进行分类,分类的总体精度达90.7%,Kappa系数为0.80,其中阔叶树种分类精度为93.33%,针叶树种分类精度为86.67%。     

Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser

Canopy height distributions were created from small-footprint airborne laser scanner data collected over 133 georeferenced field sample plots and 56 forest stands located in young and mature forest. The plot size was 300-400 m² and the average stand size was 1.7 ha. Spruce and pine were the dominant tree species. Canopy height distributions were created from both first and last pulse data. The laser data were acquired from two different flying altitudes, i.e., 530-540 and 840-850 m above ground. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were computed from the laser-derived canopy height distributions. Corresponding metrics derived from the two different flying altitudes were compared. Only 1 of 54 metrics derived from the first pulse data differed significantly between flying altitudes. For the last pulse data, the mean values of the height percentiles were up to 50 cm higher than the corresponding values of the low-altitude data. The high-altitude data yielded significantly higher values for most of the canopy density measures. The standard deviation for the differences between high and low flying altitude for each of the metrics was estimated. The standard deviations for the height percentiles ranged from 0.07 to 0.30 cm in the forest stands, indicating a large degree of stability between repeated flight overpasses.The effect of variable flying altitude on mean tree height (h_L), stand basal area (G), and stand volume (V) estimated from the laser-derived height and density measures using a two-stage inventory procedure was assessed by randomly combining laser data from the two flying altitudes for each individual sample plot and forest stand. The sample plots were used as training data to calibrate the models. The random assignment was repeated 10,000 times. The results of the 10,000 trials indicated that the precision of the estimated values of h_L, G, and V was robust against alterations in flying altitude.     

Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems

Large areas of the world's coastal marine environments remain poorly characterized because they have not been mapped with sufficient accuracy and at spatial resolutions high enough to support a wide range of societal needs. Expediting the rate of seafloor mapping requires the collection of multi-use datasets that concurrently address hydrographic charting needs and support decision-making in ecosystem-based management. While active optical and acoustic sensors have previously been compared for the purpose of hydrographic charting, few studies have evaluated the performance and cost effectiveness of these systems for providing benthic habitat maps. Bathymetric and intensity data were collected in shallow water (< 50 m depth) coral reef ecosystems using two conventional remote sensing technologies: (1) airborne Light Detection and Ranging (LiDAR), and (2) ship-based multibeam (MBES) Sound Navigation and Ranging (SoNAR). A comparative assessment using a suite of twelve metrics demonstrated that LiDAR and MBES were equally capable of discriminating seafloor topography (r = > 0.9), although LiDAR depths were found to be consistently shallower than MBES depths. The intensity datasets were not significantly correlated at a broad 4 × 5 km spatial scale (r = − 0.11), but were moderately correlated in flat areas at a fine 4 × 500 m spatial scale (r = 0.51), indicating that the LiDAR intensity algorithm needs to be improved before LiDAR intensity surfaces can be used for habitat mapping. LiDAR cost 6.6% less than MBES and required 40 fewer hours to map the same study area. MBES provided more detail about the seafloor by fully ensonifying high-relief features, by differentiating between fine and coarse sediments and by collecting data with higher spatial resolutions. Surface fractal dimensions and fast Fourier transformations emerged as useful methods for detecting artifacts in the datasets. Overall, LiDAR provided a more cost effective alternative to MBES for mapping and monitoring shallow water coral reef ecosystems (< 50 m depth), although the unique advantages of MBES may make it a more appropriate choice for answering certain ecological or geological questions requiring very high resolution data.     

Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area

There is a need for accurate inventory methods that produce relevant and timely information on the forest resources and carbon stocks for forest management planning and for implementation of national strategies under the United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). Such methods should produce information that is consistent across various geographical scales. Airborne scanning Light Detection and Ranging (LiDAR) is among the most promising remote sensing technologies for estimation of forest resource information such as timber volume and biomass, while acquisition of three dimensional data with Interferometric Synthetic Aperture Radar (InSAR) from space is seen as a relevant option for inventory in the tropics because of its ability to “see through the clouds” and its potential for frequent updates at low costs. Based on a stratified probability sample of 201 field survey plots collected in a 960 km² boreal forest area in Norway, we demonstrate how total above-ground biomass (AGB) can be estimated at three distinct geographical levels in such a way that the estimates at a smaller level always sum up to the estimate at a larger level. The three levels are (1) a district (the entire study area), (2) a village, local community or estate level, and (3) a stand or patch level. The LiDAR and InSAR data were treated as auxiliary information in the estimation. At the two largest geographical levels model-assisted estimators were employed. A model-based estimation was conducted at the smallest level. Estimates of AGB and corresponding error estimates based on (1) the field sample survey were compared with estimates obtained by using (2) LiDAR and (3) InSAR data as auxiliary information. For the entire study area, the estimates of AGB were 116.0, 101.2, and 111.3 Mg ha⁻¹, respectively. Corresponding standard error estimates were 3.7, 1.6, and 3.2 Mg ha⁻¹. At the smallest geographical level (stand) an independent validation on 35 large field plots was carried out. RMSE values of 17.1-17.3 Mg ha⁻¹ and 42.6-53.2 Mg ha⁻¹ were found for LiDAR and InSAR, respectively. A time lag of six years between acquisition of InSAR data and field inventory has introduced some errors. Significant differences between estimates and reference values were found, illustrating the risk of using pure model-based methods in the estimation when there is a lack of fit in the models. We conclude that the examined remote sensing techniques can provide biomass estimates with smaller estimated errors than a field-based sample survey. The improvement can be highly significant, especially for LiDAR.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.