Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

Many areas of forest across northern Canada are challenging to monitor on a regular basis as a result of their large extent and remoteness. Although no forest inventory data typically exist for these northern areas, detailed and timely forest information for these areas is required to support national and international reporting obligations. We developed and tested a sample-based approach that could be used to estimate forest stand height in these remote forests using panchromatic Very High Spatial Resolution (VHSR, < 1 m) optical imagery and light detection and ranging (lidar) data. Using a study area in central British Columbia, Canada, to test our approach, we compared four different methods for estimating stand height using stand-level and crown-level metrics generated from the VHSR imagery. ‘Lidar plots’ (voxel-based samples of lidar data) are used for calibration and validation of the VHSR-based stand height estimates, similar to the way that field plots are used to calibrate photogrammetric estimates of stand height in a conventional forest inventory or to make empirical attribute estimates from multispectral digital remotely sensed data. A k-nearest neighbours (k-NN) method provided the best estimate of mean stand height (R ² = 0.69; RMSE = 2.3 m, RMSE normalized by the mean value of the estimates (RMSE-%) = 21) compared with linear regression, random forests, and regression tree methods. The approach presented herein demonstrates the potential of VHSR panchromatic imagery and lidar to provide robust and representative estimates of stand height in remote forest areas where conventional forest inventory approaches are either too costly or are not logistically feasible. While further evaluation of the methods is required to generalize these results over Canada to provide robust and representative estimation, VHSR and lidar data provide an opportunity for monitoring in areas for which there is no detailed forest inventory information available.     

Leaf area and clumping indices for a boreal mixed-wood forest: lidar,hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry.

Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r ² > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r ² > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r ² < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r ² > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r ² < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.     

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).     

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.     

Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain

The amount and spatial distribution of aboveground forest biomass (AGB) are required inputs to forest carbon budgets and ecosystem productivity models. Satellite remote sensing offers distinct advantages for large area and multi-temporal applications, however, conventional empirical methods for estimating forest canopy structure and AGB can be difficult in areas of high relief and variable terrain. This paper introduces a new method for obtaining AGB from forest structure estimates using a physically-based canopy reflectance (CR) model inversion approach. A geometric-optical CR model was run in multiple forward mode (MFM) using SPOT-5 imagery to derive forest structure and biomass at Kananaskis, Alberta in the Canadian Rocky Mountains. The approach first estimates tree crown dimensions and stem density for satellite image pixels which are then related to tree biomass and AGB using a crown spheroid surface area approach. MFM estimates of AGB were evaluated for 36 deciduous (trembling aspen) and conifer (lodgepole pine) field validation sites and compared against spectral mixture analysis (SMA) and normalised difference vegetation index (NDVI) biomass predictions from atmospherically and topographically corrected (SCS+C) imagery. MFM provided the lowest error for all validation plots of 31.7 tonnes/hectare (t/ha) versus SMA (32.6 t/ha error) and NDVI (34.7 t/ha) as well as for conifer plots (MFM: 23.0 t/ha; SMA 27.9 t/ha; NDVI 29.7 t/ha) but had higher error than SMA and NDVI for deciduous plots (by 4.5 t/ha and 2.1 t/ha, respectively). The MFM approach was considerably more stable over the full range of biomass values (67 to 243 t/ha) measured in the field. Field plots with biomass > 1 standard deviation from the field mean (over 30% of plots) had biomass estimation errors of 37.9 t/ha using MFM compared with 65.5 t/ha and 67.5 t/ha error from SMA and NDVI, respectively. In addition to providing more accurate overall results and greater stability over the range of biomass values, the MFM approach also provides a suite of other biophysical structural outputs such as density, crown dimensions, LAI, height and sub-pixel scale fractions. Its explicit physical-basis and minimal ground data requirements are also more appropriate for larger area, multi-scene, multi-date applications with variable scene geometry and in high relief terrain. MFM thus warrants consideration for applications in mountainous and other, less complex terrain for purposes such as forest inventory updates, ecological modeling and terrestrial biomass and carbon monitoring studies.     

Investigating the impact of discrete-return lidar point density on estimations of mean and dominant plot-level tree height in Eucalyptus grandis plantations

The accuracy of lidar remote sensing in characterizing three-dimensional forest structural attributes has encouraged foresters to integrate lidar approaches in routine inventories. However, lidar point density is an important consideration when assessing forest biophysical parameters, given the direct relationship between higher spatial resolution and lidar acquisition and processing costs. The aim of this study was to investigate the effect of point density on mean and dominant tree height estimates at plot level. The study was conducted in an intensively managed Eucalyptus grandis plantation. High point density (eight points/m²) discrete-return, small-footprint lidar data were used to generate point density simulations averaging 0.25, one, two, three, four, five, and six points/m². Field surveyed plot-level mean and dominant heights were regressed against metrics derived from lidar data at each simulated point density. Stepwise regression was used to identify which lidar metrics produced the best models. Mean height was estimated at accuracy of R² ranging between 0.93 and 0.94 while dominant height was estimated with an R² of 0.95. Root mean square error (RMSE) was also similar at all densities for mean height (～1.0 m) and dominant height (～1.2 m); the relative RMSE compared to field-measured mean was constant at approximately 5%. Analysis of bias showed that the estimation of both variables did not vary with density. The results indicated that all lidar point densities resulted in reliable models. It was concluded that plot-level height can be estimated with reliable accuracy using relatively low density lidar point spacing. Additional research is required to investigate the effect of low point density on estimation of other forest biophysical attributes.     

Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State,Brazil

In this study, we tested the effectiveness of stand age, multispectral optical imagery obtained from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data acquired by the Sentinel-1B satellite, and digital terrain attributes extracted from a digital elevation model (DEM), in estimating forest volume in 351 plots in a 1,498 ha Eucalyptus plantation in northern Minas Gerais state, Brazil. A Random Forest (RF) machine learning algorithm was used following the Principal Component Analysis (PCA) of various data combinations, including multispectr al and SAR texture variables and DEM-based geomorphometric derivatives. Using multispectral, SAR or DEM variables alone (i.e. Experiments (ii)–(iv)) did not provide accurate estimates of volume (RMSE (Root Mean Square Error) > 32.00 m³ ha^?1) compared to predictions based on age since planting of Eucalyptus stands (Experiment (i)). However, when these datasets were individually combined with stand age (i.e. Experiments (v)–(vii)), the RF models resulted in better volume estimates than those obtained when using the individual multispectral, SAR and DEM datasets (RMSE < 28.00 m³ ha^?1). Furthermore, a model that integrated the selected variables of these data with stand age (Experiment (viii)) improved volume estimation significantly (RMSE = 22.33 m³ ha^?1). The large and increasing area of Eucalyptus forest plantations in Brazil and elsewhere suggests that this new approach to volume estimation has the potential to support Eucalyptus plantation monitoring and forest management practices.     

Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers

Researchers in lidar (Light Detection And Ranging) strive to search for the most appropriate laser-based metrics as predictors in regression models for estimating forest structural variables. Many previously developed models are scale-dependent that need to be fitted and then applied both at the same scale or pixel size. The objective of this paper is to develop methods for scale-invariant estimation of forest biomass using lidar data. We proposed two scale-invariant models for biomass: a linear functional model and an equivalent nonlinear model that use lidar-derived canopy height distributions (CHD) and canopy height quantile functions (CHQ) as predictors, respectively. The two models are called functional regression models because the predictors CHD and CHQ are themselves functions or functional data. The model formulation was justified mathematically under moderate assumptions. We also created a fine-resolution biomass map by mapping individual tree component biomass in a temperate forest of eastern Texas with a lidar tree-delineation approach. The map was used as reference data to synthesize training and test datasets at multiple scales for validating the two scale-invariant models. Results suggest that the models can accurately predict biomass and yield consistent predictive performances across a variety of scales with an R² ranging from 0.80 to 0.95 (RMSE: from 14. 3 Mg/ha to 33.7 Mg/ha) among all the fitted models. Results also show that a training data size of around 50 plots or less was enough to guarantee a good fitting of the linear functional model. Our findings demonstrate the effectiveness of CHD and CHQ as lidar metrics for estimating biomass as well as the capability of lidar for mapping biomass at a range of scales. The functional regression models of this study are useful for lidar-based forest inventory tasks where the analysis units vary in size and shape. They also hold promise for estimating other forest characteristics such as below-ground biomass, timber volume, crown fuel weight, and Leaf Area Index.     

Species stratification and upscaling of forest carbon estimates to landscape scale using GeoEye-1 image and lidar data in sub-tropical forests of Nepal

Aboveground forest biomass and carbon estimation at landscape scale is crucial for implementation of REDD+ programmes. This study aims to upscale the forest carbon estimates using GeoEye-1 image and small footprint lidar data from small areas to a landscape level using RapidEye image. Species stratification was carried out based on the spectral separability curve of GeoEye-1 image, and comparison of mean intensity and mean plot height of the trees from lidar data. GeoEye-1 image and lidar data were segmented using region growing approach to delineate individual tree crowns; and the segmented crowns (CPA) of tree were further used to establish a relationship with field measured carbon and total trees’ height. Carbon stock measured from field, individual tree crown (ITC) segmentation approach and area-based approach (ABA) was compared at plot level using one-way ANOVA and post hoc Tukey comparison test. ITC-based carbon estimates was used to establish a relationship with spectral reflectance of RapidEye image variables (NDVI, RedEdge NDVI, PC1, single band of RedEdge, and NIR) to upscale the carbon at landscape level. One-way ANOVA resulted in a highly significant difference (p-value < 0.005) between the mean plot height and lidar intensity to stratify Shorea robusta and Other species successfully. ITC carbon stock estimation models of two major tree species explained about 88% and 79% of the variances, respectively, at 95% confidence level. The ABA estimated carbon was highly correlated (R² = 0.83, RMSE = 20.04) to field measured carbon with higher accuracy than the ITC estimated carbon. A weak relationship was observed between the carbon stock and the RapidEye image variables. However, upscaling of carbon estimates from ABA is likely to improve the relationship of the RapidEye variables rather than upscaling the carbon estimates from ITC approach.     

Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA

Lidar provides enhanced abilities to remotely map leaf area index (LAI) with improved accuracies. We aim to further explore the capability of discrete-return lidar for estimating LAI over a pine-dominated forest in East Texas, with a secondary goal to compare the lidar-derived LAI map and the GLOBCARBON moderate-resolution satellite LAI product. Specific problems we addressed include (1) evaluating the effects of analysts and algorithms on in-situ LAI estimates from hemispherical photographs (hemiphoto), (2) examining the effectiveness of various lidar metrics, including laser penetration, canopy height and foliage density metrics, to predict LAI, (3) assessing the utility of integrating Quickbird multispectral imagery with lidar for improving the LAI estimate accuracy, and (4) developing a scheme to co-register the lidar and satellite LAI maps and evaluating the consistency between them. Results show that the use of different analysts or algorithms in analyzing hemiphotos caused an average uncertainty of 0.35 in in-situ LAI, and that several laser penetration metrics in logarithm models were more effective than other lidar metrics, with the best one explaining 84% of the variation in the in-situ LAI (RMSE = 0.29 LAI). The selection of plot size and height threshold in calculating laser penetration metrics greatly affected the effectiveness of these metrics. The combined use of NDVI and lidar metrics did not significantly improve estimation over the use of lidar alone. We also found that mis-registration could induce a large artificial discrepancy into the pixelwise comparison between the coarse-resolution satellite and fine-resolution lidar-derived LAI maps. By compensating for a systematic sub-pixel shift error, the correlation between two maps increased from 0.08 to 0.85 for pines (n = 24 pixels). However, the absolute differences between the two LAI maps still remained large due to the inaccuracy in accounting for clumping effects. Overall, our findings imply that lidar offers a superior tool for mapping LAI at local to regional scales as compared to optical remote sensing, accuracies of lidar-estimate LAI are affected not only by the choice of models but also by the absolute accuracy of in-situ reference LAI used for model calibration, and lidar-derived LAI maps can serve as reliable references for validating moderate-resolution satellite LAI products over large areas.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories

Strategic forest inventory programs produce forest resource estimates for large areas such as states and provinces using data collected for a large number of variables on a relatively sparse array of field plots. Management inventories produce stand-level estimates to guide management decisions using data obtained with sampling intensities much greater than for strategic inventories. The costs associated with these greater sampling intensities have motivated investigations of alternatives to traditional sample-based management inventories. This study focused on a relatively inexpensive alternative to management inventories that uses strategic forest inventory plot data, Landsat Thematic Mapper (TM) satellite imagery, and the k-Nearest Neighbors (k-NN) technique. The approach entailed constructing stem density and basal area per unit area maps from which stand-level means were estimated as averages of k-NN pixel predictions. The study included investigations of the benefits of selecting optimal combinations of k-NN feature space variables derived from the TM imagery and the benefits of modifying the k-NN technique to eliminate spurious nearest neighbors. For both the stem density and basal area per unit area training data, the selection of optimal feature space covariates produced less than 1.5% improvement in root mean square error relative to using all covariates. The k-NN modification improved the sum of mean squared deviations for stand-level stem density and basal area per unit area estimates by 7–20% depending on the k-NN feature space covariates. For the best combination of feature space covariates, estimates of stand-level means were within confidence intervals for validation estimates for 11 of 12 stands for stem density and for 10 of 12 stands for basal area per unit area.     

Estimation of above-ground forest biomass using metrics based on Gaussian decomposition of waveform lidar data

Large-footprint waveform light detection and ranging (lidar) data have been widely used in above-ground forest biomass estimation. Waveform metrics derived from basic statistics (e.g. percentile of energy) of the lidar waveform, such as canopy height and height of median energy, have been applied to biomass estimation in numerous studies. In this study, a set of metrics based on Gaussian decomposition (GD) results were developed and evaluated for forest above-ground biomass estimation using NASA’s laser vegetation imaging sensor (LVIS) data. The GD metrics were designed to explicitly incorporate lidar intensity and vertical structures of canopy layers for biomass estimation. The proposed GD metrics used information related to the above-ground height of each Gaussian centroid and the Gaussian area index (GAI), where GAI is the area covered by a Gaussian function. Two types of novel GD metrics were developed: (1) percentile-height GAI metrics expressing the GAI summation of a subset of Gaussian centroids located within a certain percentile height range; and (2) height-weighted GAI metrics, a summation of GAIs of a waveform weighted by the corresponding heights of their Gaussian centroids. A biomass regression model was built by eight newly developed GD metrics using GAI information and five pre-existing GD-derived metrics that have not previously been used for biomass estimation. The performance of the regression model was then compared to another regression model using 12 previously published metrics (non-GD metrics). The Random Forests (RF) regression algorithm was employed for predicting biomass. The RF out-of-bag results indicated that above-ground biomass estimations using GD metrics achieved significantly better results than those derived from non-GD metrics for deciduous plots (19% lower root mean square error (RMSE), 25% higher coefficient of determination (R²), and marginally better results in coniferous plots (4% lower RSME, 6% higher R²). The combination of GD and non-GD metrics achieved comparable biomass estimation results to the model using exclusively GD metrics. GD metrics also showed strong correlation with forest attributes such as mean diameter at breast height (DBH) and stem density. This study contributes to the usage of GD results for accurate estimation of forest above-ground biomass in large-footprint lidar waveform data in temperate deciduous forests, because temperate deciduous forests have been proved challenging in regard to lidar-derived biomass estimations.     

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed.In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R² of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R² of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.     

Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar

A ground-based, upward-scanning, near-infrared lidar, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stem count density (stems/area), basal area, and above-ground woody biomass with very good accuracy in six New England hardwood and conifer forest stands. Comparing forest structural parameters retrieved using EVI data with extensive ground measurements, we found excellent agreement at the site level using five EVI scans (plots) per site (R² = 0.94-0.99); very good agreement at the plot level for stem count density and biomass (R² = 0.90-0.85); and good agreement at the plot level for mean DBH and basal area (R² = 0.48-0.66). The observed variance at site and plot levels suggest that a sample area of at least 1 ha (10⁴ m²) is required to estimate these parameters accurately at the stand level using either lidar-based or conventional methods. The algorithms and procedures used to retrieve these structural parameters are dependent on the unique ability of the Echidna® lidar to digitize the full waveform of the scattered lidar pulse as it returns to the instrument, which allows consistent separation of scattering by trunks and large branches from scattering by leaves. This successful application of ground-based lidar technology opens the door to rapid and accurate measurement of biomass and timber volume in areal sampling scenarios and as a calibration and validation tool for mapping biomass using airborne or spaceborne remotely sensed data.     

Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data

In this work, the results of above-ground biomass (AGB) estimates from Landsat Thematic Mapper 5 (TM) images and field data from the fragmented landscape of the upper reaches of the Heihe River Basin (HRB), located in the Qilian Mountains of Gansu province in northwest China, are presented. Estimates of AGB are relevant for sustainable forest management, monitoring global change, and carbon accounting. This is particularly true for the Qilian Mountains, which are a water resource protection zone. We combined forest inventory data from 133 plots with TM images and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) V2 products (GDEM) in order to analyse the influence of the sun-canopy-sensor plus C (SCS+C) topographic correction on estimations of forest AGB using the stepwise multiple linear regression (SMLR) and k-nearest neighbour (k-NN) methods. For both methods, our results indicated that the SCS+C correction was necessary for getting more reliable forest AGB estimates within this complex terrain. Remotely sensed AGB estimates were validated against forest inventory data using the leave-one-out (LOO) method. An optimized k-NN method was designed by varying both mathematical formulation of the algorithm and remote-sensing data input, which resulted in 3000 different model configurations. Following topographic correction, performance of the optimized k-NN method was compared to that of the regression method. The optimized k-NN method (R² = 0.59, root mean square error (RMSE) = 24.92 tonnes ha^–1) was found to perform much better than the regression method (R² = 0.42, RMSE = 29.74 tonnes ha^–1) for forest AGB retrieval over this montane area. Our results indicated that the optimized k-NN method is capable of operational application to forest AGB estimates in regions where few inventory data are available.     

Histogram matching for the calibration of kNN stem volume estimates

The k-nearest neighbours (kNN) methods have been used successfully in many countries for the production of spatially comprehensive raster databases of forest attributes, made from the combination of National Forest Inventory (NFI) and satellite data. In Sweden, country-wide kNN estimates of forest variables have been produced to represent the forest condition in the years 2000 and 2005 by using a combination of Système Pour l'Observation de la Terre 5 (SPOT 5) satellite data and field data from the Swedish NFI. The resulting products are wall-to-wall raster maps with estimates of total stem volume, stem volume per tree species, tree height and stand age and a 25?×?25 m² pixel resolution. However, probability-based kNN stem volume estimates tend to have a suppressed variation range as large values are usually underestimated and small values are overestimated. One way to handle this problem is to calibrate the kNN stem volume estimates to the reference distribution of stem volume observations by histogram matching (HM) for a defined geographic area.

In this study, we have tested HM for the calibration of kNN total stem volume raster maps to the reference distribution captured by a forest inventory (FI) from 106 stands in Strömsjöliden, in the north of Sweden. The available field FI data set comprises 1084 circular plots, divided into a reference data set and an evaluation data set of total stem volume observations. The reference data set was used for the creation of a cumulative frequency histogram of total stem volume and the evaluation data set was used to assess the accuracy of volume estimates, before and after HM. The HM adjusted the cumulative distribution of the kNN data set to the distribution of the reference observations and resulted in a distribution of kNN estimates of total stem volume, which corresponded closely to that of the evaluation data set. The results show that the variation range of the kNN stem volume estimates can be extended by HM both on the pixel and stand levels. The extension of the range of estimates towards the range provided by the field observations allows improvement of kNN volume estimation for use in forest management planning based on stand-level analysis, given that the reference stem volume distribution can be estimated accurately, for example, using field data from NFI.     

Polar grid fraction as an estimator of montane tropical forest canopy structure using airborne lidar

The structure of a forest canopy is the key determinant of light transmission, use and understory availability. Airborne light detection and ranging (LiDAR) has been used successfully to measure multiple canopy structural properties, thereby greatly reducing the fieldwork required to map spatial variation in structure. However, lidar metrics to date do not reflect the full extent of the three-dimensional information available from the data. To this end, we developed a new metric, the polar grid fraction (GRID), based on gridding lidar returns in polar coordinates, in order to more closely match measurements provided by field instruments on leaf area index (LAI), gap fraction (GF) and percentage photosynthetically active radiation transmittance (tPAR). The metric summarizes the arrangement of lidar point returns for a single ground location rather than to an area surrounding the location.

Compared with more traditional proportion-based and height percentile-based estimators, the GRID estimator increased validation R² by 14.5% for GF and 6.0% for tPAR over the next best estimator. LAI was still best estimated with the more traditional statistic based on the proportion of ground returns in 14 m × 14 m moving kernels. By applying the models to a 2 × 2 m grid across the lidar coverage area, extreme values occurred in the estimations of all three response variables when using proportion-based and height percentile-based estimators. However, no extreme values were estimated by models using the GRID estimator, indicating that models based on GRID may be less influenced by spurious data. These results suggest that the GRID estimator is a strong candidate for any project requiring estimates of canopy metrics for large areas.     

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape

Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r²=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.