Lauri Korhonen

Retrieval of boreal forest LAI using a forest reflectance model and empirical regressions

Abstract Spectral invariants provide a novel approach for characterizing canopy structure in forest reflectance models and for mapping biophysical variables using satellite images. We applied a photon recollision probability (p) based forest reflectance model (PARAS) to retrieve leaf area index (LAI) from fine resolution SPOT HRVIR and Landsat ETM+ satellite data. First, PARAS was parameterized using an extensive database of LAI-2000 measurements from five conifer-dominated boreal forest sites in Finland, and mixtures of field-measured forest understory spectra. The selected vegetation indices (e.g. reduced simple ratio, RSR), neural networks and kNN method were used to retrieve effective LAI (Le) based on reflectance model simulations. For comparison, we established empirical vegetation index-LAI regression models for our study sites. The empirical RSR–Le regression performed best when applied to an independent test site in southern Finland [RMSE 0.57 (24.2%)]. However, the difference to the best reflectance model based retrievals produced by neural networks was only marginal [RMSE 0.59 (25.1%)]. According to this study, the PARAS model provides a simple and flexible modelling tool for calibrating algorithms for LAI retrieval in conifer-dominated boreal forests. The advantage of PARAS is that it directly uses field measurements to parameterize canopy structure (LAI-2000, hemispherical photographs) and optical properties of foliage and understory.

Bayesian Approach to Tree Detection Based on Airborne Laser Scanning Data

In this paper, we consider a computational method for detecting trees on the basis of airborne laser scanning (ALS) data. In the approach, locations, heights, and crown shapes of trees are tracked automatically by fitting multiple 3-D crown height models to ALS data of a field plot. The estimates are computed with an iterative reconstruction method based on Bayesian inversion paradigm. The formulation allows for utilizing prior information on tree shapes in the estimation. Here, the prior models are written based on field measurement data and allometric models for tree shapes. The feasibility of the approach is tested with ALS and field data from a managed boreal forest. The algorithm found 70.2% of the trees in the area, which is a clear improvement compared to a usual 2.5D crown delineation approach (53.1% of the trees detected).

Leaf Area Index (LAI) Estimation of Boreal Forest Using Wide Optics Airborne Winter Photos

A new simple airborne method based on wide optics camera is developed for leaf area index (LAI) estimation in coniferous forests. The measurements are carried out in winter, when the forest floor is completely snow covered and thus acts as a light background for the hemispherical analysis of the images. The photos are taken automatically and stored on a laptop during the flights. The R2 value of the linear regression of the airborne and ground based LAI measurements was 0.89.

Training Area Concept in a Two-Phase Biomass Inventory Using Airborne Laser Scanning and RapidEye Satellite Data

This study evaluated the accuracy of boreal forest above-ground biomass (AGB) and volume estimates obtained using airborne laser scanning (ALS) and RapidEye data in a two-phase sampling method. Linear regression-based estimation was employed using an independent validation dataset and the performance was evaluated by assessing the bias and the root mean square error (RMSE). In the phase I, ALS data from 50 field plots were used to predict AGB and volume for the 200 surrogate plots. In the phase II, the ALS-simulated surrogate plots were used as a ground-truth to estimate AGB and volume from the RapidEye data for the study area. The resulting RapidEye models were validated against a separate set of 28 plots. The RapidEye models showed a promising accuracy with a relative RMSE of 19%–20% for both volume and AGB. The evaluated concept of biomass inventory would be useful to support future forest monitoring and decision making for sustainable use of forest resources.

Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data

Forest canopy cover (C) is needed in forest area monitoring and for many ecological models. Airborne scanning lidar sensors can produce fairly accurate C estimates even without field training data. However, optical satellite images are more cost-efficient for large area inventories. Our objective was to use airborne lidar data to obtain accurate estimates of C for a set of sample plots in a boreal forest and to generalize C for a large area using a satellite image. The normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) were calculated from the satellite image and used as predictors in the regressions. RSR, which combines information from the red, near-infrared, and shortwave infrared bands, provided the best performance in terms of absolute root mean square error (RMSE) (7.3%) in the training data. NDVI produced a markedly larger RMSE (10.0%). However, in an independent validation data set, RMSE increased (13.0–17.1%) because the systematic sample of validation plots contained more variation than the training plots. Our results are better than those reported earlier, which is probably explained by more consistent C estimates derived from the lidar. Our approach provides an efficient method for creating C maps for large areas.

Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests

Leaf area index (LAI) is one descriptor of forest canopy structure and can be linked to vegetation productivity, carbon cycling, and several other ecosystem services. Airborne lidar (light detection and ranging) provides proxies of canopy gap fraction (GF) in the near-vertical direction, which can be related to LAI using a logarithmic model derived from Beer’s Law. The approach has been successful in LAI mapping in boreal and temperate forests. In this study, we evaluated the logarithmic model and several GF proxies in tropical montane forests in southeastern Kenya. We used two discrete-return lidar datasets (max. scan angle ~16°) with different flying heights and pulse densities (5.4 and 2.6 pulses m–2). GF for the 0–15° zenith angle range (GF15) and effective LAI (Le) were estimated for 29 sample plots using digital hemispherical photography. Twenty-one plots were located in indigenous forests and eight plots in plantation forests. According to the results, GF15 was best approximated by the proxies that included all canopy and ground return types (all echo cover index, ACI, root mean square error, RMSE = 0.050, bias = –0.003; Solberg’s cover index, SCI, RMSE = 0.057, bias = 0.002) although some saturation occurred when using data from the higher flight altitude. The results of the Le modelling propose that the logarithmic model needs to be fit separately for indigenous forest and plantations. Furthermore, the slope parameters of the models based on SCI suggest planophile (β ≈ 1.6) and spherical (β ≈ 2) leaf angle distribution for indigenous forests and plantations, respectively. We conclude that lidar cover indices based on all returns can estimate GF15 in closed-canopy tropical forests but the detection of the smallest gaps can be limited by the scanner or scanning parameters. The application of the logarithmic model requires stratification in the structurally heterogeneous and multi-species forest areas as β should be estimated separately for the different forest types.

Estimation of Canopy Cover, Gap Fraction and Leaf Area Index with Airborne Laser Scanning

Forest canopy cover and gap fraction are commonly used metrics in forest ecology. Airborne laser scanning is capable of measuring both very accurately, but slightly different estimation methods should be used as these metrics are defined differently. In canopy cover estimation the proportion of vertical gaps between the crowns is needed for a specific area. Canopy gap fraction includes all gaps observed from a single point with some angular view range. Canopy cover can be estimated with high accuracy as the fraction of first echoes above a specified height threshold, because only the large gaps are considered. In gap fraction estimation also last echoes should be used so that the effect of the smaller gaps within the crowns is considered. Leaf area index can be estimated from the gap fraction using a logarithmic model with a single coefficient representing leaf orientation. However, sensor effects have a strong influence on the estimates, and therefore validation with high-quality field data is recommended.

Estimation of tree crown volume from airborne lidar data using computational geometry

Although simple geometrical shapes are commonly used to describe tree crowns, computational geometry enables calculation of the individual crown properties directly from airborne lidar point clouds. Our objective was to calculate crown volumes (CVs) using this technique and validate the results by comparing them with field-measured values and modelled ellipsoidal crowns. The CVs of standing trees were obtained by measuring the crown radii at different heights, integrating the obtained crown profiles as solids of revolution, and finally averaging the volumes obtained from the four separate profiles. With the lidar data, the CVs were extracted using 3D alpha shape and 3D convex hull techniques. Crown base heights (CBHs) were also estimated from the lidar data and used to exclude echoes from the understory, which was also done using field-based CBHs to exclude this error source. The results show that the field-measured CVs had a high correlation with lidar-based estimates (best R 2 = 0.83), but the lidar-based estimates were generally smaller than the field values. The best correspondence (root mean square difference (RMSD) = 45.0%, average difference = –24.7%) was obtained using the convex hull of the point data and field-measured CBH. The CBHs were consistently overestimated (RMSD = 37.3%; average difference = –20.0%), especially in spruces with long crowns. Thus using lidar-based CBH also increased the inaccuracy of the CV estimates. While the underestimation of CV is mainly explained by the inadequate number of echoes from the lower regions of the crowns, the CVs obtained from the lidar were better than those obtained with ellipsoids fitted by using general models for crown dimensions. The utility of the estimated CVs in the prediction of stem diameter is also demonstrated.

Automatic Segment-Level Tree Species Recognition Using High Resolution Aerial Winter Imagery

Abstract Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73.

Airborne Estimation of Boreal Forest LAI in Winter Conditions: A Test Using Summer and Winter Ground Truth

A recently developed airborne method for estimation of leaf area index (LAI) in coniferous forests is used for comparing the LAI values in summer and winter conditions. The airborne measurements based on a wide-optic camera are carried out in winter when the forest floor is completely snow covered and thus acts as a light background for the image analysis. The photographs are taken automatically and stored on a laptop during the flights. The R2 value of the linear regression between the airborne and ground-based LAI measurements was 0.97 for all plots. Despite the unfavorable weather conditions, the average difference between the ground-based and airborne regression-based LAI estimates was 0.08, and in 90% of the cases, it was smaller than 0.13. The corresponding relative differences were 14% and 23%. The standard deviation of the ground-based LAI values measured within a plot was, on the average, of the same order. The winter-time values of the LAI of coniferous trees were estimated to be 24% smaller than the preceding summer-time values.