Jussi Peuhkurinen

Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands

Airborne laser scanning based forest inventories employ two major methods: individual tree detection (ITD) and the area-based statistical approach (ABSA). ITD is based on the assumption that trees are of a certain form and can be delineated using airborne laser scanning techniques, whereas ABSA is an empirical method based on the relations between area-level forest attributes and laser echo height distributions. These two methods are compared here within the same test area in terms of their usefulness for estimating mean forest stand characteristics and tree size distributions. All evaluations were performed using leave-one-out cross validation. The average errors in volume and basal area did not differ significantly between the methods. ABSA resulted in overall better accuracies when estimating the diameter and height of the basal area median tree and the number of stems, whereas ITD produced significantly biased estimates for the number of stems and the mean tree size. Tree size distributions were estim...

Predicting the spatial pattern of trees by airborne laser scanning

The spatial pattern of trees can be defined as a property of their location in relation to each other. In this study, the spatial pattern was summarized into three categories, regular, random, and clustered, using Ripleys L-function. The study was carried out at 79 sample plots located in a managed forest in Finland. The goal was to study how well the spatial pattern of trees can be predicted by airborne laser scanning (ALS) data. ALS-derived predictions were based upon individual tree detection (ITD), semi-individual tree detection (semi-ITD), and plot-level metrics calculated from the canopy height model, AREA. The kappa value for ITD was almost zero, which indicates no agreement. The semi-ITD and AREA methods performed better, although kappa values were only 0.34 and 0.24, respectively. It appears difficult to detect a particularly clustered spatial pattern.

Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery

Consistent, detailed and up-to-date forest resource information is required for allocation of forestry activities and national and international reporting obligations. We evaluated the forest stand attribute prediction accuracy when radargrammetry was used to derive height information from TerraSAR-X stereo imagery. Radargrammetric elevations were normalized to heights above ground using an airborne laser scanning (ALS)-derived digital terrain model (DTM). Derived height metrics were used as predictors in the most similar neighbor (MSN) estimation approach. In total, 207 field measured plots were used in MSN estimation, and the obtained results were validated using 94 stands with an average area of 4.1 ha. The relative root mean square errors for Loreys height, basal area, stem volume, and above-ground biomass were 6.7% (1.1 m), 12.0% (2.9 m 2 /ha), 16.3% (31.1 m 3 /ha), and 16.1% (15.6 t/ha). Although the prediction accuracies were promising, it should be noted that the predictions included bias. The respective biases were −4.6% (−0.7 m), −6.4% (−1.6 m 2 /ha), −9.3% (−17.8 m 3 /ha), and −9.5% (−9.1 t/ha). With detailed DTM, TerraSAR-X stereo radargrammetry-derived forest information

Airborne Laser Scanning for the Site Type Identification of Mature Boreal Forest Stands

In Finland, forest site types are used to assess the need of silvicultural operations and the growth potential of the forests and, therefore, provide important inventory information. This study introduces airborne laser scanner (ALS) data and the k-NN classifier data analysis technique applicable to the site quality assessment of mature forests. Both the echo height and the intensity value percentiles of different echo types of ALS data were used in the analysis. The data are of 274 mature forest stands of different sizes, belonging to five forest site types, varying from very fertile to poor forests, in Koli National Park, eastern Finland. The k-NN classifier was applied with values of k varying from 1 to 5. The best overall classification accuracy achieved for all the forest site types and for a single type, were 58% and 73%, respectively. The conclusion is that when conducting large-scale forest inventories ALS-data based analysis would be a useful technology for the identification of mature boreal site types. However, the technique could still be improved and further studies are needed to ensure its applicability under different local conditions and with data representing earlier stages of stand development.

Estimation of Forest Stand Characteristics Using Spectral Histograms Derived from an Ikonos Satellite Image

The aim of this paper was to examine the potential of Ikonos satellite images for estimating boreal forest stand characteristics using frequency distributions of radiometric values. The spectral features selected for use in the estimation were medians, standard deviations, and the parameters of the two-parametric Weibull distribution derived from the standwise spectral histograms of the Ikonos image. Ancillary map information, such as land-use and peatland classes, was also included. The method of estimation was non-parametric k-most similar neighbors (K-MSN) method. The most accurate results were achieved using spectral features that were derived from the multispectral images. The lowest RMSES for the mean total stem volume, basal area, and mean height were 52.2 m 3 /ha (31.3 percent), 5.6 m 2 /ha (25.3 percent), and 3.1 m (20.6 percent), respectively. When only the panchromatic image was used in the analysis, the RMSEs for the mean total stem volume and basal area were about 3 percentage points higher. No differences in the mean height estimates were observed between the multispectral and panchromatic images. The most efficient predictor variables were the medians and the scale parameters of the Weibull distribution. The use of classified map information did not improve the results. The findings suggest that Ikonos satellite images can be used in to estimate forest stand characteristics giving an accuracy that corresponds to that achieved with aerial photographs.

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

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

Robustness of model-based high-resolution prediction of forest biomass against different field plot designs

BackgroundParticipatory forest monitoring has been promoted as a means to engage local forest-dependent communities in concrete climate mitigation activities as it brings a sense of ownership to the communities and hence increases the likelihood of success of forest preservation measures. However, sceptics of this approach argue that local community forest members will not easily attain the level of technical proficiency that accurate monitoring needs. Thus it is interesting to establish if local communities can attain such a level of technical proficiency. This paper addresses this issue by assessing the robustness of biomass estimation models based on air-borne laser data using models calibrated with two different field sample designs namely, field data gathered by professional forester teams and field data collected by local communities trained by professional foresters in two study sites in Nepal. The aim is to find if the two field sample data sets can give similar results (LiDAR models) and whether the data can be combined and used together in estimating biomass.Results Results show that even though the sampling designs and principles of both field campaigns were different, they produced equivalent regression models based on LiDAR data. This was successful in one of the sites (Gorkha). At the other site (Chitwan), however, major discrepancies remained in model-based estimates that used different field sample data sets. This discrepancy can be attributed to the complex terrain and dense forest in the site which makes it difficult to obtain an accurate digital elevation model (DTM) from LiDAR data, and neither set of data produced satisfactory results.Conclusions Field sample data produced by professional foresters and field sample data produced by professionally trained communities can be used together without affecting prediction performance provided that the correlation between LiDAR predictors and biomass estimates is good enough.