Ole Martin Bollandsås

Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest

In this study, we present a new method for single tree segmentation and characterization from a canopy surface model (CSM), and its corresponding point cloud, based on airborne laser scanning. The method comprises new algorithms for controlling the shape of crown segments, and for residual adjustment of the canopy surface model (CSM). We present a new criterion that measures the success of locating trees, and demonstrate how this criterion can be used for optimizing the degree of CSM smoothing. From the adjusted CSM segments, we derived tree height and crown diameter, and based on all first laser pulse measurements within the segments we derived crown-base height. The method was applied and validated in a Norway spruce dominated forest reserve having a heterogeneous structure. The number of trees automatically detected varied with social status of the trees, from 93 percent of the dominant trees to 19 percent of the suppressed trees. The RMSE values for tree height, crown diameter, and crown-base height were around 1.2 m, 1.1 m, and 3.5 m, respectively. The method overestimated crown diameter (0.8 m) and crown base height (3.0 m).

Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data

Abstract A model for prediction of stand basal area and diameters at 10 percentiles of a basal area distribution was estimated from small-footprint laser scanner data from primeval conifer forest using partial least squares regression. The regression explained 44–80% and 67% of the variability of the 10 percentiles and stand basal area, respectively. The predicted percentiles, scaled by the predicted stand basal area, were used to compute diameter distributions. A cross-validation showed that the mean differences between the predicted and observed number of stems by diameter class were non-significant (p>0.05) for 22 of 29 diameter classes. Moreover, plot volume was calculated from the predicted diameter distribution and cross-validation revealed a non-significant deviation between predicted and observed volume of −3.3% (of observed volume). An independent validation showed non-significant mean differences for 20 of 21 diameter classes for data corresponding to the model calibration data. Plot volumes calculated from the predicted diameter distributions deviated from observed volume by −4.4%. The model reproduced diameter distributions corresponding to the model calibration data (uneven-sized forest) well. However, the model is not flexible enough to reproduce normal and uniform diameter distributions. Volume estimates derived from predicted diameter distributions were generally well determined, irrespective of the observed distribution.

Non-parametric prediction of diameter distributions using airborne laser scanner data

Abstract The aim of this study was to apply the non-parametric k-most similar neighbour (MSN) method and airborne laser scanner data to predict stand diameter distributions in a 960 km2 forest district in south-eastern Norway. The specific objectives of the study were (1) to examine the use of different dependent and independent variables in the canonical correlation analysis of MSN, and (2) to examine the influence of reduced number of training data plots by means of simulations. The reliability of the constructed diameter distributions was analysed using error indices and the accuracy of stand attributes derived from predicted diameter distributions. The study material included a total of 201 plots and they were reduced to 181, 161, … , 41 plots in the simulations. The results indicated that when selecting dependent variables in the canonical correlation analysis it is sufficient to have variables reflecting stand means and aggregated variables (sums) to obtain accurate predictions of diameter distributions. Furthermore, the prediction models should not to be too detailed, i.e. they should not include a great number of independent variables since cross-validation always tends to give too optimistic results. Validation on independent data will often show considerably poorer reliability figures. Finally, the results indicated that even such a low number of training plots as about 100 can produce accurate enough predictions of stand attributes and diameter distributions.

Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.

Deriving forest monitoring variables from X-band InSAR SRTM height.

The suitability of interferometric X-band radar for forest monitoring was investigated. Working in a spruce-dominated forest in southeast Norway, top height, mean height, stand density, stem volume, and biomass were related to space shuttle interferometric height above ground. A ground truth dataset was produced for each radar data pixel in the study area by combining a field inventory and automatic tree detection with airborne laser scanning data. Pixels were aggregated to forest stands. Interferometric height was strongly related to all of the five forest variables, and most strongly to top height with R2 = 0.71 and RMSE = 13% at the pixel level and R2 = 0.82 and RMSE = 5.6% at the stand level. Interferometric height was linearly related to stem volume and biomass up to 400 m3/ha and 200 t/ha, respectively, and RMSE was approximately 19% for both variables. These errors contain error components caused by the 3.5-year time lag between the radar acquisition and the laser scanning. It is concluded that interferometric X-band radar has potential for use in forest monitoring.

Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data

Successful implementation of projects under the REDD+ mechanism, securing payment for storing forest carbon as an ecosystem service, requires quantification of biomass. Airborne laser scanning (ALS) is a relevant technology to enhance estimates of biomass in tropical forests. We present the analysis and results of modeling aboveground biomass (AGB) in a Tanzanian rainforest utilizing data from a small-footprint ALS system and 153 field plots with an area of 0.06–0.12 ha located on a systematic grid. The study area is dominated by steep terrain, a heterogeneous forest structure and large variation in AGB densities with values ranging from 43 to 1147 Mg·ha−1, which goes beyond the range that has been reported in existing literature on biomass modeling with ALS data in the tropics. Root mean square errors from a 10-fold cross-validation of estimated values were about 33% of a mean value of 462 Mg·ha−1. Texture variables derived from a canopy surface model did not result in improved models. Analyses showed that (1) variables derived from echoes in the lower parts of the canopy and (2) canopy density variables explained more of the AGB density than variables representing the height of the canopy.

Predicting the growth of stands of trees of mixed species and size: A matrix model for Norway

Abstract The objective of this study was to predict the growth of forest stands of mixed tree species and size with natural recruitment. The stand state was defined by the number of spruce, pine, birch and other broadleaved trees by hectare in 15 diameter classes from 50 to 750 mm. The change in stand state over 5 years was predicted with state-dependent matrices based on equations for recruitment, growth and mortality. The data came from 7241 plots of the National Forest Inventory of Norway, measured from 1994 to 2005. A short-term validation was carried out by comparing predicted and actual growth over 10 years on 416 plots not used in model estimation. The model was also used to predict the long-term growth of stands with different initial species composition and diameter distribution. Irrespective of the initial condition the same steady state resulted, with characteristics similar to those observed in stands that had been undisturbed for 75 years.

Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data

Different approaches for estimation of change in biomass between two points in time by means of airborne laser scanner data were tested. Both field and laser data were collected at two occasions on 52 sample plots in a mountain forest in southeastern Norway. In the first approach, biomass change was estimated as the difference between predicted biomass for the two measurement occasions. Joint models for the biomass at both occasions were fitted using different height and density variables from laser data as explanatory variables. The second approach modelled the observed change directly using the change in different variables extracted from the laser data as explanatory variables. In the third approach we modelled the relative change in biomass. The explanatory variables were also expressed as relative change between measurement occasions. In all approaches we allowed spline terms to be entered. We also investigated the aptness of models for which the residual variance was modeled by allowing it to be proportional to the area of the plot on which biomass was assessed. All alternative models were initially assessed by AIC. All models were also evaluated by estimating biomass change on the model development data. This evaluation indicated that the two direct approaches (approach 2 and 3) were better than relying on modeling biomass at both occasions and taking change as the difference between biomass estimates. Approach 2 seemed to be slightly better than approach 3 based on assessments of bias in the evaluation.

Forest biomass change estimated from height change in interferometric SAR height models.

BackgroundThere is a need for new satellite remote sensing methods for monitoring tropical forest carbon stocks. Advanced RADAR instruments on board satellites can contribute with novel methods. RADARs can see through clouds, and furthermore, by applying stereo RADAR imaging we can measure forest height and its changes. Such height changes are related to carbon stock changes in the biomass. We here apply data from the current Tandem-X satellite mission, where two RADAR equipped satellites go in close formation providing stereo imaging. We combine that with similar data acquired with one of the space shuttles in the year 2000, i.e. the so-called SRTM mission. We derive height information from a RADAR image pair using a method called interferometry.ResultsWe demonstrate an approach for REDD based on interferometry data from a boreal forest in Norway. We fitted a model to the data where above-ground biomass in the forest increases with 15 t/ha for every m increase of the height of the RADAR echo. When the RADAR echo is at the ground the estimated biomass is zero, and when it is 20 m above the ground the estimated above-ground biomass is 300 t/ha. Using this model we obtained fairly accurate estimates of biomass changes from 2000 to 2011. For 200 m2 plots we obtained an accuracy of 65 t/ha, which corresponds to 50% of the mean above-ground biomass value. We also demonstrate that this method can be applied without having accurate terrain heights and without having former in-situ biomass data, both of which are generally lacking in tropical countries. The gain in accuracy was marginal when we included such data in the estimation. Finally, we demonstrate that logging and other biomass changes can be accurately mapped. A biomass change map based on interferometry corresponded well to a very accurate map derived from repeated scanning with airborne laser.ConclusionsSatellite based, stereo imaging with advanced RADAR instruments appears to be a promising method for REDD. Interferometric processing of the RADAR data provides maps of forest height changes from which we can estimate temporal changes in biomass and carbon.

Relationships between diameter and height of trees in natural tropical forest in Tanzania

The relationship between tree height (h) and tree diameter at breast height (dbh) is an important element describing forest stands. In addition, h often is a required variable in volume and biomass models. Measurements of h are, however, more time consuming compared to those of dbh, and visual obstructions, rounded crown forms, leaning trees and terrain slopes represent additional error sources for h measurements. The aim of this study was therefore to develop h–dbh relationship models for natural tropical forest in Tanzania. Both general forest type specific models and models for tree species groups were developed. A comprehensive data set with 2 623 trees from 410 different tree species collected from a total of 1 191 plots and 38 sites covering the four main forest types of miombo woodland, acacia savanna, montane forest and lowland forests was applied. Tree species groups were constructed by using a k-means clustering procedure based on the h–dbh allometry, and a number of different non-linear model forms were tested. When considering the complexity of natural tropical forests in general and in particular variations of h–dbh relationships due to high species diversity in such forests, the model fit and performance were considered to be appropriate. Results also indicate that tree species group models perform better than forest type models. Despite the fact that the residual errors level associated with the models were relatively high, the models are still considered to be applicable for large parts of Tanzanian forests with an appropriate level of reliability.