Ninni Saarinen

Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries

Abstract: The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality

Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.

Using multi-source data to map and model the predisposition of forests to wind disturbance

Natural disturbances such as wind are known to cause threats to ecosystem services as well as sustainable forest ecosystem management. The objective of this research was to better understand and quantify drivers of predisposition to wind disturbance, and to model and map the probability of wind-induced forest disturbances (PDIS) in order to support forest management planning. To accomplish this, we used open-access airborne light detection and ranging (LiDAR) data as well as multi-source National Forest Inventory (NFI) data to model PDIS in southern Finland. A strong winter storm occurred in the study area in December 2011. High spatial resolution aerial images, acquired after the disturbance event, were used as reference data. Potential drivers associated with PDIS were examined using a multivariate logistic regression model. The model based on LiDAR provided good agreement with detected areas susceptible to wind disturbance (73%); however, when LiDAR was combined with multi-source NFI data, the results were more promising: prediction accuracy increased to 81%. The strongest predictors in the model were mean canopy height, mean elevation, and stem volume of the main tree species (Norway spruce and Scots pine). Our results indicate that open-access LiDAR data can be used to model and map the probability of predisposition to wind disturbance, providing spatially detailed, valuable information for planning and mitigation purposes.

Allocating tree crown pruning with ALS-data - A case study in the city of Helsinki

Trees are an essential part of urban environments. In urban areas trees are located for example among buildings, on roadsides and in parks. When growing outside of their typical natural forest habitat, the trees need to be actively managed to ensure their well-being and the safety of the citizens. One example of urban tree management is the need to make sure that the street areas are sufficiently lit. It means that trees growing very close to street-lamps need to be pruned occasionally. In this case study the need of pruning the urban trees in the city of Helsinki was studied by means of airborne laser-scanning (ALS) and GIS-analysis. An ALS-based tree register containing the main attributes and the spatial location of the trees was compared in the analysis to a lamppost register. The lamppost register included the types and the xy-locations of the lampposts. The aim of the analysis was to map the trees and more specifically tree crowns that had an effect on the lampposts and thus needed to be pruned. First, tree crowns were delineated from the ALS-data. A GIS-analysis was used to map the trees that had a crown segment influencing to a lamppost and then the tree was classified to be pruned. The classification was then compared to a field data collected from the same areas to validate the results. Based on the results, it seems it is possible to use ALS-data and GIS in planning of the tree management in urban areas.

Aboveground forest biomass derived using multiple dates of WorldView-2 stereo-imagery: quantifying the improvement in estimation accuracy

ABSTRACT The aim of this study was to investigate the capabilities of two date satellite-derived image-based point clouds (IPCs) to estimate forest aboveground biomass (AGB). The data sets used include panchromatic WorldView-2 stereo-imagery with 0.46 m spatial resolution representing 2014 and 2016 and a detailed digital elevation model derived from airborne laser scanning data. Altogether, 332 field sample plots with an area of 256 m2 were used for model development and validation. Predictors describing forest height, density, and variation in height were extracted from the IPC 2014 and 2016 and used in k-nearest neighbour imputation models developed with sample plot data for predicting AGB. AGB predictions for 2014 (AGB2014) were projected to 2016 using growth models (AGBProjected_2016) and combined with the AGB estimates derived from the 2016 data (AGB2016). AGB prediction model developed with 2014 data was also applied to 2016 data (AGB2016_pred2014). Based on our results, the change in the 90th percentile of height derived from the WorldView-2 IPC was able to characterize forest height growth between 2014 and 2016 with an average growth of 0.9 m. Features describing canopy cover and variation in height derived from the IPC were not as consistent. The AGB2016 had a bias of −7.5% (−10.6 Mg ha−1) and root mean square error (RMSE) of 26.0% (36.7 Mg ha−1) as the respective values for AGBProjected_2016 were 7.0% (9.9 Mg ha−1) and 21.5% (30.8 Mg ha−1). AGB2016_pred2014 had a bias of −19.6% (−27.7 Mg ha−1) and RMSE of 33.2% (46.9 Mg ha−1). By combining predictions of AGB2016 and AGBProjected_2016 at sample plot level as a weighted average, we were able to decrease the bias notably compared to estimates made on any single date. The lowest bias of −0.25% (−0.4 Mg ha−1) was obtained when equal weights of 0.5 were given to the AGBProjected_2016 and AGB2016 estimates. Respectively, RMSE of 20.9% (29.5 Mg ha−1) was obtained using equal weights. Thus, we conclude that combination of two date WorldView-2 stereo-imagery improved the reliability of AGB estimates on sample plots where forest growth was the only change between the two dates.

Accuracy of High-Altitude Photogrammetric Point Clouds in Mapping

During the past decade, airborne laser scanning (ALS) has established its status as the state-of-the-art method for detailed forest mapping and monitoring. Current operational forest inventory widely utilizes ALS-based methods. Recent advances in sensor technology and image processing have enabled the extraction of dense point clouds from digital stereo imagery (DSI). Compared with ALS data, the DSI-based data are cheap and the point cloud densities can easily reach that of ALS. In terms of point density, even the high-altitude DSI-based point clouds can be sufficient for detecting individual tree crowns. However, there are significant differences in the characteristics of ALS and DSI point clouds that likely affect the accuracy of tree detection. In this study, the performance of high-altitude DSI point clouds was compared with low-density ALS in detecting individual trees. The trees were extracted from DSI- and ALS-based canopy height models (CHM) using watershed segmentation. The use of both smoothed and unsmoothed CHMs was tested. The results show that, even though the spatial resolution of the DSI-based CHM was better, in terms of detecting the trees and the accuracy of height estimates, the low-density ALS performed better. However, utilizing DSI with shorter ground sample distance (GSD) and more suitable image matching algorithms would likely enhance the accuracy of DSI-based approach.

Errors in the Short-Term Forest Resource Information Update

Currently the forest sector in Finland is looking towards the next generation’s forest resource information systems. Information used in forest planning is currently collected by using an area-based approach (ABA) where airborne laser scanning (ALS) data are used to generalize field-measured inventory attributes over an entire inventory area. Inventories are typically updated at 10-year interval. Thus, one of the key challenges is the age of the inventory information and the cost-benefit trade-off between using the old data and obtaining new data. Prediction of future forest resource information is possible through growth modelling. In this paper, the error sources related to ALS-based forest inventory and the growth models applied in forest planning to update the forest resource information were examined. The error sources included (i) forest inventory, (ii) generation of theoretical stem distribution, and (iii) growth modelling. Error sources (ii) and (iii) stem from the calculations used for forest planning, and were combined in the investigations. Our research area, Evo, is located in southern Finland. In all, 34 forest sample plots (300 m2) have been measured twice tree-by-tree. First measurements have been carried out in 2007 and the second measurements in 2014 which leads to 7 year updating period. Respectively, ALS-based forest inventory data were available for 2007. The results showed that prediction of theoretical stem distribution and forest growth modelling affected only slightly to the quality of the predicted stem volume in short-term information update when compared to forest inventory error.

Deriving canopy metrics of urban trees from airborne laser scanning data

This study describes an automatic method for assessing various crown metrics for urban trees. We used high resolution (>20 points/m2) airborne laser scanning (ALS) data to derive four key characteristics for roadside trees at individual tree level. The tree level ALS point clouds were filtered with alpha shapes to exclude non-tree objects and measurements were taken directly from the filtered point clouds. The root mean square error (RMSE) of crown length and width, crown base height, and crown volume were 1.04 m, 0.68 m, 0.57 m, and 74.65 m3 respectively. The introduced method may be utilized in urban biomass estimations as well as monitoring the state and wellbeing of individual urban trees.