Arbonaut (Finland)

ABSTRACT The aim of this study is to evaluate the suitability of very high-resolution satellite stereo-imagery data for creating forest fire-related fuel load maps in the boreal region. We acquired stereo imagery from the GeoEye-1 (GE-1) satellite, which has a ground sampling distance of 50 cm. The images were acquired in August 2021 and 2023 (hence leaf-on). Our study area was centred around the Hiidenportti national park in central Finland, dominated by natural boreal forests. The ground reference was a field dataset consisting of measurements from 33 forested plots, each of 15 m radius. The dominant height (m), foliage biomass (t ha-1) and canopy base height (m) were predicted using multivariate linear regression models, while the understory presence (categorical; present/absent) was predicted using logistic regression analysis. Prediction models using area-based metrics based on airborne laser scanning (ALS) data had the smallest associated root mean square error (RMSE) (between 2.6% and 23.9%). Meanwhile, similar type of area-based metrics of stereo satellite data combined with an ALS-based digital terrain model (DTM) resulted in RMSEs of 6.6–30.3%. We also formulated models suitable for the case when only satellite data is available (i.e. high-quality DTM is absent), such as in remote locations of the boreal forest region. In this case, the models involved several canopy texture metrics and point cloud height and colour intensity-based metrics as predictors. The associated relative RMSEs were in the range of 11–30%. Dominant height, an important global vegetation metric, was predicted with an RMSE of 2.6 m, which compares well with other model predictions under similar circumstances. Our findings suggests that very high-resolution stereo satellite image data is promising for the generation and updating of wall-to-wall boreal forest fuel load maps, including remote areas lacking high resolution DTM data.

The methodology presented here can assist in making timber markets more efficient when assessing the value of harvestable timber stands and the amounts of timber assortments during the planning of harvesting operations. Information on wood quality and timber assortments is essential for wood valuation and procurement planning as varying wood dimensions and qualities may be utilized and refined in different places, including sawmills, plywood mills, pulp mills, heating plants or combined heat and power plants. We investigate here alternative approaches for generating detailed timber assortments for Norway spruce (Picea abies (L.) H.Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.) from airborne laser scanning (ALS) data, aerial images, harvester data and field data. For this purpose, we used 665 circular plots, and logging recovery information recorded from 249 clear-cut stands using cut-to-length harvesters. We estimated timber assortment volumes, economic values and wood paying capabilities (WPC) for each stand in different bucking scenarios, and used the resulting timber assortment estimates to assess logging recoveries. The bucking scenarios were (1) bucking-to-value using maximum sawlog and pulpwood volumes excluding quality (theoretical maximum), and (2) bucking-to-value using sawlog lengths at 30 cm intervals for Norway spruce and Scots pine and veneer logs of lengths 4.7 m, 5.0 m, 6.0 m and 6.7 m for birch, either excluding quality (the usual business practice) or including quality (a novel business practice). The results showed that our procedure can assist in locating stands that are likely to be more valuable and have the desired timber assortment distributions. We conclude that the method can estimate WPC with root mean square errors of 28.7%, 66.0% and 45.7% in Norway spruce, Scots pine and birch, respectively, for sawlogs and 19.3%, 63.7% and 29.5% for pulpwood.

The ultimate goal of the research is to develop an alternative system for tracking non-cooperative aircraft based on the VHF Doppler phenomenon by exploiting transmitters of opportunity and radio receivers. Doppler-only information is used to track aircraft, which as a configuration is a challenging way of tracking aircraft. A particularly challenging sub-task is track initialization. The current paper presents an algorithm for that, based on a look-up table. After the initialization, tracking is done with the Extended Kalman filter from the Doppler-only measurements. The validity of the tracks is verified with reference data based on Automatic Dependent Surveillance - Broadcast (ADS-B) information.

Information on forest trafficability (i.e. carrying capacity of the forest floor) is required before harvesting operations in Southern Boreal forest conditions. It describes the seasons when harvesting operations may take place without causing substantial damage to the forest soil using standard logging machinery. The available trafficability information have been based on subjective observations made during the wood procurement planning. For supporting forest operations, an open access map product has been developed to provide information on trafficability of forests. The forest stands are distributed into classes that characterize different harvesting seasons based on topographic wetness index, amount of vegetation, ground water height and ditch depth. The main goal of this case study was to evaluate the information of the static forest trafficability map in relation to the detected rutting within logging tracks measured in the field. The analysis concentrated on thinning stands since the effect of rutting is significant on the growth of the remaining trees. The results showed that the static trafficability map provided reliable and slightly conservative estimation of the forest trafficability. The majority (91.7%) of the evaluated stands were harvested without causing significant damage if harvesting was timed correctly compared to the trafficability information. However, it should be pointed out that the weather history at small scale, the skills of a driver, and effects of used machinery are not considered in the map product although they can have a considerable impact on the rutting.

Biomass assessment of the Mau Forest Ecosystem (MFE) was done as part of Kenya’s greenhouse gas inventory. Trans Mara and Mount Londiani forest blocks representing extremes of vegetation types in the MFE were selected for ground data. Based on canopy closure, four forest strata were identified as very dense, moderately dense, open and bamboo. In each stratum, 5 clusters each with 4 plots measuring 30 m × 30 m were located. Big trees (D1.3 ≥ 10 cm) were measured per species for diameter at breast height (D1.3) in the whole plot while height was measured for every 5th tree. Poles (10 cm > D1.3 ≤ 5) were measured for D1.3 in a 10 × 10 m concentric sub plot. Saplings (5 cm > D1.3; ht ≥ 1.5 m) and seedlings (ht × 5 m and 2 × 2 m concentric sub plots, respectively. Data were recorded in a Personal Digital Assistant (PDA) and quality checked with Open Foris Collect software. Allometric equations that have been used for similar vegetation in Kenya were used to relate D1.3 and height with biomass. The tree data were uploaded to ArboWebForest (AWF) cloud-service and using the AWF-SIMO calculation tool, average values of diameter, height, and biomass were calculated for each plot. The data were generalised to cover all areas for each block using the Sparse Bayesian linear regression process on the vegetation characteristics with 10 m resolution ALOS-AVNIR-2 images of the MFE. ANOVA was used to compare biomass generated from several allometric equations. Results show that the average biomass of the MFE was 236 Mg·ha1. Degradation that converts dense forests into open and moderately dense forests contributed to a biomass loss of 228 Mg·ha1 and 194 Mg·ha1 respectively. Four allometric equations gave no significant difference (P

Abstract. Accurate and reliable estimation of biomass in tropical forest has been a challenging task because a large proportion of forests are difficult to access or inaccessible. So, for effective implementation of REDD+ and fair benefit sharing, the proper designing of field plot sampling schemes plays a significant role in achieving robust biomass estimation. The existing forest inventory protocols using various field plot sampling schemes, including FAO's regular grid concept of sampling for land cover inventory at national level, are time and human resource intensive. Wall to wall LiDAR scanning is, however, a better approach to assess biomass with high precision and spatial resolution even though this approach suffers from high costs. Considering the above, in this study a sampling design based on a LiDAR strips sampling scheme has been devised for Ghanaian forests to support field plot sampling. Using Top-of-Atmosphere (TOA) reflectance value of satellite data, Land Use classification was carried out in accordance with IPCC definitions and the resulting classes were further stratified, incorporating existing GIS data of ecological zones in the study area. Employing this result, LiDAR sampling strips were allocated using systematic sampling techniques. The resulting LiDAR strips represented all forest categories, as well as other Land Use classes, with their distribution adequately representing the areal share of each category. In this way, out of at total area of 15,153km2 of the study area, LiDAR scanning was required for only 770 km2 (sampling intensity being 5.1%). We conclude that this systematic LiDAR sampling design is likely to adequately cover variation in above-ground biomass densities and serve as sufficient a-priori data, together with the Land Use classification produced, for designing efficient field plot sampling over the seven ecological zones.