Jussi Rasinmäki

Short communication: Soil carbon model Yasso07 graphical user interface

In this article, we present a graphical user interface software for the litter decomposition and soil carbon model Yasso07 and an overview of the principles and formulae it is based on. The software can be used to test the model and use it in simple applications. Yasso07 is applicable to upland soils of different ecosystems worldwide, because it has been developed using data covering the global climate conditions and representing various ecosystem types. As input information, Yasso07 requires data on litter input to soil, climate conditions, and land-use change if any. The model predictions are given as probability densities representing the uncertainties in the parameter values of the model and those in the input data - the user interface calculates these densities using a built-in Monte Carlo simulation.

Effects of individual tree detection error sources on forest management planning calculations

Abstract: The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees ( e td ), error in tree height prediction ( e h ) and error in tree diameter prediction ( e d ). The effects of errors were analyzed with Monte Carlo simulations. e td was modeled empirically based on a tree’s relative size. A total of five different tree detection scenarios were tested. Effect of e h was investigated using 5% and 0% and effect of e d using 20%, 15%, 10%, 5%, 0% error levels, respectively. The research material comprised 15 forest stands located in Southern Finland. Measurements of 5,300 trees and their timber assortments were utilized as a starting point for the Monte Carlo simulated ITD inventories. ITD carried out for the same study area provided a starting point (Scenario 1) for e td . In Scenario 1, 60.2% from stem number and 75.9% from total volume (V

Modelling spatio-temporal environmental data

Abstract A conceptual model for environmental data is presented with special emphasis on the ability to store spatio-temporal references of the data. Other aspects of the model are the ability to handle hierarchical data and semantics of the measurements. The model was tested with an implementation on an object-relational database management system. As a part of the test implementation, a forestry data set covering 75 years and 4900 hectares was loaded onto the database.

Effect of tree-level airborne laser-scanning measurement accuracy on the timing and expected value of harvest decisions

The objective was to compare tree-level airborne laser-scanning (ALS) data accuracy with standwise estimation data accuracy as input data for forest planning, using tree- and stand-level simulators. The influence of the input data accuracy was studied with respect to (1) timing of the next thinning or clear-cutting and (2) the relative variation in the predicted income of the next logging expressed as the net present value (NPV). The timing and predicted NPV of thinning and clear-cutting operations were considered separately. The research was based on Monte Carlo simulations carried out with the tree- and stand-level simulators using a simulation and optimisation (SIMO) framework. The simulations used treewise measurements taken on 270 circular plots measured at the Evo Field Station, Finland, as input data. Deviations in the tree data measured were generated according to the mean standard errors found in standwise field estimation and tree-level ALS. The accuracy factors of ALS individual tree detection were based on the EUROSDR/ISPRS Tree Extraction Project. The results show that input data accuracy significantly affects both the timing and relative NPV of loggings. Tree-level ALS produces more accurate simulation results than standwise estimation with the error levels assumed. Diameter-based characteristics are the most important input data in all simulations. Accurate tree height estimates cannot be fully utilised in current simulators.

A method for estimating tree composition and volume using harvester data

This research article introduces a method that can be used to estimate tree composition and volume of arbitrary subdivisions of a logged stand. The method uses spatial data that is generated with a harvester to simulate individual tree locations. The simulation uses two probability density functions: the distance and the angle from the harvester at which the tree is cut. The average estimated volume root mean squared error varied from 4% for 0.4 ha subregions to 29% for 0.03 ha subregions. The stand subdivision method affected the accuracy of volume estimation only in the smallest subregions. Compared with the use of harvester data as such, i.e. without tree location simulation, the improvement in total and species-wise volume estimates varied between 5 and 35%. The data produced by the method can be used as a field data source for remote sensing methods as well as a verification data set for field inventories. However, a question remains over the generality of the model parameters used.

From a strategic to a tactical forest management plan using a hierarchic optimization approach

The Finnish state forest enterprise, Metsähallitus, defines the regional harvest levels for a 10-year period in a strategic-level natural resources plan. Although this plan defines stand-level harvest schedules for all stands, in practice, it cannot be used, as the harvests need to be clustered in time and in space. It is applied by giving each subregion goals they need to fulfill in a tactical level planning process, and the harvests are manually clustered into predefined groups of adjacent stands (departments). In this study, we developed a hierarchical optimization process making use of departments for clustering the harvests. For each of the departments, 91 different stand-level harvest schedules (plans) were determined using incomes from one period and the forest value at the end as objectives. The department-level plans were then used as alternatives in a region-level goal optimization problem. The resulting hierarchic plan was compared to the stand-level solution of the strategic-level plan which served as a benchmark plan. The hierarchical plan clustered the harvests and achieved the goals set better than the benchmark plan, but the net present income was 3.3% lower. The approach turned out usable, but further developing of the approach is needed to reduce the costs of clustering.

XQuery as a retrieval mechanism for longitudinal multiscale forest resource data

In most forested environments, boreal forest in particular, monitoring and evaluation of management activities would be facilitated by storage of multiscale data sets spanning long time periods. For this study, such a data repository based on a generic spatio-temporal conceptual data model was created using XML. The data repository containing changes in data content for a 1000ha forest estate between 1871 and 1994 was used to evaluate the data retrieval capabilities of XQuery. The data retrieval operations consisted of pivoting, sequencing, and aggregation. Pivoting transforms the generic attribute storage used in the data model into a more compact column-wise format. Sequencing is used to return a temporal sequence of values, and for the traversal of the object hierarchy. Aggregation aggregates the values of a variable over a grouping variable. The combinations of the basic retrieval operations were also investigated as well as the effect of time and object hierarchy on the retrieval operations. The conceptual match between the generic spatio-temporal data model and the XML data model was apparent at the data retrieval operations. The key properties of XQuery for the implementation of the retrieval operations were extendability, support for nested structures, and data transformation functionality.