Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.

The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency.

Sirro L., Häme L., Pitkänen T.P., Hostikka A., Törmä M., Häme T., Kangas A., Rauste Y., Huotari R., Norppa J. (2019). Metsävaratiedon ajantasaistus – satelliittikuviin perustuva muutostulkinta. Metsätieteen aikakauskirja 2019-10260. Tieteen tori: Metsätieto ja sähköiset palvelut. 6 s. https:// doi.org/10.14214/ma.10260 Yhteystiedot 1Teknologian tutkimuskeskus VTT Oy, Espoo; 2 Satellio Oy (Terramonitor), Helsinki; 3 Luonnonvarakeskus, Biotalous ja ympäristö, Helsinki; 4 Suomen metsäkeskus, Tarkastukset ja valvonta, Lahti; 5 Suomen ympäristökeskus, Geoinformatiikkatutkimus, Helsinki; 6 Luonnonvarakeskus, Biotalous ja ympäristö, Joensuu Sähköposti laura.sirro@vtt.fi Hyväksytty 30.10.2019

The dial-a-ride problem DARP involves the dispatching of a fleet of vehicles to transport customers requesting service and is one of the most challenging tasks of combinatorial optimization. We study the DARP as a constraint satisfaction problem, where the goal is to find a feasible solution with respect to the time, capacity, and precedence constraints, or to prove infeasibility. The main contribution of our work is a new robust method for this problem formulation. The algorithm is based on a dynamic subroutine that finds for any set of customers a maximum cluster, that is, a maximal set of customers that can be served by a single vehicle. The performance of the algorithm is analyzed and evaluated by means of computational experiments, justifying the efficiency of the solution method.

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Lauri Hame Name of the doctoral dissertation Demand-Responsive Transport: Models and Algorithms Publisher School of Science Unit Department of Mathematics and Systems Analysis Series Aalto University publication series DOCTORAL DISSERTATIONS 80/2013 Field of research Mathematics Manuscript submitted 18 January 2013 Date of the defence 31 May 2013 Permission to publish granted (date) 16 April 2013 Language English Monograph Article dissertation (summary + original articles) Abstract Demand-responsive transport is a form of public transport between bus and taxi services, involving flexible routing of small or medium sized vehicles. This dissertation presents mathematical models for demand-responsive transport and methods that can be used to solve combinatorial problems related to vehicle routing and journey planning in a transport network.Demand-responsive transport is a form of public transport between bus and taxi services, involving flexible routing of small or medium sized vehicles. This dissertation presents mathematical models for demand-responsive transport and methods that can be used to solve combinatorial problems related to vehicle routing and journey planning in a transport network. Public transport can be viewed as a market where demand affects supply and vice versa. In the first part of the dissertation related to vehicle routing, we show how a given demand for transportation can be satisfied by using a fleet of vehicles, assuming that the demand is known at the individual level. In the second part, by considering the journey planning problem faced by commuters, we study how the demand adapts to the supply of transport services, assuming that the supply remains unchanged for a short period of time. We also present a stochastic network model for determining the economic equilibrium, that is, the point at which the demand meets the supply, by assuming that commuters attempt to minimize travel time and transport operators aim to maximize profit. The mathematical models proposed in this work can be used to simulate the operations of public transport services in a wide range of scenarios, from paratransit services for the elderly and disabled to large-scale demand-responsive transport services designed to compete with private car traffic. Such calculations can provide valuable information to public authorities and planners of transportation services, regarding, for example, regulation and investments. In addition to public transport, potential applications of the proposed methods for solving vehicle routing and journey planning problems include freight transportation, courier and food delivery services, military logistics and air traffic.

The dial-a-ride problem involves the dispatching of a fleet of vehicles in order to transport a set of customers from specific pick-up nodes to specific drop-off nodes. Using a modified version of hyperlink-induced topic search (HITS), we characterize hubs as nodes with many out-links to other hubs and calculate a hub score for each pick-up and drop-off node. Ranking the nodes by hub score gives guidance to a backtracking algorithm for efficiently finding feasible solutions to the dial-a-ride problem.

We study a dynamic-journey planning problem for multimodal transportation networks. The goal is to find a journey, possibly involving transfers between different transport modes, from a given origin to a given destination within a specified time horizon. Transport services are represented as sequences of scheduled legs between nodes in the transportation network. Due to uncertainty in transport services, we assume for each pair of adjacent legs i and j a probability of a successful transfer from i to j . If a transfer between two legs is unsuccessful, the customer needs to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process, and the main contribution is a backward induction algorithm that generates an optimal policy for traversing the public transport network in terms of a given objective, e.g., reliability, ride time, waiting time, walking time, or the number of transfers. A straightforward method for maximizing reliability is also suggested, and the algorithms are tested on real-life Helsinki area public transport data. Computational examples show that, with a given input, the proposed algorithms rapidly solve the journeying problem.

We introduce a journey planning problem in multi-modal transportation networks under uncertainty. The goal is to find a journey, possibly involving transfers between different transport services, from a given origin to a given destination within a specified time horizon. Due to uncertainty in travel times, the arrival times of transport services at public transport stops are modeled as random variables. If a transfer between two services is rendered unsuccessful, the commuter has to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process in which states are defined as paths in the transport network. The main contribution is a backward induction method that generates an optimal policy for traversing the public transport network in terms of maximizing the probability of reaching the destination in time. By assuming history independence and independence of successful transfers between services we obtain approximate methods for the same problem. Analysis and numerical experiments suggest that while solving the path dependent model requires the enumeration of all paths from the origin to the destination, the proposed approximations may be useful for practical purposes due to their computational simplicity. In addition to on-time arrival probability, we show how travel and overdue costs can be taken into account, making the model applicable to freight transportation problems.

We introduce a novel dierential approach to the Traveling Salesman Problem with Disk Neighborhoods (TSPDN), in which each node may be relocated within radius r from the original location in order to decrease the length of the shortest tour visiting all nodes. When r is small compared to the distance between the nodes, the optimal solution to the TSPDN is achieved by shortening the cycle corresponding to the optimal TSP tour without reordering the nodes. Looking at the shortening rate of a cycle, defined as the ratio of the decrease in tour length to r when r tends to zero, gives us an insight on how the movement of nodes can be converted into savings in tour length. We study the optimal direction for shortening and show how the shortening rate relates to the tightness of turns, number of U-turns and the distance from the origin in Euclidean, Manhattan and hyperbolic metrics, respectively.

The dial-a-ride problem (DARP) is a widely studied theoretical challenge related to dispatching vehicles in demand-responsive transport services, in which customers contact a vehicle operator requesting to be carried from specified origins to specified destinations. An important subproblem arising in dynamic dial-a-ride services can be identified as the single-vehicle DARP, in which the goal is to determine the optimal route for a single vehicle with respect to a generalized objective function. The main result of this work is an adaptive insertion algorithm capable of producing optimal solutions for a time constrained version of this problem, which was first studied by Psaraftis in the early 1980s. The complexity of the algorithm is analyzed and evaluated by means of computational experiments, implying that a significant advantage of the proposed method can be identified as the possibility of controlling computational work smoothly, making the algorithm applicable to any problem size.

We study a variant of dynamic vehicle routing problem with pickups and deliveries where a vehicle is allocated to each service (i.e., trip) request immediately upon the arrival of the request. Solutions to this problem can be characterized as dynamic policies that define how each customer is handled by operating a fleet of vehicles. Evaluation of such policies is beyond the grasp of analytical studies and requires extensive simulations. We present an efficient and modular simulation tool developed for studying the performance of a large scale system with different policies under given trip arrival process. Numerical and analytical observations on the model are utilized to provide guidelines for solving the routing problem efficiently, and to support the validation of the simulation results. Application of the developed framework is demonstrated by several numerical examples, e.g., policy parameter optimization, which all give insight on the viability of this type of transportation system.

The aim of transport services is to find a good balance between efficiency and quality of service. Passenger-pooling and trip-combining increase efficiency but in most cases require passengers to compromise on the quality. In this paper, we analyze the passenger-pooling potential (PPP) and trip-combining potential (TCP) in Demand Responsive Transport (DRT) in high demand density situations. The studied DRT system operates on a real-time basis without pre-order times. Simulation study is used to analyze the effect of demand density on PPP and TCP, without relying on any particular control and routing algorithms. We also examine the effect of spatial and temporal dimensions of a DRT system to the potential of pooling and combining. The results indicate that both PPP and TCP have a strong positive correlation with demand density. With low demand densities no distinct potential for trip-combining and large-scale passenger-pooling is possible except with large walking distances. Increasing the demand density increases the possibilities of producing more efficient DRT services with better quality of service. The presented results argue for further research of DRT in high demand density situations, even though DRT system has conventionally been seen applicable mainly for low demand density situations.