Paikkatietojärjestelmien ja kaukokartoituksen integraatio

Kirjallisuusarvostelu

Alankosademetsien kasvilajiston vaihtelun kartoittaminen satelliittikuvilta

Seloste väitöskirjasta: Remote sensing of floristic patterns in the lowland rain forest landscape. Dissertationes Forestales 59.

Automated, adaptive methods for forest inventory

Forest inventories are used to estimate forest characteristics and the condition of forest for many different applications: operational tree logging for forest industry, forest health state estimation, carbon balance estimation, land-cover and land use analysis in order to avoid forest degradation etc. Recent inventory methods are strongly based on remote sensing data combined with field sample measurements, which are used to define estimates covering the whole area of interest. Remote sensing data from satellites, aerial photographs or aerial laser scannings are used, depending on the scale of inventory. To be applicable in operational use, forest inventory methods need to be easily adjusted to local conditions of the study area at hand. All the data handling and parameter tuning should be objective and automated as much as possible. The methods also need to be robust when applied to different forest types. Since there generally are no extensive direct physical models connecting the remote sensing data from different sources to the forest parameters that are estimated, mathematical estimation models are of "black-box" type, connecting the independent auxiliary data to dependent response data with linear or nonlinear arbitrary models. To avoid redundant complexity and over-fitting of the model, which is based on up to hundreds of possibly collinear variables extracted from the auxiliary data, variable selection is needed. To connect the auxiliary data to the inventory parameters that are estimated, field work must be performed. In larger study areas with dense forests, field work is expensive, and should therefore be minimized. To get cost-efficient inventories, field work could partly be replaced with information from formerly measured sites, databases. The work in this thesis is devoted to the development of automated, adaptive computation methods for aerial forest inventory. The mathematical model parameter definition steps are automated, and the cost-efficiency is improved by setting up a procedure that utilizes databases in the estimation of new area characteristics.

Jäätiköiden kaukokartoittamista eurooppalaisittain

Kirjallisuusarvostelu

People and the intertidal : human induced changes, biodiversity loss, livelihood implications and management in the Western Indian Ocean

Vad händer i tidvattenzonen? Var går gränsen mellan land och hav, vad händer i tidvattenzonen och vem ansvarar för detta? I västra Indiska oceanen (VIO) kan avståndet mellan den lägsta nivån för lågvattnet och den högsta nivån för högvattnet vara flera kilometer och nivåskillnaderna upp till 6 meter och detta skapar ett stort och föränderligt område. Syftet med min avhandling är att öka förståelsen för tidvattenzonen i tropiska och subtropiska västra Indiska oceanen. Sammanfattningsvis visar mina studier att det finns ett mycket stort värde i den komplexa tidvattenzonen, men också att det här området hotas från både land och hav, genom t.ex. överexploatering, erosion och föroreningar. Uttnyttjandet av tidvattenzonen är stort och min avhandling har visat att aktiviteter såsom fiske i form av plocking av musslor och andra ryggradslösa djur och hamnaktiviteter påverkar den biologiska mångfalden negativt, vilket leder till försämrad levnadsstandard för resursutnyttjande människor i regionen. För att förbättra situationen krävs det mer forskning, miljöövervakning och bättre förvaltning av tidvattenzonen. Experter i regionen har rangordnat förslag på förvaltningsstrategier som skulle kunna testas för att förbättra miljön och skapa ett mer hållbart nyttjande. Avhandlingen visar även att det är möjligt att använda fjärranalysteknik såsom satellitbildsanalys för att kvantifiera mängden sjögräsvegetation (i form av biomassa), vilket kan ha stor betydelse för att förbättra storskalig miljöövervakning av kustnära naturtyper (habitat). I avhandlingsarbetet har jag använt mig av ett multidisciplinärt tillvägagångssätt och använt metoder såsom ekologisk och biologisk provtagning, intervjuer, observationer, diskussionsgrupper, frågeformulär och fjärranalys. Resultaten presenterade i denna avhandling ger en ökad kunskap om tidvattenzonen i utvecklingsländerna inom VIO-regionen som kan användas för att initiera och fortsätta att utveckla hållbara förvaltningsstrategier av biologiska resurser.

Forest cover and its change in Unguja Island, Zanzibar

Tropical forests are sources of many ecosystem services, but these forests are vanishing rapidly. The situation is severe in Sub-Saharan Africa and especially in Tanzania. The causes of change are multidimensional and strongly interdependent, and only understanding them comprehensively helps to change the ongoing unsustainable trends of forest decline. Ongoing forest changes, their spatiality and connection to humans and environment can be studied with the methods of Land Change Science. The knowledge produced with these methods helps to make arguments about the actors, actions and causes that are behind the forest decline. In this study of Unguja Island in Zanzibar the focus is in the current forest cover and its changes between 1996 and 2009. The cover and changes are measured with often used remote sensing methods of automated land cover classification and post-classification comparison from medium resolution satellite images. Kernel Density Estimation is used to determine the clusters of change, sub-area –analysis provides information about the differences between regions, while distance and regression analyses connect changes to environmental factors. These analyses do not only explain the happened changes, but also allow building quantitative and spatial future scenarios. Similar study has not been made for Unguja and therefore it provides new information, which is beneficial for the whole society. The results show that 572 km2 of Unguja is still forested, but 0,82–1,19% of these forests are disappearing annually. Besides deforestation also vertical degradation and spatial changes are significant problems. Deforestation is most severe in the communal indigenous forests, but also agroforests are decreasing. Spatially deforestation concentrates to the areas close to the coastline, population and Zanzibar Town. Biophysical factors on the other hand do not seem to influence the ongoing deforestation process. If the current trend continues there should be approximately 485 km2 of forests remaining in 2025. Solutions to these deforestation problems should be looked from sustainable land use management, surveying and protection of the forests in risk areas and spatially targeted self-sustainable tree planting schemes.

Suitability of LiDAR technology for forest inventory in Russia

LiDAR is an advanced remote sensing technology with many applications, including forest inventory. The most common type is ALS (airborne laser scanning). The method is successfully utilized in many developed markets, where it is replacing traditional forest inventory methods. However, it is innovative for Russian market, where traditional field inventory dominates. ArboLiDAR is a forest inventory solution that engages LiDAR, color infrared imagery, GPS ground control plots and field sample plots, developed by Arbonaut Ltd. This study is an industrial market research for LiDAR technology in Russia focused on customer needs. Russian forestry market is very attractive, because of large growing stock volumes. It underwent drastic changes in 2006, but it is still in transitional stage. There are several types of forest inventory, both with public and private funding. Private forestry enterprises basically need forest inventory in two cases – while making coupe demarcation before timber harvesting and as a part of forest management planning, that is supposed to be done every ten years on the whole leased territory. The study covered 14 companies in total that include private forestry companies with timber harvesting activities, private forest inventory providers, state subordinate companies and forestry software developer. The research strategy is multiple case studies with semi-structured interviews as the main data collection technique. The study focuses on North-West Russia, as it is the most developed Russian region in forestry. The research applies the Voice of the Customer (VOC) concept to elicit customer needs of Russian forestry actors and discovers how these needs are met. It studies forest inventory methods currently applied in Russia and proposes the model of method comparison, based on Multi-criteria decision making (MCDM) approach, mainly on Analytical Hierarchy Process (AHP). Required product attributes are classified in accordance with Kano model. The answer about suitability of LiDAR technology is ambiguous, since many details should be taken into account.

Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.