Errors related to the automatized satellite-based change detection of boreal forests in Finland

Highlights • Forest changes were automatically modelled from multitemporal Sentinel-2 images. • Errors were evaluated based on visually interpreted VHR images. • Extraction of clear-cuts was accurate whereas thinnings had more variation. • Image quality and translucent clouds had most significant effect on errors. • Results were regarded applicable for operational change monitoring.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

Evaluation of the Finnish National Biodiversity Action Plan 1997-2005

The results of the evaluation of the Finnish National Biodiversity Action Plan 1997-2005 indicate clear changes towards better consideration of biodiversity in the routines and policies of many sectors of the administration and economy. There are many indications that actors across society have recognized the need to safeguard biodiversity and have begun to adjust their practices accordingly. Several concrete measures have been undertaken in forests, agricultural habitats and in other habitats significantly affected by human activities. Biodiversity research has expanded significantly and the knowledge of Finland´s biological diversity has increased. In general, the Action Plan has supported public discussion of the need to safeguard biodiversity and this discussion has resulted in more positive attitudes towards nature conservation.So far, however, the implemented measures have not been sufficiently numerous or efficient to stop the depletion of original biological diversity. Many habitats remain far from their original state. More species will become endangered in the immediate future unless more effective and far-reaching measuresare taken. The objective of the EU to halt the decline of biodiversity by 2010 will not be achieved given the current development. Although the deterioration in biodiversity may have slowed down in several cases, many economic activities continue to have a negative impact on biodiversity. The scale of these activities is normally greater than that of the measures taken to manage and restore biodiversity.The evaluation focused on detecting changes in the administration of key sectors, analysing the recent development of biodiversity and observing interlinkages between these two. The analysis of administrative measures was based on interviews and on examining policy documents, reports and other relevant literature. The analysis covered changes in the administration of nature conservation, forestry,  agriculture, land use and regional and development cooperation. The analysis of the development of biodiversity was based on employing 75 pressure, state, impact and response indicators. There were 5 to 15 indicators for each of the nine major habitat types of Finland.Three separate case studies were made to provide further insights into some key issues: 1) A GISanalysis was made of the development of land use patterns in North Karelia and south-west Finland between 1990 and 2000, 2) two scenarios on the development of forest structure in North Karelia until 2050 were developed using a special MELA-model and 3) the cost-effectiveness of the agri-environmental support scheme was examined by comparing different land allocation choices and their effects on biodiversity on an average farm in southern Finland. The evaluation also paid special attention to the role of research in safeguarding biodiversity and reflected Finnish experiences against an international background

Preface to the special issue on Monitoring and Modelling of Carbon-Balance-, Water- and Snow-Related Phenomena at Northern Latitudes

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Maaseudun kehittämisohjelmien teema-arviointi. Ohjelmakausi 2000-2006

Kyseessä on vuosina 2000–2006 Suomessa toteutettujen maaseudun kehittämisohjelmien ALMA, ELMA, Pohjois-Suomen tavoite 1, Itä-Suomen tavoite 1, POMO+ ja LEADER+ -ohjelmien teema-arviointi. Maaseudun kehittämisohjelmien tavoitteena on ollut maaseudun työ- ja elinkeinomahdollisuuksien parantaminen, yhteisöllisyyden lisääminen, maatalouden rakennemuutoksen edistäminen ja maatilatalouden monipuolistaminen. Arvioinnin koordinointivastuu on ollut Helsingin yliopiston Ruralia-instituutilla

Comparison Between Three Different Clustering Algorithms

New approaches like neural networks and fuzzy sets have been used more and more in pattern recognition during recent years. In this article, neural network and fuzzy clustering algorithms are compared to the traditional clustering algorithm

KOHONENSELF-ORGANIZINGFEATUREMAP INPATTERNRECOGNITION

Traditional statistical pattern recognition models have been studied quite a long time. Due to the difficulty of pattern recognition task (for example classification) there are many different models for that task. The need for a general model which could be successful in most pattern recognition tasks has led to study new approaches like neural networks and fuzzy sets. This paper introduces one self-organizing neural network, the Kohonen self-organizing feature map, and represents its use in different pattern recognition tasks. The pattern recognition process for satellite image goes as follows. First, the instrument in the satellite makes its measurements and transfers the data to ground. Then in the preprocessing stage, the errors introduced by the measurement situation are corrected. These errors include geometrical errors due to the round earth an