A hierarchical clustering method for land cover change detection and identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis

Energy from biomass : Assessing sustainability by geoinformation technology

Publisher Copyright: © 2021 Austrian Acedemy of Sciences Press. All rights reserved.Forest Flux https://www.forestflux.eu/ will renew forestry value-added services in Earth Observation (EO) by creating and piloting cloud-based services for committed users on forest carbon assimilation and structural variable prediction. Forest Flux exploits the explosive increase of high-resolution EO data from the Copernicus program and developments of cloud computing technology. It implements a world-first service platform for high-resolution maps of traditional forestry variables together with forest carbon fluxes. Forest Flux will allow the users to improve the profitability of forest management while taking care of ecological sustainability. The Forest Flux services are implemented on the Forestry Thematic Exploitation cloud platform https://f-tep.com/. In 2020, nearly 700 thematic maps on forest stand and carbon flux variables were delivered to nine specific users in a form that was applicable to their operational forest management systems. The last project year 2021 focuses on map product refinement and improving user services, which eventually lead to operational service concepts.Peer reviewe

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

Rakennusten ja teiden havaitseminen ilmakuvasta

Tämän työn tavoitteena oli kehittää menetelmä, jolla voidaan havaita rakennukset ja tiet yksikanavaisesta korkeakuvauksen ilmakuvasta, jonka alueellinen erotuskyky on yksi metri. Kouvolan alueelta otettua ilmakuvaa käytettiin korvaamaan vastaavan erotuskyvyn satelliittikuvia, joita tulee kaupallisesti saataville vuoden 1999 lopussa. Jos rakennukset onnistutaan havaitsemaan kyllin hyvin, niiden perusteella voidaan arvioida esimerkiksi tutkittavan alueen asukasmäärän tai ostovoiman jakautumista. Havaittujen teiden perusteella voidaan selvittää asukkaiden mahdolliset liikkumisreitit. Rakennusten havaitsemiseksi kokeiltiin neljää erilaista lähestymistapaa: maankäytön luokittelua, mallikuvien sovitustekniikkaa, alueenkasvatusmenetelmällä tapahtuvaa segmentointia ja rakennusten vastakkaisten reunojen havaitsemiseen perustuvaa menetelmää. Näistä viimeinen antoi parhaat tulokset. Kuvasta pystytään tällä menetelmällä paikallistamaan melko hyvin alueet, joilla on rakennuksia. Sen sijaan kaikkien rakennusten täsmällisiä sijainteja ja tyyppejä ei pystytä selvittämään. Tien osat erotettiin ilmakuvasta gaussin funktion toiseen derivaattaan perustuvan viivasuotimen avulla. Tieverkko muodostettiin yhdistelemällä havaittujen tienosien vapaita päitä. Tiesegmentin kunkin pään eri yhdistämisvaihtoehdoista valittiin se, joka vastasi parhaiten teihin kuuluvien pikseleiden oletettua intensiteettiä, intensiteetin varianssia ja jonka viivasuotimen vaste oli kyllin suuri. Menetelmää sovellettiin erilaisilla alueilla. Tiheästi asutuilla alueilla koko tieverkon havaitseminen osoittautui vaikeaksi. mutta tärkeimmät taajamien väliset tiet onnistuttiin löytämään