Use of remotely sensed auxiliary data for improving sample-based forest inventories

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dc.contributor Helsingin yliopisto, maatalous-metsätieteellinen tiedekunta, metsatieteiden laitos fi
dc.contributor Helsingfors universitet, agrikultur-forstvetenskapliga fakulteten, institutionen för skogsvetenskaper sv
dc.contributor University of Helsinki, Faculty of Agriculture and Forestry, Department of Forest Sciences en
dc.contributor Swedish University of Agricultural Sciences, Department of Forest Resource Management, Forest Resource Analysis Division en
dc.contributor.author Saarela, Svetlana
dc.date.accessioned 2015-09-04T11:34:20Z
dc.date.available 2015-09-15 fi
dc.date.available 2015-09-04T11:34:20Z
dc.date.issued 2015-09-25
dc.identifier.uri URN:ISBN:978-951-651-491-1
dc.identifier.uri http://hdl.handle.net/10138/156392
dc.description.abstract Over the past decades it has been shown that remotely sensed auxiliary data have a potential to increase the precision of key estimators in sample-based forest surveys. This thesis was motivated by the increasing availability of remotely sensed data, and the objectives were to investigate how this type of auxiliary data can be used for improving both the design and the estimators in sample-based surveys. Two different modes of inference were studied: model-based inference and design-based inference. Empirical data for the studies were acquired from a boreal forest area in the Kuortane region of western Finland. The data comprised a combination of auxiliary information derived from airborne LiDAR and Landsat data, and field sample plot data collected using a modification of the 10th Finnish National Forest Inventory. The studied forest attribute was growing stock volume. In Paper I, remotely sensed data were applied at the design stage, using a newly developed design which spreads the sample efficiently in the space of auxiliary data. The analysis was carried out through Monte Carlo sampling simulation using a simulated population developed by way of a copula technique utilizing empirical data from Kuortane. The results of the study showed that the new design resulted in a higher precision when compared to a traditional design where the samples were spread only in the space of geographical data. In Paper II, remotely sensed auxiliary data were applied in connection with model-assisted estimation. The auxiliary data were used mainly in the estimation stage, but also in the design stage through probability-proportional-to-size sampling utilizing Landsat data. The results showed that LiDAR auxiliary data considerably improved the precision compared to estimation based only on field samples. Additionally, in spite of their low correlation with growing stock volume, adding Landsat data as auxiliary data further improved the precision of the estimators. In Paper III, the focus was set on model-based inference and the influence of the use of different models on the precision of estimators. For this study, a second simulated population was developed utilizing the empirical data, including only non-zero growing stock volume observations. The results revealed that the choice of model form in model-based inference had minor to moderate effects on the precision of the estimators. Furthermore, as expected, it was found that model-based prediction and model-assisted estimation performed almost equally well. In Paper IV, the precision of model-based prediction and model-assisted estimation was compared in a case where field and remotely sensed data were geographically mismatched. The same simulated population as used in Paper III was employed in this study. The results showed that the precision in most cases decreased considerably, and more so when LiDAR auxiliary data were applied, compared to when Landsat auxiliary data were used. As for the choice of inferential framework, it was revealed that model-based inference in this case had some advantages compared to design-based inference through model-assisted estimators. The results of this thesis are important for the development of forest inventories to meet the requirements which stem from an increasing number of international commitments and agreements related to forests. Keywords: design-based, Landsat, LiDAR, model-based, multivariate probability distribution, sampling. en
dc.description.abstract Under de senaste årtiondena har det visat sig att hjälpdata från fjärranalys har potential att öka precisionen för skattningar i stickprovsbaserade skogsinventeringar. Denna avhandling motiverades av den ökade tillgängligheten av fjärranalysdata, och målet var att undersöka hur den här typen av hjälpdata kan användas för att förbättra både stickprovsdesign och skattningar vid stickprovsbaserade inventeringar. Två olika typer av statistisk inferens studerades: modellbaserad inferens och designbaserad inferens. Empiriska data för studierna förvärvades från ett borealt skogsområde i Kuortane regionen i västra Finland. Data bestod av en kombination av hjälpinformation från luftburen LiDAR, Landsat och fältdata från provytor som samlats in med hjälp av en intensifierad version av Riksskogstaxeringen. Det attribut som studerades var volym för skogsbeståndet. Resultaten från denna avhandling är viktiga för utvecklingen av skogsinventeringar så att de kan uppfylla de krav som följer av ett ökande antal internationella åtaganden och överenskommelser med anknytning till skogen. Nyckelord: designbaserad, Landsat, LiDAR, modellbaserad, multivariat sannolikhetsfördelning, stickprov. sv
dc.description.abstract Viime vuosikymmeninä kaukokartoituksen avulla hankitulla aputiedolla on potentiaalia lisätä otantapohjaisen metsien inventoinnin tärkeimpien estimaattoreiden täsmällisyyttä. Tämän opinnäytetyön motivaatio perustuu kaukokartoitusaineistojen lisääntyvään saatavuuteen ja tavoitteena oli selvittää miten tämän tyyppistä aputietoa voidaan käyttää parantamaan otantapohjaisten tutkimusten asetelmaa ja estimaattoreita. Tutkittiin kahta erilaista lähestymistapaa: malliperusteista ja asetelmaperusteista. Tutkimuksia varten kerättiin empiirinen aineisto boreaalisesta metsäalueelta Kuortaneelta Länsi-Suomesta. Aineisto käsitti yhdistelmän aputietona hyödynnettyjä laserkeilaus- ja Landsat-aineistoja, maastokoeala-aineisto kerättiin 10. valtakunnan metsien inventoinnin muunnelmana. Tutkittavana metsikkötunnuksena oli runkotilavuus. Tämän opinnäytetyön tulokset ovat tärkeitä metsien inventoinnin kehittämisessä vastaamaan vaatimuksia, jotka pohjautuvat kasvavaan määrään metsiin liittyviä kansainvälisiä sopimuksia ja velvoitteita. Avainsanat: asetelmaperusteinen, Landsat, LiDAR, malliperusteinen, moniuloitteinen todennäköisyysjakauma, otanta. fi
dc.format.mimetype application/pdf fi
dc.language.iso en
dc.publisher Helsingin yliopisto fi
dc.publisher Helsingfors universitet sv
dc.publisher University of Helsinki en
dc.relation.isformatof URN:ISBN:978-951-651-492-8 fi
dc.relation.isformatof Finnish Society of Forest Sciences, 2015, Dissertationes Forestales 201. 2323-9220 fi
dc.relation.ispartof URN:ISSN:1795-7389 fi
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. sv
dc.subject Forest Resource Science and Technology fi
dc.title Use of remotely sensed auxiliary data for improving sample-based forest inventories en
dc.title.alternative Användning av fjärranalysdata för att förbättra stickprovsbaserade skogsinventeringar sv
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Doktorsavhandling (sammanläggning) sv
dc.ths Dahlin, Bo
dc.ths Grafström, Anton
dc.opn Gregoire, Timothy G.
dc.type.dcmitype Text

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