The impacts of training data sample size and sampling method on the accuracy of forest/nonforest classifications of three mosaicked Landsat ETM+ images with the nearest neighbor decision rule were explored. Large training data pools of single pixels were used in simulations to create samples with three sampling methods (random, stratified random, and systematic) and eight sample sizes (25, 50, 75, 100, 200, 300, 400, and 500). Two forest area estimation techniques were used to estimate the proportion of forest in each image and to calculate forest area precision estimates. Training data editing was explored to remove problem pixels from the training data pools. All possible band combinations of the six non-thermal ETM+ bands were evaluated for every sample draw. Comparisons were made between classification accuracies to determine if all six bands were needed. The utility of separability indices, minimum and average Euclidian distances, and cross-validation accuracies for the selection of band combinations, prediction of classification accuracies, and assessment of sample quality were determined. Larger training data sample sizes produced classifications with higher average accuracies and lower variability. All three sampling methods had similar performance. Training data editing improved the average classification accuracies by a minimum of 5.45%, 5.31%, and 3.47%, respectively, for the three images. Band combinations with fewer than all six bands almost always produced the maximum classification accuracy for a single sample draw. The number of bands and combination of bands, which maximized classification accuracy, was dependent on the characteristics of the individual training data sample draw, the image, sample size, and, to a lesser extent, the sampling method. All three band selection measures were unable to select band combinations that produced higher accuracies on average than all six bands. Cross-validation accuracies with sample size 500 had high correlations with classification accuracies, and provided an indication of sample quality. Collection of a high quality training data sample is key to the performance of the nearest neighbor classifier. Larger samples are necessary to guarantee classifier performance and the utility of cross-validation accuracies. Further research is needed to identify the characteristics of "good" training data samples. / Ph. D.

Granbarkborre är en av Sveriges mest destruktiva skadeinsekter som angriper granskog. Insekten har medfört förödande konsekvenser för granskog, framför allt sedan2018 där stora arealer granskog nästan har eliminerats. Insekten trivs i varmt ochtorrt klimat. Växthuseffekten i form av värmeböljor och perioder av minskad nederbörd tros gynna denna skadliga insekt då de kan fortplanta sig flera gånger och erövra nya områden under en enda sommarsäsong. En vital och nödvändig åtgärd vid bekämpning av skadeinsekter är att föra bort angripna träd innan granbarkborren lämnar barken. Dock är det nästan omöjligt attundersöka all granskog på det traditionella sättet, det vill säga till fots eftersom detär mycket tids- och resurskrävande. I det tidiga skedet visar det angripna trädet ingabetydande färgförändringar i det synliga spektrumet inom fjärranalys, vilket försvårar tidig upptäckt. Men för att försöka göra detta möjligt ämnar det här arbetet undersöka skillnaderna hos friska och angripna träd i tid, där det användes band i detosynliga spektrumet som ShortWave Infrared. Detta användes bland annat i form avbandkombinationer, som Atmospheric Penetration och Agriculture. Dessutom utfördes empiriska experiment på olika vegetationsindex (VI) som var NormalizedSimple Ratio, Enhanced Vegetation Index, Green Chlorophyll Vegetation Index,Normalized Difference Vegetation Index, Normalized Difference Moisture Indexoch Normalized Distance RED and SWIR. I denna studie användes satellitbilder från Sentinel-2 över studieområdet i Mellansverige under månaderna maj-september från 2020 till juli 2022. Inrapporterade dataför angrepp av granbarkborren i studieområdet hämtades från databasarkiven GlobalBiodiversity Information Facility och Holmen AB. Skogsstyrelsens öppna karttjänstanvändes för att erhålla data över Sveriges skogsarter, för att säkerställa att studieområdet bestod av granskog. Genom att utföra empiriska experiment av de olika VI och bandkombinationer sompresenteras i denna studie kunde några indikationer utmärkas. På grund av problematiken med de olika påverkande faktorerna, som bland annat lokalt klimat i kombination med tröskelvärden, var det svårt att fastställa en fullständig bedömning. Vårslutsats visar att de använda vegetationsindex och de två bandkombinationer tillsammans med den spatiala upplösningen, som Sentinel-2 erbjuder, inte uppnår det someftersträvas i denna studie. Anledningen till detta var att möjligheten att identifieraenstaka sjuka träd i studieområdet saknades. / The spruce bark beetle is one of Sweden's most destructive pests that attack spruceforests. The insect has had devastating consequences for spruce forests, especiallysince 2018 where large areas of spruce forest have been almost eliminated. The insect thrives in warm and dry climates. The greenhouse effect in the form of heatwaves and periods of reduced rainfall is believed to favor this harmful insect as theycan reproduce several times and conquer new areas in a single summer season. A vital and necessary measure in combating pests is to remove infested trees beforethe spruce bark beetle leaves the bark. However, it is almost impossible to examineall the spruce forest in the traditional way, which is on foot, because it is time- andresource-consuming. In the early stage, the infested tree shows no significant colorchanges in the visible spectrum in remote sensing, which makes early detection difficult. To try to make this possible, this work intends to investigate the differences inhealthy and infested trees in time, where bands in the invisible spectrum such asShortWave Infrared were used. This was used, among other things, in the form ofband combinations, such as Atmospheric Penetration and Agriculture. In addition,empirical experiments were performed on different vegetation indices (VI) whichwere Normalized Simple Ratio, Enhanced Vegetation Index, Green ChlorophyllVegetation Index, Normalized Difference Vegetation Index, Normalized DifferenceMoisture Index and Normalized Distance RED and SWIR. In this study, satellite images from Sentinel-2 were used over the study area in central Sweden during the months of May-September from 2020 to July 2022. Reported data for attacks by the spruce bark beetle in the study area were retrievedfrom the Global Biodiversity Information Facility and Holmen AB database archives.The Forestry Agency's open map service was used to obtain data on Sweden's forestspecies, to ensure that the study area consisted of spruce forest. By performing empirical experiments of the different VI and band combinationspresented in this study, some indications could be distinguished. Due to the problems with the various influencing factors, such as local climate in combination withthreshold values, it was difficult to establish a complete assessment. Our conclusionshows that the used vegetation indices and the two band combinations together withthe spatial resolution offered by Sentinel-2 do not achieve what is sought in thisstudy. The reason for this was that the possibility of identifying individual diseasedtrees in the study area was missing.