Submitted by Romulus Lima (romulus.lima@ufpe.br) on 2015-03-04T18:14:30Z No. of bitstreams: 2 DISSERTAÇÃO Bruna Patrícia Barbosa de Alencar.pdf: 2179196 bytes, checksum: 8c3cf557099a57319a7f100868e020c5 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-04T18:14:30Z (GMT). No. of bitstreams: 2 DISSERTAÇÃO Bruna Patrícia Barbosa de Alencar.pdf: 2179196 bytes, checksum: 8c3cf557099a57319a7f100868e020c5 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-26 / As imagens de RADAR são uma alternativa para a realização de mapeamentos em áreas de grande cobertura de nuvens, inerentes às imagens de sensores passivos. Entretanto, devido à variação de declive dos terrenos, que influência o ângulo de incidência do feixe emitido pelo RADAR, alguns erros geométricos ocorrem nas imagens. Esses erros são o encurtamento de rampa, inversão do relevo e o sombreamento. O objeto de estudo desta dissertação é a análise da ocorrência da sombra nas imagens ALOS/PALSAR, por ser uma área que não tem sinal de retorno e por gerar confusão no mapeamento de áreas como, por exemplo, as de vegetação. O sombreamento pode ocorrer em regiões de terreno movimentado e onde a face do relevo está voltada perpendicularmente ao pulso enviado pela antena ALOS/PALSAR. Para analisar a geração da sombra, são estudadas duas áreas de terreno movimentado. As áreas estão nos municípios de Petrolândia em Pernambuco e Glória na Bahia. Elas apresentam relevo ondulado controlado por zonas de cisalhamento, dobramentos e falhas geológicas. Uma metodologia de detecção da sombra ALOS/PALSAR é realizada, através da binarização da imagem ALOS/PALSAR supondo que para a área de sombra o retorno de sinal é muito baixo. Curvas de nível, mapa de orientação de vertentes e mapa de declividade foram adquiridos do projeto TOPODATA para verificar as características do terreno que influenciam na ocorrência do sombreamento, a escala utilizada foi de 1:100000. As imagens LANDSAT5/TM permitiram localizar as feições e compará-las com as regiões de sombra localizadas nas imagens ALOS/PALSAR. A área de sombra foi identificada na região de declividade, predominantemente, maior que 75% e nas orientações Sudeste e Sul. Os dados de orientação de vertentes e declividade serviram como dados fundamentais na identificação das áreas de sombra, juntamente com a imagem binarizada para identificar o baixo retorno do sinal que ocorre nas imagens de RADAR.

Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and development of integrated approach for the management of water resources. With the recent advent of computational power and the growing availability of spatial data, RS and GIS technologies can augment to a great extent the conventional methods used in rainfall runoff studies; it is possible to accurately describe watershed characteristics in particularly when determining runoff response to rainfall input. The main objective of this study is to apply the potential of spaceborne SAR data for soil moisture retrieval in order to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, high resolution 2 m aerial laser scanning Digital Terrain Model (DTM), soil map, and landuse map were used. Rainfall records were transformed into a runoff through hydrological parameterisation of the watershed and the river network using HEC-HMS software for rainfall runoff simulation. The Soil Conservation Services Curve Number (SCS-CN) and Soil Moisture Accounting (SMA) loss methods were selected to calculate the infiltration losses. In microwave remote sensing, the study of how the microwave interacts with the earth terrain has always been interesting in interpreting the satellite SAR images. In this research soil moisture was derived from two different types of Spaceborne SAR data; TerraSAR-X and ALOS PALSAR (L band). The developed integrated hydrological model was applied to the test site of the Gottleuba Catchment area which covers approximately 400 sqkm, located south of Pirna (Saxony, Germany). To validate the model historical precipitation data of the past ten years were performed. The validated model was further optimized using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Quantitatively the study concluded that based on SAR data, the model could be used as an expeditious tool of soil moisture mapping which required for hydrological modelling.

Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-04-20Bitstream added on 2014-06-13T20:21:13Z : No. of bitstreams: 1 sartori_lr_dr_prud.pdf: 4148637 bytes, checksum: 5616600e595cbbe65fef21b03cd5309c (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O propósito deste trabalho foi avaliar o potencial dos dados PALSAR polarimétricos para discriminar e mapear espécies de macrófitas (vegetação aquática) de uma área alagável da Amazônia, a planície de inundação do Lago Grande de Monte Alegre, no estado do Pará. A coleta de dados foi realizada quase simultaneamente à aquisição dos dados de radar. Três principais espécies de macrófitas foram encontradas na área: Paspalum repens (PR), Hymenachne amplexicaulis (HA) e Paspalum elephantipes (PE). Variáveis morfológicas foram medidas em campo e usadas para derivar outras variáveis tais como a biomassa. Atributos foram gerados a partir da matriz de covariância [C] extraída da imagem ALOS/PALSAR em modo SLC (single look complex). Os atributos polarimétricos foram analisados para as três espécies e identificados aqueles capazes de discriminar as espécies. Foram aplicadas as seguintes abordagens de classificação: baseada em regras, baseada em modelos de decomposição (Decomposições de Freeman-Durden e Cloude-Pottier), baseada em estatística (Classificação supervisionada baseada na distância Wishart) e híbrida (Classificador Wishart com classes de entrada baseadas na decomposição de Cloude-Pottier). Finalmente, a variável morfológica “volume da haste” foi modelada por regressão múltipla em função de alguns atributos polarimétricos. Os resultados sugerem que a imagem polarimétrica banda L possui potencial para discriminar as espécies de macrófitas, sendo os principais atributos para isso sigma zero HH ( ), sigma zero HV ( ) e sigma zero VV ( ), índice de estrutura da copa... / The purpose of this work was to evaluate the potential of fully polarimetric PALSAR data to discriminate and map macrophyte species in the Amazon floodplain, more specifically in the Monte Alegre Lake, in the state of Pará, Brazil. Fieldwork was carried out almost simultaneously to the radar acquisition. Three main species were found in the study area: Paspalum repens (PR), Hymenachne amplexicaulis (HA) and Paspalum elephantipes (PE). Macrophyte morphological variables were measured on the field and used to derive others variables, like the biomass. Attributes were calculated from the covariance matrix [C] derived from the SLC (single look complex) data. The polarimetric attributes were analyzed for the three species and it was identified that ones capable of discriminating them. The following classification approaches were applied: a rule-based classification, model-based classifications (Freeman-Durden and Cloude-Pottier), a statistical-based classification (supervised classification using Wishart distance measure) and a hybrid classification (Wishart classifier with the input classes based on the H/a plane). Finally, the morphological variable “stem volume” was modeled using multiple regression. The findings suggest that the fully polarimetric image has potential for discriminating plant species, being the main attributes sigma-nought HH ( ), sigma-nought HV ( ) and sigma-nought VV ( ), canopy structure index ( ), HH-VV polarimetric coherence ( ), helicity of the third scattering mechanism (τ ), orientation angle of the first scattering mechanism ( ) and scattering type phase of the first mechanism ( ); among the different classifications, only the supervised (Wishart) and the rule-based discriminated the species, with overall accuracy of 75,04% and 87,18%, respectively; the stem volume was modeled using the following attributes: biomass index ( ), volumetric scattering ... (Complete abstract click electronic access below)

Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and development of integrated approach for the management of water resources. With the recent advent of computational power and the growing availability of spatial data, RS and GIS technologies can augment to a great extent the conventional methods used in rainfall runoff studies; it is possible to accurately describe watershed characteristics in particularly when determining runoff response to rainfall input. The main objective of this study is to apply the potential of spaceborne SAR data for soil moisture retrieval in order to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, high resolution 2 m aerial laser scanning Digital Terrain Model (DTM), soil map, and landuse map were used. Rainfall records were transformed into a runoff through hydrological parameterisation of the watershed and the river network using HEC-HMS software for rainfall runoff simulation. The Soil Conservation Services Curve Number (SCS-CN) and Soil Moisture Accounting (SMA) loss methods were selected to calculate the infiltration losses. In microwave remote sensing, the study of how the microwave interacts with the earth terrain has always been interesting in interpreting the satellite SAR images. In this research soil moisture was derived from two different types of Spaceborne SAR data; TerraSAR-X and ALOS PALSAR (L band). The developed integrated hydrological model was applied to the test site of the Gottleuba Catchment area which covers approximately 400 sqkm, located south of Pirna (Saxony, Germany). To validate the model historical precipitation data of the past ten years were performed. The validated model was further optimized using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Quantitatively the study concluded that based on SAR data, the model could be used as an expeditious tool of soil moisture mapping which required for hydrological modelling.

The aim of this thesis is to evaluate the capability of radio detection and ranging (radar) data collected by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture radar (PALSAR), supported by field measurements obtained through ground survey, to predict and map Above Ground Woody Biomass (AGWB) in the tropical savannas of the developing country of Belize, and to understand how the forest structure may influence the backscatter observed. Firstly, an extensive inventory of the woody vegetation of the tropical savannas of Belize was created by measuring the diameter at breast height (dbh), the total height (ht) and the location of 6547 trees in plots covering a total woodland area of 30.8 hectares, located within four protected areas (the Rio Bravo Conservation and Management Area (11×1ha), Deep River (108×0.1ha) and Manatee Forest Reserve (1ha) and the Bladen Nature Reserve (1ha) and also from plots located in unprotected areas (7×1ha). These measurements of forest structure, when combined with information about forest management practices obtained from local organisations revealed that different forms of protection and management may lead to the development of pine woodlands with different structural characteristics in these savannas. Secondly, a case-study was conducted to establish the sensitivity of the ALOS PALSAR backscatter data to AGWB and determine the effect of sample plot size to their relationship. The findings of this case-study show that the L-band backscatter in these low density pine woodlands is a possible predictor of AGWB and confirm that the appropriate sample plot size for predicting AGWB is one hectare; while the sensitivity degrades significantly with decreasing sample plot size. Taken together, the findings described above were combined to assess the capability of ALOS PALSAR backscatter to predict AGWB in these woodlands. A semi-empirical Water Cloud Model (WCM) describing the interaction between the backscatter and vegetation was re-arranged to enable the prediction of AGWB. Non-linear regression analysis revealed that the ALOS PALSAR backscatter predicted AGWB with an R2=0.92; an external validation conducted with additional ground reference data estimated this AGWB prediction to have an RMSE ~13 t/ha. The form of the regression model linking backscatter to AGWB appears to be particularly influenced by sample plots with higher tree numbers and by plots in which the trees were more homogeneous. The presence of many similar sized individuals within some plots is postulated as one explanation for the elevated saturation level for predictions in this study (> 100 t/ha) compared to other models. The model developed here predicts complete saturation in the backscatter - AGWB relationship to occur primarily as a result of increases in the tree number density and often concurrently in basal area, two parameters which are usually strongly correlated with AGWB in these woodlands. Thirdly, the locally validated relationship between ALOS PALSAR backscatter and AGWB is used to map AGWB for the lowland pine savannas of Belize at a spatial resolution of 100m. The mapping estimates that over 90% of these pine woodlands have an AGWB below 60 t/ha, with the average woody biomass estimated at 23.5 t/ha. When these new predictions are mapped and aggregated over the extents of two protected areas (Rio Bravo and Deep River), the totals obtained agree closely (error ≤20%) with previous estimates of AGWB obtained from ground data and previous research. The combined evidence suggests that woodland protection may produce a small, positive effect upon AGWB, with the mean of the AGWB/ha predictions higher in areas that are protected and managed for biodiversity (29.55 ± 0.84 t/ha) than in other areas that are not protected (23.29 ± 0.19 t/ha). When the fine scale local AGWB mapping produced using ALOS PALSAR is compared cell-by-cell with global biomass products at coarser spatial resolutions (500m and 1000m), the AGWB differences observed range from 115-120%. When the coarser AGWB estimates are aggregated over the extents of Deep River and Rio Bravo, the AGWB totals obtained differ significantly (~280 – 300%) from AGWB estimates from ground data and previous research. Overall, these findings suggest that where sufficient ground data exists to build a reliable local relationship to radar backscatter, more detailed biomass mapping can be produced from ALOS and similar satellite sensor data at resolutions of ~100m. This more accurate and spatially detailed information about the distribution of woody biomass within tropical lowland savannas is more appropriate for monitoring local changes in forest cover and for supporting management decisions for forested areas of around ~10,000ha than estimates based upon previously available, but coarser scale, global biomass products.

Electrical resistivity tomography (ERT) is a commonly used geophysical method to investigate permafrost in the mountain environment, but few studies have employed this method in a permafrost affected peatland. For this thesis, 5 ERT profiles were measured over 17 palsas and peat-plateaus in a palsa peatland environment in Tavvavuoma, northern Sweden, where the primary aim was to investigate the depth to the base of permafrost under the mounds. These depths are also used to estimate the excess ice fraction (EIF), which is indicative of the proportion of segregation ice in the frozen core under the mounds. The internal structure of palsas and the spatial distribution of permafrost was also investigated from the inverted resistivity models. Permafrost thickness was found to range from 5 – 17 m, with the thickest permafrost in the west end of the study area. EIF values range between 0,04 to 0,58, with the lowest values in the same end as the deepest permafrost, where also low mound elevations are found. The deep permafrost combined with low mound elevations are suggested to be attributed to the presence of coarse grained (glaciofluvial) sediments where ice segregation formation is limited, thus small amounts of frost heave. Deep permafrost is possibly underlying at least two thermokarst depressions/fens in the area, which is suggested to obstruct their drainage. The height of the mounds was surprisingly found to decrease with permafrost thickness, a relationship that is likely to be an effect of the varied underlying sediment cover. This thesis demonstrates the applicability of ERT in peatland permafrost research, but also considers the limitations of the method. / Elektrisk resistivitetstomografi (ERT) är en geofysisk metod som har använts flitigt vid undersökningar av alpin permafrost, men få studier har hittills tillämpat denna teknik vid undersökningar av permafrost i myrmarker. Under detta examensarbete på masternivå mättes 5 ERT-profiler över 17 palsar samt torvplatåer i ett palsmyrkomplex med sporadisk permafrost i Tavvavuoma, norra Sverige, med det primära målet att undersöka permafrostens mäktighet under dessa. De beräknade permafrostdjupen används även för att uppskatta isöverskottsfraktionen (EIF), vilket ger en indikation på andelen segregationsis i den frusna kärnan under respektive pals/torvplatå. Palsarnas interna struktur och den rumsliga utbredningen av permafrost i myren diskuteras också kvalitativt utifrån resistivitetsmodellerna. Permafrostmäktigheten under palsar och torvplatåer bestämdes till mellan 5 – 17 meter, med den djupaste permafrosten i den västra delen av studieområdet. Isöverskottsfraktionen varierar mellan 0,04 – 0,58, med de lägsta värdena i samma del av studieområdet som den djupaste permafrosten fanns, här är även palshöjderna låga. Den djupa permafrosten i kombination med låga palshöjder föreslås tillskrivas förekomst av grovkorniga (glaciofluviala) sediment i denna del av studieområdet, där bildningen av segregationsis begränsas. I och med detta begränsas mängden frosthävning, med låga palshöjder som resultat. Djup permafrost kan finnas under minst två thermokarstsänkor i området, vilket kan hindra dränering av dessa. Ett oväntat resultat var att palshöjd minskar med ökat permafrostdjup bland de studerade objekten, vilket bäst kan förklaras med det varierade sedimentunderlaget, som ger mycket olika förutsättningar för segregationsisbildning. Denna masteruppsats visar på tillämpligheten av ERT i myrmarker med permafrost, men beaktar även metodens begränsningar för denna tillämpning.

The boreal forest covers 11% of the earth’s land surface and contains 37 percent of the planet’s terrestrial carbon, which is more than the combined total of both the tropical and the temperate forests [1]. This estimate translates to 703 Pg of carbon with the vast majority contained within the organic soils and peat layers [2-4]. The western-north American boreal forest is a fire ecosystem [2, 5-7] where fires typically occur every 50 to 200 years [8, 9], allowing vast quantities of carbon to re-enter the atmosphere. Understanding and estimating past fire history and the related changes in carbon budget [3, 4, 7, 10] in this biome is of significant importance for climate researchers as they attempt to model for future changes in the planet’s climate [2, 4, 11-14]. Many techniques are available to remotely sense wildfires - using optical, thermal and passive microwave remote sensors - during and immediately after an event - although resolution and availability of images due to cloud cover can make these techniques operationally challenging. Radar remote sensing can provide a complement to these optical and passive microwave techniques, since radar is not affected by cloud cover and solar illumination levels. The Advanced Land Observatory Satellite (ALOS) operates a phased array L band synthetic aperture radar (PALSAR) and Canada’s Radarsat-2 contains a C-Band (SAR) instrument. These radar satellites can be used to detect information about the boreal forest environment including the effects of wildfire. Polarimetric radar is an emerging technology whose full potential is still being actively explored and discovered. More specifically, this research is ground-breaking since very little work has been performed investigating the relationship between polarimetric radar data and historical boreal wildfire events. This area of investigation is a complex marriage of forestry, geospatial information and radar engineering that requires an extensive array of data sets to facilitate analysis. This research has demonstrated that both PALSAR L-Band and Canada’s Radarsat-2 C-Band full polarimetric radars can be used to detect and classify wildfire scars within individual images. The boreal forest is a dynamic ecosystem where both the level of burn severity and the subsequent regeneration of the forest is affected by many factors that can vary widely across small distances. This work contributes to the understanding of the relationships between remotely sensed quad-pol radar signals and both the boreal ecosystem and how wildfire interacts in this environment. / Graduate / 0478 / 0538 / 0984 / ghobart@nrcan.gc.ca

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No. of bitstreams: 1 Dissertação - Mônica Alves de Vasconcelos.pdf: 4440019 bytes, checksum: c44d2ef502b2db9e99beafff1edcc52e (MD5) Previous issue date: 2012-11-30 / CNPQ - Conselho Nacional de Desenvolvimento Científico e Tecnológico / The regional importance of the Tarumã-Açu River basin, its location and extent, which represents about 12 percent in the city of Manaus, and its current situation of threat due to disorderly occupation problems, forest conversion to other types of use and soil cover, motivated the study of landscape structure analysis of this area. For this analysis a map of use and soil of the basin and cover, and indexes of landscape ecology were produced. Creating the map was based on digital supervised classification (maximum likelihood algorithm) of orbital images active sensor digital ALOSPALSAR of the year 2007, with global accuracy of 77.47. The elements of the landscape studied in this work were those of smearing and array, and the rates of landscape ecology used, that make the fragments-level characterization and use class-level and soil cover. The largest class of land cover found in the rain forest was 64.98 ha, representing 46,001 ha and demonstrating that the studied area had great natural vegetation cover in 2007. The second largest class of soil coverage was represented by the urban area by 18.02, totaling 12,759.53 acres, showing that already at the time (5 years by now), the basin was already in advanced process of occupation and man-made impacts. This can still be justified by the fact the bowl is partially located within the urban area of the city of Manaus. The porosity of the matrix of the basin was represented by 64,282 fragments. This high value is justified by the size of the study area bounded and the spatial resolution of the image ALOSPALSAR with 12.5 meters, which results in a zoom of the study area, highlighting areas usually omitted for images of lower spatial resolution. This high value is justified by the size of the study area bounded and the spatial resolution of the image ALOSPALSAR with 12.5 meters, which results in a zoom of the study area, highlighting areas usually omitted for images of lower spatial resolution. These fragments totaled 24,796.35 ha, total study area 47.07. Of these, 18.02% fragments formed by the urban area, followed by agriculture and secondary by forest, soil and water. The fragment had 95.94 % area class less than 1.0 ha, 3.78% has area between 1.0 and 10 hectares, and only 0.27% has area greater than 10 ha, totaling 174 large fragments. Most of the fragments of size less than 1.0 ha belong to the urban area, showing 48,467 fragments, and in the area greater than 10 ha this kind of coverage presented 59 spots. In relation to the average values and sets, the shape of fragments results were similar for all classes studied - around 2. The results were satisfactory for the studied region, since become required the use of remote sensing techniques due to the climatic characteristics and territorial extension of the Amazon / importância regional da Bacia do Rio Tarumã-Açu, sua localização e extensão, a qual representa cerca de 12% do município de Manaus, e sua atual situação de ameaça devido a problemas de ocupação desordenada, conversão de floresta em outros tipos de uso e cobertura do solo, motivaram o estudo da análise da estrutura paisagem dessa área. Para essa análise foi produzido o mapa de uso e cobertura do solo da bacia e foram utilizados índices de ecologia da paisagem. O mapa de uso e cobertura do solo foi produzido pela classificação digital supervisionada (algoritmo de máxima verossimilhança) de imagens orbitais digitais do sensor ativo ALOS/PALSAR do ano de 2007, com exatidão global de 77,47%. Os elementos da paisagem estudados neste trabalho foram os de mancha e matriz, sendo os índices de ecologia da paisagem utilizados, os que fazem a caracterização em nível de fragmentos e em nível de classes de uso e cobertura do solo. A maior classe de cobertura do solo encontrada na classificação foi a de floresta ombrófila, 64,98%, representando 46.001 hectares, demonstrando que a área de estudo para o ano de 2007 apresentava grande cobertura vegetal natural possivelmente primária. A segunda maior classe de cobertura do solo foi representada pela área urbana por 18,02%, totalizando 12.759,53 hectares, mostrando que já na época, há 5 anos, a bacia já se encontrava em avançado processo de ocupação e impactos antrópicos, isto pode ainda ser justificado pelo fato da bacia está parcialmente localizada dentro da área urbana do município de Manaus. A porosidade da matriz da bacia foi representada por 64.282 fragmentos. Este valor elevado justifica-se pelo tamanho da área de estudo delimitada e da resolução espacial da imagem ALOS/PALSAR com 12,5 metros, que resulta em um "zoom" da área de estudo, destacando áreas geralmente omitidas por imagens de menor resolução espacial. Esses fragmentos totalizaram 24.796,35 hectares, 47,07% da área total de estudo. Destes, 18,02% formados por área urbana, seguidos por agricultura e solo exposto, floresta secundária e água. Os 95,94% dos fragmentos apresentaram classe de área menor que 1,0 ha, 3,78% possuem área entre 1,0 e 10 hectares e apenas 0,27% possui área maior que 10 ha, totalizando 174 grandes fragmentos. A maioria dos fragmentos com área menor que 1,0 ha pertencem à classe área urbana, apresentando 48.467 fragmentos, e na classe de área maior que 10 ha esta classe de cobertura apresentou 59 manchas. Em relação aos valores de média, mediana e moda da forma dos fragmentos os resultados foram semelhantes para todas as classes estudadas, em torno de 2. Os resultados mostraram-se satisfatórios para o referido estudo na região, visto que tornam-se necessários a utilização de técnicas de sensoriamento remoto devido as características climáticas e extensão territorial da Amazônia.

Remote sensing has been widely used in disaster management. However, application of optical imageries in damage detection is not always feasible for immediate damage assessment. In the case of the Sichuan earthquake in 2008, the damaged areas were covered by cloud and fog for most of the time. The all weather SAR imageries could instead provide information of the damaged area. Therefore, more efforts are needed to explore the usability of SAR data. In regards to this purpose, this research focuses on studying the ability of using various SAR data in damage identification through image classification, and furthermore the effectiveness of fusion of various sensors in classification is evaluated.   Three different types of SAR imagery were acquired over the heavily damaged zone Qushan town in the Sichuan earthquake. The 3 types of SAR data are ALOS PALSAR L-band, RADARSAT-1 C-band and the TerraSAR-X X- band imageries.   Maximum likelihood classification method is applied on the imageries.  Four classes: Water, collapsed area, built-up area and landslide area are defined in the study area. The ability of each band in identifying these four classes is studied and the overall classification accuracy is analysed. Furthermore, fusion of these 3 types of imageries is performed and the effectiveness and accuracy of image fusion classification are evaluated.   The results show that classification accuracy from individual SAR imagery is not ideal. The overall accuracy which PALSAR gives is 30.383%, RADARSAT-1 is 31.268% while TerraSAR-X only achieves 37.168%. Accuracy statistics demonstrate that TerraSAR-X performs the best in classifying these four classes.   SAR image fusion shows a better classification result. Double image fusion of PALSAR and RADARSAT-1, PALSAR and TerraSAR-X, and RADARSAT-1 and TerraSAR-X give an overall classification accuracy of 41.88%, 42.478% and 37.758% respectively. The result from triple image fusion even reaches 52.507%. They are all higher than the result given by the individual images.   The study illustrates that the VHR TerraSAR X band SAR data has a higher ability in classification of damages, and fusion of different band can improve the classification accuracy.

The climate is changing at a rapid rate. This causes both an increase in temperature and precipitation in the northern high latitudes. Subarctic peatlands contain 30% of the carbon storage in the northern permafrost zone. Palsa mires are found in the subarctic peatlands in the northern hemisphere limited by the climate zone, which are expected to disappear within the next few decades. Degradation of permafrost can lead to carbon being released into the atmosphere, which contributes to carbon feedback. The Environmental Protection Agency gave the County Administrative Board in Norrbotten an assignment to carry out a total mapping of northern Sweden's palsa mires in 2014. Through aerial image interpretation, the County Administrative Board in Norrbotten made a grid with 100 x 100-meter squares used as an indicator where palsa mires were mapped in Sweden as presence/absence and percentage cover. This was done without any contour digitization. In my study, a total of 60 palsa mires were digitized over two transects in Norrbotten County using ArcGIS Pro and the 100 x 100-meter squares. Spatial and climate data were documented for each of the palsa mires to answer the questions of how the surface of the palsa mires has decreased and how the climate changed between 1960 and 2020. The spatial data consisted of altitude and geographical coordinates. The transects were examined both separately and together to examine differences within and between transects. The climate data consisted of the annual average temperature, seasonal winter temperature, annual average precipitation, seasonal winter precipitation, the length of the vegetation period, and frost days. The results showed that the palsas had a considerable degradation between 1960-2020. There was a difference between the northern and southern transects, for which the palsas had decreased the most in the southern transect. There was an association between reduced palsa surface and higher altitude above sea level. There was also an association between higher N coordinates and greater palsa size, and reduced palsa size with distance from the Scandinavian Mountain range. There has been an increase in both the annual temperatures and the seasonal winter temperatures. More precipitation both annually, and seasonally during the winter. The vegetation period length has increased while frost days have decreased. The palsas size has likely degraded due to the worsening climate conditions that were measured for all the palsas in this study between 1960 and 2020. Reduction of palsa mires leads to large emissions of stored carbon and contributes to positive carbon feedback that is predicted to increase the course of climate change now and in the future.