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The extent, frequency and intensity of forest fires in Mediterranean regions have become an important problem in recent decades. Nowadays, remote sensing is an essential tool for the planning and management of the land at different scales. In the field of forest fires remote sensing images have been used in many different types of studies and currently applied to detect burned areas by means of images, providing quickly, easily and affordable the limits of burned areas immediately during or after the fire season. The importance of these products lies in the possibility to obtain perimeter, area and damage level caused by wildfires. The objective of this study was the evaluation of multi-scale remotely sensed images and various mapping methods for the identification and estimation of burned areas. The area of the study was situated in Galicia, a region of Spain punished year after year by important wildfires. By employing 7 images before, during and after the occurrence of forest fires, and working with different methods it was possible the collection of several products and results. The satellite imagery used was Landsat TM5 and MODIS, and the methods carried out were mainly spectral indices such as Normalized Burnt Ratio (NBR), Short Wave InfraRed Index (SWIR), Burnt Area Index (BAI), Burnt Area Index for MODIS (BAIM) and supervised classifications. Based on a wide literature review there were selected as suitable techniques for assess, localize and quantify burned areas. The work was separated in two sections, being differenced monotemporal and multitemporal analyses, depending on the images involved in each part. The results showed that which indices can distinguish burned areas with the high precision. There were found common problems of all indices as the classification of burned areas in shaded regions as unburned areas. Landsat images proved to be the most accurate images to perform studies with burned areas due to its high spatial resolution comparing with MODIS images. As a final products were obtained with precision the total burned area, the perimeter, the localization and the burn severity of the regions affected by wildfires. The data obtained could be used to create a database of burned areas, or based in the repetitive patterns, as useful information in order to prevent future forest fires.

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The mining company LKAB extracts iron ore in three areas in the north of Sweden, Kiruna, Malmberget and Svappavaara. The iron ore is refined and in the pelletizing plants the material is rolled to finished pellet product. The different steps of pelletizing plants causes among other things emissions to the air. It occurs occasionally stops in the production and the emissions are vented via an emergency chimney without purification. The aim for this work is to look into how LKAB may improve their monitoring of air emissions and control of ambient air quality and the analysis methods which are suitable for measuring. More recently, optical methods have been included in the measurements, which have an advantage over the collection of sample for analysis in a laboratory. The work has been limited to air emissions and optical methods. Through extensive literature review, questions has been answered on regarding which are the most important parameters to measure concerning air emissions, which methods that are suitable for continuous measurement and the parameters that are important in meteorological dispersion modeling. Three different methods of measurement are included, DOAS, FT-IR and LIDAR. The optical methods are discussed on the basis of how they can apply for adjustment to LKAB’s operations and its northern geographical location. A method suited better than the others, DOAS, which is the measuring method to be adapted best to LKAB’s operations and for measurement of the emergency chimney. Measurement results from DOAS can be used to make a dispersion model.

Klarälven är en av Sveriges längsta älvar och är unik i Skandinavien för sitt karaktäristiska meanderlopp. Det unika i Klarälvens fall är att älvfåran är instängd mellan två bergssidor. Klarälvens meanderlopp innebär erosion och förändringar i älvens lopp. Syftet med studien är att jämföra och analysera hur erosionen längs en sträcka av Klarälven har förändrats över tid. Underlaget består av en historisk karta från 1883 samt ortofoton från 1961 och 2014. Studien är avgränsad till en 25 km lång sträcka norr om Ekshärad i Värmland. Metoder som används omfattar georeferering av den historiska kartan samt skärmdigitalisering av samtliga data. Därefter granskades och jämfördes resultaten genom överlagringsanalys uppdelat i två perioder samt alla perioder på en gång. Resultatet visar att omfattande erosion och avlagring har skett. I älvens ytterkurvor där vattnets hastighet är som högst har störst erosion skett och i innerkurvorna där vattnet rinner långsammare har det eroderade materialet avlagrats. De största skillnaderna är uppmätta mellan åren 1883 och 1961. Men erosionsförändring i mindre skala har skett även mellan 1961 och 2014. / River Klarälven is one of Sweden's longest rivers and is unique in Scandinavia because of its characteristic meander course.  The uniqueness in the case of Klarälven is that the riverbed is trapped between two mountain sides. The meandering course of Klarälven means major changes in its path due to extensive erosion along some reaches. The purpose of this study is to compare and analyze how some reaches of River Klarälven have changed over time. The data consists of a historical map from 1883 as well as orthophotos from 1961 and 2014. The study is limited to a 25 km meandering reach north of the town Ekshärad in northern Värmland. Methods involved are georeferencing of the historical map and screen digitizing of all data. Subsequently, the results were reviewed and compared by overlay analysis divided into two periods and all periods at once. The result shows that extensive erosion and deposit have taken place. In the river's outer curves where the velocity of the water is highest is also the place where most erosion occurs. In the inner curves where the water velocity is slower, the eroded material is deposited. The largest differences were measured between the years 1883 and 1961. However, erosion changes on a smaller scale also occurred between 1961 and 2014.

Remote sensing technologies, such as satellite imagery, have proven to be a powerful tool for land cover classification when combined with machine learning algorithms. Depending on which type of sensor is used for the imagery, different properties of land cover classes may be distinguished. Because of this, a data set containing a combination of data from different sensors could potentially further improve the classification accuracy. To determine if adding data from the radar sensor on the satellite constellation Sentinel-1 to data from the multispectral optical sensor on the satellite constellation Sentinel-2 could improve the accuracy of land cover classification, a tool for combining data from both satellites was developed. The classification accuracy using the combined data was then compared to using non-combined Sentinel-2 data with a neural network and a random forest classifier. We found that the random forest classifier produced a higher accuracy than the neural network for both the combined data and non-combined data. The combined data increased the accuracy further compared to the non-combined data. However, the increase produced by the combined data was small and most likely not worth the extra computational power required to implement Sentinel-1 data to Sentinel-2 data.

GNSS Radio Occultation (GNSS-RO) is an opportunistic Earth sensing technique where GNSS signals passing through the atmosphere are received in low Earth orbit and processed to extract meteorological parameters. As signals are received along an orbit, the measured Doppler shift is transformed to a bending angle profile (commonly referred to as bending angle retrieval), which, in turn, is inverted to a refractivity profile. Thanks to its high vertical resolution and SI traceability, GNSS-RO is an important complement to other Earth sensing endeavors. In the lower troposphere, GNSS-RO measurements often get degraded and biased due to sharp refractive gradients and other complex structures. The main objective of this thesis is to explore contemporary retrieval methods such as phase matching and full spectrum inversion to improve their performance in these conditions. To avoid the bias caused by the standard inversion, we attempt to derive additional information from the amplitude output of the examined retrieval operators. While simulations indicate that such information could be found, it is not immediately straightforward how to achieve this with real measurements. The approach chosen is to examine reflected signal components and their effect on the amplitude output.

Creating 3D models of our surrounding world has seen a rapid increase in research and development over the last few years. A common method is to use laser scanners. Mapping is done either by ground based systems or airborne systems. With stationary ground-based laser scanning, or terrestrial laser scanning (TLS), it is possible to obtain high accuracy point clouds. But stationary TLS can often be a cumbersome and time-demanding task due to its lack of mobility. Because of this, much research has gone into mobilised TLS systems, referred commonly to as mobile laser scanning (MLS). Georeferencing point clouds to a world coordinate system is a difficult task in environments where global navigation satellite systems (GNSS) is unreliable. One such environment is forests, where the GNSS signal can be blocked, absorbed or reflected from the trees and canopy. Accurate georeference of points clouds for MLS systems in forests is difficult task that can be solved by using additional measurement instruments and post-processing algorithms to reduce the accumulation of errors, also known as drift. In this thesis a backpack-based MLS system to be used in forests was tested. The MLS system was composed of a GNSS, an inertial navigation unit (INS) and a laser scanner. The collected data was post-processed and analyzed to reduce the effects of detecting multiple ground layers and multiples of the same tree due to drift. The post-processing algorithm calculated tree and ground features to be used for adjusting the point cloud in the horizontal and vertical planes. The forest survey was done for an area roughly 40 meters in diameter. The MLS data was compared against TLS data as well as manual caliper data - where the caliper data was only measured in an area roughly 24 meters in diameter. The results indicated that the effects of multiple ground layers and multiple tree copies were removed after post-processing. Out of the total 214 TLS trees, 185 managed to be co-registered to MLS trees. The root mean square error (RMSE) and bias of the diameter at breast height (DBH) between the MLS andTLS data were 27.00 mm and -9.33 mm respectively. Co-registrationof the MLS and manual caliper data set gave 36 successful matches out of the total 43 manually measured DBH. The DBH RMSE and bias were 16.95 mm and -10.58 mm respectively. A Swedish TLS forest study obtained a DBH RMSE and bias (between TLS and caliper) of approximately 10 mm and +0.06 mm respectively. A Finnish backpack MLS forest study obtained a DBH RMSE and bias (between MLS and TLS) of 50.6 mm and +11.1 mm respectively. Evaluating the difference in radius at different heights along the tree stems between the MLS and TLS revealed a slight dependence on height, as the radius difference increased slightly closer to the stem base. The results indicated that backpack-based MLS systems has the potential for accurate lidar mapping in forests, and future development is of great interest to improve this system further.

Urbanization is one of the most significant phenomena of the anthropogenic influence on the Earth’s environment. One of the principal results of the urbanization is the creation of megacities, with their local climate and high impact on the surrounding area. The design and evolution of an urban area leads to higher absorption of solar radiation and heat storage in which is the foundation of the urban heat island phenomenon. Remote sensing data is a valuable source of information for urban climatology studies. The main objective of this thesis research is to examine the relationship between land use and land cover types and corresponding land surface temperature, as well as the urban heat island effect and changes in these factors over a 10 year period. 10 megacities around the world where included in this study namely Beijing (China), Delhi (India), Dhaka (Bangladesh), Los Angeles (USA), London (UK), Mexico City (Mexico), Moscow (Russia), New York City (USA), Sao Paulo (Brazil) and Tokyo (Japan). Landsat satellite data were used to extract land use/land cover information and their changes for the abovementioned cities. Land surface temperature was retrieved from Landsat thermal images. The relationship between land surface temperature and landuse/land-cover classes, as well as the normalized vegetation index (NDVI) was analyzed. The results indicate that land surface temperature can be related to land use/land cover classes in most cases. Vegetated and undisturbed natural areas enjoy lower surface temperature, than developed urban areas with little vegetation. However, the cities show different trends, both in terms of the size and spatial distribution of urban heat island. Also, megacities from developed countries tend to grow at a slower pace and thus face less urban heat island effects than megacities in developing countries.

Image classification based on remotely sensed data is the primary domain of automatic mapping research. With the increasing of urban development, keeping geographic database updating is imminently needed. Automatic mapping of land cover types in urban area is one of the most challenging problems in remote sensing. Traditional database updating is time consuming and costly. It has usually been performed by manual observation and visual interpretation, In order to improve the efficiency as well as the accuracy, new technique in the data collection and extraction becomes increasingly necessary. This paper studied an object-based decision tree classification based on orthophoto and lidar data, both alone and integrated. Four land cover types i.e. Forest, Water, Openland as well as Building were successfully extracted. Promising results were obtained with the 89.2% accuracy of orthophoto based classification and 88.6% accuracy of lidar data based classification. Both lidar data and orthophoto showed enough capacity to classify general land cover types alone. Meanwhile, the combination of orthophoto and lidar data demonstrated a prominent classification results with 95.2% accuracy. The results of integrated data revealed a very high agreement. Comparing the process of using orthophoto or lidar data alone, it reduced the complexity of land cover type discrimination. In addition, another classification algorithm, support vector machines (SVM) classification was preformed. Comparing to the decision tree classification, it obtained the same accuracy level as decision tree classification in orthophoto dataset (89.2%) and integration dataset (97.3%). However, the SVM results of lidar dataset was not satisfactory. Its overall accuracy only reached 77.1%. In brief, object-based land cover classification demonstrated its effectiveness in land cover map generation. It could exploit spectral and spatial features from input data efficiently and classifying image with high accuracy.