Skattning av skogliga variabler är inte längre begränsade till datainsamling genom manuell fältmätning. Numera kan skattningar göras genom insamling av data från såväl laserskanning (Airborne Laser Scanning, ALS) som mobilapplikationen Katam Forest (KF) som företaget Katam utvecklat. Dessutom kan varje enskilt träd skattas genom att kombinera Katam Treemap (KT), som använder sig av fotogrammetri för att mäta trädhöjd och identifiera träd, med KF (KF+KT) . I denna studie jämfördes skattningar av grundytevägd medeldiameter (DGV) och grundyta (GY) utförda med manuell fältmätning, ALS, KF samt KF+KT, i tre olika grandominerande bestånd. KF+KT beräknades felaktigt varför inga slutsatser kan dras om metoden. KF visade ett lägre relativt RMSE än ALS, 7,8 % jämfört med 9,6 % vid skattning av DGV. För GY var relativ RMSE 22,5 % vid KF, och 23 % vid ALS. KF bedöms med fördel kunna användas likvärdigt med manuell fältmätning.

Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. The study attempted to investigate the potential of using Google Earth Engine (GEE), and the combinations of different datasets from Sentinel-1 (SAR), Sentinel-2 multispectral imagery, and LiDAR data to estimate AGB, by using the random forest algorithm (RF). Two models were proposed: the first one (Model 1) detected the AGB temporal changes from 2016 to 2021 in Southern Sweden; while the second one (Model 2) focused on Hultsfred municipality and studied the influence of different variables including the canopy height. Besides, six experimental groups of variables were tested to determine the performance of using different types of remote sensing data. We validated these two models with the observed AGB, and the findings showed that the combination of SAR polarization, multisprectral bands, vegetation indices able to estimate AGB for Model 1. In addition, Model 2 showed that further using the canopy height data can further improve the estimation.  We also found out that the spectral bands from Sentinel-2 contributed the most to AGB estimation for Model 1 in terms of: bands B3 (Green), B4 (Red), B5 (Red edge), B11 (SWIR), B12 (SWIR); and, vegetation indices of RVI, DVI, and EVI. On the other hand, for Model 2, B1(Ultra blue), B4 (Red), EVI, SAVI, and the canopy height are the most crucial variables for estimating AGB. Besides, the radar backscatter values using VV and VH modes from Sentienl-1 were both important for Models 1 and 2. For Model 1, the experimental group with the best accuracy was the group that used all variable combinations from Sentinel-1 and 2, and its   was 0.33~0.74. For Model 2, the group that used all the variables, in addition to the canopy height performed the best, where its   is 0.91. These therefore showed the benefit of integrating different remote sensing data sources.  In conclusion, this study showed the potential of using RF and GEE to estimate AGB in Southern Sweden. Furthermore, this study also shows the possibility of handling large dataset for a large scale area, at the resolution of 10 m, and producing time series AGB maps from 2016 to 2021. This can help enhance our understanding of AGB temporal changes and carbon stock detection in Southern Sweden, that can provide valuable insights for forest management and carbon monitoring.

No description available.

The Arctic cryosphere is experiencing a higher rate of warming compared to the rest of the world due to Arctic amplification. As glacier elevation change provide reliable evidence of climate change it is routinely measured by satellite altimeters. Satellite altimetry, while a valuable tool for monitoring elevation change over time, is subject to inherent uncertainties caused by, among other factors, the small scale surface roughness of the target surfaces. Previous studies have identified surface roughness as a key source of uncertainty when measuring sea ice freeboard and studies suggest the surface roughness strongly influences the Synthetic Aperture Radar (SAR) signatures of sea ice. Similar studies over snow- and glacier surfaces, are rare. In this context, we attempt to conduct a small scale calibration and validation (cal/val) campaign over glacier surfaces, using the ideal location and infrastructure of the University Centre in Svalbard. We demonstrate the process, from planning through field data collection and data analysis. By doing so, we identify good as well as bad practices. Using high resolution in-situ LiDAR data, collected under two ICESat-2 (IS2) overpasses in Svalbard we generated Digital Elevation Models (DEM) and calculated surface roughness estimates across glacier- and snow surfaces. The surface roughness was quantified by calculating the Root Mean Square (RMS) of deviations from the overall topography of the surfaces. The DEMs were used for direct comparison with the satellite elevation retrievals and the observed elevation differences were tested for correlation with surface roughness at different length scales. We then investigated the effect of surface roughness on the photon cloud of the lower level ATL03 ICESat-2 data products, by quantifying the precision in the data. We found little to no correlation between RMS roughness and the observed elevation differences between in-situ and satellite data sets, possibly explained by errors in georeferencing the DEMs. We show moderate to strong correlation between photon cloud precision and along- and across-track absolute surface slopes, with correlation coefficients of 0.6–0.8. Correlation between photon cloud precision and RMS roughness was found, with a maximum correlation coefficient of 0.9 for a roughness length scale of 1m. The results suggest IS2 is sensitive to surface roughness at similar length scales but we identify a need for more data, covering a wider range of surfaces and potential roughness scenarios, to draw strong conclusions. We demonstrate how a small team can carry out a cal/val campaign in the high arctic and collect coincident data under satellite overpasses, data which is typically rare for the remote high Arctic regions.

BiG

Disasters, whether natural or man-made have become an issue of mounting concern all over the world. Natural disasters such as floods, earthquakes, landslides, cyclones, tsunamis and volcanic eruptions are yearly phenomena that have devastating effect on infrastructure and property and in most cases, results in the loss of human life. Floods are amongst the most prevalent natural disasters. The frequency with which floods occur, their magnitude, extent and the cost of damage are escalating all around the globe. Accra, the capital city of Ghana experiences the occurrence of flooding events annually with dire consequences. Past studies demonstrated that remote sensing and geographic information system (GIS) are very useful and effective tools in flood risk assessment and management.  This thesis research seeks to demarcate flood risk areas and create a flood risk map for the Greater Accra Metropolitan Area using remote sensing and Geographic information system. Multi Criteria Analysis (MCA) is used to carry out the flood risk assessment and Sentinel-1A SAR images are used to map flood extend and to ascertain whether the resulting map from the MCA process is a close representation of the flood prone areas in the study area.  The results show that the multi-criteria analysis approach could effectively combine several criteria including elevation, slope, rainfall, drainage, land cover and soil geology to produce a flood risk map. The resulting map indicates that over 50 percent of the study area is likely to experience a high level of flood.  For SAR-based flood extent mapping, the results show that SAR data acquired immediately after the flooding event could better map flooding extent than the SAR data acquired 9 days after.  This highlights the importance of near real-time acquisition of SAR data for mapping flooding extent and damages.  All parts under the study area experience some level of flooding. The urban land cover experiences very high, and high levels of flooding and the MCA process produces a risk map that is a close depiction of flooding in the study area.  Real time flood disaster monitoring, early warning and rapid damage appraisal have greatly improved due to ameliorations in the remote sensing technology and the Geographic Information Systems.

Sea ice is a vital component of the cryosphere and does not only influence the polar regions but has a more global influence. Indeed, sea ice plays a major role in the regulation of the global climate system as the sea ice cover reflects the sun radiation back to the atmosphere keeping the polar regions cool. The shrinkage of the sea ice cover entails the warming up of the oceans and as a consequence, a further amplification of the melting of sea ice. Therefore, the polar regions are sensitive to climate change and monitoring the sea ice cover is very important. To assess sea ice change in the polar regions, satellite active microwave sensors, scatterometers, are used to observe the evolution of sea ice extent and sea ice types. Thus, this research aims at creating a historic record of daily global Arctic and Antarctic sea ice extents and analysing the change in sea ice types with scatterometer data. A Bayesian sea ice detection algorithm, developed for the Advanced scatterometer (ASCAT), is applied and tuned to the configurations of the scatterometers on board the European Remote Sensing satellites, ERS\textendash 1 and ERS\textendash 2. The sea ice geophysical model functions (GMFs) of ERS and ASCAT are studied together to validate the use of ASCAT sea ice GMF extrapolated to the lower incidence angles of ERS. The main adaptations from the initial algorithm aim at compensating for the lower observation densities afforded by ERS with a refined spatial filter and time\textendash variable detection thresholds. To further analyse the backscatter response from sea ice and derive information on the different sea ice types, a new model of sea ice backscattering at C\textendash band is proposed in this study. This model has been derived using ERS and ASCAT backscatter data and describes the variation of sea ice backscatter with incidence angle as a function of sea ice type. The improvement of the sea ice detection algorithm for ERS\textendash 1 and ERS\textendash 2, operating between 1992 and 2001, leads to the extension of the existing records of daily global sea ice extents from the Quick scatterometer (QuikSCAT) which operated from 1999 to 2009 and ASCAT operating from 2007 onwards. The sea ice extents from ERS, QuikSCAT and ASCAT show excellent agreement during the overlapping periods, attesting to the consistency and homogeneity of the long\textendash term scatterometer sea ice record. The new climate record is compared against passive microwave derived sea ice extents, revealing consistent differences between spring and summer which are attributed to the lower sensitivity of the passive microwave technique to melting sea ice. The climate record shows that the minimum Arctic summer sea ice extent has been declining, reaching the lowest record of sea ice extent in 2012. The new model for sea ice backscatter is used on ERS and ASCAT backscatter data and provides a more precise normalization of sea ice backscatter than was previously available. An application of this model in sea ice change analysis is performed by classifying sea ice types based on their normalized backscatter values. This analysis reveals that the extent of multi\textendash year Arctic sea ice has been declining remarkably over the period covered by scatterometer observations.

Agriculture faces significant challenges with current climate change and a growing global population. Increasing dry periods, even in Sweden, require increased field irrigation to secure harvests while water availability decreases. Research in remote sensing has led to significant technological advancements where soil moisture can now be detected with precision at 100x100 meters. This study aims to evaluate how well satellite analysis of soil moisture aligns with field experiments and its usefulness for irrigation decisions. Through the collection and analysis of data from both field measurements and satellites, the agreement between these has been examined where statistical measures such as NNSE, RMSE, and MAE have been used. A challenge with satellite analysis is its limitation to measuring soil moisture only in the top layer (3-5 cm) of the soil, which does not estimate the water content in the root zone required for irrigation decisions. To address this, the cumulative function matching method (CDF-method) was used to estimate the water content in the root zone based on satellite measurements of the top soil layer. The results show that satellite data aligns with field measurements for various locations in Sweden, with NNSE values between 0.08-0.61 and an accuracy (MAE) of 4.51-6.99 % water content. Furthermore, the results indicate that water content in the root zone can be estimated from surface soil moisture using the CDF matching method with an accuracy of 1.28-6.41 %. However, this method requires cross-validation in the field where the estimation is to be performed, and the use of satellite analysis for irrigation decisions is limited to the field level. To effectively utilize satellite analysis as a global decision support, methods for estimating water content in the root zone need to be developed to be more applicable for the different soil types.

Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.       Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.

Det växande behovet av metoder för tillförlitlig identifiering och kartering av markfuktighet i landskapet inom naturvård och skogsbruk ställer ökade krav på kartmaterialet. Dagens kartor är generellt baserade på flygbildstolkning vilket ofta resulterar i en ofullständig och generaliserad redovisning av våtmarker. Med LiDAR-data och moderna GIS-program kan topografiska markfuktighetsindex med hög upplösning genereras, vilket potentiellt kan ge en mer realistisk och detaljerad redovisning. I detta examensarbete utvärderades det etablerade Topgraphic Wetness Index (TWI) och det mer nyligen utvecklade Depth to Water Index (DTW). Fem TWI-raster med upplösning 2–20 m skapades. Fyra DTW-raster med varierad Flow Initiation Threshold (1–8 ha) skapades, samtliga med 2 m upplösning. Som referensmaterial användes Vegetationskartan (GSD-Vegetationsdata), vilken bedömdes vara det bästa tillgängliga alternativet. Resultatet från studien visar att indexkartorna överlag identifierar fler våtmarker och visar en större andel våtmark än Vegetationskartan. DTW ger en tydligare och mer realistisk kartbild jämfört med TWI. De använda metoderna för generering av indexbaserad markfuktighetskartering innehåller vissa osäkerheter. För DTW finns god förbättringspotential för att minimera dessa osäkerheter men även i att vidareutveckla metoden. Indexkartor (främst DTW) kan trots detta anses användbara i syfte att identifiera markfuktighet, särskilt i kombination med andra typer av kartdata. / The increasing need for reliable identification of wet soils in nature preservation and forestry requires more detailed and accurate maps.  Previous maps are mostly based on aerial photos which may result in an incomplete and generalized representation of wetlands. Using high resolution LiDAR-data and modern GIS software, topographic indices can be generated which has the potential to model wetlands in a more realistic and detailed manner. This study evaluated the widely used Topgraphic Wetness Index (TWI) and the more recently developed Depth to Water Index (DTW). Five TWI-raster with a resolution of 2-20 m was created. Four DTW-raster with 2 m resolution was created with Flow Initiation Threshold varying between 1-8 ha. A vegetation map (GSD-Vegetationsdata) was used as reference since it was judged to be the best available option for comparison. The results show that the index-maps overall identify more wetland area than the vegetation map. The DTW-index generates a clearer and realistic map compared to the TWI. The methods used for index-based mapping of soil moisture has some uncertainties. However, the DTW-index has good potential for further development. It was concluded that index-based maps (primarily DTW) can be useful for identification of soil moisture, especially if combined with other sources of map data.