For some time now, there has been a growing awareness in society about climate change, pollution, energy and the use of natural resources. This thinking has permeated society, mainly because the extreme natural phenomena that we are experiencing nowadays are no longer outliers in our time series of meteorological records. In this regard, it has been proven that the actual high temperatures are not only unparalleled, but also consistent around the globe which is something that had not happened until now (Neukom et al., 2019). The XX century was a turning point when it comes to the increase of the landuse for crops. In a context where the population doubled, the crop production for food from 1960 to 2010 tripled, helping to reduce the hungry population. When the world’s population is expected to continue to grow up to 9 billion people (Goodfray et al., 2010) by middle XXI century, it is essential to provide ourselves with the necessary tools to maximise crop production by taking advantage of all the resources available under a sustainable point of view. Under this context, all farmers in the European Union (EU) have the possibility to benefit from the Common Agricultural Policy (CAP), which came into force in 1960. The CAP is responsible for the financing of aid to farmers on a cross-compliance basis, based on the declaration of crop types. Traditionally, the authorities have checked the veracity of declarations in person through field inspections, which is clearly a highly inefficient, impractical and very expensive system. However, in 2018 the European Commission drafted an amendment to the CAP (European Commission, 2018), to be implemented in 2020, recommending the establishment of newprocedures for checking declarations, including the use of satellite data from the Copernicus programme or other new technologies. Among the various satellite technologies, Synthetic Aperture Radar (SAR) (Brown,1967; Curlander and McDonough, 1992) has proven the most reliable,as the images are acquired with a constant pass period and they are not subject to cloud problems (as is the case with sensors working in the optical domain) and information can be acquired both day and night. They are based in a SAR microwave sensor installed on a satellite platform with a forward trajectory which offers side-looking imaging geometries. Working in a range between 300 MHz and 30 GHz, the SAR sensor is in charge of emitting electromagnetic pulses and receiving the resulting echoes from the imaged target, which can help retrieve information about its dielectric properties, geometry, orientation, shape, and its behaviour along time. For a given target, the SAR backscattering response σ0 is function of many parameters (Lee and Pottier, 2017; Dobson et al., 1985): wave frequency, polarisation, imaging configuration, roughness, geometrical structure and dielectric properties. This makes the information extraction a major problem, as identical radar responses from two different targets may lead to the same result. To cope with this problem, the main techniques are based on extending the observation space by working with the full diversity of data. Thus, the main axes of SAR data are: • Time • Polarimetry • Interferometry • Frequency. Time series of radar data constitutes a major source of information for the classification of crops and land cover, since it makes it possible to distinguish between classes by their temporal behaviour: some land covers show a uniform response along time (e.g. urban areas), whereas there are others subject to seasonal changes (e.g. crops). It may happen that different crop species give the same radar response at a given time, however, when the time window becomes larger, and consecutive acquisitions are taken over a shorter time span, the more one can detect abrupt changes in the target over a longer time interval. Polarimetry is sensitive to the shape, orientation and the scattering mechanisms of the scatterers (Boerner et al.,1981; Zyl, Zebker, and Elachi, 1987). In that sense, when using different polarisations it is possible to discern better the true nature of the target, as some features may be visible in one polarisation but not in the others. Regarding multi-spectral data, it also constitutes a major source of information which can be exploited for classification purposes. Working with sensors operating at different frequencies, or wavelengths, provides diversity in the size of the elements of the scene to which the radar is sensitive as the radar backscattering will come from elements the size of the wavelength used it. For all of the above, multifrequency data provide complementary information, as each frequency operates and interacts with elements of the same wavelength or longer, and being transparent to all others. In addition, different bands are also associated with different spatial resolutions, so a high-frequency sensor can complement the classification performance of a low-frequency sensor when there are sufficiently small details in the scene that cannot be appreciated with the spatial resolution available at the lower frequency. From all the 4 axes exposed above, Interferometry (Graham, 1974) is without a doubt the least exploited for classification purposes. While polarimetry is sensitive to the scattering mechanisms of the scene by means of the polarisation information, interferometry adds the third dimension by being sensitive to the spatial distribution of the scatterers (Treuhaft et al., 1996). Coherence and phase difference computed between two complex-valued SAR images are the main descriptors of interferometry (Bamler and Hartl, 1998), and together, can be used to derive topographic information, vegetation structure, and deformation (volcanoes, landslides, etc.). For this reason, interferometry is especially suited for classification of covers in which there is vertical distribution of elements, e.g. urban areas and vegetation (forests and crops). Polarimetric interferometric SAR (PolInSAR) (Cloude and Papathanassiou, 1998; Treuhaft and Cloude, 1999), constitutes the next step forward, and is based on the application of interferometry to all polarisation channels. Polarimetry can identify the different scattering mechanisms in the scene by using the polarisation information, whilst interferometry is able to locate the effective scattering phase centres, which are mainly dependent on frequency, the polarisation employed, the physical, geometrical structure and orientation of the scatterer. By using the combination of both we can retrieve the vertical structure of the scene, which shows a great potential for classification purposes, since classes characterised by similar backscattering or polarimetric responses can be separated if their heights are different (e.g. types of buildings, forests, crops, etc.), whereas classes with similar heights, and hence similar interferometric coherence values (e.g. grass, crops, bare soil, etc.) can be resolved using their polarimetric response. In summary, PolInSAR-based classification is attractive since polarimetric ambiguities are resolved by interferometric information and vice-versa. The lack of exploitation of the 4 data axes in the literature, plus the arrival of a new generation of SAR sensors in the near future such as ROSE-L, BIOMASS and NISAR among others, offers a new range of possibilities in terms of new types of features for classification whose results and impact must be analysed. In this context, there are many types of SAR data (i.e. features) that have not been used yet, acquired from different sensors (Sentinel-1, PAZ, TanDEMX, TerraSAR-X and ALOS-2), and whose diversity axes, either used individually or jointly, have not yet been explored for classification applications. Therefore, the exploration of these new types of SAR data, whose contribution to classification is unknown regarding crop-type mapping, is the main objective of this doctoral thesis, and consequently also its main novelty. Based on the current state of the art of the research topic the main objective of this PhD thesis is to explore the added value of new SAR features, and their potential, alone or used together, for crop type and land cover classification. In the end, several experiments will be carried out, in different test sites, in which the proposed new features will be evaluated and compared with the traditional observables used so far, with the aim of evaluating their internal potential in classification applications. / Work supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22. Mario Busquier received a grant from the University of Alicante UAFPU20-08.

Human-caused forest fires have increased in magnitude and frequency, affecting global vegetation and requiring a re-evaluation of fire regimes. Changing fire regimes have led to reduced burned areas in fire- dependent ecosystems and increased areas in fire-independent ecosystems, resulting in changes in land cover and posing a threat to native plant communities. This study focuses on monitoring vegetation recovery after fires in California, USA, using the Enhanced Vegetation Index (EVI) from MODIS time series. The goal is to determine the full recovery time and half recovery time (HRT) after forest fires in year 2017 and analyze the influence of burn severity on three land cover classes in two different climate zones in California.Analyzes show that the "Closed Forest" land cover type exhibits the longest recovery period, followed by the "Open Forest" type and “Herbaceous/Shrub” type in both climate zones but no general connection between recovery time and climate zone was observed. It is found that burn severity degree affects HRT but not the full recovery time in both Mediterranean and Semi-arid climate zones. The study mainly highlights the variations in forest fire recovery patterns between land cover types, as well as differences observed between climate zones.

Forest degradation is a significant yet underestimated source of carbon emissions. Traditionally, monitoring forest degradation has been difficult due to a lack of sufficiently frequent satellite observations and reliable analysis methods. Recent advancements in satellite remote sensing provide new opportunities to monitor, attribute and analyze forest degradation. This dissertation develops methods to monitor and attribute forest degradation and analyzes the spatial-temporal patterns of forest degradation and associated carbon emissions. A new method, Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA), was developed on Google Earth Engine (GEE) to monitor abrupt and gradual forest degradation in temperate climate zones using Landsat time series. CCDC-SMA was applied to the Republic of Georgia from 1987-2019. Results show that forest degradation affected a much larger area than deforestation. In addition, CCDC-SMA was extended to monitor forest degradation in the tropics and applied in Laos. Attribution of the drivers of forest degradation was based on a combination of CCDC-SMA results, post-disturbance land cover classification and object-based image analysis. Shifting cultivation is the largest kind of forest disturbance in Laos, affecting 32.9% ± 1.9% of Laos during 1991-2020. The results show that shifting cultivation has been expanding and intensifying in Laos, especially in the last five years. Furthermore, the length of fallow periods has been continuously declining, which indicates that shifting cultivation is becoming increasingly unsustainable. Combining biomass estimates from the Global Ecosystem Dynamics Investigation (GEDI) and area estimates of shifting cultivation, the net carbon emissions from shifting cultivation during 1991-2020 in Laos are 1.28 ± 0.12 petagrams of CO2 equivalent (Pg CO2 eq). Tree canopy height and aboveground biomass density are strongly correlated with the years of regrowth since the latest year of slash-and-burn activities, which can be expressed using logarithmic models. It takes 131 years for the biomass to recover to pre-disturbed levels based on the logarithmic models. In addition to advancements in remote sensing of forest degradation, the results of this dissertation provide valuable information for policy related to forest management and reduction of carbon emissions.

The Gulf of Mexico dune system is a broad and dynamic environment that varies greatly in geomorphology and vegetative composition across the Gulf coastline. Beach mice (Peromyscus polionotus spp.) are an endangered species that rely on coastal habitat structure. I hypothesized that beach mouse occupancy would be dependent upon coastal dune land cover and landform features. I identified coastal landforms using high-resolution elevation data and landform models in GRASS GIS and identified coastal dune vegetation classes using high-resolution aerial imagery and object oriented vegetation classification. These features were used to create a dynamic occupancy model to determine occupancy patterns in three subspecies of beach mice over multiple years of sampling. Beach mice demonstrated no distinct pattern in habitat occupancy over the study period. However, dynamic occupancy models demonstrated that habitat occupancy varied between individual sites, indicating that habitat selection may be population specific.

Earth observation data has increased significantly over the last decades with satellites collecting and transmitting to Earth receiving stations in excess of three terabytes of data a day. This data acquisition rate is a major challenge to the existing data exploitation and dissemination approaches. The lack of content and semantics based interactive information searching and retrieval capabilities from the image archives is an impediment to the use of the data. The proposed framework (Intelligent Interactive Image Knowledge retrieval-I3KR) is built around a concept-based model using domain dependant ontologies. An unsupervised segmentation algorithm is employed to extract homogeneous regions and calculate primitive descriptors for each region. An unsupervised classification by means of a Kernel Principal Components Analysis (KPCA) method is then performed, which extracts components of features that are nonlinearly related to the input variables, followed by a Support Vector Machine (SVM) classification to generate models for the object classes. The assignment of the concepts in the ontology to the objects is achieved by a Description Logics (DL) based inference mechanism. This research also proposes new methodologies for domain-specific rapid image information mining (RIIM) modules for disaster response activities. In addition, several organizations/individuals are involved in the analysis of Earth observation data. Often the results of this analysis are presented as derivative products in various classification systems (e.g. land use/land cover, soils, hydrology, wetlands, etc.). The generated thematic data sets are highly heterogeneous in syntax, structure and semantics. The second framework developed as a part of this research (Semantics-Enabled Thematic data Integration (SETI)) focuses on identifying and resolving semantic conflicts such as confounding conflicts, scaling and units conflicts, and naming conflicts between data in different classification schemes. The shared ontology approach presented in this work facilitates the reclassification of information items from one information source into the application ontology of another source. Reasoning on the system is performed through a DL reasoner that allows classification of data from one context to another by equality and subsumption. This enables the proposed system to provide enhanced knowledge discovery, query processing, and searching in way that is not possible with key word based searches.

This study compares suspended sediment and land use/land cover in the watershed of Weeks Bay, Alabama. Using Landsat thematic mapper imagery, potential high and low erosion sites were determined based on the increase in urban development form 2002 to 2005. In situ sediment sampling was used to test the hypothesis that the high erosion potential sites have larger amounts of suspended sediments. Additionally, sampling was performed along the Fish and Magnolia rivers to establish a background total suspended sediment level. The background study established an average total suspended sediment concentration of 18.71 mg/L for the Fish River and 17.47 mg/L for the Magnolia River, which are higher than previous studies. The results of the comparison between suspended sediments and land use/land cover proved to be more complex than expected due to variation in precipitation, to the 30 m satellite resolution, and to the criteria for classifying urban land use.

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