Spatial and temporal patterns of dissolved organic matter (DOM) were characterized using a combination of spectroluorometric measurements and multivariate analysis techniques. The study was conducted over a four-year (2012-2016) period in multiple watersheds located in the Gulf-Atlantic Coastal Plain Physiographic region of the southeast USA as well as in the Indo-Gangetic Plain of India. Surface water samples were collected from five major lakes in the Mississippi, an estuarine region in the southeastern Louisiana, and from the coastal region in the eastern Mississippi Sound in the USA, and a large river (Ganges River) in India. Absorption and fluorescence measurements were performed to generate absorption spectra and excitation-emission matrices (EEMs). Using parallel factor analyses (PARAFAC), EEM models were developed to characterize the biogeochemistry of DOM in three studies in this project. Principal component analysis and regression analyses of DOM data indicated that the northern Mississippi lakes were majorly influenced by agricultural land use, estuarine region was affected by natural DOM export from forests and wetlands, while the coastal waters were affected by a mix of anthropogenic and natural inputs of DOM. Spatial analyses indicated that DOM derived from watershed with increased wetland coverage was humic and aromatic while the DOM derived from agricultural watersheds was bioavailable. Temporal patterns of DOM in the estuary indicated the influence of hydrologic conditions and summer temperatures, and revealed strong seasonality in DOM evolution in the watershed. During high discharge periods (spring), aromatic and humic DOM was exported from the watershed while strong photochemical degradation during summer resulted bioavailable DOM. Comparison between two river systems, a highly urbanized large river and a small pristine river, indicated the influence of anthropogenic inputs of DOM in the large river system. DOM was bioavailable during summer due to anthropogenic activities in the large river system while it varied with hydrological connectivity in a small river system during summer and winter. In conclusion, this study has improved my understandings of the DOM properties, which are critical for a comprehensive assessment of biogeochemical processes undergoing in important water bodies on which our society is heavily dependent upon.

Variation of dissolved of organic carbon concentration in stream water is a consequence of process changes in the surrounding terrestrial environment. This study will focus on 1) Identify significant environmental factors controlling the spatial and temporal variation of DOC in terrestrial ecosystems of a watershed southeast of Boston, Massachusetts; 2) Model the DOC leaching from different land cover and examine the relationship between leaching flux and in-stream DOC. Our hypothesis is variations of in stream DOC is closely related to watershed properties and environmental factors at annual, seasonal, and daily scales, especially land cover type, watershed size and hydrology. To explore the relationship of hydrology and DOC variation at ungauged sub-basin, we examined the effectiveness of using simulated stream flow from Soil Water Assessment Tool (SWAT) to study terrestrial DOC dynamics. Our results demonstrated that streamflow, drainage area, and percent of wetland and forest were particularly strong predictors in watersheds with a large proportion of developed area. The resulting linear model is able to explain about 70.2% (R2=0.702) and 65.1% (R2=0.651) of the variance of in-stream DOC concentrations at seasonal and annual scales respectively. Results also suggest that more frequent DOC sampling is necessary to establish the quantitative relationship between simulated stream flows from the SWAT and in-stream DOC concentrations at daily scale. The physically based ecosystem model developed in this study shows that DOC leaching from various land cover are highly correlated (up to 80%) with in-stream DOC by using ecological process with incorporated different hydrological pathways. It shows that leaching of DOC from soil is a significant contributor to the in-stream DOC. The production of DOC is largely controlled by the vegetation type and soil texture. Considering the hydrologic control on DOC transport with different pathways of water at finer spatial and temporal scale highlights the need to identify the quantitative relationships between water and carbon flux.

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Urban represents one of the most dynamic areas in the global change context. To support rational policies for sustainable urban development, remote sensing technologies such as Synthetic Aperture Radar (SAR) enjoy increasing popularity for collecting up-to-date and reliable information such as urban land cover/land-use. With the launch of advanced spaceborne SAR sensors such as RADARSAT-2, multitemporal fully polarimetric SAR data in high-resolution become increasingly available. Therefore, development of new methodologies to analyze such data for detailed and accurate urban mapping is in demand.   This research investigated multitemporal fine resolution spaceborne polarimetric SAR (PolSAR) data for detailed urban land cover mapping. To this end, the north and northwest parts of the Greater Toronto Area (GTA), Ontario, Canada were selected as the study area. Six-date C-band RADARSAT-2 fine-beam full polarimetric SAR data were acquired during June to September in 2008. Detailed urban land covers and various natural classes were focused in this study.   Both object-based and pixel-based classification schemes were investigated for detailed urban land cover mapping. For the object-based approaches, Support Vector Machine (SVM) and rule-based classification method were combined to evaluate the classification capacities of various polarimetric features. Classification efficiencies of various multitemporal data combination forms were assessed. For the pixel-based approach, a temporal-spatial Stochastic Expectation-Maximization (SEM) algorithm was proposed. With an adaptive Markov Random Field (MRF) analysis and multitemporal mixture models, contextual information was explored in the classification process. Moreover, the fitness of alternative data distribution assumptions of multi-look PolSAR data were compared for detailed urban mapping by this algorithm.   Both the object-based and pixel-based classifications could produce the finer urban structures with high accuracy. The superiority of SVM was demonstrated by comparison with the Nearest Neighbor (NN) classifier in object-based cases. Efficient polarimetric parameters such as Pauli parameters and processing approaches such as logarithmically scaling of the data were found to be useful to improve the classification results. Combination of both the ascending and descending data with appropriate temporal span are suitable for urban land cover mapping. The SEM algorithm could preserve the detailed urban features with high classification accuracy while simultaneously overcoming the speckles. Additionally the fitness of the G0p and Kp distribution assumptions were demonstrated better than the Wishart one. / <p>QC 20110315</p>

Deforestation of tropical forests is one of the most pressing environmental problems of the twenty-first century, leading to the loss of environmental services such as climate regulation and biodiversity. The expansion of the agricultural frontier by small landholder farmers continues to be one of the major drivers of land use change in the Amazon region. Much of the recent research in the Brazilian Amazon has been focused on modeling their behavior in order to prescribe policies that can curb current deforestation rates and promote more sustainable land use practices. The availability of more sophisticated remote sensing and economic modeling tools has led to the proliferation of agricultural household level models that attempt to explain land use change processes at the farm level. This dissertation tests the household life cycle theory in one of the oldest colonization fronts in the Brazilian Amazon: Rondônia, now a post-frontier. The study examines household and farm level changes over time for specific aspects of the frontier process that can be tested using the household life cycle theory. This study introduces important additions to the life cycle theory in order to consider the more dynamic and complex set of factors that characterize modern frontier processes. Specifically the study examines: (1) property fragmentation and expansion processes, (2) property ownership, turnover and change, and (3) land use change processes at the property level. These are linked to changes in the social and economic features of the smallholder farmer as it moves along its life cycle. The central hypothesis is that these changes in property and land use dynamics can be explained by the corresponding changes in the life cycle of the household as the frontier evolves over time into a post-frontier. It was found that the household life cycle theory did not adequately explain land use change processes over time. As the frontier evolved into the modern post-frontier, the labor and drudgery constraints associated with the initial frontier processes, as exemplified in the household life cycle theory, became less relevant. The Sauerian concept of cultural successions and the concept of scale from hierarchical ecology are used in order to explain the apparent inconsistencies found between the household life cycle theory and land use change processes over time and at different scales of analysis. The household life cycle theory is a useful theoretical framework from which to examine the effects of household level factors on land use; however, this must be embedded within concepts of time and scale that determine their differentiated impact and behavior. Existing plans to expand road infrastructure into the Amazon region will open-up previously inaccessible rainforest regions to agricultural frontier expansion at a scale unprecedented since the mid-eighties. Findings from this study reveal that policies based on household life cycle postulates will have limited impacts in reducing deforestation rates and promoting sustainable land use practices. Appropriate accounting of the social and environmental costs of future infrastructure development projects should consider associated frontier agricultural expansion costs to discourage further deforestation. / Ph. D.

Over the past couple of decades, fires have experienced changes on a global scale. These changing fire regimes point to an alarming direction where fire-dependent ecosystems are experiencing a decline in burned area, while fire-independent ecosystems are experiencing an increase. As a result, land cover change is seen in both types of ecosystems where the native plant communities run the risk of disappearing, and recovery becomes increasingly important. One of the areas experiencing a notable increase in fires is California, US. Here, both observed and projected changes indicate increasing frequency of fires, fire size and fire severity. In this study, post-fire recovery for 5 land cover types in California is compared using Normalized Difference Vegetation Index (NDVI) time-series. Two metrics are used for post-fire recovery, where a metric that describes short-term recovery is found most appropriate for a comparison between land cover types. It is found that the land cover type “Trees” has the longest recovery, followed by “Herbaceous/Shrubs”. Faster recovery times are found in the late fire season compared to the early fire season, indicating an influence of precipitation on post-fire vegetation recovery. Similarly, faster recovery times are found in a semi-arid climate zone compared to the Mediterranean climate zones. This indicates the potential influence of species composition on post-fire vegetation recovery. Results particularly show differences in post-fire recovery between land cover types, but also between fire seasons and climate zones. To examine these details in further detail, fire severity, meteorological data, and a more detailed classification for vegetation types could be implemented as factors determining post-fire recovery.

Excessive nutrient concentrations from a different point and non-point sources are the main cause of water impairment in the United States. Appropriate management practices, according to the source and quantity of pollutions, need to be implemented to control excessive nutrient influx in the water body. Various types of hydrological and water quality models with diverse function, capability and degree of complexity are employed to quantify watershed hydrologic processes and nutrient pollution. Multiple models can be applied to a watershed but the suitable model must be selected based on watershed type and simulation need. Two watershed-scale models, Soil and Water Assessment Tool (SWAT) and Hydrologic Simulation Program-Fortran (HSPF) were chosen for this study to simulate runoff, sediment yield, and nutrient load from the Big Sunflower River Watershed (BSRW) of Mississippi. The objectives of this study are to access the nutrient sources within the watershed, determine the appropriate model to quantify them, develop and evaluate model considering spatial and temporal variations in input data, and evaluate the effectiveness of different Best Management Practices (BMPs) on surface runoff, sediment yield and nutrient load at watershed scale. This study has identified a potential source of nutrients in BSRW and provided a suitable BMP for its management. Similarly, the study found both SWAT and HSPF were efficient in the simulation of streamflow, sediment yield and nutrient load, where SWAT was more efficient during simulation streamflow and sediment yield. Likewise, the study established that both water-quantity and water-quality are sensitive to the change in LULC data layers and thus, seasonal LULC data applied to SWAT will better explain variation in hydrology and water quality as compared to the annual cropland data layer. Moreover, the study showed that well managed vegetative filter strip was very efficient in reducing sediment yield, TN, and TP at both field and watershed scale among different BMPs evaluated at field and watershed scale. This study will be beneficial in developing efficient nutrient management strategy at field and watershed scale, selecting appropriate model and input according to the need and type of watershed, and providing further research opportunities to the scientific community.

<p dir="ltr">Land cover classification has always been a crucial topic in the remote sensing domain. Utilizing data collected by unmanned aerial vehicles and satellites, researchers can detect land degradation, monitor environmental changes, and provide insights for urban planning. Recent advancements in large multi-modal models have enabled open-vocabulary classification, which is particularly beneficial in this field. Becuase of the pre-training method, these models can perform zero-shot inference on unseen data, significantly reducing the costs associated with data collection and model training. This open-vocabulary feature of large-scale vision-language pre-training aligns well with the requirements of land cover classification, where benchmark datasets in the remote sensing domain comprise various categories, and transferring results from one dataset to another through supervised learning methods is challenging.</p><p dir="ltr">In this thesis, the author explored the performance of zero-shot CLIP and linear probe CLIP to assess the feasibility of using the CLIP model for land cover classification tasks. Further, the author fine-tuned CLIP by creating hierarchical label sets for the datasets, leading to better zero-shot classification results and improving overall accuracy by 2.5%. Regarding data engineering, the author examined the performance of zero-shot CLIP and linear probe CLIP across different categories and proposed a categorization method for land cover datasets. In summary, this work evaluated CLIP's overall performance on land cover datasets of varying spatial resolutions and proposed a hierarchical classification method to enhance its zero-shot performance. The thesis also offers a practical approach for modifying current dataset categorizations to better align with the model.</p>

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.