791 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Using hydrological models and digital soil mapping for the assessment and management of catchments: A case study of the Nyangores and Ruiru catchments in Kenya (East Africa)

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    Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................8

    Deep learning methods applied to digital elevation models: state of the art

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    Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing

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    This PhD project develops methods for the assessment of forest condition utilising modern remote sensing technologies, in particular optical imagery from unmanned aerial systems and with Structure from Motion photogrammetry. The research focuses on health threats to the UK’s native oak trees, specifically, Chronic Oak Decline (COD) and Acute Oak Decline (AOD). The data requirements and methods to identify these complex diseases are investigatedusing RGB and multispectral imagery with very high spatial resolution, as well as crown textural information. These image data are produced photogrammetrically from multitemporal unmanned aerial vehicle (UAV) flights, collected during different seasons to assess the influence of phenology on the ability to detect oak decline. Particular attention is given to the identification of declined oak health within the context of semi-natural forests and heterogenous stands. Semi-natural forest environments pose challenges regarding naturally occurring variability. The studies investigate the potential and practical implications of UAV remote sensing approaches for detection of oak decline under these conditions. COD is studied at Speculation Cannop, a section in the Forest of Dean, dominated by 200-year-old oaks, where decline symptoms have been present for the last decade. Monks Wood, a semi-natural woodland in Cambridgeshire, is the study site for AOD, where trees exhibit active decline symptoms. Field surveys at these sites are designed and carried out to produce highly-accurate differential GNSS positional information of symptomatic and control oak trees. This allows the UAV data to be related to COD or AOD symptoms and the validation of model predictions. Random Forest modelling is used to determine the explanatory value of remote sensing-derived metrics to distinguish trees affected by COD or AOD from control trees. Spectral and textural variables are extracted from the remote sensing data using an object-based approach, adopting circular plots around crown centres at individual tree level. Furthermore, acquired UAV imagery is applied to generate a species distribution map, improving on the number of detectable species and spatial resolution from a previous classification using multispectral data from a piloted aircraft. In the production of the map, parameters relevant for classification accuracy, and identification of oak in particular, are assessed. The effect of plot size, sample size and data combinations are studied. With optimised parameters for species classification, the updated species map is subsequently employed to perform a wall-to-wall prediction of individual oak tree condition, evaluating the potential of a full inventory detection of declined health. UAV-acquired data showed potential for discrimination of control trees and declined trees, in the case of COD and AOD. The greatest potential for detecting declined oak condition was demonstrated with narrowband multispectral imagery. Broadband RGB imagery was determined to be unsuitable for a robust distinction between declined and control trees. The greatest explanatory power was found in remotely-sensed spectra related to photosynthetic activity, indicated by the high feature importance of nearinfrared spectra and the vegetation indices NDRE and NDVI. High feature importance was also produced by texture metrics, that describe structural variations within the crown. The findings indicate that the remotely sensed explanatory variables hold significant information regarding changes in leaf chemistry and crown morphology that relate to chlorosis, defoliation and dieback occurring in the course of the decline. In the case of COD, a distinction of symptomatic from control trees was achieved with 75 % accuracy. Models developed for AOD detection yielded AUC scores up to 0.98,when validated on independent sample data. Classification of oak presence was achieved with a User’s accuracy of 97 % and the produced species map generated 95 % overall accuracy across the eight species within the study area in the north-east of Monks Wood. Despite these encouraging results, it was shown that the generalisation of models is unfeasible at this stage and many challenges remain. A wall-to-wall prediction of decline status confirmed the inability to generalise, yielding unrealistic results, with a high number of declined trees predicted. Identified weaknesses of the developed models indicate complexity related to the natural variability of heterogenous forests combined with the diverse symptoms of oak decline. Specific to the presented studies, additional limitations were attributed to limited ground truth, consequent overfitting,the binary classification of oak health status and uncertainty in UAV-acquired reflectance values. Suggestions for future work are given and involve the extension of field sampling with a non-binary dependent variable to reflect the severity of oak decline induced stress. Further technical research on the quality and reliability of UAV remote sensing data is also required

    Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images

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    Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difícil. Además, en el escenario actual, donde el número de sensores en el espacio crece constantemente, desarrollar metodologías para transferir modelos que funcionen con datos de nuevos satélites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detección de nubes que exploten las diferentes propiedades de las imágenes de satélite y desarrollar metodologías para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribución, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detección de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribución, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detección de nubes basado en aprendizaje profundo de Landsat-8 (resolución 30m, 12 bandas espectrales y muy buena calidad radiométrica) a Proba-V, que tiene una resolución de 333m, solo cuatro bandas y una calidad radiométrica peor. El tercer artículo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformación de adaptación de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribución, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detección de inundaciones y nubes con un único modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (ϕSat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cómo transferir este modelo a la cámara del ϕSat-I. Este modelo se lanzó en junio de 2021 a bordo de la misión WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (ϕSat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theϕSat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space

    Floodplain Vegetation Productivity Response to Wetting and Drying: Testing the Adaptive Cycle Model

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    Dryland floodplains are characterized by highly variable flooding and drying regimes. The spatial and temporal variability in flooding plays a significant role in the productivity of these ecosystems and is a key influence on the composition and distribution of vegetation in these floodplains. Dryland floodplains have been perceived to be boom and bust systems, in which the boom is characterised by an inundated floodplain and the bust is characterised by a dry floodplain in moisture deficit. The boom stimulates great primary and secondary production where as the bust brings contraction of the ecosystem into refugia and a reduction in primary and secondary production. This relatively simple two state model may not account for the transitions that may occur between flooding (boom) and dry (bust) floodplain states. Understanding the patterns of response at different scales is critical to our ability to manage these complex dryland systems and to be able to make predictions about their future condition over time. This thesis applied an adaptive cycle model in order to understand change in floodplain vegetation productivity through multiple periods of flooding and drying. Adaptive cycles are a key component of resilience thinking. In this adaptive cycle model, vegetation productivity is the ecosystem responder and hydrology, or floodplain flooding and drying, the main driver of change. I derived a series of sequential hypotheses that explored the applicability of an adaptive cycle for the response of vegetation productivity in the Narran floodplain. The Normalized Difference Vegetation Index (NDVI) which measures vegetation greenness was used as a surrogate for vegetation productivity. In this adaptive cycle floodplain inundation was considered to drive vegetation productivity response through a cycle of exploitation, conservation, release and reorganization phases. The adaptive cycle starts as floodwater inundates the floodplain in the wetting phase. The wetting phase corresponds to the exploitation part of adaptive loop, where the area of vegetation productivity and quality will increase because of the availability of water as an exploitable resource. The wet phase is the phase of maximum inundation and corresponds to the conservation phase of the adaptive loop. The conservation phase is a period of increased vegetation productivity and a stability of vegetation productivity. The contraction of floodwater triggers the drying phase and corresponds to the release phase of an adaptive cycle. Further, desiccation of the floodplain occurs with the draining of floodwaters until the floodplain reaches a dry phase, a phase of no surface water availability. The dry phase corresponds to the reorganization phase of an adaptive cycle. The results of this thesis represents an advance on previous studies of dryland floodplains as an approach for characterising and understanding the response of vegetation communities in large floodplains. The findings of this thesis demonstrated there to be marked differences in NDVI class area, number of transitions, directions of transitions, probability of transitions and NDVI class diversity between the dry phase and the combined wetting, wet and drying phases of inundation. Overall an anti-clockwise hysteresis relationship occurred between flooding and vegetation productivity, indicating a cyclic nature of vegetation response to floodplain inundation through dry, wetting, wet and drying phases. These results support the hypothesised adaptive cycle model for the response of vegetation productivity and its appropriateness for understanding the complexity of dryland floodplain vegetation response to wetting and drying. These results were also repeated over four flood events of different size. Although the four events exhibited an adaptive cycle, the duration and the nature of vegetation within each phase of the adaptive cycle differed. Likewise, the four different vegetation communities also exhibited response patterns in relation to flooding and drying that fit the adaptive cycle model. However, differences were evident in the timing of transitions between adaptive cycle phases and the duration spent in those phases in each vegetation community. The woodland community types of the Narran floodplain showed a higher productivity response during the drying or release phase. By comparison the highest productivity response for the grassland and shrubland was observed during the wetting or exploitation phase. Overall, the results showed the four vegetation communities are sensitive at different points in the adaptive cycle. A unique finding of this study result was the location of the exit point from the adaptive cycle, which is the potential point for a state change. The exit point from an adaptive cycle is characterized by a period of enhanced high instability. In the Narran floodplain, the patterns of response in vegetation productivity to flooding and drying indicate this occurred between the conservation and release phases and not between the reorganization and exploitation phases as hypothesised by adaptive cycle theory. Thus, the potential for a change in state in dryland floodplains is highest between the wet (conservation) and drying (release) phases

    On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring

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    Marine plastic pollution is an emerging environmental problem since it pollutes the ocean, air and food whilst endangering the ocean wildlife via the ingestion and entanglements. During the last decade, an enormous effort has been spent on finding possible solutions to marine plastic pollution. Remote sensing imagery sits in a crucial place for these efforts since it provides informative earth observation products, and the current technology offers further essential development. Despite the advances in the last decade, there is still a way to go for marine plastic monitoring research where challenges are rarely highlighted. This paper contributes to the literature with a critical review and aims to highlight literature milestones in marine debris and suspected plastics (MD&SP) monitoring by promoting the computational imaging methodology behind these approaches along with detailed discussions on challenges and potential future research directions

    Exploratory mapping of blue ice regions in Antarctica using very high resolution satellite remote sensing data

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    Mapping spatiotemporal changes in the distribution of blue ice regions (BIRs) in Antarctica requires repeated, precise, and high-resolution baseline maps of the blue ice extent. This study demonstrated the design and application of a newly-developed semi-automatic method to map BIRs in the Antarctic environment using very high-resolution (VHR) WorldView-2 (WV-2) satellite images. We discussed the potential of VHR satellite data for the mapping of BIRs in the Antarctic environment using a customized normalized-difference blue-ice index (NDBI) method devised using yellow, green, and near-infrared spectral bands of WV-2 data. We compared the viability of the newly developed customized NDBI approach against state-of-the-art target detection (TD), spectral processing (SP) and pixel-wise supervised (PSC) feature extraction (FE) approaches. Four semi-automatic FE approaches (three existing plus one newly developed) consisting of 16 standalone FE methods (12 existing + four customized) were evaluated using an extensive quantitative and comparative assessment for mapping BIRs in the vicinity of Schirmacher Oasis, on the continental Antarctic coastline. The results suggested that the customized NDBI approach gave a superior performance and the highest statistical stability when compared with existing FE techniques. The customized NDBI generally rendered the lowest level of misclassification (average RMSE = 654.48 ± 58.26 m2), followed by TD (average RMSE = 987.81 ± 55.05 m2), SP (average RMSE = 1327.09 ± 127.83 m2) and PSC (average RMSE = 2259.43 ± 115.36 m2) for mapping BIRs. Our results indicated that the use of the customized NDBI approach can greatly improve the semi-automatic mapping of BIRs in the Antarctic environment. This study presents the first refined map of distribution of BIRs around the Schirmacher Oasis. The total area of blue ice in the study area was estimated to be 106.875 km2, approximately 61% of the study area. The WV-2 derived BIR map area presented in this study locally refined the existing BIR map derived using Landsat Enhanced Thematic Mapper Plus (ETM+) and the Moderate Resolution Imaging Spectroradiometer (MODIS)-based mosaic of Antarctica (MOA) dataset by ~31% (~33.40 km2). Finally, we discussed the practical challenges and future directions in mapping BIRs across Antarctica

    Explotación sinérgica de datos multiespectrales y radar para la estimación de variables biofísicas de la vegetación mediante tecnologías de sensoramiento remoto

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    Las variables biofísicas de la vegetación (VBV) son indicadores directos del crecimiento y productividad de los cultivos. Los sistemas de observación de la Tierra (EO–Earth observation) presentan oportunidades sin precedentes para el monitoreo de las variables biofísicas del trigo. Sentinel–2 (S2) es una constelación de satélites que forma parte de las misiones Sentinel del programa Copernicus de EO. El período de revisita, así como su resolución espacial y espectral, han convertido a S2 en un sistema de EO trascendental para el monitoreo de VBV. Los sistemas ópticos de EO se ven limitados con frecuencia por las condiciones climáticas tales como nubosidad o precipitaciones. En este sentido, la tecnología radar, presenta nuevas oportunidades para el monitoreo de VBV que deben explorarse en profundidad. Sentinel–1 (S1) es una constelación radar de la familia Sentinel. Debido a la complejidad de la interacción de la señal radar con las superficies cultivadas y al ruido aditivo inherente de speckle, la estimación de VBV con tecnología radar aún sigue siendo un desafío. El objetivo de esta tesis doctoral es desarrollar modelos de estimación de variables biofísicas del trigo, en una zona irrigada de cultivo intensivo al sureste de Argentina, basados en medidas in situ de la vegetación, a partir de: i) datos multiespectrales de S2; ii) datos radar de S1; y iii) la sinergia S1 & S2. Para abordar la problemática planteada, se desarrollaron en primer lugar, modelos de estimación del índice de área foliar, del contenido de clorofila de la cubierta vegetal y del contenido de agua del trigo, utilizando una base de datos multitemporal de VBV tomadas in situ, algoritmos de aprendizaje automático, una base de datos de espectros de reflectividad bidireccional de la vegetación simulados con un modelo de transferencia radiativa y datos multiespectrales de S2. Se obtuvieron modelos híbridos de estimación de estas VBV que se ajustaron con alta precisión a los datos de campo y se logró reconstruir con éxito la curva fenológica del cultivo de trigo. En segundo lugar, se implementó un modelo de estimación de LAI basado en datos radar de S1 adquiridos en diferentes geometrías de adquisición. Se probó que la estructura tridimensional de la vegetación cuando es observada desde ángulos de incidencia local diferentes proporciona información muy valiosa que puede ser utilizada para mejorar los modelos existentes. Por último, se desarrolló una estrategia de fusión de datos de S1 & S2 para reconstruir series temporales de VWC. Se aplicaron varios modelos de procesos Gaussianos de salidas múltiples para analizar la correlación cruzada existente, en el dominio de la frecuencia, entre los canales ópticos y radar. La combinación sinérgica de datos radar y ópticos mostró ser un novedoso enfoque para abordar el monitoreo de variables biofísicas del trigo en regiones intensamente cultivadas con frecuente nubosidad
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