The main objectives of this master thesis are to evaluate and compare chosen classification algorithm for the tree species classification. With usage of satellite imagery Sentinel-2 and Landsat 8 is examined whether the better spatial resolution affects the quality of the resulted classification. According to past case studies and literature was chosen supervised algorithms Support Vector Machine, Neural Network and Maximum Likelihood. To achieve the best possible results of classification is necessary to find a suitable choice of parameters and rules. Based on literate was applied different settings which were subsequently evaluated by cross validation. All results are accompanied by tables, charts and maps which comprehensively and clearly summarize the answers to the main objectives of the thesis.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21089号 / 工博第4453号 / 新制||工||1692(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 立川 康人, 准教授 後藤 忠徳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Supraglacial lakes in the ablation zone of the Greenland Ice Sheet (GrIS) often drain rapidly (in hours to days) by hydraulically-driven fracture (“hydrofracture”) in the summer. Hydrofracture can deliver large meltwater volumes to the ice-bed interface and open-up surface-to-bed connections, thereby routing surface meltwater to the subglacial system, altering basal water pressures and, consequently, the velocity profile of the GrIS. The study of rapidly draining lakes is thus important for developing coupled hydrology and ice-dynamics models, which can help predict the GrIS’s future mass balance. Remote sensing is commonly used to identify the location, timing and magnitude of rapid lake-drainage events for different regions of the GrIS and, with the increased availability of high-quality satellite data, may be able to offer additional insights into the GrIS’s surface hydrology. This study uses new remote-sensing datasets and develops novel analytical techniques to produce improved knowledge of rapidly draining lake behaviour in west Greenland over recent years. While many studies use 250 m MODerate-resolution Imaging Spectroradiometer (MODIS) imagery to monitor intra- and inter-annual changes to lakes on the GrIS, no existing research with MODIS calculates changes to individual and total lake volume using a physically-based method. The first aim of this research is to overcome this shortfall by developing a fully-automated lake area and volume tracking method (“the FAST algorithm”). For this, various methods for automatically calculating lake areas and volumes with MODIS are tested, and the best techniques are incorporated into the FAST algorithm. The FAST algorithm is applied to the land-terminating Paakitsoq and marine-terminating Store Glacier regions of west Greenland to investigate the incidence of rapid lake drainage in summer 2014. The validation and application of the FAST algorithm show that lake areas and volumes (using a physically-based method) can be calculated accurately using MODIS, that the new algorithm can identify rapidly draining lakes reliably, and that it therefore has the potential to be used widely across the GrIS to generate novel insights into rapidly draining lakes. The controls on rapid lake drainage remain unclear, making it difficult to incorporate lake drainage into models of GrIS hydrology. The second aspect of this study therefore investigates whether various hydrological, morphological, glaciological and surface-mass-balance controls can explain the incidence of rapid lake drainage on the GrIS. These potential controlling factors are examined within an Exploratory Data Analysis statistical technique to elicit statistical similarities and differences between the rapidly and non-rapidly draining lake types. The results show that the lake types are statistically indistinguishable for almost all factors, except lake area. It is impossible, therefore, to elicit an empirically-supported, deterministic method for predicting hydrofracture in models of GrIS hydrology. A frequent problem in remote sensing is the need to trade-off high spatial resolution for low temporal resolution, or vice versa. The final element of this thesis overcomes this problem in the context of monitoring lakes on the GrIS by adapting the FAST algorithm (to become “the FASTER algorithm”) to use with a combined Landsat 8 and Sentinel-2 satellite dataset. The FASTER algorithm is applied to a large, predominantly land-terminating region of west Greenland in summers 2016 and 2017 to track changes to lakes, identify rapidly draining lakes, and ascertain the extra quantity of information that can be generated by using the two satellites simultaneously rather than individually. The FASTER algorithm can monitor changes to lakes at both high spatial (10 to 30 m) and temporal (~3 days) resolution, overcoming the limitation of low spatial or temporal resolution associated with previous remote sensing of lakes on the GrIS. The combined dataset identifies many additional rapid lake-drainage events than would be possible with Landsat 8 or Sentinel-2 alone, due to their low temporal resolutions, or with MODIS, due to its inferior spatial resolution.

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Anthropological influences are threatening the state of many savanna ecosystems in most rural landscapes around the world. Effective monitoring and management of these landscapes requires up to date maps and data on the state of the environment. Degradation data over a range of scales is often not readily available due to a lack of financial resources, time and technical capabilities. The aim of this research was to use a medium resolution multispectral SPOT 5 image from 2010 and Landsat 8 images from 2014 to map habitat intactness in the Bushbuckridge and Kruger National Park (KNP) region. The images were pre-processed and segmented into meaningful image objects using an object based image analysis (OBIA) approach. Five image derivatives namely: brightness, compactness, NIR standard deviation, area and the normalised difference vegetation index (NDVI) were evaluated for their capability to model habitat intactness. A habitat intactness index was generated by combining the five derivatives and rescaling them to a data range of 0 to 10, with 0 representing completely transformed areas, 10 being undisturbed natural vegetation. Field data were collected in October 2014 using a field assessment form consisting of 10 questions related to ecosystem state, in order to facilitate comparisons with the remote sensing habitat intactness index. Both satellite data sets yielded low overall accuracies below 30%. The results were improved by applying a correction factor to the reference data. The results significantly improved with SPOT 5 producing the highest overall accuracy of 62.6%. The Landsat 8 image for May 2014 achieved an improved accuracy of 60.2%. The SPOT 5 results showed to be a better predictor of habitat intactness as it assigned natural vegetation with better accuracy, while Landsat 8 correctly assigned mostly degraded areas. These findings suggest that the method was not easily transferable between the different satellite sensors in this savanna landscape, with a high occurrence of forest plantations and rural settlements too. These areas caused high omission errors in the reference data, resulting in the moderate overall accuracies obtained. It is recommended that these sites be clipped out of the analysis in order to obtain acceptable accuracies for non-transformed areas. The study nevertheless demonstrated that the habitat intactness index maps derived can be a useful data source for mapping general patterns of degradation especially on a regional scale. Therefore, the methods tested in this study can be integrated in habitat mapping projects for effective conservation planning. / AFRIKAANSE OPSOMMING: Antropologiese invloede bedreig die toestand van savanna-ekostelsels in die meeste landelike landskappe regoor die wêreld. Doeltreffende monitering en bestuur van hierdie landskappe vereis op datum kaarte en inligting oor die toestand van die omgewing. Agteruitgangsdata van verskillende skale is dikwels nie geredelik beskikbaar nie weens 'n gebrek aan finansiële hulpbronne, tyd en tegniese vermoëns. Die doel van hierdie navorsing was om ‘n hoë resolusie multispektrale SPOT 5 beeld van 2010 en Landsat 8 beelde van 2014 te gebruik om die habitatongeskondenheid in die Bushbuckridge en Kruger Nasionale Park (KNP) streek te karteer. Die beelde is voorverwerk en gesegmenteer om sinvolle beeldvoorwerpe te skep deur die gebruik van ‘n voorwerp gebaseerde beeldanalise (OBIA) benadering. Vyf beeldafgeleides naamlik: helderheid, kompaktheid, NIR standaardafwyking, area en die genormaliseerde verskil plantegroei-indeks (NDVI) is geëvalueer vir hul vermoë om habitat ongeskondenheid te modelleer. ‘n Habitatongeskondenheidsindeks is gegenereer deur die kombinasie van die vyf afgeleides wat herskaal is na 'n datareeks van 0 tot 10, met 0 om totaal getransformeerde gebiede te verteenwoordig en 10 om ongestoorde natuurlike plantegroei voor te stel. Velddata is versamel in Oktober 2014 met gebruik van 'n veldassesseringsvorm, bestaande uit 10 vrae wat verband hou met die toestand van die ekostelsel, om vergelykings met die afstandswaarneming habitatongeskondenheidsindeks te fasiliteer. Beide satellietdatastelle het lae algehele akkuraatheid onder 30% opgelewer. Die resultate is deur die toepassing van 'n regstellingsfaktor tot die verwysing data verbeter. Die resultate het aansienlik verbeter met SPOT 5 wat die hoogste algehele akkuraatheid van 62.6% gelewer het. Die Landsat 8 beeld vir Mei 2014 bereik 'n verbeterde akkuraatheid van 60.2%. Die SPOT 5 resultate het geblyk om ‘n beter voorspeller van habitatongeskondenheid te wees as gevolg van ‘n beter akkuraatheid vir natuurlike plantegroei, terwyl Landsat meestal gedegradeerde gebiede kon voorspel. Hierdie bevindinge dui daarop dat die metode nie maklik oordraagbaar was tussen die verskillende satelliet sensors in hierdie savanna landskap nie, veral as gevolg van ‘n hoë voorkoms van bosbouplantasies en landelike nedersettings. Hierdie gebiede veroorsaak hoë weglatingsfoute in die verwysing data, wat lei tot gematigde algehele akkuraatheid. Dit word aanbeveel dat hierdie areas gemasker word tydens die ontleding om aanvaarbare akkuraatheid te verkry vir nie-getransformeerde gebiede. Nogtans het die studie getoon dat die afgeleide habitatongeskondenheidsindekskaarte ‘n nuttige bron van data kan wees vir die kartering van algemene patrone van agteruitgang, veral op 'n plaaslike skaal. Daarom kan die getoetsde metodes in die studie in habitatkarteringsprojekte vir doeltreffende bewaring beplanning geïntegreer word. Stellenbosch University https://scholar.sun.ac.za

A sustainable society is a society that satisfies its resource requirements without endangering the sustainability of these resources. The mineral endowment on the African continent is estimated to be the first or second largest of world reserves. Therefore, it is recognised that the African continent still heavily depends on mineral exports as a key contributor to the gross domestic product (GDP) of various countries. These mining activities, however, do introduce primary and secondary environmental degradation factors. They attract communities to these mining areas, light and heavy industrial establishments occur, giving rise to artisanal activities. This study focussed on satellite RS products as an aid to a mine’s operations and the monitoring of its environment. Effective operational mine management and control ensures a more sustainable and profitable lifecycle for mines. Satellite based RS holds the potential to observe the mine and its surrounding areas at high temporal intervals, different spectral wavelengths and spatial resolutions. The combination of SAR and optical information creates a spatial platform to observe and measure the mine’s operations and the behaviour of specific land cover and land use classes over time and contributes to a better understanding of the mining activities and their influence on the environment within a specific geographical area. This study will introduce an integrated methodology to collect, process and analyse spatial information over a specific targeted mine. This methodology utilises a medium resolution land cover base map, derived from Landsat 8, to understand the predominant land cover types of the surrounding area. Using very high resolution mono- and stereoscopic satellite imagery provides a finer scale analysis and identifies changes in features at a smaller scale. Combining these technologies with the synthetic aperture radar (SAR) applications for precise measurement of surface subsidence or upliftment becomes a spatial toolbox for mine management. This study examines a combination of satellite remote sensing products guided by a systematic workflow methodology to integrate spatial results as an aid for mining operations and environmental monitoring. Some of the results that can be highlighted is the successful land cover classification using the Landsat 8 satellite. The land cover that dominated the Kolomela mine area was the “SHRUBLAND/GRASS” class with a 94% coverage and “MINE” class of 2.6%. Sishen mine had a similar dominated land cover characteristic with a “SHRUBLAND/GRASS” class of 90% and “MINE” class of 4.8%. The Pléiades time-series classification analysis was done using three scenes each acquired at a different time interval. The Sishen and Kolomela mine showed especially changes from the bare soil class to the asphalt or mine class. The Pléiades stereoscopic analysis provided volumetric change detection over small, medium, large and recessed areas. Both the Sishen and Kolomela mines demonstrated height profile changes in each selected category. The last category of results focused on the SAR technology to measure within millimetre accuracy the subsidence and upliftment behaviour of surface areas over time. The Royal Bafokeng Platinum tailings pond area was measured using 74 TerraSAR-X scenes. The tailings wall area was confirmed as stable with natural subsidence that occurred in its surrounding area due to seasonal changes of the soil during rainy and dry periods. The Chuquicamata mine as a large open pit copper mine area was analysed using 52 TerraSAR-X scenes. The analysis demonstrated significant vertical surface movement over some of the dumping sites. It is the wish of the researcher that this dissertation and future research scholars will continue to contribute in this scientific field. These contributions can only assist the mining sector to continuously improve its mining operations as well as its monitoring of the primary as well as the secondary environmental impacts to ensure improved sustainability for the next generation. / Environmental Sciences / M. Sc. (Environmental Science)

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Tesis por compendio / [ES] Los humedales son uno de los ecosistemas que reciben mayor atención por parte de la comunidad científica. Su importancia se puede explicar teniendo en cuenta que ayudan a mitigar los efectos de inundaciones, pueden actuar como filtros de agua y constituyen hábitats de valiosas especies de fauna y flora. En los últimos años este tipo de ambientes están cada vez más amenazados como consecuencia de la contaminación, alteraciones de los niveles de agua asociadas a los efectos del cambio climático y usos antrópicos, introducción de especies invasoras y los efectos negativos de algunos cambios de usos del suelo y determinadas prácticas asociados a ellos (agrícolas, pastoreo y urbanización). Para analizar estos ecosistemas, la teledetección se presenta como una herramienta con alto potencial que permite identificar, evaluar y monitorizar estos espacios. En esta tesis se utilizaron imágenes Sentinel-2A/B, Landsat-5 TM y Landsat-8 OLI para extraer láminas de agua libre en dos entornos: humedales mediterráneos costeros (Prat Cabanes-Torreblanca, Marjal de Sagunto, Marjal de La Safor y Marjal Pego-Oliva) y el humedal de llanura aluvial situado en el centro de España las Tablas de Daimiel. Para ello, se realizó un análisis de siete índices de agua (NDWI, mNDWI, Cedex, Re-NDWI, Awei(sh), AWEI (nsh) y B_Blue) y de umbrales para obtener la cartografía de las masas de agua libre en estos espacios. El objetivo principal es definir el índice y el umbral que permitan un uso más amplio de la metodología para su aplicación en otras zonas húmedas. También se utilizó información LiDAR (Laser Imaging Detection and Ranging) en el humedal de La Safor para analizar los efectos de la superficie inundada en diferentes usos del suelo. La evaluación de los resultados se realizó a partir de la validación con un conjunto de muestras obtenidas a partir de imágenes de elevada resolución espacial. Se calcularon la fiabilidad global y el índice kappa en los humedales analizados para distintas fechas y sensores. En el caso de los humedales costeros, el índice de agua NDWI con un umbral de ¿0,30 proporcionó los resultados con mayor precisión obteniendo un valor promedio de 0,89 en fiabilidad global. En el caso del humedal de las Tablas de Daimiel, se seleccionaron el índice MNDWI y umbral ¿0,15 para imágenes Landsat-5 (fiabilidad global 0,88), el índice MNDWI y umbral ¿0,25 para imágenes Landsat-8 (fiabilidad global 0,99) y el índice NDWI y umbral ¿0,20 (fiabilidad global 0,99) en el caso de imágenes Sentinel-2A/B. En el humedal de las Tablas de Daimiel se realizó un análisis temporal desde el año 2000 al 2021 que permitió calcular las anomalías de la superficie de agua, de la precipitación, del nivel piezométrico y del caudal hidrológico. Esta reconstrucción temporal también permitió comparar los resultados derivados a partir de las imágenes Sentinel-2A/B y de las imágenes Landsat 8. Se realizó un análisis de correlación entre los índices de anomalías calculados, que revela una correlación no significativa entre las anomalías precipitación y de superficie de agua. Mientras que el índice de anomalías de superficie de agua si presentó una correlación estadísticamente significativa con los índices de anomalía de caudal y de niveles piezométricos. En cuanto al análisis comparativo entre las imágenes Landsat-8 y Sentinel-2 se obtuvo una relación lineal entre la superficie de agua estimada por ambos sensores con un valor de R2 = 0,87. No obstante, la mayor resolución espacial de Sentinel-2 permite detectar masas de agua más pequeñas contribuyendo a un mejor análisis de los patrones de variabilidad en el área de estudio. La información derivada de esta tesis presenta una aplicabilidad de interés medioambiental para el seguimiento del estado de los humedales ayudando a adaptar planes de gestión que conduzcan a un estado de conservación adecuado. / [CA] Els aiguamolls són un dels ecosistemes que reben major atenció per part de la comunitat científica. La seua importància es pot explicar tenint en compte que ajuden a mitigar els efectes d'inundacions, poden actuar com a filtres d'aigua i constitueixen hàbitats de valuoses espècies de fauna i flora. En els últims anys aquest tipus d'ambients estan cada vegada més amenaçats a conseqüència de la contaminació, alteracions dels nivells d'aigua associades a l'efecte del canvi climàtic i usos antròpics, introducció d'espècies invasores i els efectes negatius d'alguns canvis d'usos del sòl i determinades pràctiques associades a ells (agrícoles, pasturatge i urbanització). Per a analitzar aquests ecosistemes, la teledetecció es presenta com una eina amb alt potencial que permet identificar, avaluar i monitorar aquests espais. En aquesta tesi es van utilitzar imatges Sentinel-2A/B, Landsat-5 TM i Landsat-8 OLI per a extraure làmines d'aigua lliure en dos entorns: aiguamolls mediterranis costaners (Prat Cabanes-Torreblanca, Marjal de Sagunt, Marjal de La Safor i Marjal Pego-Oliva) i l'aiguamoll de plana al·luvial situat en el centre d'Espanya les Taules de Daimiel. Per a això, es va realitzar una anàlisi de set índexs d'aigua (NDWI, mNDWI, Cedex, Re-NDWI, Awei (sh), AWEI (nsh) i B_Blue) i de llindars per a obtindre la cartografia de les masses d'aigua lliure en aquests espais. L'objectiu principal és definir l'índex i el llindar que permeten un ús més ampli de la metodologia per a la seua aplicació en altres zones humides. També es va utilitzar informació LiDAR (Laser Imaging Detection and Ranging) en l'aiguamoll de La Safor per a analitzar els efectes de la superfície inundada en diferents usos del sòl. L'avaluació dels resultats es va realitzar a partir de la validació amb un conjunt de mostres obtingudes a partir d'imatges d'elevada resolució espacial. Es van calcular la fiabilitat global i l'índex kappa en els aiguamolls analitzats per a diferents dates i sensors. En el cas dels aiguamolls costaners, l'índex d'aigua NDWI amb un llindar de ¿0,30 va proporcionar els resultats amb major precisió obtenint un valor mitjà de 0,89 en fiabilitat global. En el cas de l'aiguamoll de les Taules de Daimiel, es van seleccionar l'índex MNDWI i llindar ¿0,15 per a imatges Landsat-5 (fiabilitat global 0,88), l'índex MNDWI i llindar ¿0,25 per a imatges Landsat-8 (fiabilitat global 0,99) i l'índex NDWI i llindar ¿0,20 (fiabilitat global 0,99) en el cas d'imatges Sentinel-2A/B. En l'aiguamoll de les Taules de Daimiel es va realitzar una anàlisi temporal des de l'any 2000 al 2021 que va permetre calcular les anomalies de la superfície d'aigua, de la precipitació, del nivell piezomètric i del cabal hidrològic. Aquesta reconstrucció temporal també va permetre comparar els resultats derivats a partir de les imatges Sentinel-2A/B i de les imatges Landsat 8. Es va realitzar una anàlisi de correlació entre els índexs d'anomalies calculats, que revela una correlació no significativa entre les anomalies precipitació i de superfície d'aigua. Mentre que l'índex d'anomalies de superfície d'aigua si va presentar una correlació estadísticament significativa amb els índexs d'anomalia de cabal i de nivells piezomètrics. Quant a l'anàlisi comparativa entre les imatges Landsat-8 i Sentinel-2 es va obtindre una relació lineal entre la superfície d'aigua estimada per tots dos sensors amb un valor de R2 = 0,87. No obstant això, la major resolució espacial de Sentinel-2 permet detectar masses d'aigua de menor grandària contribuint a una millor anàlisi dels patrons de variabilitat en l'àrea d'estudi. La informació derivada d'aquesta tesi presenta una aplicabilitat d'interés mediambiental per al seguiment de l'estat dels aiguamolls ajudant a adaptar plans de gestió que condueixen a un estat de conservació adequat. / [EN] Wetlands are one of the ecosystems that receive the most attention from the scientific community. Their importance can be explained by the fact that they help mitigate the effects of flooding, can act as water filters, and provide habitats for valuable species of fauna and flora. In recent years, these types of environments are increasingly threatened as a result of pollution, alterations in water levels associated with the effects of climate change and anthropic uses, the introduction of invasive species and the negative effects of some changes in land use and certain practices associated with them (agriculture, grazing and urbanization). To analyze these ecosystems, remote sensing is presented as a tool with high potential to identify, evaluate and monitor these areas. In this thesis, Sentinel-2A/B, Landsat-5 TM and Landsat-8 OLI images were used to extract free water bodies in two environments: coastal Mediterranean wetlands (Prat Cabanes-Torreblanca, Marjal de Sagunto, Marjal de La Safor and Marjal Pego-Oliva) and the alluvial plain wetland located in the center of Spain, the Tablas de Daimiel. For this purpose, an analysis of seven water indices (NDWI, mNDWI, Cedex, Re-NDWI, Awei (sh), AWEI (nsh) and B_Blue) and thresholds were carried out to obtain the mapping of free water bodies in these areas. The main objective is to define the index and threshold that allow a wider use of the methodology for its application in other wetlands. LiDAR (Laser Imaging Detection and Ranging) information was also used in La Safor wetland to analyze the effects of the flooded surface on different land uses. The evaluation of the results was carried out based on the validation with a set of samples obtained from high spatial resolution images. The overall accuracy and the kappa index were calculated for the wetlands analyzed for different dates and sensors. In the case of the coastal wetlands, the NDWI water index with a threshold of ¿0.30 provided the most accurate results with an average value of 0.89 in global accuracy. In the case of Las Tablas de Daimiel wetland, the MNDWI index and threshold ¿0.15 were selected for Landsat-5 images (overall accuracy 0.88), the MNDWI index and threshold ¿0.25 for Landsat-8 images (overall accuracy 0.99) and the NDWI index and threshold ¿0.20 (overall accuracy 0.99) in the case of Sentinel-2A/B images. In Las Tablas de Daimiel wetland, a temporal analysis was carried out from 2000 to 2021 to calculate the anomalies of the water surface, precipitation, piezometric level and hydrological flow. This temporal reconstruction also made it possible to compare the results derived from Sentinel-2A/B images and Landsat 8 images. A correlation analysis was performed between the calculated anomaly indices, which revealed a non-significant correlation between the precipitation and water surface anomalies. However, the water surface anomaly index did show a statistically significant correlation with the flow anomaly and piezometric level indexes. As for the comparative analysis between Landsat-8 and Sentinel-2 images, a linear relationship was obtained between the water surface estimated by both sensors with a value of R2 = 0.87. However, the higher spatial resolution of Sentinel-2 allows the detection of smaller water masses contributing to a better analysis of the variability patterns in the study area. The information derived from this thesis presents an application of environmental interest for monitoring the state of wetlands helping to adapt management plans that lead to an adequate conservation status. / Pena Regueiro, J. (2023). Monitorización de cambios en la lámina libre de agua en humedales mediante teledetección [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/196109 / Compendio