Uso de Sentinel-2 y datos auxiliares para la generación, mediante clasificación de imágenes, del Mapa de Usos y Cubiertas del Suelo de Cataluña 2017

[EN] This paper details the process of generating the 2017 Land Use/Land Cover Map of Catalonia (MUCSC) using automatic classification of satellite imagery and auxiliary cartographic and remote sensing data. A total of 60 images (6 dates for each of the 10 tiles covering Catalonia) captured by the Sentininel-2A and Sentinel-2B satellites were used. These images as well as texture variables, terrain models derived from lidar processing, and vegetation and wetness indices were classified using the k-Nearest Neighbor algorithm (kNN) to obtain a map with 25 categories. The categories related to urbanized areas (urban areas, urbanizations and industrial zones/ commercial areas), road infrastructures and burned areas were edited using official cartographic datasets of the Catalan Government [Generalitat]. The results have an overall accuracy greater than 98 %, which was evaluated with a set of more than 8.6 million independent test pixels. This work represents an important milestone in terms of the computational effort it involves due to the territorial extension (32 000 km2), the spatial detail of between 2 and 20 m, the use of up to 58 variables, the relative completeness of the legend and the level of success achieved. The MUCSC 2017, which is part of a 30-year quinquennial series beginning in 1987, can be downloaded in different formats (also in MMZX: new ISO 19165-2) and at resolutions of 10 m and 30 m pixel side from the Ministry of Territory and Sustainability website of the Catalan Government.[ES] En este artículo se detalla el proceso de generación del Mapa de Usos y Cubiertas del Suelo de Cataluña (MUCSC) 2017 mediante clasificación automática de imágenes de satélite y datos cartográficos y de teledetección auxiliares. Con este propósito se han utilizado un total de 60 imágenes (6 fechas sobre cada una de las 10 teselas que cubren Cataluña) captadas por los satélites 2A y 2B de la constelación Sentinel-2. Estos datos, junto con variables de textura, modelos del terreno derivados del procesado lidar para todo el territorio e índices de vegetación y humedad, han sido clasificados con el algoritmo de inteligencia artificial kNN para obtener un mapa de 25 categorías, de las cuales las referentes a zonas urbanizadas (zonas urbanas, urbanizaciones y zonas industriales y comerciales), vías de comunicación y zonas quemadas han sido editadas utilizando bases cartográficas oficiales de la Generalitat [Gobierno] de Catalunya. Los resultados muestran un acierto global superior al 98 % evaluado mediante un conjunto de más de 8,6 millones de píxeles independientes de test. Este trabajo representa un hito importante tanto por el esfuerzo de cálculo que ha supuesto (extensión territorial de 32.000 km2, detalle espacial de entre 2 y 20 m y uso de hasta 58 variables), como por la relativa completitud de la leyenda y por el nivel de acierto conseguido. El MUCSC 2017, que forma parte de una serie quinquenal de 30 años desde 1987, está disponible para descarga en distintos formatos (también en MMZX: nueva ISO 19165-2) y a resoluciones de 10 m y 30 m de lado de píxel a través de la página web en el Departamento de Territorio y Sostenibilidad de la Generalitat de Catalunya.This study was funded by the Generalitat (Grumets SGR2014-1491) and by the Spanish MCIU through the NEWFORLAND project (RTI2018-099397-B-C21 MCIU/AEI/ERDF, EU). X. Pons is the recipient of an ICREA Academia 2016 -2020 Excellence in Research Grant.González-Guerrero, O.; Pons, X. (2020). The 2017 Land Use/Land Cover Map of Catalonia based on Sentinel-2 images and auxiliary data. Revista de Teledetección. 0(55):81-92. https://doi.org/10.4995/raet.2020.13112OJS8192055Cea, C., Cristóbal, J., Pons, X. 2007. An improved methodology to map snow cover by means of Landsat and MODIS imagery. En: Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. Barcelona. IEEE International, 4217-4220. DOI https://doi.org/10.1109/IGARSS.2007.4423781Elvidge, C.D., Sutton, P.C., Wagner, T.W., Ryznar, R., Goetz, S.J., Smith, A.J., Jantz, C., Seto, K., Imhoff, M.L., Vogelmann, J., wang, Y.Q., Milesi, C., Nemani, R. 2004. Urbanization. En: Gutman, G. et al. (ed.). Land change science: Observing, monitoring, and understanding trajectories of change on the earth's surface. Dordrecht, Países Bajos: Kluwer Academic Publishers, pp. 315-328. https://doi.org/10.1007/978-1-4020-2562-4_18ESA. 2015. Sentinel-2 User Handbook. Recuperado de https://sentinel.esa.int/documents/247904/685211/ Sentinel-2_User_Handbook Último acceso: 4 de febrero, 2020.González-Guerrero, O., Pons-Fernández, X., Bassols- Morey, R., Camps-Fernandez, F.J. 2019. Dinàmica de les superfícies de conreu a Catalunya mitjançant Teledetecció en el període 1987-2012. Quaderns Agraris, 46, 59-91.Hansen, M.C., Loveland, T.R. 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66-74. https://doi.org/10.1016/j.rse.2011.08.024ICC. 1992. Mapa d'usos del sòl de Catalunya. Institut Cartogràfic de Catalunya. Barcelona. 118 p.ICGC (2017). Datos lidar. Institut Cartogràfic i Geològic de Catalunya. Recuperado de https://www.icgc.cat/ es/Descargas/Elevaciones/Datos-lidar Último acceso: 1 de mayo, 2020.Loveland, T.R., Dwyer, J.L. 2012. Landsat: Building a strong future. Remote Sensing of Environment, 122, 22-29. https://doi.org/10.1016/j.rse.2011.09.022Moré, G., Pons, X. 2007. Influencia del número de imágenes en la calidad de la cartografía detallada de vegetación forestal. Revista de Teledetección, 28, 61- 68. Recuperado de http://www.aet.org.es/revistas/ revista28/7-111_More_revisado.pdf Último acceso: 1 de mayo, 2020.Padial, M., Vidal-Macua, J.J., Serra, P., Ninyerola, M., Pons, X. 2019. Aplicación de filtros multicriterio basados en NDVI para la extracción de áreas de entrenamiento desde la base de datos SIOSE. Ruiz L.A., Estornell J., Calle A., Antuña-Sánchez J.C. (eds) Teledetección: hacia una visión global del cambio climático, pp. 311-314. ISBN: 978- 84-1320-038-5. Libro de actas XVIII Congreso de la Asociación Española de Teledetección, 24 - 27 Septiembre, Valladolid (Spain).Padró, J.C., Pons, X., Aragonés, D., Díaz-Delgado, R., García, D., Bustamante, J., Pesquer, L., Domingo- Marimon, C., González-Guerrero, O., Cristóbal, J., Doktor, D., Lange, M. 2017. Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy. Remote Sensing, 9(12), 1319. https://doi.org/10.3390/rs9121319Padró, J.C., Muñoz, F.J., Ávila, L.A., Pesquer, L., Pons, X. 2018. Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry. Remote Sensing, 10(11), 1687. https://doi.org/10.3390/rs10111687Pons, X. 2004. MiraMon. Sistema de Información Geográfica y software de Teledetección. Centre de Recerca Ecològica i Aplicacions Forestals, CREAF. Bellaterra. ISBN: 84-931323-4-9. Recuperado de http://www.miramon.cat/Index_es.htm Último acceso: 1 de mayo, 2020.Pons, X., Ninyerola, M. 2008. Mapping a topographic global solar radiation model implemented in a GIS and refined with ground data. International Journal of Climatology, 28(13), 1821-1834. https://doi.org/10.1002/joc.1676Pons, X., Pesquer, L., Cristóbal, J., González- Guerrero, O. 2014. Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images. International Journal of Applied Earth Observation and Geoinformation, 33, 243-254. https://doi.org/10.1016/j.jag.2014.06.002Pons X., Masó J. 2016. A comprehensive open package format for preservation and distribution of geospatial data and metadata. Computers & Geosciences, 97, 89-97. https://doi.org/10.1016/j.cageo.2016.09.001Townshend, J.R., Masek, J.G., Huang, C., Vermote, E.F., Gao, F., Channan, S., Sexton, J.O., Feng, M., Narasimhan, R., Kim, D., Song, K., Song, D., Song, X. P., Noojipady, P., Tan, B., Hansen, M.C., Li, M., Wolfe, R.E. 2012. Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges. International Journal of Digital Earth, 5(5), 373-397. https://doi.org/10.1080/17538947.2012.713190Woodcock, C.E., Allen, R., Anderson, M., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Goward, S.N., Helder, D., Helmer, E., Nemani, R., Oreopoulos, L., Schott, J., Thenkabail, P.S., Vermote, E.F., Vogelmann, J., Wulder, M.A., Wynne, R. 2008. Free access to Landsat imagery. Science, 320(5879), 1011. https://doi.org/10.1126/science.320.5879.1011aWulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., Woodcock, C.E. 2012. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122, 2-10. https://doi.org/10.1016/j.rse.2012.01.01

Ten years of local water resource management : Integrating satellite remote sensing and geographical information systems

On 2002, a novel initiative was undertaken by the local water administration of Catalonia (the Agència Catalana de l'Aigua) and the Universitat Autònoma de Barcelona, leading to a ten-year project where a high number of medium resolution satellite images (MODIS and Landsat) were integrated to the daily water management to improve decision making effectiveness. This paper describes the methodology followed in the successful application of remote sensing, as well as the main problems that had to be overcome during its execution. It also presents the products that have been calculated. These are integrated into the Agency's corporate GIS and immediately available via the intranet for the staff, and a selection is available on the Internet

Investigating the Spatio-Temporal Relationships Between Snowmelt Timing and Wildfire Area Burned in the US Mountain West

Wildfire is a growing problem in the US mountain west, with suppression costs exceeding $2 billion in 2015. Wildfire outbreaks occur in climate-driven synchronous events, and by studying the climate patterns that lead to dangerous fire conditions scientists have been able to identify numerous climatic factors that contribute to large fires. Low snow years and early snowmelt have long been hypothesized as indicators of large fire years, though there are few papers that identify this link explicitly, and those that do show great variation between the different mountainous areas of the west. In this thesis I, along with my co-investigators, explore the relationship between snowmelt timing and wildfire area burned among the many ecological systems of the US mountain west. We begin by defining a new way to identify snowmelt timing using time-series satellite imagery. We then form a theoretical and statistical framework for comparing snowmelt timing with area burned, modeled from previous climate/fire investigations. We further refined the snowmelt timing data using the complete MODIS record for the northern hemisphere. Finally, we use the MODIS-derived snowmelt timing data to investigate snowmelt/fire relationships at a moderate scale across hundreds of ecological systems of the US mountain west. Once we identified specific ecological systems that exhibit a link between snowmelt timing and wildfire we discussed the ecological implications of this relationship as well as fire-management strategies for land managers and public officials. This body of work demonstrates a substantial contribution to the fields of cryosphere studies, remote sensing, and fire ecology

Evaluación Espacio temporal de la evapotranspiración desde satélite. Aplicación a la gestión de recursos hídricos en la Cuenca del Segura

[SPA] La Cuenca del Río Segura, localizada en el Sureste de España, es un área semiárida donde las necesidades hídricas del sector agrícola superan el 80 % del uso total del agua disponible. En esta cuenca considerada la “huerta de Europa”, las sequías son recurrentes por ello la gestión sostenible del uso del agua representa un reto a alcanzar. En este sentido, el aumento de fiabilidad en la estimación de la evapotranspiración real (ETreal) se vería traducido en un mayor conocimiento de la necesidad hídrica real del cultivo, y por consiguiente en un uso más racional del agua, siendo por tanto el principal objetivo de la presente Tesis. La información necesaria para alcanzar este objetivo, ha sido obtenida en su mayor parte desde satélite; valiosa fuente de información que permite derivar componentes del balance hídrico. Desde observaciones multiescala se pueden identificar las zonas regadas con problemas de estrés hídrico, sus necesidades de riego, y realizar por tanto recomendaciones acertadas de volúmenes de riego. En la presente Tesis, se han propuesto y desarrollado metodologías en base a imágenes satélites que han sido validadas con datos al suelo desde torres de medida de flujo (mediante la técnica Eddy Covariance), permitiendo una estimación espacio-temporal de la ETreal a distintas escalas temporales y espaciales. Primeramente se abordó un análisis a escala de parcela o distrito de riego, centrándose el área de estudio en el Campo de Cartagena. El método aplicado consistió en una aproximación gráfica basado en la ecuación de Priestley-Taylor (PT), donde condiciones no limitantes del agua y de suelo seco fueron definidas por la temperatura superficial (LST) y el índice de vegetación (VI) Normalized Difference Vegetation Index (NDVI), ambos obtenidos mediante teledetección. Utilizando mediciones de ETreal e imágenes Landsat 5-TM en un área regada, se realizó un análisis de sensibilidad de las distribuciones espaciales de ETreal para el período 2009-11 con respecto a: (i) la forma (trapezoidal o rectangular) del espacio LST versus VI; y (ii) el valor del coeficiente PT (α). Los resultados de la validación fueron satisfactorios. Ambas formas proporcionaron resultados similares en la estimación de ETreal, con un error cuadrático medio (RMSE) ~30 W/m2 y una diferencia relativa ~10 % con respecto a las observaciones basadas en las torres de medida. El mejor ajuste con datos medidos fue para α cercano a 1, un valor algo diferente del valor comúnmente utilizado de 1.27, lo que indica que podría producirse un error sustancial al usar este último valor. En segundo lugar, se afronta un análisis a escala de cuenca hidrográfica más apropiado en estudios y modelos hidrológicos orientados a planificación de recursos hídricos, y usos agrícolas, pues éstos exigen el conocimiento de largas series temporales de ETreal empleando escenarios dinámicos relacionados con el uso del suelo o el cambio climático. Esta información a escala mensual y de cuenca hidrográfica permite la mejora de series temporales hidrológicas, uno de los objetivos buscados por la política del agua de la Unión Europea. En este sentido, a nivel europeo se pretende incentivar la contabilidad del agua a escala de cuenca, como herramienta para determinar ahorros potenciales de agua y homogeneizar resultados para ser comparados entre territorios y periodos. Debido a ello, con el propósito de integrar ETreal en un sistema de contabilidad hídrica, se empleó un método basado en un ajuste de regresión no lineal, usando para ello el IV Enhanced Vegetation Index (EVI) provisto por el sensor MODIS a bordo de la plataforma TERRA, datos de temperatura del aire para la estimación de la ETo, y observaciones de ETreal, proporcionando estimaciones mensuales y a escala de cuenca. Los resultados fueron validados con datos al suelo desde torres de medida de flujos, mostrando un error inferior a un 15 % y un RMSE inferior a 0.5 mm/d (mensual). Con esta información, se completaron balances hídricos en el marco de la implantación del sistema SEEA-Water de Naciones Unidas para toda la Demarcación del Río Segura, diseñándose una herramienta que combina la información hidrometeorológica necesaria desde distintas fuentes. De este modo, constituye una metodología innovadora para la incorporación de datos hidrometeorológicos agregando diferentes redes de medición. A modo de aplicación, en esta Tesis se detallan únicamente los resultados referentes a en la cuenca alta del Río Segura, donde se localizan y generan las reservas más importantes de la cuenca. y validación para diferentes condiciones de cobertura vegetal. Se destaca la dificultad en la aplicación de la metodología SEEA-Water de las cuentas del agua a escala de demarcación hidrográfica, dada la falta de datos y la integración de diferentes fuentes de información y escalas espacio-temporales de trabajo. Sin embargo, quedan por resolver incertidumbres relacionadas con otras variables así como la integración de diferentes fuentes de información y escalas espacio-temporales de trabajo. En este sentido se debe hacer un esfuerzo, que permita llegar a un equilibrio entre lo establecido en la Directiva Marco de Aguas y la disponibilidad real de la información requerida. [ENG] The Segura River Basin, located in the Southeast of Spain, is a semi-arid area where water need of agricultural sector exceed 80 % of the total use of available water. In this basin, which is considered the "orchard of Europe", droughts are frequent, so the sustainable management of water use represents a challenge to be achieved. In this sense, the increase of reliability in the estimation of the actual evapotranspiration (ETact), would be reflected in a better knowledge of the crop water requirements, and consequently in a more rational use of agricultural water resources, which is one of the main objectives of this Thesis. The necessary information to achieve this objective have been obtained mostly from satellite, which is known to be a valuable source of information for retrieving the components of land energy and water balance. From multiscale observations, it can be identified irrigated areas with water stress problems, their irrigation needs, and therefore, it could provide accurate recommendations for irrigation scheduling. In this Thesis, methodologies have been proposed and developed based on satellite images, which have been validated from ground truth using flux towers and the Eddy-Covariance technique, allowing estimation of ETact at different temporal and spatial scales. Firstly, an analysis was carried out at the scale of the irrigation plot or district. The applied method consisted of a graphical approximation based on the Priestley-Taylor (PT) equation, where non-limiting conditions of water and dry soil were defined by the surface temperature (LST) and a vegetation index (VI) Normalized Difference Vegetation Index (NDVI), both obtained by remote sensing. Using ET measurements from flux towers, and Landsat 5-TM images in a irrigated area, a sensitivity analysis of ETact spatial distributions was performed for the period 2009-11 with respect to: (i) the form (trapezoidal or rectangular) of the LST-VI space; and (ii) the value of the PT coefficient (α). The results from ground truth validation were satisfactory, both shapes providing similar performances in estimating ETact, with root mean square error (RMSE) ~30 W/m2 and relative difference ~10 % with respect to tower-based measurements. Importantly, the best fit with ground data was found for α close to 1, a somewhat different value from the commonly used value of 1.27, indicating that substantial error might arise when using the latter value. Secondly, an analysis at the hydrographic basin scale is, more appropriate in hydrological and agricultural studies and models, but requires the knowledge of long time series of ETact using dynamic scenarios related to land use or climate change. This information at a monthly scale and in a watershed allows for the improvement of hydrological time series, the purpose of the European Union's water policy. In this sense, at European level the water accounting is pretended to encourage, as a tool to determine potential water savings and homogenize results to be compared between territories and periods. Therefore, with the purpose of integrating ETact in a water accounting system, a method based on a nonlinear regression adjustment was used, which uses the IV Enhanced Vegetation Index (EVI) provided by the MODIS sensor on board the platform TERRA, air temperature data for the estimation of ETo, and observations of ETact, provided monthly and basin scale estimates. The results were validated with soil data from flow measurement towers, showing an error of less than 15 % and an RMSE of less than 0.5 mm/d (monthly). With this information, water balances were filled out within the framework of the implementation of the United Nations SEE-Water system, for the entire Segura River Demarcation, designing a tool that combines different hydrometeorological information necessary for the completion of water balances. In this way, it constitutes an innovative methodology for the incorporation of hydrometeorological data by the integration of different measurement networks. However, this thesis only details the results respect to the upper basin of the Segura River, where the most important stocks of the basin are located and generated. Finally, it is emphasized that obtaining reliable estimates of ETact for different vegetal coverage conditions by means of large-scale satellite data is a difficult objective to achieve given the multitude of variables involved in this process. To which must be added the scarcity of ground true data for its contrast under different environmental conditions. The difficulty in the implementation of SEEA-Water methodology of water accounting at basin scale, is justified by the unavailability of all data at the spatio-temporal scale Abstract required. However although there are still unresolved uncertainties related to other variables and the integration of different sources of information and spatio-temporal scales of work. Therefore, an effort must be made in this sense, which allows achieving a balance between what is established in the Water Framework Directive and the actual availability of the required information.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma Doctorado en Técnicas Avanzadas en Investigación y Desarrollo Agrario y Alimentari

Multispectral classification and reflectance of glaciers: in situ data collection, satellite data algorithm development, and application in Iceland & Svalbard

Glaciers and ice caps (GIC) are central parts of the hydrological cycle, are key to understanding regional and global climate change, and are important contributors to global sea level rise, regional water resources and local biodiversity. Multispectral (visible and near-infrared) remote sensing has been used for studying GIC and their changing characteristics for several decades. Glacier surfaces can be classified into a range of facies, or zones, which can be used as proxies for annual mass balance and also play a significant role in understanding glacier energy balance. However, multispectral sensors were not designed explicitly for snow and ice observation, so it is not self-evident that they should be optimal for remote sensing of glaciers. There are no universal techniques for glacier surface classification which have been optimized with in situ reflectance spectra. Therefore, the roles that the various spectral, spatial, and radiometric properties of each sensor play in the success and output of resulting classifications remain largely unknown. Therefore, this study approaches the problem from an inverse perspective. Starting with in situ reflectance spectra from the full range of surfaces measured on two glaciers at the end of the melt season in order to capture the largest range of facies (Midtre Lovénbreen, Svalbard & Langjökull, Iceland), optimal wavelengths for glacier facies identification are investigated with principal component analysis. Two linear combinations are produced which capture the vast majority of variance in the data; the first highlights broadband albedo while the second emphasizes the difference in reflectance between blue and near-infrared wavelengths for glacier surface classification. The results confirm previous work which limited distinction to snow, slush, and ice facies. Based on these in situ data, a simple, and more importantly completely transferrable, classification scheme for glacier surfaces is presented for a range of satellite multispectral sensors. Again starting with in situ data, application of relative response functions, scaling factors, and calibration coefficients shows that almost all simulated multispectral sensors (at certain gain settings) are qualified to classify glacier accumulation and ablation areas but confuse classification of partly ash-covered glacier surfaces. In order to consider the spatial as well as the spectral properties of multispectral sensors, airborne data are spatially degraded to emulate satellite imagery; while medium-resolution sensors (~20-60 m) successfully reproduce high-resolution (2 m) observations, low-resolution sensors (i.e. 250 m+) are unable to do so. These results give confidence in results from current sensors such as ASTER and Landsat ETM+ as well as ESA’s upcoming Sentinel-2 and NASA’s recently launched LDCM. In addition, images from the Landsat data archive are used to classify glacier facies and calculate the albedo of glaciers on the Brøgger Peninsula, Svalbard. The time series is used to observe seasonal and interannual trends and investigate the role of melt-albedo feedback in thinning of Svalbard glaciers. The dissertation concludes with recommendations for glacier surface classification over a range of current and future multispectral sensors. Application of the classification schemes suggested should help to improve the understanding of recent and continuing change to GIC around the world.My doctoral studies were supported by a graduate studentship from Trinity College, Cambridge as well as by the National Science Foundation Graduate Research Fellowship Programme under Grant No. DGE-1038596. Further research support came from UK Natural Environment Research Council’s Field Spectroscopy Facility, ARCFAC (the European Centre for Arctic Environmental Research), Trinity College Cambridge, Sigma Xi, the Norwegian Marshall Fund, the Explorers Club, the National Geographic Society Young Explorers Program, the Scott Polar Research Institute, the Cambridge University Geography Department, the Cambridge University Department of Anglo-Saxon, Norse, and Celtic Studies, and the Cambridge University Worts Fund