A Human-Centered Framework for the Understanding of Synthetic Aperture Radar Images

The limited usage of SAR data in the end-user community and in applicative contexts testified the failure of the recent literature, in which the research privileged the automatic extraction of information at the expense of users' experience with data. The development of new products and processing frameworks providing user-friendly representations and extraction of the physical information is a necessary condition for the full exploitation of SAR sensors. In this Book, the necessity to restore users’ centrality in remote sensing data analysis is analyzed and achieved through the introduction of two new classes of RGB SAR products obtained via multitemporal processing, whose principal characteristics are the ease of interpretation and the possibility to be processed with simple, end-user-oriented technique. These proposed approach aims to definitely fill the gap between the academy and the applications. The rationale is to provide ready-to-use images, in which the technical expertise with electromagnetic models, SAR imaging and image processing has been absorbed in the products formation phase. In such way, the idea that SAR images are too complicated to be interpreted and processed without a high technical expertise in order to extract physical information is overcame

SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms.

In this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested

Multisource Data Integration in Remote Sensing

Papers presented at the workshop on Multisource Data Integration in Remote Sensing are compiled. The full text of these papers is included. New instruments and new sensors are discussed that can provide us with a large variety of new views of the real world. This huge amount of data has to be combined and integrated in a (computer-) model of this world. Multiple sources may give complimentary views of the world - consistent observations from different (and independent) data sources support each other and increase their credibility, while contradictions may be caused by noise, errors during processing, or misinterpretations, and can be identified as such. As a consequence, integration results are very reliable and represent a valid source of information for any geographical information system

Assessing the Vitality of Urban Trees using Remote Sensing and Deep Learning

Abstract The use of Convolutional Neural Networks (CNN) has been widely implemented in forestry-related tasks as species classification, crown detection and mortality identification. The usage of several sources as images, point clouds and elevation models have generated relevant results in different forested areas, but unfortunately these studies have not been focused on urban trees. Therefore, the objective of this study is to investigate the performance of CNN for classifying the vitality of urban trees, which are increasingly affected and stressed by the Urban Heat Island Effect. Aerial and Sentinel-2 images are sampled for feeding the CNN model. The prediction of the vitality classes shows a precision of 74,69%, especially for the most represented class (healthy trees). The achieved results allow to better understand the performance of a CNN network for determining the vitality of trees in an urban context where diversity of vegetation patterns can represent a big challenge for classification tasks

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Development of burned area algorithms on a global scale

El trabajo de tesis titulado "Desarrollo de algoritmos de área quemada a escala global - Development of burned area algorithms on a global scale" ha sido desarrollado y financiando en el marco del proyecto fire_cci dentro del programa de cambio climático de la Agencia Espacial Europea. El objetivo principal de esta tesis doctoral ha sido desarrollar un algoritmo para la caracterización de áreas quemadas (AQ) a escala global a partir de información del sensor MERIS. Dentro de la tesis se ha buscado contextualizar la relevancia del fuego a escala global. Se han revisado los métodos para caracterizar los incendios desde el espacio, llevando a cabo una revisión bibliográfica del estado del arte. Se ha desarrollado y probado el algoritmo de área quemada, basando su configuración final en los distintos métodos implementados y en los resultados de las pruebas realizadas. El algoritmo obtenido puede clasificarse dentro de la categoría de algoritmo híbrido, ya que combina la información obtenida del contraste térmico (proporcionada por el producto MODIS HS) y de los cambios temporales en las reflectividades de los datos MERIS. El algoritmo consta de dos fases: semillado y crecimiento. En la primera fase, se identifican los píxeles semilla, es decir los puntos más claramente clasificables como quemados. Para ello se obtienen de forma dinámica estadísticas locales (basadas en regiones de 10x10 grados) de forma mensual que permiten definir condiciones para clasificar los píxeles semilla. En la fase de crecimiento se realiza un análisis de los píxeles vecinos a estas semillas, estableciendo su carácter quemado si verifican a su vez una serie de condiciones. Se ha llevado a cabo un análisis y discusión de las estimaciones de área quemada obtenidas mediante este algoritmo a nivel global para los años 2006 a 2008. Estos resultados se han validado e inter-comparado con otros productos de área quemada. Se incluyen así mismo en la tesis las conclusiones obtenidas del desarrollo del algoritmo, y los posibles futuros pasos a seguir. El principal logro del trabajo realizado en el marco de este trabajo de investigación ha sido el desarrollo del primer algoritmo de áreas quemadas a escala global a partir del sensor MERIS. Esto permite obtener productos de AQ a mayor resolución que la proporcionada por las colecciones de AQ existentes en la actualidad, y mejorando la calidad de las colecciones obtenidas a nivel europeo

Implementing an Agro-Environmental Information System (AEIS) Based on GIS, Remote Sensing, and Modelling -- A case study for rice in the Sanjiang Plain, NE-China

Information on agro-ecosystems is crucial for understanding the agricultural production and its impacts on the environment, especially over large agricultural areas. The Sanjiang Plain (SJP), covering an area of 108 829 km², is a critical food base located in NE-China. Rice, soya bean and maize are the major crops in the SJP which are sold as commercial grain throughout China. The aim of this study is to set up an Agro-Environmental Information System (AEIS) for the SJP by employing the technologies of geographic information systems (GIS), remote sensing (RS), and agro-ecosystem modelling. As the starting step, data carrying interdisciplinary information from multiple sources are organized and processed. For an AEIS, geospatial data have to be acquired, organized, operated, and even regenerated with good positioning conditions. Georeferencing of the multi-source data is mandatory. In this thesis, high spatial accuracy TerraSAR-X imagery was used as a reference for georeferencing raster satellite data and vector GIS topographic data. For the second step, the georeferenced multi-source data with high spatial accuracy were integrated and categorized using a knowledge-based classifier. Rice was analysed as an example crop. A rice area map was delineated based on a time series of three high resolution FORMOSAT-2 (FS-2) images and field observed GIS topographic data. Information on rice characteristics (i.e., biomass, leaf area index, plant nitrogen concentration and plant nitrogen uptake) was derived from the multi-temporal FS-2 images. Spatial variability of rice growing status on a within-field level was well detected. As the core part of the AEIS, an agro-ecosystem modelling was then applied and subsequently crops and the environmental factors (e.g., climate, soil, field management) are linked together through a series of biochemical functions inherent in the modelling. Consequently, the interactions between agriculture and the environment are better interpreted. In the AEIS for the SJP, the site-specific mode of the DeNitrification-DeComposition (DNDC) model was adapted on regional scales by a technical improvement for the source code. By running for each pixel of the model input raster files, the regional model assimilates raster data as model inputs automatically. In this study, detailed soil data, as well as the accurate field management data in terms of crop cultivation area (i.e. rice) were used as model inputs to drive the regional model. Based on the scenario optimized from field observation, rice yields over the Qixing Farm were estimated and the spatial variability was well detected. For comparison, rice yields were derived from multi-temporal FS-2 images and the spatial patterns were analysed. As representative environmental effects, greenhouse gas of nitrous oxide (N2O) and carbon dioxide (CO2) emitted from the paddy rice fields were estimated by the regional model. This research demonstrated that the AEIS is effective in providing information about (i) agriculture on the region, (ii) the impacts of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies, especially for the regional areas (e.g. the SJP) that is lacking of geospatial data

D6.6: 7 conference papers

The Deliverable 6.6 with the title “7 conference papers”, is part of WP6 “Dissemination and Exploitation” of Athena project with a basic aim to knowledge sharing, network development and exposure to an international environment. Three conference attendances were foreseen (e.g. CAA; SPIE; EARSeL) within the project duration whereas more than 30 posters and oral presentations were presented during the project in the conferences such as: SPIE 2016, SPIE 2018, EUROMED 2016, EUROMED 2018, EGU 2016, EGU 2017, EGU 2018, RSCy2016, RSCy 2017, RSCy 2018, etc

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications