Methodology for the Automatic Inventory of Olive Groves at the Plot and Polygon Level

The aim of this study was to develop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques. To do so, tools and protocols have been developed that have made it possible to automate the capture of images of different characteristics and origins, enable the use of open data sources, as well as integrating and metadating them. They can then be used for the development and validation of algorithms that allow for improving the characterization of olive grove surfaces at the plot and cadastral polygon scales. With the proposed system, an inventory of the Andalusian olive grove has been automatically carried out at the level of cadastral polygons and provinces, which has accounted for a total of 1,519,438 hectares and 171,980,593 olive trees. These data have been contrasted with various official statistical sources, thus ensuring their reliability and even identifying some inconsistencies or errors of some sources. Likewise, the capacity of the Sentinel 2 satellite images to estimate the FCC at the cadastral polygon, parcel and 10 × 10 m pixel level has been demonstrated and quantified, as well as the opportunity to carry out inventories with temporal resolutions of approximately up to 5 days

Abordagem Big Data a dados de mobilidade em transportes públicos

A necessidade de armazenar, processar e analisar os dados é uma realidade cada vez presente nas empresas onde as decisões de negócio dependem muito das plataformas digitais. A introdução do conceito de Data Warehouse teve como finalidade facilitar e melhorar o processo de recolha de indicadores de negócio imprescindíveis. O conceito de Big Data veio com o aumento da variedade e do volume de dados para fins de análise. Com esse conceito em mente, foram desenvolvidas tecnologias para fazerem face aos desafios impostos. A transformação digital no registo de entradas e saídas nos transportes público leva a grandes volumes de dados que podem ser usados para aplicar análises de negócio na área [1]. Este projeto visa a recolha de um conjunto de tecnologias na vertente do Big Data e a avaliação da capacidade de armazenamento, do método de elaboração dos métodos de ETL e do desempenho na obtenção de resposta a um conjunto de queries, consoante o aumento do volume de dados de mobilidade, referentes às entradas dos autocarros da companhia de transportes públicos Horários do Funchal. É introduzida neste projeto uma revisão de literatura sobre os conceitos de Data Warehouse, de modelos dimensionais e de Big Data, bem como nas tecnologias existentes e trabalhos relacionados com manipulação de Big Data. Foi também objeto de análise do estado da arte a aplicação destas tecnologias nos transportes públicos. Os resultados apresentados revelam que algumas plataformas conseguem adequar-se bem ao um aumento do volume de dados, com boas capacidades de desempenho, tanto na execução de processos de ETL, como na execução de queries de consulta, em comparação a outras tecnologias, cujo resultados são pouco práticos neste tipo de estudo.The need to store, process and analyse data is a increasingly present reality in companies where business decisions depend heavily on digital platforms. The purpose of introducing the Data Warehouse concept was to facilitate and improve the process of collecting essential business indicators. The concept of Big Data came with the increase in the variety and the volume of data for analysis purposes. With the concept in mind, technologies were developed to face the imposed challenges. The digital transformation in the registration of entrances and exits in the public transport lead to large volumes of data that can be used to apply business analysis [1]. This project aims to collect a set of technologies in the field of Big Data and evaluate the storage capacity, the method of elaborating ETL methods and the performance in obtaining a response to a set of queries, referring to the entrances of the buses of public transport company Horários do Funchal. This project introduces a literature review on the concepts of Data Warehouse, dimensional models and Big Data, as well as existing technologies and work related to Big Data manipulation. The application of these technologies in public transport was also subject to a state-of-the-art analysis. The presented results reveal that some platforms are able to adapt well to an increase in the volume, with good performance capabilities, both in the execution of ETL processes and in the execution of queries, in comparison to other technologies, whose results are impractical in this type of study

Analysis of photograms and satellite images for the automatic identification and quantification of trees and units with ecological significance in olive groves and dehesa ecosystems

La gestión sostenible e integración de servicios para el manejo de ecosistemas se ha convertido en un factor clave en las últimas décadas, viéndose impulsado gracias a la digitalización. Dentro de los ecosistemas presentes en el sur de España destaca la dehesa, el cual es un ecosistema antrópico complejo típico de algunas zonas de España y Portugal, con un papel clave en la conservación del suelo, la biodiversidad y en la búsqueda del equilibrio entre producción, conservación y servicios ecosistémicos. Por otro lado, el olivar es uno de los cultivos más representativos de la cuenca mediterránea, por su interés cultural, paisajístico y económico. Por ello, es fundamental disponer de herramientas que permitan su caracterización, así como el seguimiento y apoyo a la toma de decisiones para mejorar su sostenibilidad. El objetivo de esta Tesis es el desarrollo de herramientas polivalentes que permitan realizar inventarios agroforestales automatizados, identificando y cuantificando unidades con significancia ecológica, a través de protocolos de análisis de imagen y analítica de datos, con el foco en los ecosistemas de dehesa y olivar. Para alcanzar el objetivo, se desarrolló un prototipo de espacio compartido de datos a través de la integración, preprocesado, limpieza e interpretación de diferentes fuentes de datos abiertas, a partir del cual se crearon sendos almacenes de datos para el ecosistema de la dehesa y el cultivo de olivar, que sirvieron como soporte a las herramientas. En lo referente a la dehesa, los elementos identificados fueron: árboles, grupos de árboles, corredores ecosistémicos, áreas regeneradas y láminas de agua. Para su identificación, se desarrolló un análisis de imagen según el enfoque OBIA (Object-Based Image Analysis). Respecto al olivar, el objetivo fue la caracterización automática de la superficie de olivar de Andalucía, obteniendo información singularizada a nivel de árbol y Fracción de Cabida Cubierta (FCC). Esta investigación también propuso evaluar el NDVI (Índice Diferencial Normalizado de Vegetación) procedente de imágenes de teledetección para estimar la FCC de distintas tipologías de olivares obtenida en la caracterización. Las herramientas propuestas en esta tesis permiten la escalabilidad y generalización en los ecosistemas planteados y en otros cultivos y ecosistemas, ofreciendo la posibilidad de segmentar la superficie ocupada por los árboles y otras unidades ecológicas lo que abre una gran oportunidad para mejorar la construcción de modelos de interpretación de imágenes de satélite.The sustainable management and integration of services for ecosystem management has become a key factor in recent decades, boosted by digitization. Among the ecosystems present in southern Spain, the dehesa stands out, which is a complex anthropic ecosystem typical of some areas of Spain and Portugal, with a key role in soil conservation, biodiversity and in the search for balance between production, conservation and ecosystem services. On the other hand, the olive grove is one of the most representative crops of the Mediterranean basin, due to its cultural, landscape and economic interest. Therefore, in order to improve its sustainability, it is essential to have tools that allow its characterization, as well as monitoring and supporting for decision making. The objective of this Thesis is the development of multipurpose tools based on a prototype of shared data space that allow automated agroforestry inventories, identifying and quantifying units with ecological significance, through image analysis protocols and data analytics, with a focus on the ecosystems of dehesas and olive groves. To achieve the objective, a prototype of a shared data space was developed through the integration, preprocessing, cleaning and interpretation of different open data sources, from which two data warehouses were created for the ecosystem of the dehesa and the olive grove, which served as support for the tools. Regarding the dehesa, the elements identified were: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water, for which an image analysis was developed according to the OBIA approach (Object-Based Image Analysis). Regarding the olive grove, the objective was the automatic characterization of the olive grove area in Andalusia, obtaining singularized information at tree and Covered Crown Fraction (FCC). This research also proposed to evaluate the NDVI (Normalized Difference Vegetation Index) from remote sensing images to estimate the FCC of different types of olive groves. The tools proposed in this Thesis allow scalability and generalization in the proposed ecosystems and in other crops and ecosystems, offering the possibility of segmenting the area occupied by trees and other ecological units, which opens a great opportunity to improve the construction of satellite image interpretation models

Designing and implementing data warehouse for agricultural big data

In recent years, precision agriculture that uses modern information and communication technologies is becoming very popular. Raw and semi-processed agricultural data are usually collected through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, farmers and agribusinesses, etc. Besides, agricultural datasets are very large, complex, unstructured, heterogeneous, non-standardized, and inconsistent. Hence, the agricultural data mining is considered as Big Data application in terms of volume, variety, velocity and veracity. It is a key foundation to establishing a crop intelligence platform, which will enable resource efficient agronomy decision making and recommendations. In this paper, we designed and implemented a continental level agricultural data warehouse by combining Hive, MongoDB and Cassandra. Our data warehouse capabilities: (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) distributed and cloud deployment. We also evaluate the performance of our data warehouse.Science Foundation IrelandOrigin Enterprises12 month embargo - A