A Contribution to land cover and land use mapping: in Portugal with multi-temporal Sentinel-2 data and supervised classification

Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceRemote sensing techniques have been widely employed to map and monitor land cover and land use, important elements for the description of the environment. The current land cover and land use mapping paradigm takes advantage of a variety of data options with proper spatial, spectral and temporal resolutions along with advances in technology. This enabled the creation of automated data processing workflows integrated with classification algorithms to accurately map large areas with multi-temporal data. In Portugal, the General Directorate for Territory (DGT) is developing an operational Land Cover Monitoring System (SMOS), which includes an annual land cover cartography product (COSsim) based on an automatic process using supervised classification of multi-temporal Sentinel-2 data. In this context, a range of experiments are being conducted to improve map accuracy and classification efficiency. This study provides a contribution to DGT’s work. A classification of the biogeographic region of Trás-os-Montes in the North of Portugal was performed for the agricultural year of 2018 using Random Forest and an intra-annual multi-temporal Sentinel-2 dataset, with stratification of the study area and a combination of manually and automatically extracted training samples, with the latter being based on existing reference datasets. This classification was compared to a benchmark classification, conducted without stratification and with training data collected automatically only. In addition, an assessment of the influence of training sample size in classification accuracy was conducted. The main focus of this study was to investigate whether the use of vi classification uncertainty to create an improved training dataset could increase classification accuracy. A process of extracting additional training samples from areas of high classification uncertainty was conducted, then a new classification was performed and the results were compared. Classification accuracy assessment for all proposed experiments was conducted using the overall accuracy, precision, recall and F1-score. The use of stratification and combination of training strategies resulted in a classification accuracy of 66.7%, in contrast to 60.2% in the case of the benchmark classification. Despite the difference being considered not statistically significant, visual inspection of both maps indicated that stratification and introduction of manual training contributed to map land cover more accurately in some areas. Regarding the influence of sample size in classification accuracy, the results indicated a small difference, considered not statistically significant, in accuracy even after a reduction of over 90% in the sample size. This supports the findings of other studies which suggested that Random Forest has low sensitivity to variations in training sample size. However, the results might have been influenced by the training strategy employed, which uses spectral subclasses, thus creating spectral diversity in the samples independently of their size. With respect to the use of classification uncertainty to improve training sample, a slight increase of approximately 1% was observed, which was considered not statistically significant. This result could have been affected by limitations in the process of collecting additional sampling units for some classes, which resulted in a lack of additional training for some classes (eg. agriculture) and an overall imbalanced training dataset. Additionally, some classes had their additional training sampling units collected from a limited number of polygons, which could limit the spectral diversity of new samples. Nevertheless, visual inspection of the map suggested that the new training contributed to reduce confusion between some classes, improving map agreement with ground truth. Further investigation can be conducted to explore more deeply the potential of classification uncertainty, especially focusing on addressing problems related to the collection of the additional samples

Instituciones políticas, procesos de diseño de políticas y resultados de las políticas en Uruguay

Uruguay genera una variedad de resultados políticos. Primero, hay políticas relativamente estables que permiten la apertura comercial y financiera del país. También, hay políticas de baja calidad e inflexibles relacionadas con políticas sociales, algunas áreas de reforma estatal (los salarios de los funcionarios del estado y mecanismos de contratación), el régimen de bancarrota, etc. Finalmente, estan los resultados volátiles que son generalmente los efectos de choques económicos, algunas veces relacionados con los gastos públicos. En los casos en que hay un precedente histórico o que la disponibilidad de mecanismos externos de cumplimento no conducen a políticas relativamente estables, la principal característica saliente de las políticas Uruguayas es la rigidez. La fuente de rigidez de las políticas Uruguayas parece ser una mezcla de factores institucionales (múltiples vetos, partidos fraccionados, y mecanismos de democracia directa) y conflictos políticos (preferencias de políticas divergentes), en los cuales es muy costoso moverse del status-quo debido a la gran amenaza de un reverso de las políticas. Las instituciones políticas en el Uruguay son propicias a alcanzar un acuerdo político a corto plazo, pero no pueden cooperar efectivamente y establecer políticas estables y flexibles al largo plazo. La dificultad está en conseguir intercambios políticos ínter temporales que son consistentes con las principales características del ambiente político: una cifra alta de principales actores políticos y vetos, una cifra considerable de maniobras políticas inobservables, una pobre aplicación de tecnología en el área económica, una burocracia políticamente influenciada, intercambios políticos que ocurren fuera del ruedo legislativo, y una particular constelación de partidos y preferencias además de un diseño costoso de políticas y cambios institucionales. (Disponible en Inglés)

Comment on "Spin-1 aggregation model in one dimension"

M. Girardi and W. Figueiredo have proposed a simple model of aggregation in one dimension to mimic the self-assembly of amphiphiles in aqueous solution [Phys. Rev. E 62, 8344 (2000)]. We point out that interesting results can be obtained if a different set of interactions is considered, instead of their choice (the s=1 Ising model).Comment: Accepted for publication in Phys. Rev.

Causal Stability Ranking

Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs. We therefore consider inferring stable rankings of genes, according to their causal effects on a phenotype, from observational data only. Our method allows for efficient design and prioritization of future experiments, and due to its generality it is useable for a broad spectrum of applications

UMA BREVE REFLEXÃO SOBRE O CAMPO CIENTÍFICO DA ADMINISTRAÇÃO E O PAPEL DO PROFESSOR PESQUISADOR NAS UNIVERSIDADES BRASILEIRAS

Discussões sobre o campo científico, suas relações e contradições inerentes aos sistemas avaliativos no Brasil têm se enveredado para o papel do professor-pesquisador, as condições a ele oferecidas, as exigências condicionadas e as limitações reconhecidas. Nesse sentido, este artigo teórico visa a tecer uma breve reflexão a respeito do campo científico da Administração e o papel do professor-pesquisador nas universidades brasileiras. Para isso, são apresentados pressupostos teóricos sobre o campo científico, de modo geral e também de forma pontual no contexto da Administração; bem como sobre a vida do professor pesquisador nas universidades brasileiras. Reflexões levam a crer que o professor-pesquisador é constantemente submetido a desafios no campo científico, que evidenciam a semelhança do campo com o sistema de trocas econômico, em que se manifestam práticas embebidas de disputas por poder, por recursos e por visibilidade

a review

Moraes, D., Campagnolo, M. L., & Caetano, M. (2024). Training data in satellite image classification for land cover mapping: a review. European Journal of Remote Sensing, 57(1), 1-16. Article 2341414. https://doi.org/10.1080/22797254.2024.2341414 --- This research was funded by Fundação para a Ciência e Tecnologia [FCT] grant number [PRT/BD/153517/2021], the Forest Research Centre and Associated Laboratory TERRA [UIDB/00239/2020]. Mário Caetano acknowledges the financial support provided by Fundação para a Ciência e a Tecnologia, Portugal [FCT] under the project [UIDB/ 04152/2020] - Centro de Investigação em Gestão de Informação [MagIC].The current land cover (LC) mapping paradigm relies on automatic satellite imagery classification, predominantly through supervised methods, which depend on training data to calibrate classification algorithms. Hence, training data have a critical influence on classification accuracy. Although research on specific aspects of training data in the LC classification context exists, a study that organizes and synthetizes the multiplicity of aspects and findings of these researches is needed. In this article, we review the training data used for LC classification of satellite imagery. A protocol of identification and selection of relevant documents was followed, resulting in 114 peer-reviewed studies included. Main research topics were identified and documents were characterized according to their contribution to each topic, which allowed uncovering subtopics and categories and synthetizing the main findings regarding different aspects of the training dataset. The analysis found four research topics, namely construction of the training dataset, sample quality, sampling design and advanced learning techniques. Subtopics included sample collection method, sample cleaning procedures, sample size, sampling method, class balance and distribution, among others. A summary of the main findings and approaches provided an overview of the research in this area, which may serve as a starting point for new LC mapping initiatives.publishersversionepub_ahead_of_prin