A contour matching approach for accurate NOAA-AVHRR image navigation

Although different methods for NOAA AVHRR image navigation have already been established, the multitemporal and multi-satellite character of most studies requires automatic and accurate methods for navigation of satellite images. In the proposed method, a simple Kepplerian orbital model for the NOAA satellites is considered as reference model, and mean orbital elements are given as input to the model from ephemeris data. In order to correct the errors caused by these simplifications, errors resulting from inaccuracies in the positioning of the satellite and failures in the satellite internal clock, an automatic global contour matching approach has been adopted. First, the sensed image is preprocessed to obtain a gradient energy map of the reliable areas (sea-land contours) using a cloud detection algorithm and a morphological gradient operator. An initial estimation of the reliable contour positions is automatically obtained. The final positions of the contours are obtained by means of an iterative local minimization procedure that allows a contour to converge on an area of high image energy (edge). Global transformation parameters are estimated based on the initial and final positions of all reliable contour points. Finally, the performance of this approach is assessed using NOAA 14 AVHRR images from different geographic areas.Postprint (published version

TerraSenseTK: a toolkit for remote soil nutrient estimation

Intensive farming endangers soil quality in various ways. Researchers show that if these practices continue, humanity will be faced with food production issues. For this matter, Earth Observation, more concretely Soil Sensing, along with Machine Learning, can be employed to monitor several indicators of soil degradation, such as soil salinity, soil heavy metal contamination and soil nutrients estimation. More concretely, Soil Nutrients are of great importance. For instance, to understand which crop better suits the land, the soil nutrients must be identified. However, sampling soil is a laborous and expensive task, which can be leveraged by Remote Sensing and Machine Learning. Several studies have already been developed in this matter, although many gaps still exist. Among them, the lack of cross-dataset evaluations of existing algorithms, and also the steep learning curve to the Earth Observation domain that prevents many researchers from embracing this field. In this sense, we propose TerraSense ToolKit (TSTK), a python toolkit that addresses these challenges. In this work, the possibility to use Remote sensing along with Machine Learning algorithms to per form Soil Nutrient Estimation is explored, additionally, a nutrient estimation toolkit is proposed, and the effectivity of it is tested in a soil nutrient estimation case study. This toolkit is capable of simplifying Remote Sensing experiments and aims at reducing the barrier to entry to the field of Earth Observation. It comes with a preconfigured case study which implements a soil sensing pipeline. To evaluate the usability of the toolkit, experiments with five different crops were executed, namely with Wheat, Barley, Maize, Sunflower and Vineyards. This case study gave visibility to an underlying unbalanced data problem, which is not well addressed in the current State of the Art.A agricultura intensiva poe em perigo a qualidade do solo de v ˜ arias formas. Os investigadores ´ mostram que, se continuarmos com estas praticas, a humanidade ser ´ a confrontada com quest ´ oes de ˜ produc¸ao alimentar. Para este efeito, a Observac¸ ˜ ao da Terra, mais concretamente o Sensoriamento ˜ do Solo, juntamente com a aprendizagem automatica, podem ser utilizadas para monitorizar v ´ arios ´ indicadores da degradac¸ao do solo, tais como a salinidade do solo, a contaminac¸ ˜ ao do solo por metais ˜ pesados e a quantificac¸ao dos nutrientes do solo. Mais concretamente, os Nutrientes do Solo s ˜ ao de ˜ grande importancia. Por exemplo para compreender qual a cultura que melhor se adapta ao solo, os ˆ nutrientes do solo devem ser identificados. No entanto, a amostragem do solo e uma tarefa trabalhosa ´ e dispendiosa, que pode ser impulsionada pela percepc¸ao remota e pela aprendizagem autom ˜ atica. ´ Ja foram desenvolvidos v ´ arios estudos sobre este assunto, embora ainda existam muitas lacunas. ´ Entre eles, a falta de avaliac¸oes cruzadas dos algoritmos existentes, e tamb ˜ em a curva de aprendiza- ´ gem acentuada para o campo de Observac¸ao da Terra que impede muitos investigadores de enveredar ˜ por este campo. Neste sentido, propomos TSTK, um toolkit em python que aborda estes desafios. Neste trabalho, e explorada a possibilidade de usar a Percepc¸ ´ ao Remota juntamente com os algo- ˜ ritmos de Aprendizagem Automatica para realizar a Estimativa de Nutrientes do Solo. Al ´ em disso, ´ e´ proposto um toolkit de estimativa de nutrientes e tambem um pipeline para o devido efeito, a efetividade ´ do toolkit e testada num caso de estudo de Estimac¸ ´ ao de Nutrientes no Solo. ˜ Este toolkit e capaz de simplificar as experi ´ encias de Percepc¸ ˆ ao Remota e visa reduzir a barreira ˜ de entrada no campo da Observac¸ao da Terra. Para avaliar a usabilidade do toolkit, foram executadas ˜ experiencias com cinco culturas diferentes, nomeadamente Trigo, Cevada, Milho, Girassol e Vinha. Este ˆ caso de estudo deu visibilidade a um problema subjacente de dados desiquilibrados, o qual nao˜ e bem ´ identificado no Estado da Arte atual

Analysis of information systems for hydropower operations

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined

Monitoring the Coastal Environment Using Remote Sensing and GIS Techniques

The coastal zone has been of importance for economic development and ecological restoration due to their rich natural resources and vulnerable ecosystems. Remote sensing techniques have proven to be powerful tools for the monitoring of the Earth’s surface and atmosphere on a global, regional, and even local scale, by providing important coverage, mapping and classification of land cover features such as vegetation, soil, water and forests. This chapter introduced the methods for monitoring the coastal environment using remote sensing and GIS techniques. Case studies of port expansion monitoring in typical coastal regions, together with the coastal environment changes analysis were also presented

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work