Articles de revistahttp://hdl.handle.net/2117/1847252024-03-28T16:22:16Z2024-03-28T16:22:16ZInSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studiesMirmazloumi, SeyedmohammadWassie, YismawNava, LorenzoCuevas González, MaríaCrosetto, MicheleMontserrat, Oriolhttp://hdl.handle.net/2117/3935542024-02-25T12:10:23Z2023-09-15T11:32:27ZInSAR time series and LSTM model to support early warning detection tools of ground instabilities: mining site case studies
Mirmazloumi, Seyedmohammad; Wassie, Yismaw; Nava, Lorenzo; Cuevas González, María; Crosetto, Michele; Montserrat, Oriol
Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.
2023-09-15T11:32:27ZMirmazloumi, SeyedmohammadWassie, YismawNava, LorenzoCuevas González, MaríaCrosetto, MicheleMontserrat, OriolEarly alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.Fresh volcanic aerosols injected in the atmosphere during the volcano eruptive activity at the Cumbre Vieja area (La Palma, Canary Islands): Temporal evolution and vertical impactCordoba Jabonero, CarmenSicard, MichaëlBarreto Velasco, ÁfricaToledano, CarlosLópez Cayuela, María ÁngelesComerón Tejero, AdolfoGarcía Rodríguez, OmairaCarvajal Pérez, Clara VioletaGil Díaz, CristinaRamos López, RamónMuñoz Porcar, ConstantinoRodríguez Gómez, Alejandro Antoniohttp://hdl.handle.net/2117/3924432023-11-12T04:06:41Z2023-07-27T17:12:28ZFresh volcanic aerosols injected in the atmosphere during the volcano eruptive activity at the Cumbre Vieja area (La Palma, Canary Islands): Temporal evolution and vertical impact
Cordoba Jabonero, Carmen; Sicard, Michaël; Barreto Velasco, África; Toledano, Carlos; López Cayuela, María Ángeles; Comerón Tejero, Adolfo; García Rodríguez, Omaira; Carvajal Pérez, Clara Violeta; Gil Díaz, Cristina; Ramos López, Ramón; Muñoz Porcar, Constantino; Rodríguez Gómez, Alejandro Antonio
For the first time in fifty years, the Cumbre Vieja volcanic area (La Palma, Canary Islands, Spain) erupted on September 19, 2021, giving birth to a new volcano. Fresh volcanic aerosols were continuously injected into the troposphere at different height levels, decreasing with time until the end of December 2021 (15 weeks duration). A wide set of different instrumentation was deployed all over the Island in order to evaluate the effects of the volcanic plumes on the atmosphere and the air quality. For the first time, a long-term study of the relative mass contribution and vertical impact of the volcanic components, ash and non-ash particles separately, during the eruptive activity was carried out in this work. In particular, a polarized Micro-Pulse Lidar (P-MPL) was deployed at Tazacorte (at around 8 km west from the volcano) in 24/7 operation from October 17, 2021 until the end of the volcano activity (11 weeks) for vertical monitoring of the volcanic particles. First, a statistical study of the mass conversion factors for mass concentration estimation of the volcanic (ash and non-ash) particles was performed by using the AERONET sun/sky-photometer dataset at Fuencaliente (at around 18 km south from the volcano). A representative mass conversion factor was obtained for ash and non-ash particles: 1.89 ± 0.53 and 0.31 ± 0.06 g m-2, respectively, with no dependence on time and optical depth. Second, these factors were used to calculate the ash and non-ash mass concentrations from P-MPL observations. Ash particles dominated 11% of the time and mostly until week 3 (i.e. week 7 from the volcanic eruption). Their mass concentration decreased by one order of magnitude: the relative ash mass contribution was 73 ± 18% with a total mass loading of 566 ± 281 mg m-2 at week 1, reducing gradually down to 38 ± 32% and 120 ± 49 mg m-2, respectively, at week 11. Layer-to-layer, it decreased with increasing layer-height; no ash was detected above 4 km at the end of the volcanic period. Third, in order to analyse the potential AERONET underestimation of the coarse mass conversion factor due to the 15 µm cutoff effect in the AERONET retrieval, two worst-case-scenarios (WCS) were examined, representing aged-like ash particles (WCS1, 4-µm radius) and fresh-like (WCS2, 10-µm radius). For both scenarios, the mass concentration of the volcanic plumes exceeded the first contamination level (>200 µg m-3, as defined by the UK Meteorological Office) up to 5–6 km height mostly during week 1 and up to 1–2 km until week 9. The extreme contamination level (>2000 µg m-3, aircraft flight limitations) was only exceeded from week 1 to week 6 under WCS2 conditions. This work infers a new long-term insight on the volcanic matter injected in the atmosphere with relevance for Air Quality issues and air traffic safety policies.
2023-07-27T17:12:28ZCordoba Jabonero, CarmenSicard, MichaëlBarreto Velasco, ÁfricaToledano, CarlosLópez Cayuela, María ÁngelesComerón Tejero, AdolfoGarcía Rodríguez, OmairaCarvajal Pérez, Clara VioletaGil Díaz, CristinaRamos López, RamónMuñoz Porcar, ConstantinoRodríguez Gómez, Alejandro AntonioFor the first time in fifty years, the Cumbre Vieja volcanic area (La Palma, Canary Islands, Spain) erupted on September 19, 2021, giving birth to a new volcano. Fresh volcanic aerosols were continuously injected into the troposphere at different height levels, decreasing with time until the end of December 2021 (15 weeks duration). A wide set of different instrumentation was deployed all over the Island in order to evaluate the effects of the volcanic plumes on the atmosphere and the air quality. For the first time, a long-term study of the relative mass contribution and vertical impact of the volcanic components, ash and non-ash particles separately, during the eruptive activity was carried out in this work. In particular, a polarized Micro-Pulse Lidar (P-MPL) was deployed at Tazacorte (at around 8 km west from the volcano) in 24/7 operation from October 17, 2021 until the end of the volcano activity (11 weeks) for vertical monitoring of the volcanic particles. First, a statistical study of the mass conversion factors for mass concentration estimation of the volcanic (ash and non-ash) particles was performed by using the AERONET sun/sky-photometer dataset at Fuencaliente (at around 18 km south from the volcano). A representative mass conversion factor was obtained for ash and non-ash particles: 1.89 ± 0.53 and 0.31 ± 0.06 g m-2, respectively, with no dependence on time and optical depth. Second, these factors were used to calculate the ash and non-ash mass concentrations from P-MPL observations. Ash particles dominated 11% of the time and mostly until week 3 (i.e. week 7 from the volcanic eruption). Their mass concentration decreased by one order of magnitude: the relative ash mass contribution was 73 ± 18% with a total mass loading of 566 ± 281 mg m-2 at week 1, reducing gradually down to 38 ± 32% and 120 ± 49 mg m-2, respectively, at week 11. Layer-to-layer, it decreased with increasing layer-height; no ash was detected above 4 km at the end of the volcanic period. Third, in order to analyse the potential AERONET underestimation of the coarse mass conversion factor due to the 15 µm cutoff effect in the AERONET retrieval, two worst-case-scenarios (WCS) were examined, representing aged-like ash particles (WCS1, 4-µm radius) and fresh-like (WCS2, 10-µm radius). For both scenarios, the mass concentration of the volcanic plumes exceeded the first contamination level (>200 µg m-3, as defined by the UK Meteorological Office) up to 5–6 km height mostly during week 1 and up to 1–2 km until week 9. The extreme contamination level (>2000 µg m-3, aircraft flight limitations) was only exceeded from week 1 to week 6 under WCS2 conditions. This work infers a new long-term insight on the volcanic matter injected in the atmosphere with relevance for Air Quality issues and air traffic safety policies.Aislamiento y confinamiento: análisis del rendimiento humano de una tripulación análoga en simulación analógica espacialSequeda, JosephBejarano, IngridCampos, CristianMalpica, DiegoCortés, DiegoJiménez Sánchez, GiovanniBuitrago Leiva, Jeimmy Natalyhttp://hdl.handle.net/2117/3913832023-10-15T05:13:55Z2023-07-18T10:28:32ZAislamiento y confinamiento: análisis del rendimiento humano de una tripulación análoga en simulación analógica espacial
Sequeda, Joseph; Bejarano, Ingrid; Campos, Cristian; Malpica, Diego; Cortés, Diego; Jiménez Sánchez, Giovanni; Buitrago Leiva, Jeimmy Nataly
Los tripulantes en misiones análogas participan en misiones espaciales simuladas que experimentan aislamiento y confinamiento con el fin de educar y realizar experimentos de ciencia, tecnología, ingeniería entre otros. Estos tripulantes análogos suelen ser estudiantes o personas dispuestos a desarrollar una carrera en la ciencia espacial y la industria. En este artículo se describe la primera simulación análoga lunar realizada por profesionales militares de la Fuerza Aérea Colombiana, misión que fue diseñada para proporcionar capacitación eficiente para futuras operaciones que van encaminadas en la línea del desarrollo del programa nacional de capacitación de astronautas en Colombia. La misión THOR (Team of Human Operation Research) se realizó en agosto del 2022, con el apoyo de un Centro de Control de Misión (MCC), misión análoga de aislamiento y confinamiento de siete días la cual tenía como objetivo promover el desarrollo cognitivo, físico, fisiológico, psicológico y tecnológico durante esta misión espacial simulada. La misión THOR fue la misión número 50 del Centro de Entrenamiento para Astronautas Análogos (AATC), se dividió en una tripulación dentro del hábitat compuesta por 5 tripulantes análogos con roles específicos basados en su experiencia, antecedentes y adecuados a los roles proporcionados por el AATC, y dos miembros de tripulación externos adicionales que brindaron apoyo remoto e investigación externa. Durante el periodo de misión se realizaron pruebas tales como aprendizaje espacial, memoria de trabajo, abstracción, velocidad sensorio-motora, orientación espacial, identificación de emociones, razonamiento abstracto, toma de decisiones de riesgo, dinámica e equipo, calidad y cantidad de sueño, puntuaciones de fatiga, intervalo R-R, mediante actigrafia de muñeca y antropometría, la vigilancia psicomotora, la percepción del tiempo y tareas críticas en el hábitat se midió simulando una misión corta en la superficie lunar mediante encuesta NASA-TLX, así como las aplicaciones móviles Brainess y Subjective TimePerception. Los sujetos fueron expuestos a crioterapia y aceleraciones.
2023-07-18T10:28:32ZSequeda, JosephBejarano, IngridCampos, CristianMalpica, DiegoCortés, DiegoJiménez Sánchez, GiovanniBuitrago Leiva, Jeimmy NatalyLos tripulantes en misiones análogas participan en misiones espaciales simuladas que experimentan aislamiento y confinamiento con el fin de educar y realizar experimentos de ciencia, tecnología, ingeniería entre otros. Estos tripulantes análogos suelen ser estudiantes o personas dispuestos a desarrollar una carrera en la ciencia espacial y la industria. En este artículo se describe la primera simulación análoga lunar realizada por profesionales militares de la Fuerza Aérea Colombiana, misión que fue diseñada para proporcionar capacitación eficiente para futuras operaciones que van encaminadas en la línea del desarrollo del programa nacional de capacitación de astronautas en Colombia. La misión THOR (Team of Human Operation Research) se realizó en agosto del 2022, con el apoyo de un Centro de Control de Misión (MCC), misión análoga de aislamiento y confinamiento de siete días la cual tenía como objetivo promover el desarrollo cognitivo, físico, fisiológico, psicológico y tecnológico durante esta misión espacial simulada. La misión THOR fue la misión número 50 del Centro de Entrenamiento para Astronautas Análogos (AATC), se dividió en una tripulación dentro del hábitat compuesta por 5 tripulantes análogos con roles específicos basados en su experiencia, antecedentes y adecuados a los roles proporcionados por el AATC, y dos miembros de tripulación externos adicionales que brindaron apoyo remoto e investigación externa. Durante el periodo de misión se realizaron pruebas tales como aprendizaje espacial, memoria de trabajo, abstracción, velocidad sensorio-motora, orientación espacial, identificación de emociones, razonamiento abstracto, toma de decisiones de riesgo, dinámica e equipo, calidad y cantidad de sueño, puntuaciones de fatiga, intervalo R-R, mediante actigrafia de muñeca y antropometría, la vigilancia psicomotora, la percepción del tiempo y tareas críticas en el hábitat se midió simulando una misión corta en la superficie lunar mediante encuesta NASA-TLX, así como las aplicaciones móviles Brainess y Subjective TimePerception. Los sujetos fueron expuestos a crioterapia y aceleraciones.Retrieval of aged biomass-burning aerosol properties by using GRASP code in synergy with polarized micro-pulse lidar and sun/sky photometerLópez Cayuela, María ÁngelesHerrera, MilagrosCordoba Jabonero, CarmenPérez Ramírez, DanielCarvajal Pérez, Clara VioletaDubovik, OlegGuerrero Rascado, Juan Luishttp://hdl.handle.net/2117/3913752023-11-12T04:00:28Z2023-07-18T10:08:56ZRetrieval of aged biomass-burning aerosol properties by using GRASP code in synergy with polarized micro-pulse lidar and sun/sky photometer
López Cayuela, María Ángeles; Herrera, Milagros; Cordoba Jabonero, Carmen; Pérez Ramírez, Daniel; Carvajal Pérez, Clara Violeta; Dubovik, Oleg; Guerrero Rascado, Juan Luis
The aim of this study was to analyze the potential of the GRASP code to retrieve optical and microphysical properties vertically-resolved using a synergy of polarized Micro-Pulse Lidar and Sun/sky photometer observations. The focus was on the long-range transport of Canadian aged-smoke plumes observed at El Arenosillo/Huelva (Spain) from 7 to 8 September 2017. Both the columnar and height-resolved microphysical and optical properties were assessed in comparison with AERONET data and vertical lidar-retrieved profiles, respectively. In particular, the vertical properties were also derived using the POLIPHON approach, which serves as a comparison for GRASP retrievals. The retrieved columnar aerosol microphysical properties (volume concentration and effective radius) showed an excellent agreement, with negligible differences, and were within the uncertainties. Nevertheless, for the retrieved columnar optical properties, we could only perform an individual comparison, due to the strong AERONET limitations, and although the agreements were generally good, no conclusions were obtained, due to differences in the real refractive index and due to the large uncertainties obtained in the retrievals. For the vertical profiles, however, we present a large advance that permits obtaining aerosol backscatter and extinction coefficients, plus volume concentrations, without the need for internal assumptions (extinction-to-backscatter ratios and depolarization measurements), due to the very good agreement observed between GRASP and the lidar-derived methodologies. However, the separation of the properties into their fine and coarse modes was not feasible using the one-wavelength elastic lidar measurements with the GRASP retrieval configuration used in this work. Therefore, current studies are being addressed to assessing the introduction of lidar depolarization in the GRASP code as an encouraged added-value, for the improvement of the retrieval of vertical aerosol properties.
2023-07-18T10:08:56ZLópez Cayuela, María ÁngelesHerrera, MilagrosCordoba Jabonero, CarmenPérez Ramírez, DanielCarvajal Pérez, Clara VioletaDubovik, OlegGuerrero Rascado, Juan LuisThe aim of this study was to analyze the potential of the GRASP code to retrieve optical and microphysical properties vertically-resolved using a synergy of polarized Micro-Pulse Lidar and Sun/sky photometer observations. The focus was on the long-range transport of Canadian aged-smoke plumes observed at El Arenosillo/Huelva (Spain) from 7 to 8 September 2017. Both the columnar and height-resolved microphysical and optical properties were assessed in comparison with AERONET data and vertical lidar-retrieved profiles, respectively. In particular, the vertical properties were also derived using the POLIPHON approach, which serves as a comparison for GRASP retrievals. The retrieved columnar aerosol microphysical properties (volume concentration and effective radius) showed an excellent agreement, with negligible differences, and were within the uncertainties. Nevertheless, for the retrieved columnar optical properties, we could only perform an individual comparison, due to the strong AERONET limitations, and although the agreements were generally good, no conclusions were obtained, due to differences in the real refractive index and due to the large uncertainties obtained in the retrievals. For the vertical profiles, however, we present a large advance that permits obtaining aerosol backscatter and extinction coefficients, plus volume concentrations, without the need for internal assumptions (extinction-to-backscatter ratios and depolarization measurements), due to the very good agreement observed between GRASP and the lidar-derived methodologies. However, the separation of the properties into their fine and coarse modes was not feasible using the one-wavelength elastic lidar measurements with the GRASP retrieval configuration used in this work. Therefore, current studies are being addressed to assessing the introduction of lidar depolarization in the GRASP code as an encouraged added-value, for the improvement of the retrieval of vertical aerosol properties.Ocean water quality monitoring using remote sensing techniques: a reviewMohseni, FarzaneSaba, FatemehMirmazloumi, SeyedmohammadAmani, MeisamMokhtarzade, MehdiJamali, SadeghMahdavi, Sahelhttp://hdl.handle.net/2117/3913632024-02-25T13:18:43Z2023-07-18T09:36:07ZOcean water quality monitoring using remote sensing techniques: a review
Mohseni, Farzane; Saba, Fatemeh; Mirmazloumi, Seyedmohammad; Amani, Meisam; Mokhtarzade, Mehdi; Jamali, Sadegh; Mahdavi, Sahel
Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.
2023-07-18T09:36:07ZMohseni, FarzaneSaba, FatemehMirmazloumi, SeyedmohammadAmani, MeisamMokhtarzade, MehdiJamali, SadeghMahdavi, SahelOcean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.Diseño del sistema de compras aeronáuticas para consolidar los canales de suministro en la Fuerza Aérea ColombianaDaza Escorcia, Julio-MarioBuitrago Leiva, Jeimmy NatalyRincón Cuta, Yeisson AlexánderVarela Castillejo, Ricardo Rafaelhttp://hdl.handle.net/2117/3913402023-10-15T03:34:55Z2023-07-18T08:43:16ZDiseño del sistema de compras aeronáuticas para consolidar los canales de suministro en la Fuerza Aérea Colombiana
Daza Escorcia, Julio-Mario; Buitrago Leiva, Jeimmy Nataly; Rincón Cuta, Yeisson Alexánder; Varela Castillejo, Ricardo Rafael
Este artículo presenta el rediseño de un sistema de compras aeronáuticas para consolidar los canales de suministro en la Fuerza Aérea Colombiana, basado en una reingeniería de procesos que se enfoque en la simplificación de operaciones innecesarias mediante la aplicación de herramientas de ingeniería. El rediseño propuesto se desarrolla en dos fases: la caracterización de las operaciones logísticas del proceso de compras y el diseño de un sistema de inteligencia de negocios. Este planteamiento presenta mejoras considerables en cuando a la planeación y el seguimiento del proceso de compras, y en la reducción de tiempos en la entrega de material aeronáutico, entre otros.
2023-07-18T08:43:16ZDaza Escorcia, Julio-MarioBuitrago Leiva, Jeimmy NatalyRincón Cuta, Yeisson AlexánderVarela Castillejo, Ricardo RafaelEste artículo presenta el rediseño de un sistema de compras aeronáuticas para consolidar los canales de suministro en la Fuerza Aérea Colombiana, basado en una reingeniería de procesos que se enfoque en la simplificación de operaciones innecesarias mediante la aplicación de herramientas de ingeniería. El rediseño propuesto se desarrolla en dos fases: la caracterización de las operaciones logísticas del proceso de compras y el diseño de un sistema de inteligencia de negocios. Este planteamiento presenta mejoras considerables en cuando a la planeación y el seguimiento del proceso de compras, y en la reducción de tiempos en la entrega de material aeronáutico, entre otros.Global evaluation of SMAP/Sentinel-1 soil moisture productsMohseni, FarzaneMirmazloumi, SeyedmohammadMokhtarzade, MehdiJamali, SadeghHomayouni, Saeidhttp://hdl.handle.net/2117/3911372024-02-25T04:17:24Z2023-07-17T13:34:04ZGlobal evaluation of SMAP/Sentinel-1 soil moisture products
Mohseni, Farzane; Mirmazloumi, Seyedmohammad; Mokhtarzade, Mehdi; Jamali, Sadegh; Homayouni, Saeid
MAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.
2023-07-17T13:34:04ZMohseni, FarzaneMirmazloumi, SeyedmohammadMokhtarzade, MehdiJamali, SadeghHomayouni, SaeidMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.Ocean remote sensing techniques and applications: a review (Part II)Amani, MeisamMehravar, SorooshAsiyabi, Reza MohammadiMoghimi, ArminGhorbanian, ArsalanAhmadi, Seyed AliEbrahimy, HamidMoghaddam, Sayyed Hamed AlizadehNaboureh, AminRanjgar, BabakMohseni, FarzaneNazari, Mohsen EslamiMahdavi, SahelMirmazloumi, SeyedmohammadOjaghi, SaeidJin, Shuanggenhttp://hdl.handle.net/2117/3911312024-02-25T05:24:18Z2023-07-17T13:19:54ZOcean remote sensing techniques and applications: a review (Part II)
Amani, Meisam; Mehravar, Soroosh; Asiyabi, Reza Mohammadi; Moghimi, Armin; Ghorbanian, Arsalan; Ahmadi, Seyed Ali; Ebrahimy, Hamid; Moghaddam, Sayyed Hamed Alizadeh; Naboureh, Amin; Ranjgar, Babak; Mohseni, Farzane; Nazari, Mohsen Eslami; Mahdavi, Sahel; Mirmazloumi, Seyedmohammad; Ojaghi, Saeid; Jin, Shuanggen
As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.
2023-07-17T13:19:54ZAmani, MeisamMehravar, SorooshAsiyabi, Reza MohammadiMoghimi, ArminGhorbanian, ArsalanAhmadi, Seyed AliEbrahimy, HamidMoghaddam, Sayyed Hamed AlizadehNaboureh, AminRanjgar, BabakMohseni, FarzaneNazari, Mohsen EslamiMahdavi, SahelMirmazloumi, SeyedmohammadOjaghi, SaeidJin, ShuanggenAs discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Ocean remote sensing techniques and applications: a review (Part I)Amani, MeisamMoghimi, ArminMirmazloumi, SeyedmohammadRanjgar, BabakGhorbanian, ArsalanOjaghi, SaeidEbrahimy, HamidNaboureh, AminNazari, Mohsen EslamiMahdavi, SahelMoghaddam, Sayyed Hamed AlizadehAsiyabi, Reza MohammadiAhmadi, Seyed AliMehravar, SorooshMohseni, FarzaneJin, Shuanggenhttp://hdl.handle.net/2117/3911252024-02-25T03:08:57Z2023-07-17T13:07:17ZOcean remote sensing techniques and applications: a review (Part I)
Amani, Meisam; Moghimi, Armin; Mirmazloumi, Seyedmohammad; Ranjgar, Babak; Ghorbanian, Arsalan; Ojaghi, Saeid; Ebrahimy, Hamid; Naboureh, Amin; Nazari, Mohsen Eslami; Mahdavi, Sahel; Moghaddam, Sayyed Hamed Alizadeh; Asiyabi, Reza Mohammadi; Ahmadi, Seyed Ali; Mehravar, Soroosh; Mohseni, Farzane; Jin, Shuanggen
Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic appli- cations. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.
2023-07-17T13:07:17ZAmani, MeisamMoghimi, ArminMirmazloumi, SeyedmohammadRanjgar, BabakGhorbanian, ArsalanOjaghi, SaeidEbrahimy, HamidNaboureh, AminNazari, Mohsen EslamiMahdavi, SahelMoghaddam, Sayyed Hamed AlizadehAsiyabi, Reza MohammadiAhmadi, Seyed AliMehravar, SorooshMohseni, FarzaneJin, ShuanggenOceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic appli- cations. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.Supervised machine learning algorithms for ground motion time series classification from InSAR dataMirmazloumi, SeyedmohammadGambin Fernandez, AngelPalamá, RiccardoCrosetto, MicheleWassie, Yismaw AberaNavarro, José AntonioBarra, AnnaMonserrat Hernández, Oriolhttp://hdl.handle.net/2117/3911142024-03-10T03:38:38Z2023-07-17T12:14:10ZSupervised machine learning algorithms for ground motion time series classification from InSAR data
Mirmazloumi, Seyedmohammad; Gambin Fernandez, Angel; Palamá, Riccardo; Crosetto, Michele; Wassie, Yismaw Abera; Navarro, José Antonio; Barra, Anna; Monserrat Hernández, Oriol
The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.
2023-07-17T12:14:10ZMirmazloumi, SeyedmohammadGambin Fernandez, AngelPalamá, RiccardoCrosetto, MicheleWassie, Yismaw AberaNavarro, José AntonioBarra, AnnaMonserrat Hernández, OriolThe increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.