Satellite images have been used for more than 60 years to better understand the atmosphere, land, and water of our planet. In this study, a historical analysis was carried out for a period of 23 years of satellite images and specific measurements of 2.5 µm particulate matter of anthropogenic origin obtained through the NASA Giovanni website [1]. Atmospheric aerosols have a direct effect on the energy balance, cloud formation, and an indirect effect on rainfall, and consequently on flora and fauna regionally and globally [2]. In addition, they have a harmful impact on air quality, affecting the health of the population. These effects indicate the need for a quantitative study of this type of aerosols, especially in the cities of the world, which is where most sources of anthropogenic aerosols are located, mainly emitted by industries and vehicles. The Guadalajara Metropolitan Zone (GMZ), the third-largest metropolis in Mexico [3], The GMZ does not currently have ground-based measurements of particulate matter for the entire city. In this quantitative study, the seasonal behavior of aerosols was modeled. This allowed us to quantify and discover seasonal patterns in the geographic region of interest. This knowledge may contribute to future research related to the effects of aerosols in the GMZ and to the generation of an approximate forecast that allows preventive and corrective actions to be taken to reduce their harmful effects that affect the city and its inhabitants.

Earth observation involves the analysis of satellite and aerial imagery, social media data, in-situ observations, mathematical models, and other sources. Among the variety of data sources, geophysical methods have been employed to understand the underground soil characteristics. Particularly, in precision agriculture, electrical resistivity tomography (ERT) is one of the most popular methods due to its advantages in terms of non-destructiveness, data acquisition, processing facilities, and multiscale measurements. On the other hand, satellite imagery is a very popular tool to survey the use of soil across the agriculture territories, with the advantage of its wide field of view, relatively large temporal resolution, and ease to obtain data of surface observation. In this manuscript, a conceptual structure is presented with the general description of the most relevant building blocks of a machine learning system that combine earth observation data. Similarly, a proposal based on a mixture of experts strategy is described for ERT and satellite imagery combination in agriculture.

Human history can be traced through the impact of their actions upon the environment. The use of remote sensing technology offers the archeologist the opportunity to detect these impacts which are often invisible to the naked eye. The extraction and classification of regions within a multispectral remotely sensed scene from a particular geographical region allows the generation of training data that could be used to train a machine learning model that automatically could perform the identification of regions in new and larger scenes. This can be achieved using a developed multispectral image classification approach based on supervised machine learning models, which is referred to as the Archaeological Learning Classification (ALC) model. This paper presents some prospective results of the proposed methodology for supervised segmentation and classification of sensed archaeological sites for land use analysis. The results obtained with this study uses real multispectral scenes obtained with remote sensing techniques to probe the efficiency of the classification technique.

This paper presents the results from a research project that is focused on land-use, land-mapping and human behavior analysis to evaluate its movement through information sources that contains geo-referenced information. The source used is data provided by MiBici, which is a bicycle sharing platform established in the city of Guadalajara (state of Jalisco) in Mexico. The database includes a combination of different data for every month of recent years, and the access to this information is free and totally available. The methodologies used for this research were agile development techniques for project planning, k-Nearest Neighbors, Decision Trees, and K-Means for the clusterization of different zones. The programming language used for the development was Python and in addition an implementation proposal was performed using the Amazon Web Services platform. The results obtained shows that the different clusters obtained are well defined, the data is classified among them and physically the information have a valuable relationship to the human activity and behavior within the Metropolitan Area of Guadalajara.

The extraction of hydrological characteristics from a particular geographical region through remote sensing data processing allows the generation of electronic signature maps, which are the basis to create a high -resolution collection atlas processed in time for a particular geographical zone. The use of remote sensing technologies combined with deep learning techniques offers an opportunity for flood-risk assessment and detection using the signature maps applied to a metropolitan area within an image. This can be achieved using a multispectral image classification approach based on convolutional neural networks, this is referred to as the Convolutional Flood Assessment method. This paper presents the prospective study for flood-risk assessment and detection using multispectral remote sensing data provided by SPOT-5 imagery and applied to a particular metropolitan area. The results provided probe the efficiency of the developed technique for applications in detection of natural hazards.

The diverse and rapidly growing number of low-cost systems dedicated to the surveying of land, oceans and agricultural ecosystems, require affordable sensors and processing units. Typical applications of these systems rely on the estimation of their trajectories while sensing, where conventionally low-cost global positioning system (GPS) receivers are used. Consequently, there is a direct relation between the accuracy of the position determination of the system and the quality of the generated surveying products. In this paper, we propose a methodology for the estimation of the navigation trajectory, based exclusively on one on-board low-cost GPS receiver combined with aerial images, employing a discrete Kalman filter. A full-state formulation of the filter is implemented over a reference trajectory to assess its performance. Additionally, error-state corrections are applied to the filter based on landmarks in aerial images.

The human history can be traced through the impacts of their actions upon the environment. The use of remote sensing technologies combined with deep learning techniques offers to the archeologist an opportunity to detect these impacts which are often invisible to the naked eye. The extraction of remote sensing signatures from a particular geographical region allows the generation of geophysical signature maps, which can be achieved using a multispectral image classification approach based on convolutional neural networks, this is referred to as the Convolutional Pixel Classification method. This paper presents the prospective study for archaeological land use characterization using multispectral remote sensing data provided by SPOT-5 imagery.

The number of applications that use low-cost global positioning system (GPS) receivers have dramatically increased over the years in the scientific, military and civil fields. The accuracy in the position estimation of these low-cost GPS receivers is crucial for a successful operation. This research focuses on the accuracy estimation of a low-cost GPS receiver based only on position measurements, and landmarks in aerial images. In addition, a comparison with respect to a commercial receiver and coordinates obtained using differential GPS (DGPS) is performed. The accuracy estimation procedure shows a meter level accuracy for pedestrian outdoors applications for a low-cost u-blox NEO-6M GPS receiver.

The analysis of dynamical models for urban knowledge analysis using the information extracted from a geographical region processed from the data provided by multispectral remote sensing systems provides useful information for urban planning and resource management. However, several topics of interest on this particular matter are still to be properly studied. Using the remote sensing data that has been extracted from multispectral images from a particular geographic region in discrete time, its dynamic study is performed in both, spatial resolution and time evolution, in order to obtain the dynamical model of the physical variables and the evolutionary information about the data. This provides a background for understanding the future trends in development of the dynamics inherent in the multispectral and high-resolution images. This proposition is performed via an intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of the data extracted from multispectral remote sensing images and using highperformance computational techniques to unify the available data scheme with its dynamic analysis and, therefore, provide a behavioral model of the sensed data.

The classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate identification model that is based on multispectral data and uses pixel statistics for the class description. This methodology is referred to as the Multispectral Identification Model. This paper presents this particular methodology applied to large remote sensing datasets (multispectral images obtained from the SPOT-5 satellite sensors) with the objective to perform environmental and land use analysis for regions within Mexico, taking advantage of high-performance computing techniques to improve the processing time and computational load. The results obtained uses real multispectral scenes (high-resolution optical images) to probe the efficiency of the classification technique.

The classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate image classification approach which is based on multispectral sets and uses pixel statistics for the class description, and it is referred to as the Multispectral Pixel Classification method. This paper presents a study of the performance that this approach provides for supervised segmentation and classification of sensed signatures for land use analysis and using high-performance computing techniques compared with traditional programming methodologies. The results obtained with this study uses real multispectral scenes obtained with remote sensing techniques (high-resolution optical images) to probe the efficiency of the classification technique.

A novel algorithm to generate a tridimensional reconstruction of an object using a series of images obtained with sensors within low-cost cameras is proposed. As a matter of particular study, this paper present the methodology employed to set an array of sensors to extract the necessary information from a particular group of acquired images surrounding the sample, the processing schema for its interpretation and its mapping, in order to approximate a tridimensional model with the use of real data. The simulation results can verify the efficiency of the proposed approach, showing an application where it could be a useful tool for decision support or resource management.

Aerosols play an important role in global climate change. [Reference new IPCC report]. These particles directly affect the radiative budget due to absorption and scattering of radiation. Additionally, aerosols are known to alter various ecosystems, cloud formation and properties, precipitation, air quality and visibility. They also have well-documented impacts on human health. In the last 30 years, progress has been made in remote sensing retrievals of aerosol distribution and properties, using different satellite sensors as well as ground-based sun photometer instruments, monitoring programs, and intensive field campaigns around the world. This paper describes how the study and analysis of satellite-based (CALIPSO) measurements and the Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model make it possible to identify some of the predominant tropospheric aerosols, black carbon and organic carbon. We present the black carbon and organic carbon aerosols and their optical properties by season, within the region of Jalisco México.

An intelligent post-processing computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of remote sensing signatures based on SPOT-5 imagery is proposed. As a matter of particular study, a robust algorithm is reported for the analysis of the dynamic behavior of geophysical indexes extracted from remotely sensed scenes. Simulations are reported to probe the efficiency of the proposed technique.

The extraction of remote sensing signatures from a particular geographical region allows the generation of electronic signature maps, which are the basis to create a high-resolution collection atlas processed in discrete time. This can be achieved using an image classification approach based on pixel statistics for the class description, referred to as the multispectral pixel neighborhood method. This paper explores the effectiveness of this approach developed for supervised segmentation and classification of high-resolution remote sensing imagery using SPOT-5 data. Moreover, an analysis of the proposition for implementation as an embedded system is provided, to improve the processing time and reducing computational load, using a scheme based on hardware/software codesign techniques. Simulations are reported to probe the efficiency of the proposed technique.

The remote sensing methods for understanding physical phenomena are being used since the last 50 years. Satellite-based sensors and ground-based sun photometers provides quantitative and qualitative knowledge about the composition of elements within the Earth's atmosphere. One actual problem is the changes on the climate of different regions of the Earth; one of them is related to aerosol climate forcing. Improvement in measurement-based systems is necessary to identify remaining issues and improve quantification of aerosol effects on climate. Also the improvement in modeling is necessary to confidently extend estimates of forcing to prior times and to project future emissions. Achieving these capabilities will require a synergistic approach between observational systems and modeling. This paper describes how the study and analysis of satellite-based and ground-based measurements can be used to develop an innovative method, based in the existent methods to calculate some optical properties that will help in characterization of the dominant temporal aerosols found in and around the city of Guadalajara in Mexico, based on previous algorithms. The quantifiable knowledge about the temporal and regional aerosols' optical properties will contribute to future investigations related to their quantitative effects on atmospheric processes in this region.

Satellite' sensors as well as ground-based sun photometers instruments surface are utilized gather data about the quantitative composition of components in the Earth's atmosphere. The relationships among these components produce effects in different phenomena, like regional climate change. Aerosols, consisting of particles from 0.01 to 10 μm, are atmosphere components, whose effects are still poorly understood. Through Remote Sensing, it is possible to classify them and gain information about their role in different atmospheric processes: low visibility, solar energy balance, cloud formation and increases or decreases of the quantity of precipitation. This paper describes how the study and analysis of satellite-based and ground-based measurements can be used to develop and validate a novel procedure to calculate the optical properties of temporal aerosols found in and around Guadalajara, based on previous algorithms:, to determine the aerosol size distribution function, scattering phase function, single scattering albedo, complex refractive index and asymmetry parameter. The quantifiable knowledge about the temporal and regional aerosols' optical properties will contribute to future investigations related to their quantitative effects on atmospheric processes in this region. These effects include: alteration of weather and climate, change of the tropospheric temperature, contribution to environmental ills, the formation and properties of the clouds, effects on the ecosystems, local solar energy balance, and the impacts on human health.

Image classification techniques can be applied to a geographical image to obtain its land use characteristics. Multispectral and high-resolution remote sensing images are able to provide sufficient information for a more accurate segmentation, nevertheless, the classification algorithms applied to images with high spatial resolution requires many computational cycles, even for modern computers. This paper explores the effectiveness of a novel approach developed for supervised segmentation and classification of high-resolution remote sensing images using distributed processing techniques to improve the computational time required. This is referred to as the distributed pixel statistics method. Examples of remote sensing signatures extracted from real world and high-resolution remote sensing images are reported to probe the efficiency of the developed technique.

In this paper we address a prospective look at the problem of computational simulation for different distributed tasks related to analysis of multispectral data obtained with the use of remote sensing systems as required for end-user-oriented environmental monitoring, urban assessment/planning and natural resources management. This virtual processing software for analysis of multispectral remote sensing imagery employs and unifies some previously developed enhancement, reconstruction, segmentation, classification, quantification and dynamical post- processing methodologies in a simulation tool referred to as the Geophysics Dynamic Laboratory (GDL). Simulation examples are reported to illustrate the usefulness of the elaborated GDL software for algorithmic-level analysis of high-resolution multispectral remote sensing imagery.

The extraction of hydrological characteristics from a particular geographical region through remote sensing (RS) data processing allows the generation of electronic signature maps, which are the basis to create a high-resolution collection atlas processed in time for a particular geographical zone. This can be achieved using a developed tool for supervised segmentation and classification of hydrological remote sensing signatures (HRSS) via the combination of both statistical strategies defined as the Weighted Order Statistics (WOS) and the Minimum Distance to Means (MDM) techniques, unifying their particular advantages. This is referred to as the Hydrological Signatures Classification (HSC) method. The extraction of HRSS from multispectral/high-resolution RS maps using SPOT-5 satellite data for the regional case of the State of Jalisco in Mexico is reported to probe the efficiency of the developed technique in hydrological resources management applications.

Much of human history can be traced through the impacts of human actions upon the environment. The use of remote sensing technology offers the archeologist the opportunity to detect these impacts which are often invisible to the naked eye. The extraction of remote sensing signatures from a particular geographical region allows the generation of geophysical signature maps; this can be achieved using an accurate and recently developed multispectral image classification approach based on pixel statistics for the class description, which is referred to as the Weighted Pixel Statistics method. This paper presents the prospective study for archaeological land use characterization using multispectral remote sensing data provided by SPOT-5 imagery. The results obtained with this study probe the efficiency of the classification technique.

An intelligent post-processing computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of geophysical signatures based on Spot-5 imagery is proposed. As a matter of particular study, a robust algorithm is reported for the analysis of the dynamic behavior of geophysical indexes extracted from the real-world remotely sensed scenes. The simulation results verify the efficiency of the approach as required for decision support in resources management.

Much of human history can be traced through the impacts of human actions upon the environment. The use of remote sensing technology offers the archeologist the opportunity to detect these impacts which are often invisible to the naked eye. The extraction of remote sensing signatures from a particular geographical region allows the generation of geophysical signature maps; this can be achieved using an accurate and recently developed multispectral image classification approach based on pixel statistics for the class description, which is referred to as the Weighted Pixel Statistics method. This paper presents the prospective study of the effectiveness that this approach provides for supervised segmentation and classification of sensed archaeological signatures for land use analysis. The results obtained with this study uses real multispectral scenes obtained with remote sensing techniques (high-resolution synthetic aperture radar) to probe the efficiency of the classification technique.

The extraction of hydrological characteristics from a particular geographical region through remote sensing (RS) data processing allows the generation of electronic signature maps, which are the basis to create a high-resolution collection atlas processed in time for a particular geographical zone. This can be achieved using a novel tool developed for supervised segmentation and classification of hydrological remote sensing signatures (HRSS) via the combination of both statistical strategies defined as the Weighted Order Statistics (WOS) and the Minimum Distance to Means (MDM) techniques, unifying their particular advantages. This is referred to as the Hydrological Signatures Classification (HSC) method. The extraction of HRSS from real-world high-resolution environmental RS imagery is reported to probe the efficiency of the developed technique in hydrological resources management applications.

A new intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of physical characteristics of environmental electronic maps extracted from the large scale remote sensing imagery is proposed. A robust Kalman filter- based algorithm is developed for the analysis of the dynamic behavior of hydrological indexes extracted from the real-world remotely sensed scenes. The simulation results verify the efficiency of the proposed approach as required for decision support in environmental resources management.

In this paper, the problem of estimating from a finite set of measurements of the radar remotely sensed complex data signals, the power spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian minimum risk (MR) paradigm unified with the experiment design (ED) regularization technique. The fused MR-ED regularization of the ill-posed nonlinear inverse problem of the SSP reconstruction is performed via incorporating into the MR estimation strategy the projection-regularization ED constraints. The simulation examples are incorporated to illustrate the efficiency of the proposed unified MR-ED technique.