Synthesis of geophysical data with space-acquired imagery: A review

Geophysical data obtained from ground and airborne platforms have been used in the development of regional geologic models for many years. Space-acquired data and imagery have a shorter but similar history of applications. All these data may be synthesized either manually or digitally. Manual synthesis methods consist of overlaying and comparing maps, whereas digital synthesis methods consist of computer storage and analysis of registered digital data sets.A data base may include topographic, geologic, soils, aeromagnetic, gravity, radiometric, electromagnetic and geochemical data, and Landsat, Seasat, and Heat Capacity Mapping Mission (HCMM) images, all of which can be evaluated individually or compared in multiple layers (overlays).Stereographic models, useful in the correlation and interpretation of geophysical data, have been created from Landsat images by using aeromagnetic, gravity, geochemical, or topographic values to offset Landsat pixels, thus introducing parallax and permitting stereoscopic viewing.Statistical correlation has been used to determine the applicability of specific data sets to the development of geologic or exploration models. Various arithmetic functions have proven useful in developing models from such data sets.     

Development of digital interactive processing system for NOAA satellites AVHRR data

The paper discusses the digital image processing system for NOAA/AVHRR data including Land applications — configured around VAX 11/750 host computer supported with FPS 100 Array Processor, Comtal graphic display and HP Plotting devices; wherein the system software for relational Data Base together with query and editing facilities, Man-Machine Interface using form, menu and prompt inputs including validation of user entries for data type and range; preprocessing software for data calibration, Sun-angle correction, Geometric Corrections for Earth curvature effect and Earth rotation offsets and Earth location of AVHRR image have been accomplished. The implemented image enhancement techniques such as grey level stretching, histogram equalization and convolution are discussed. The software implementation details for the computation of vegetative index and normalized vegetative index using NOAA/AVHRR channels 1 and 2 data together with output are presented; scientific background for such computations and obtainability of similar indices from Landsat/MSS data are also included. The paper concludes by specifying the further software developments planned and the progress envisaged in the field of vegetation index studies.     

Metropolitan expansion and population density patterns in third world supercities as indicated by integration of space and ground data

Monitoring of the urbanization process in the less developed countries of the world is limited by the inadequacy of ground data such as censuses and by their explosive rates of population growth and spatial expansion. By integrating Landsat satellite imagery and available population data, more accurate analyses are possible than by using either medium alone. Using this technique, built-up area, population and patterns of population density for the year 1978 are quantified for six Third World supercities with population in excess of six million: Calcutta, Shanghai, Cairo, Mexico City, Sao Paulo and Rio de Janeiro. Significant differences in density patterns between these cities appear to be related to economic level and culture.     

Accurate orbit predictions for debris orbit manoeuvre using ground-based lasers

Orbit manoeuvre of low Earth orbiting (LEO) debris using ground-based lasers has been proposed as a cost-effective means to avoid debris collisions. This requires the orbit of the debris object to be determined and predicted accurately so that the laser beam can be locked on the debris without the loss of valuable laser operation time. This paper presents the method and results of a short-term accurate LEO (<900 km in altitude) debris orbit prediction study using sparse laser ranging data collected by the EOS Space Debris Tracking System (SDTS). A main development is the estimation of the ballistic coefficients of the LEO objects from their archived long-term two line elements (TLE). When an object is laser tracked for two passes over about 24 h, orbit prediction (OP) accuracy of 10–20 arc seconds for the next 24–48 h can be achieved – the accuracy required for laser debris manoeuvre. The improvements in debris OP accuracy are significant in other applications such as debris conjunction analyses and the realisation of daytime debris laser tracking.     

Evaluation of geostationary and polar orbiting satellites to collect meteorological data from automatic stations in the Antarctic Peninsula

This paper summarizes the main results, needs and perspectives on the use of Satellite Data Collection Systems (DCS) in the Antarctic Peninsula as derived from the operation of an experimental net of five Data Collection Platforms (DCPs) installed to collect meteorological data from remote sites, during the period 1978–1984. Main logistics problems have been solved and also the maintenance of a continuous data link along the year through Landsat (initially) and GOES satellites (actually). It is hoped to solve in the near future the remaining data quality problems on the DCP sensors.     

Canadian Landsat studies for monitoring resource development: A summary

This report summarizes a major paper reviewing Canadian work using Landsat imagery for monitoring the environmental effects of the development of both renewable and non-renewable resources. Landsat has definite advantages for studying resources. In particular, the data are able to show changes in surface cover indicating in many cases development or the exploitation of resources. Monitoring of areas and rates of change over time is possible. Outlined in the paper are examples related to three aspects of resource development. First is the use of Landsat for updating topographic maps for areas where recent developments have taken place. The second aspect is forestry, and examples related to the detection and measurement of clear cuts and recording of areas affected by spruce budworm are reported. Finally, studies of landscape changes produced by mining activities are presented.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex,south Iran

Spectral transformation methods, including correlation coefficient (CC) and Optimum Index Factor (OIF), band ratio (BR) and principal component analysis (PCA) were applied to ASTER and Landsat TM bands for lithological mapping of Soghan ophiolitic complex in south of Iran. The results indicated that the methods used evidently showed superior outputs for detecting lithological units in ophiolitic complexes. CC and OIF methods were used to establish enhanced Red–Green–Blue (RGB) color combination bands for discriminating lithological units. A specialized band ratio (4/1, 4/5, 4/7 in RGB) was developed using ASTER bands to differentiate lithological units in ophiolitic complexes. The band ratio effectively detected serpentinite dunite as host rock of chromite ore deposits from surrounding lithological units in the study area. Principal component images derived from first three bands of ASTER and Landsat TM produced well results for lithological mapping applications. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinite dunite in ophiolitic complexes. The developed approach used in this study offers great potential for lithological mapping using ASTER and Landsat TM bands, which contributes in economic geology for prospecting chromite ore deposits associated with ophiolitic complexes.     

An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images

Shorelines constantly vary due to natural, urbanization and anthropogenic effects such as global warming, population growth, and environmental pollution. Sustainable monitoring of coastal changes is vital in terms of coastal resource management, environmental preservation and planning. Publicly available Landsat 8 OLI (Operational Land Manager) images provide accurate, reliable, temporal and up-to-date information about coastal areas. Recently, the use of machine learning and deep learning algorithms have become widespread. In this study, we used our public Landsat 8 OLI satellite image dataset to create a majority voting method which is an ensemble automatic shoreline segmentation system (WaterNet) to obtain shorelines automatically. For this purpose, different deep learning architectures have been utilized namely as Standard U-Net, Dilated U-Net, Fractal U-Net, FC-DenseNet, and Pix2Pix. Also, we have suggested a novel framework to create labeling data from OpenStreetMap service to create a unique dataset called YTU-WaterNet. According to the results, IoU and F1 scores have been calculated as 99.59% and 99.79% for the WaterNet. The results indicate that the WaterNet method outperforms other methods in terms of shoreline extraction from Landsat 8 OLI satellite images.     

Visual thematic mapping from Landsat and collateral data in support of mineral development

Over the past ten years, the authors and their associates have used Landsat images and collateral data to prepare visual thematic maps. The data are used in a projection-compositor (Procom) system that enlarges Landsat images and other data to scales as great as 1:15 000. Revisions of existing maps for simple themes (e.g. topographic maps) can be completed quickly, accurately and inexpensively. Complex themes, such as geological formations and structures, can be mapped through the optical merging of collateral data sets (e.g. Landsat, geophysics, geology) at a common scale. Such mapping has led to the recognition of a non-parallactic means of obtaining whole image stereo-models with large vertical exaggerations using Landsat images.     

Monitoring the changing aereal extent of irrigated lands of the gefara plain,libya

The inventory of large often non-homogeneous tracts of land by resource agencies has led to the development of estimation methods and sampling strategies that produce estimates of the aereal extent of various features from ‘limited’ observations of the universe of interest. Landsat data has furnished a tool that allows for potential improvement in estimates of resource parameters over extensive areas. This paper examines the methodology and results of a procedure which uses an economical sampling procedure allied with the large area covering qualities of the satellite imaging system to make improved estimates of irrigated land in a more timely, efficient and ‘accurate’ manner.     

Landsat 8地表温度产品验证与分析

Landsat系列卫星热波段具有60~120m的空间分辨率，对各种环境监测起到了重要的作用。随着Landsat系列卫星在全球范围内地表温度（land surface temperature，LST）产品的发布，其验证工作也随之展开，然而对于长时间序列的精度验证工作仍然缺乏。以黑河流域中游为研究区，利用研究区内湿地站（SD）、戈壁站（GB）和大满超级站（CJZ）三个气象站的地面测量数据对2013-2016年清晰无云的31景Landsat 8地表温度产品进行了验证与分析，并将Landsat 8地表温度产品与广泛使用的普适性单通道算法（JMS）反演结果进行了对比。结果表明，Landsat 8地表温度产品与普适性单通道算法反演结果精度均较高，在各个站点处R2均优于0.949。基于所有站点分析，Landsat 8地表温度产品精度稍高于普适性单通道算法反演结果。     

Low cost training in remote sensing applications for developing countries

This presentation introduces the Polish Remote Sensing Centre /OPOLiS/, which offers various types of training possibilities in remote sensing techniques and applications. It deals with a wide range of applications and experiences reached by OPOLiS for various branches of national economy, as well as introduction to analog and digital satellite data processing possibilities with the existing equipment.The offered training possibilities include analog and digital data handling and its interpretation such as visual and computer assisted integration of multiple data sets, as well as image processing, enhancements, mosaics etc.It also includes training and applications of interpretations results for exploration of minerals, energy, and ground water resources, as well as inventories, monitoring and forecasting for agriculture, forest, and natural environmental purposes.The cost factor of the offered training possibilities includes a reasonable input from the Polish Government, which results in low cost input by the participants from developing countries. It also accepts the participation fee in non - convertible currency.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.     

The private sector: A global pool of technical talent for remote sensing training and program support

The growing global pool of highly trained, technical talent in the private sector throughout the world reflects the degree of maturation that remote sensing from air and space vehicles has undergone, especially during the past ten years. This has been further spawned by the US Government move to commercialize the Landsat system. The number of qualified technicians and scientists has grown in proportion to the amount of data that has become available for public and private use. Applications of the data have matured from research investigations to operational use in many disciplines and geographic areas. Private and public industry corporations have established their own image processing and analytical laboratories to ensure their competitive positions. This has been further enhanced by the simultaneous availability of lower cost, analytical computer systems. Training and operational project development remain prominent objectives.While these are all very positive aspects a note of caution must be raised. Concern has recently been expressed by representatives of developing nations that not enough attention is given by remote sensing specialists to spelling out the limitations of the sensing systems or qualifying the degree of accuracy of the results obtained. Such criticisms are probably justified and it is recommended that remote sensing workers be mindful of such concerns.     

Selection of classification techniques for land use/land cover change investigation

The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.     

Radar auroral observations and ionospheric electric fields

The ground-based systems STARE and SABRE utilize radar auroral phenomena to estimate ionospheric electric fields. Some of the assumptions underlying these systems have been tested and general agreement with expectations have been found. However, as the results have been analysed in detail, it has become clear that the error in the irregularity drift velocity can at times amount to 100 ms^?1. Direct comparisons with other E-field measurements, as well as assessments of the results of applications of the Stare data clearly demonstrate that the electric field, calculated on the basis of the irregularity drift velocity, is a useful estimate of the actual horizontal electric field in the ionosphere and is sufficiently accurate for a great variety of geophysical studies.     

Comparison of Landsat OLI,ASTER, and Sentinel 2A data in lithological mapping : A Case study of Rich area (Central High Atlas,Morocco)

The eastern part of the Rich area consists of the massive Paleozoic and Meso-Cenozoic cover formations that present the geodynamic development of the study area, where is characterized by various carbonate facies of Jurassic age. The geographical characteristic of the study area leaves the zone difficult to map by conventional methods. The objective of this work focuses on the mapping of the constituent lithological units of the study area using multispectral data of Landsat OLI, ASTER, and Sentinel 2A MSI. The processing of these data is based on a precise methodology that distinguishs and highlights the limits of the different lithological units that have an approximate similarity of spectral signature. Three techniques were used to enhance the image including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). Lithological mapping was performed using two types of supervised classification : Maximum likelihood classifier (MLC) and Support Vector Machine (SVM).The results of processing data show the effectiveness of Sentinel 2A data in mapping of lithological units than the ASTER and Landsat OLI data. The classification evaluation of two methods of the Sentinel 2A MSI image showed that the SVM method give a better classification with an overall accuracy of 93,93% and a Kappa coefficient of 0.93, while the MLC method present an overall accuracy of 82,86% and a Kappa coefficient of 0.80. The results of mapping obtained show a good correlation with the geological map of the study area as well as the efficiency of remote sensing in identification of different lithological units in the Central High Atlas.     

Specification of the Earth’s plasmasphere with data assimilation

In this paper we report on initial work toward data assimilative modeling of the Earth’s plasmasphere. As the medium of propagation for waves which are responsible for acceleration and decay of the radiation belts, an accurate assimilative model of the plasmasphere is crucial for optimizing the accurate prediction of the radiation environments encountered by satellites. On longer time-scales the plasmasphere exhibits significant dynamics. Although these dynamics are modeled well by existing models, they require detailed global knowledge of magnetospheric configuration which is not always readily available. For that reason data assimilation can be expected to be an effective tool in improving the modeling accuracy of the plasmasphere. In this paper we demonstrate that a relatively modest number of measurements, combined with a simple data assimilation scheme, inspired by the ensemble Kalman filtering data assimilation technique does a good job of reproducing the overall structure of the plasmasphere including plume development. This raises hopes that data assimilation will be an effective method for accurately representing the configuration of the plasmasphere for space weather applications.     

Mapping recent agricultural developments in China from satellite data

Landsat data have been employed to study and map agricultural developments in three regions of China: 1) Pearl River delta; 2) Nen River basin; and 3) Xinjiang Autonomous Region. Manual interpretation procedures used in conjunction with multi-date Landsat images and collateral information permitted rice yields to be estimated for the Pearl River delta in 1978. A combination of manual and computer-assisted analyses of Landsat data of Northeast China revealed that more than 15,000 km² of agricultural land in a 184,500 km² study area had been reclaimed from rangeland and marshland. These analyses also indicated a shift in cropping practices, with the foodcrops wheat and corn replacing cash crops such as soybeans. In the arid west, Landsat image data provided valuable input to a geographic information system (GIS). It appears the GIS approach will prove useful for evaluating agricultural land potential in the remote areas of China.