A Methodology for Effective Metadata Design in Earth Observation

2017 ◽  
pp. 65-97
Author(s):  
Jean-Christophe Desconnets ◽  
Isabelle Mougenot ◽  
Hatim Chahdi
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Data Access ◽  
Earth Observation ◽  
Data Discovery ◽  
Dublin Core ◽  
Earth Observations ◽  
Application Profile ◽  
Methodological Considerations ◽  
Source Of Information

The satellite images have become an essential source of information to address and analyze environmental issues quickly, repeatedly and in a reliable way. The increasing number of remotely sensed images are the first impediments for data discovery, access and processing. In this context, it is critical to simplify efficient multi-sensors image-based data access and query processing to provide accessibility to a variety of users in remote sensing. Describing satellite images through a metadata application profile may leverage capabilities to promote easy use of satellite image for environmental analysis. Accordingly, an application profile conforming to the Dublin Core Application Profile guidelines and designed for Earth observations data have been developed. The aim is to provide insights of key methodological considerations in relation to the design of this profile called EOAP (Earth Observation Application Profile).

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

scholarly journals Applicability of KOMPSAT Image Data in Open Data Cube

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Kwangseob Kim ◽  
Kiwon Lee
Keyword(s):  
Satellite Data ◽  
Satellite Images ◽  
Open Data ◽  
Image Data ◽  
Earth Observation ◽  
Data Cube ◽  
Observation System ◽  
Human Society ◽  
Earth Observations ◽  
Data Ingestion

<p><strong>Abstract.</strong> Data cube terms a multi-dimensional stack of gridded datasets aligned for analysis. Open Data Cube (ODC) is an open-source based information processing and managing platform on the viewpoint from web-based infrastructure. This open platform is for a large volume of geo-spatial information with geo-rectified coordinates, and it has been applied by non-profit international organizations such as the Committee on Earth Observation Satellites (CEOS) for an international coordination and management of space-borne missions, Global Earth Observation System of Systems (GEOSS) in the Group on Earth Observations (GEO), as an intergovernmental organization to improve the applicability, accessibility and usability of Earth observations for benefit of human society.</p><p>The building of Analysis Ready Data (ARD), which means the preparation of radiometric calibration and geo-rectification, is for the data cube utilization. The platform converts large-scale satellite image data into analytic information, providing functions for time series analysis. Internationally, there has been an ever-increasing number of country-based data cube deployments with freely available satellite images, including Australia Data Cube, Vietnam Data Cube, Swiss Data Cube, and Colombia Data Cube, as a computing environment for information distribution, sharing, and analysis.</p><p>However, there is no program yet to register Korea Multi-purpose satellite (KOMPSAT) optical and radar images on this platform, so this study developed the registering and ingestion script codes for KOMPSAT optical and radar image sets into ODC. Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. Thus, an ingestion process is required to add satellite data to the ODC platform and the process can be divided into three main stages in Figure 1. First, it is to define the data type in the YAML format. Then, the indexing process of datasets for metadata registration is necessary. The next step is a data ingestion process that users can be used directly data sets collected in ODC.</p><p>Figure 2 shows some of the Python module results for index datasets and the process of metadata generation. The metadata YAML, which is required for indexing, has many advantages in many respects in the creation of metadata through Python modules. This is why we added Python modules to create metadata YAML. In particular, the KOMPSAT data ingestion process was designed so that all of them were possible through one module.</p><p>Using script modules for these steps, the functional accuracy was tested with actual satellite data. Color composite images using RGB bands of KOMPSAT optical images were generated in the ODC environment in Figure 3. In this process, image data formats of GeoTiff and netCDF are also supported.</p><p>In this study, consideration points for implementation of ODC applications are also discussed. KOMPSAT data is basically commercial-based products, unlike other freely accessible satellite images in the ODC applications. For the practical contribution for ODC-GEOSS, careful considerations for data policy are needed, because it can be applied as a reference model for other commercial satellite data for GEOSS.</p>

scholarly journals Estimating the Earth’s Outgoing Longwave Radiation Measured from a Moon-Based Platform

2021 ◽  
Vol 13 (11) ◽  
pp. 2201
Author(s):  
Hanlin Ye ◽  
Huadong Guo ◽  
Guang Liu ◽  
Jinsong Ping ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Large Scale ◽  
Single Point ◽  
Longwave Radiation ◽  
Earth Observation ◽  
Artificial Satellites ◽  
Earth Observations ◽  
The Earth ◽  
Observation Geometry ◽  
Global Mean

Moon-based Earth observations have attracted significant attention across many large-scale phenomena. As the only natural satellite of the Earth, and having a stable lunar surface as well as a particular orbit, Moon-based Earth observations allow the Earth to be viewed as a single point. Furthermore, in contrast with artificial satellites, the varied inclination of Moon-based observations can improve angular samplings of specific locations on Earth. However, the potential for estimating the global outgoing longwave radiation (OLR) from the Earth with such a platform has not yet been fully explored. To evaluate the possibility of calculating OLR using specific Earth observation geometry, we constructed a model to estimate Moon-based OLR measurements and investigated the potential of a Moon-based platform to acquire the necessary data to estimate global mean OLR. The primary method of our study is the discretization of the observational scope into various elements and the consequent integration of the OLR of all elements. Our results indicate that a Moon-based platform is suitable for global sampling related to the calculation of global mean OLR. By separating the geometric and anisotropic factors from the measurement calculations, we ensured that measured values include the effects of the Moon-based Earth observation geometry and the anisotropy of the scenes in the observational scope. Although our results indicate that higher measured values can be achieved if the platform is located near the center of the lunar disk, a maximum difference between locations of approximately 9 × 10−4 W m−2 indicates that the effect of location is too small to remarkably improve observation performance of the platform. In conclusion, our analysis demonstrates that a Moon-based platform has the potential to provide continuous, adequate, and long-term data for estimating global mean OLR.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

2021 ◽  
Author(s):  
Sarah Bauermeister ◽  
Joshua R Bauermeister ◽  
R Bridgman ◽  
C Felici ◽  
M Newbury ◽  
...  
Keyword(s):  
Data Model ◽  
Data Access ◽  
Data Discovery ◽  
Standard Data ◽  
Technology Standard ◽  
Access Request ◽  
Nested Structure ◽  
Using Data ◽  
Model C ◽  
Cohort Analyses

Abstract Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

scholarly journals Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

2020 ◽  
Vol 1 (4) ◽  
pp. 125-134
Author(s):  
Pawan Rachee
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Object Segmentation ◽  
Satellite Image ◽  
Bilateral Filter ◽  
Histogram Equalization ◽  
Saliency Map ◽  
Enhancement Technique ◽  
Map Segmentation ◽  
Quantitative Manner

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed   Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

scholarly journals Revealing glacier flow and surge dynamics from animated satellite image sequences: examples from the Karakoram

2015 ◽  
Vol 9 (2) ◽  
pp. 2597-2623 ◽  
Author(s):  
F. Paul
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Time Lapse ◽  
Image Sequences ◽  
Flow Dynamics ◽  
Mountain Range ◽  
Time Period ◽  
Climatic Control ◽  
Long Time ◽  
Glacier Flow

Abstract. Although animated images are very popular on the Internet, they have so far found only limited use for glaciological applications. With long time-series of satellite images becoming increasingly available and glaciers being well recognized for their rapid changes and variable flow dynamics, animated sequences of multiple satellite images reveal glacier dynamics in a time-lapse mode, making the otherwise slow changes of glacier movement visible and understandable for a wide public. For this study animated image sequences were created from freely available image quick-looks of orthorectified Landsat scenes for four regions in the central Karakoram mountain range. The animations play automatically in a web-browser and might help to demonstrate glacier flow dynamics for educational purposes. The animations revealed highly complex patterns of glacier flow and surge dynamics over a 15-year time period (1998–2013). In contrast to other regions, surging glaciers in the Karakoram are often small (around 10 km2), steep, debris free, and advance for several years at comparably low annual rates (a few hundred m a−1). The advance periods of individual glaciers are generally out of phase, indicating a limited climatic control on their dynamics. On the other hand, nearly all other glaciers in the region are either stable or slightly advancing, indicating balanced or even positive mass budgets over the past few years to decades.

scholarly journals Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Model Calibration ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Soil Mapping ◽  
Spectral Signatures ◽  
Novel Approach ◽  
Multispectral Satellite Images ◽  
Reduced Costs ◽  
Multispectral Satellite Image

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

PRISMA Hyperspectral – First insights in the performance in urban surface cover and coastal seascape analysis

2021 ◽  
Author(s):  
Dimitris Poursanidis ◽  
Nektarios Chrysoulakis
Keyword(s):  
Land Surface ◽  
Satellite Image ◽  
Surface Characteristics ◽  
Spectral Width ◽  
Earth Observation ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Geospatial Information ◽  
Surface Cover ◽  
Spectral Channels

&lt;p&gt;The characterization of the Earth&amp;#8217;s surface cover based on predefined classes is among the fundamental activities in the domain of satellite image analysis image since the early 70s. It was the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites that start to continuously acquired images of the Earth's land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment. However, in 2020, the collected data even if are of continuous flow in terms volume of terrabytes per day from various optical and radar systems, are limited in terms of spectral resolution since almost all sensors are limited to a maximum of 25 spectral channels in the visible, near-and-shortwave-and-thermal infrared spectrum. The need of denser spectral information has been highlighted in early 80s and the first satellite-based hyperspectral sensor, AVIRIS, start to provide data allowing the extraction information on material composition and precise surface cover information. Since then few attempt appear but more are undergoing for launching. In 2019, the Italian Space Agency launch the PRISMA hyperspectral satellite which collect spectral data in the 400-2500nm spectrum; in total 250 spectral channels with a spectral width of ~ 12nm, at 30m pixel size. Here we present first results of the use of Level 2D PRISMA hyperspectral data in mapping the surface characteristics of the urban and periurban area of Heraklion city along with the coastal zone of the urban front aiming at the simultaneous creation of a land-and-coastal cover map along with the extraction of coastal bathymetry information using artificial intelligence approaches within open access platforms. The use of hyperspectral information allow the separation of urban surfaces based on material signatures, while the availability of dense spectral information in the blue-green spectrum allow the more accurate retrieval of coastal seascape characteristics. It is envisaged that hyperspectral missions soon to be the normal in Earth Observation, allowing the accurate creation of geospatial information for further use in several applications.&lt;/p&gt;

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