Applications Of Earth Observation Data Research Articles

A Review Of Perspectives From Earth Observation Data To Investigate The Effects Of COVID-19 On The Environment

Background: Earth observation data has established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China. This study aims to systematically review and synthetize perspectives from earth observation data to investigate the effects of COVID-19 on the environment. Material and Methods: A total of 41articles were first collected from four main digital databases including Web of Science, SCOPUS, PubMed/MEDLINE, and Google scholar. It will go on comprehensively review and synthesize applications of earth observation data in studies the COVID-19 impacts on environment. Specifically, the content is presented under three sub-sections; namely the use of earth observation data in (i) studies of impacts on water quality, (ii) studies of impacts on air quality, and (iii) studies of other impacts on the environment, respectively. Results: It was found that change in the intensity of air and water pollution after reduced anthropogenic activities around the world were captured by remote sensing – supplying concrete evidence that can help inform improved environmental policy during the COVID-19 pandemic. Conclusion: It can be concluded that the varied use of remote sensing techniques affirms the value of earth observation data to studies of infectious diseases to environment, especially in times of such large-scale disasters as the COVID-19 pandemic.

Assessing the Applications of Earth Observation Data for Monitoring Artisanal and Small-Scale Gold Mining (ASGM) in Developing Countries

This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on the detection and monitoring of ASGM activities across the globe. The study covers the use of spaceborne sensors and available opportunities for data access and processing and emphasizes the important role that freely-available data has played in understanding ASGM activities. It discusses applications and opportunities offered in assessing the geospatial and temporal characteristics of ASGM and its impacts on the surrounding environment. Furthermore, it examines different indicators for the detection of ASGM in the landscape. Finally, technological capabilities described in the study are illustrated with case studies in the Democratic Republic of Congo and in Colombia using cloud computing with the Open Data Cube. The case studies demonstrate the identification and quantification of impacts of ASGM activities on land degradation and water turbidity in remote areas and results are dissiminated using the MapX platform. This facilitates policy development, implementation, and evaluation in the ASGM context.

Land Cover Change Detection Techniques: Very-high-resolution optical images: A review

Land cover change detection (LCCD) with remote sensing images is an important application of Earth observation data because it provides insights into environmental health, global warming, and city management. In particular, very-high-resolution (VHR) remote sensing images can capture details of a ground object and offer an opportunity to detect land cover changes in detail.

Leveraging the application of Earth observation data for mapping cropland soils in Brazil

Despite the natural spatial variability, cropland soils are subject to many interventions that can lead to alterations of soil functioning. As the cropland expansion took place in Brazil the last decades, leading to significant land-use change and environmental impacts, detailed information about soils is fundamental for sustainable development. Thus, considering the lack of spatially explicit information about cropland soils in Brazil, we aimed at performing high-resolution mapping of key topsoil attributes using spectral and terrain features extracted from Earth observation data (EOD). With the resulting information, we also aimed at performing a general examination of the main agricultural regions and estimate the total organic carbon stocks on croplands soils. For this, we obtained environmental predictors from the historical collection of Landsat data and the digital elevation model from Shuttle Radar Topographic Mission at the cloud-based platform of Google Earth Engine. The environmental predictors (30 m spatial resolution) with georeferenced soil samples (n = 5097) were used for predicting the topsoil content (0–20 cm) of clay, sand, silt, cation exchange capacity, pH, soil organic carbon (SOC) and SOC stock. Prediction models of clay, sand, SOC content, and SOC stocks had the best performance metrics, achieving a R2 ranging from 0.44 to 0.74 and ratio of performance to the interquartile range higher than 1.5. The predicted maps revealed the variability of topsoil among the cropped areas, indicating that the agricultural expansion took place on sandy soils. The SOC stock map provided consistent estimates compared to previous datasets but revealed additional information at the local and regional scales. Thus, this study supports the proposition that EOD is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion over large geographical areas.

Mapping New Zealand 2025 – A National Perspective

Mapping New Zealand 2025 – A National Perspective

Geospatial Methods and Tools for Natural Risk Management and Communications

In the last decade, real-time access to data and the use of high-resolution spatial information have provided scientists and engineers with valuable information to help them understand risk. At the same time, there has been a rapid growth of novel and cutting-edge information and communication technologies for the collection, analysis and dissemination of data, re-inventing the way in which risk management is carried out throughout its cycle (risk identification and reduction, preparedness, disaster relief and recovery). The applications of those geospatial technologies are expected to enable better mitigation of, and adaptation to, the disastrous impact of natural hazards. The description of risks may particularly benefit from the integrated use of new algorithms and monitoring techniques. The ability of new tools to carry out intensive analyses over huge datasets makes it possible to perform future risk assessments, keeping abreast of temporal and spatial changes in hazard, exposure, and vulnerability. The present special issue aims to describe the state-of-the-art of natural risk assessment, management, and communication using new geospatial models and Earth Observation (EO)architecture. More specifically, we have collected a number of contributions dealing with: (1) applications of EO data and machine learning techniques for hazard, vulnerability and risk mapping; (2) natural hazards monitoring and forecasting geospatial systems; (3) modeling of spatiotemporal resource optimization for emergency management in the post-disaster phase; and (4) development of tools and platforms for risk projection assessment and communication of inherent uncertainties.

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Abstract Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

Application of Earth Observation Data and Standardized Precipitation Index Based Approach for Meteorological Drought Monitoring, Assessment and Prediction Over Kutch, Gujarat, India

Drought is a natural phenomenon which differs from other natural hazards by its slow accumulating process and its indefinite commencement and termination. The present study addresses water deficiency and drought occurrence over Kutch district, Gujarat, because nearly 45% of the whole Kutch district is severely suffering by deficiency of water. Earth observation data (LANDSAT ETM+) and Standardized Precipitation Index were used to analyze drought severity. Daily rainfall data over the study area were obtained from Indian Meteorological Department (IMD) for the period of study (1990-2014) and geo-referenced for further analyses. Using Remote Sensing and GIS techniques, rainfall variability map over the period of study has been prepared to show rainfall distribution and land use and land cover map is prepared to show the area under different land use classes and impacts of drought over land uses. Standardized Precipitation Index (SPI) was generated for each block wise and scenario of drought development has been analyzed using decadal data set for the study period (1990-2014). The present study suggests method and techniques for continuous drought monitoring by linking temporal earth observation and rainfall data. The methodology will be very useful for the development of a regional drought monitoring system.

Object-based change detection

Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change detection and provide a conceptual overview of solutions, which are followed by a detailed review of current OBCD algorithms. In particular, OBCD offers unique approaches and methods for exploiting high-spatial-resolution imagery, to capture meaningful detailed change information in a systematic and repeatable manner, corresponding to a wide range of information needs.

Current Status and Perspectives for the Estimation of Crop Water Requirements from Earth Observation

This paper presents an overview of current techniques and recent developments in the application of Earth Observation data for assessing crop water requirements. During recent years there has been much progress in understanding land surface-atmosphere processes and their parameterisation in the management of land and water resources. This knowledge can be combined with the potentiality of Earth Observation techniques from space, which are able to provide detailed information for monitoring agricultural systems. As today, two main developments in the field of Earth Observation data acquisition and analysis have occurred: a) availability of new generations of sensors, with enhanced spectral and spatial resolution; b) detailed knowledge of the processes that determine the response of land surface as detected from remote sensors in different regions of the electromagnetic spectrum. These advancements have made possible a “quantitative” approach in the interpretation of Earth Observation data, ready for being transferred to operative applications i.e. for irrigation scheduling and water management. This paper presents a review of current applications of optical data in the visible and near infrared spectral regions, with particular emphasis to the experiences developed by the author within AQUATER and other research projects project.

Forecasting the View of Mt. Fuji Using Earth Observation Data

In this paper, we present a forecasting method for the view of Mt. Fuji as an application of Earth observation data (EOD) obtained by satellites. We defined the Mt. Fuji viewing index (FVI) that characterises how well the mountain looks on a given day, based on photo data produced by a fixed-point observation. A long-term predictor of FVI, ranging from 0 to 30 days, was constructed through support vector machine regression on climate and earth observation data. It was found that the aerosol mass concentration (AMC) improves prediction performance, and such performance is particularly significant in the long-term range.

The use of remote sensing to support the application of multilateral environmental agreements

Abstract There is a growing realisation of the increasingly varied and interesting possibilities for the use of Earth observation data to ensure compliance with international obligations generally, and treaty obligations in particular. Most examinations of the application of Earth observation data to monitoring states’ compliance with international obligations focus on the environmental sector. This paper proposes the use of remote sensing satellites for the support of multilateral environmental agreements (MEAs), especially land monitoring MEAs such as the Convention on Biological Diversity (1992) and the Kyoto Protocol (1997). It discusses the uses of remote sensing for treaty implementation or enforcement in general, and the admissability of satellite imagery as legal proof, before examining how Earth observation-derived data could be of benefit to specific MEAs. As sensors become increasingly sophisticated the use of remote sensing in this area should grow but it needs to be supported by its more widespread legal recognition as proof.

Remote sensing in the information age

A large percentage of the public today perceives the majority of applications of Earth observation data from satellite and aircraft altitudes to be focused on the understanding and management of the renewable and non-renewable resources of the Earth and its environment. Originally conceived as a tool for gathering intelligence information, remote sensing has just fully emerged from its military womb to the public domain. Advances in the technology, a variety of indirect benefits that could be derived from space exploration, commercialization of remote sensing and the drive of the value-added companies – all of these hold promise for new opportunities for many other novel applications of Earth observation data and related information. In the advent of the more advanced, user-friendly, cost effective, and problem solving operations being championed by the private sector, particularly in the industrialized countries, it appears that the commercial future for remote sensing programmes and related information generated in the process is promising. This paper examines how the information age is influencing the metamorphosis of remote sensing technology particularly through international legal instruments and converging technologies. In spite of the progress attained to-date, of international concern is possible radio frequency interference between remote sensing satellite and communication satellite services. There is also a major knowledge gap between the providers of raw remote sensing data and the user community, particularly those interested in the new high-level information. A resolution of these issues will enhance the contributions of remote sensing to the information economy.