Suchergebnisse

Abstract
Coastlines mapping techniques or the coastline automated analyses have been sought after. In practice, various sorts of seacoasts, for example, biological, silty, arenaceous, artificial, and bedrock coasts, have their own attributes, which force various degrees of intricacy on coastline mapping. As an extraordinary kind of complex artificial coast, aquaculture coast is shaped by the farming of aquatic organisms on silt tidal flats. With the rapid growth of coastal aquaculture in recent years, aquaculture coasts have increased in some developing countries. It has been estimated that aquaculture coasts constitute about 30% of all coastlines in mainland China. In order to identify, monitor, model, and manage the vast expanse of coastal aquaculture, effective methods of extracting aquaculture coastlines from remotely sensed imagery are desired. Secondly, with the rapid economic development in coastal areas, the development of coastal zone resources is also increasing day by day, which benefits the development of island coastal zone. Using oneself has become an important link in the development of marine economy. Due to the limited coastal resources and low environmental carrying capacity, the overexploitation and utilization of coastal resources will lead to a series of problems, such as coastal erosion, coastal migration and accumulation, island area reduction, etc., Both man-made activities and natural factors will lead to coastline changes, which will lead to corresponding changes in coastal ecological environment, thus affecting the coordinated development of coastal economy and the survival of coastal residents. Therefore, efficient, accurate and timely acquisition of coastline information and research on the spatial-temporal changes of coastline are of great significance to the protection of the living environment of coastal residents, the effective development of island and coastal resources, the coordination of sustainable economic development in coastal areas and the mitigation of marine disasters. This paper presents a review of those papers reporting coastline extraction and land use and land cover (LULC) change analysis using remote sensing (RS) and geographic information system (GIS) technology.

The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice-oriented approaches are evaluated too, finally widely-used dimension metrics like Root Mean Squared Error (RMSE) or confusion matrix are discussed. The authors present data quality features of well-defined and poorly defined object. The central part of the study is the land cover mapping, describing its accuracy management model, presented relevance and uncertainty measures of its influencing quality dimensions. In the paper theory is supported by a case study, where the remote sensing technology is used for supporting the area-based agricultural subsidies of the European Union, in Hungarian administration.

The technological developments in remote sensing (RS) during the past decade has contributed to a significant increase in the size of data user community. For this reason data quality issues in remote sensing face a significant increase in importance, particularly in the era of Big Earth data. Dozens of available sensors, hundreds of sophisticated data processing techniques, countless software tools assist the processing of RS data and contributes to a major increase in applications and users. In the past decades, scientific and technological community of spatial data environment were focusing on the evaluation of data quality elements computed for point, line, area geometry of vector and raster data. Stakeholders of data production commonly use standardised parameters to characterise the quality of their datasets. Yet their efforts to estimate the quality did not reach the general end-user community running heterogeneous applications who assume that their spatial data is error-free and best fitted to the specification standards. The non-specialist, general user group has very limited knowledge how spatial data meets their needs. These parameters forming the external quality dimensions implies that the same data system can be of different quality to different users. The large collection of the observed information is uncertain in a level that can decry the reliability of the applications. Based on prior paper of the authors (in cooperation within the Remote Sensing Data Quality working group of ISPRS), which established a taxonomy on the dimensions of data quality in GIS and remote sensing domains, this paper is aiming at focusing on measures of uncertainty in remote sensing data lifecycle, focusing on land cover mapping issues. In the paper we try to introduce how quality of the various combination of data and procedures can be summarized and how services fit the users' needs. The present paper gives the theoretic overview of the issue, besides selected, practice-oriented approaches are evaluated too, finally widely-used dimension metrics like Root Mean Squared Error (RMSE) or confusion matrix are discussed. The authors present data quality features of well-defined and poorly defined object. The central part of the study is the land cover mapping, describing its accuracy management model, presented relevance and uncertainty measures of its influencing quality dimensions. In the paper theory is supported by a case study, where the remote sensing technology is used for supporting the area-based agricultural subsidies of the European Union, in Hungarian administration.

This article examines the dynamics of an emerging multi-use paradigm for satellite remote sensing (RS). From the beginning of the space age, RS satellites have served as dual-use technologies. The advancements to RS technology since that time have largely been driven by the security and economic interests of States. While these interests are to some extent mutually supporting, they have also created a complex environment where non-governmental actors such as commercial satellite operators are increasingly involved in matters of national security and defence. Furthermore, the growing number of RS data providers globally, and more open and easily accessible data and analysis tools online, are creating a new use paradigm for RS in civil-military relations. These developments are extending the dual-use technology dilemma to one of multi-use, where non-governmental actors, including ordinary individuals, are becoming entangled in government affairs. This article traces the trajectory of these processes, and discusses potential implications for RS capable States.

A systematic use of Remote Sensing (RS) data opens a door for evaluators to better address evaluation questions by adding a spatial dimension. This policy brief highlights DEval's methodological approach to the analysis of high-resolution RS data through the application of image classification and machinelearning (ML) techniques. DEval has been developing this approach in close cooperation with RS experts from the Faculty of Geo-information Science and Earth Observation (ITC) at the University of Twente in the Netherlands.

International audience ; Monitoring crops with Remote Sensing (RS) at the national scale is usually an operational work acted as a normal business for the government needs to the crop field conditions. The crop information is main content of agricultural condition. It mainly includes crop growth, crop areas and crop yields, which can be named 3 factors for crop monitoring with RS. Diversification is the general feature of crop monitoring with RS, which reflects in 3 parts of labor objects, labor materials and labor process. Monitoring the 3 factors with RS has similar process summarized as 3 periods which are data acquisition and transmission, model development and application, producing products. Monitoring crops with RS at the national scale needs to found an organizational and technical system, using the System Theory according to the 3 factors, the 3 parts and the 3 periods, mentioned above. The operational work of monitoring the 3 factors have a common goal, which is that the monitoring result is more accurate, the monitoring process is faster, more economic and more convenient. In China, Remote Sensing Application Centre (RSAC) has been working on monitoring the main crops as an operational task and a research project based on its system for several years. The monitoring methods to the 3 factors are presented in this paper along with the cases coming from the monitoring products produced by RSAC in 2014.

Uyo is one of the fastest-growing cities in Nigeria. In recent years, there has been a widespread change in land use, yet to date, there is no thorough mapping of vegetation change across the area. This study focuses on land use change, urban development, and the driving forces behind natural vegetation loss in Uyo. Based on time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) image data, the relationships between urban land development and its influencing factors from 1985 to 2018 were analyzed using remote sensing (RS) and time series data. The results show eight land use cover classes. Three of these (forest, swamp vegetation, and mixed vegetation) are related to natural vegetation, and three (sparse built-up, dense built-up, and borrow pit) are direct consequences of urban infrastructure development changes to the landscape. Swamp vegetation, mixed vegetation, and forest are the most affected land use classes. Thus, the rapid growth of infrastructure and industrial centers and the rural and urban mobility of labor have resulted in an increased growth of built-up land. Additionally, the growth pattern of built-up land in Uyo corresponds with socioeconomic interviews conducted in the area. Land use changes in Uyo could be attributed to changes in economic structure, urbanization through infrastructure development, and population growth. Normalized difference vegetation index (NDVI) analysis shows a trend of decreasing vegetation in Uyo, which suggests that changes in economic structure represent a key driver of vegetation loss. Furthermore, the implementation of scientific and national policies by government agencies directed at reducing the effects of urbanization growth should be strengthened, in order to calm the disagreement between urban developers and environmental managers and promote sustainable land use.

La competizione per l'uso della risorsa territoriale è considerata motivo scatenante e fattore perpetuante dei conflitti nella parte orientale della Repubblica Democratica del Congo (RDC). La letteratura esistente dimostra che il telerilevamento (RS) è uno strumento utile per monitorare sistematicamente le dinamiche spazio-temporali di uso e copertura del suolo in molte regioni del mondo. Per tale ragione in questo studio proponiamo una metodologia per l'integrazione di informazioni provenienti da diverse fonti, in particolare il telerilevamento e le interviste condotte in campo, volta alla messa a punto di un sistema spaziale di supporto alle decisioni finalizzato alla valutazione multicriteriale di potenziali siti pilota per lo sviluppo agricolo e il re-insediamento di rifugiati congolesi. ; Land disputes are considered both key sources and perpetuating factors of conflict in the eastern Democratic Republic of the Congo (DRC). Existing literature demonstrates that remote sensing (RS) is a useful tool for systematically monitor the spatial-temporal land use/land cover dynamics in many regions of the world. For this reason, in this paper we propose a methodology for the integration of different sources of information, namely satellite imagery and census information, in order to set up a Spatial Decision Support System aimed at Multi-Criteria Evaluation of potential pilot sites for agricultural development and refugees resettlement.