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1.
The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive. In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations – the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement. We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25?m resolution observations. Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.  相似文献   

2.
Protected areas are experiencing increased levels of human pressure. To enable appropriate conservation action, it is critical to map and monitor changes in the type and extent of land cover/use and habitat classes, which can be related to human pressures over time. Satellite Earth observation (EO) data and techniques offer the opportunity to detect such changes. Yet association with field information and expert interpretation by ecologists is required to interpret, qualify and link these changes to human pressure. There is thus an urgent need to harmonize the technical background of experts in the field of EO data analysis with the terminology of ecologists, protected area management authorities and policy makers in order to provide meaningful, context-specific value-added EO products. This paper builds on the DPSIR framework, providing a terminology to relate the concepts of state, pressures, and drivers with the application of EO analysis. The type of pressure can be inferred through the detection of changes in state (i.e. changes in land cover and/or habitat type and/or condition). Four broad categories of changes in state are identified, i.e. land cover/habitat conversion, land cover/habitat modification, habitat fragmentation and changes in landscape connectivity, and changes in plant community structure. These categories of change in state can be mapped through EO analyses, with the goal of using expert judgement to relate changes in state to causal direct anthropogenic pressures. Drawing on expert knowledge, a set of protected areas located in diverse socio-ecological contexts and subject to a variety of pressures are analysed to (a) link the four categories of changes in state of land cover/habitats to the drivers (anthropogenic pressure), as relevant to specific target land cover and habitat classes; (b) identify (for pressure mapping) the most appropriate spatial and temporal EO data sources as well as interpretations from ecologists and field data useful in connection with EO data analysis. We provide detailed examples for two protected areas, demonstrating the use of EO data for detection of land cover/habitat change, coupled with expert interpretation to relate such change to specific anthropogenic pressures. We conclude with a discussion of the limitations and feasibility of using EO data and techniques to identify anthropogenic pressures, suggesting additional research efforts required in this direction.  相似文献   

3.
ABSTRACT

Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.  相似文献   

4.
In order to secure the necessary image acquisitions for global agricultural monitoring applications, we must first articulate Earth observation (EO) requirements for diverse agricultural landscapes and cropping systems. Crucial to this task is the identification of agricultural growing season timing at a meaningful spatial scale, so as to better define the necessary periods of image acquisition. To this end, 10 years of MODIS Terra Surface Reflectance imagery have been used to determine phenological transition dates including start of season, peak period, and end of season at 0.5° globally. This is the first set of global, satellite-derived, cropland-specific calendar dates for major field crops within a 0.5°, herein called agricultural growing season calendars Preliminary comparison against ground-based crop-specific calendars is performed, highlighting the utility of this approach for articulating growing season timing and its interannual and within-region variability. This research provides critical inputs for defining the EO requirements for the Global Agricultural Monitoring initiative (GEOGLAM), an effort by the Group on Earth Observations (GEO) to synergize existing national and regional observation systems for improved agricultural production and food security monitoring.  相似文献   

5.
Abstract

Digital Earth is an important field of information technology and a research frontier of geosciences in the 21st century. So far, the Grid computing technique is one of the best solutions for Digital Earth infrastructure. Digital Earth can only be realised through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organisationally dispersed. Earth observation (EO) includes information acquisition, processing and applications. Information acquisition provides a vast amount of spatial data for building the fabric resource infrastructure. Information processing means that spatial information processing middleware is used with large amounts of secure Grid computing resources for real-time processing of all kinds of spatial data. We are currently working on the development of core-middleware for EO data processing and applications for the Digital Earth Prototype System, which is available in the Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences (CAS) The further results will be available soon.  相似文献   

6.
ABSTRACT

For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.  相似文献   

7.
ABSTRACT

The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).  相似文献   

8.
Earth observation (EO)-based mapping and analysis of natural hazards plays a critical role in various aspects of post-disaster aid management. Spatial very high-resolution Earth observation data provide important information for managing post-tsunami activities on devastated land and monitoring re-cultivation and reconstruction. The automatic and fast use of high-resolution EO data for rapid mapping is, however, complicated by high spectral variability in densely populated urban areas and unpredictable textural and spectral land-surface changes. The present paper presents the results of the SENDAI project, which developed an automatic post-tsunami flood-extent modelling concept using RapidEye multispectral satellite data and ASTER Global Digital Elevation Model Version 2 (GDEM V2) data of the eastern coast of Japan (captured after the Tohoku earthquake). In this paper, the authors developed both a bathtub-modelling approach and a cost-distance approach, and integrated the roughness parameters of different land-use types to increase the accuracy of flood-extent modelling. Overall, the accuracy of the developed models reached 87–92%, depending on the analysed test site. The flood-modelling approach was explained and results were compared with published approaches. We came to the conclusion that the cost-factor-based approach reaches accuracy comparable to published results from hydrological modelling. However the proposed cost-factor approach is based on a much simpler dataset, which is available globally.  相似文献   

9.
Measuring forest degradation and related forest carbon stock changes is more challenging than measuring deforestation since degradation implies changes in the structure of the forest and does not entail a change in land use, making it less easily detectable through remote sensing. Although we anticipate the use of the IPCC guidance under the United Framework Convention on Climate Change (UNFCCC), there is no one single method for monitoring forest degradation for the case of REDD+ policy. In this review paper we highlight that the choice depends upon a number of factors including the type of degradation, available historical data, capacities and resources, and the potentials and limitations of various measurement and monitoring approaches. Current degradation rates can be measured through field data (i.e. multi-date national forest inventories and permanent sample plot data, commercial forestry data sets, proxy data from domestic markets) and/or remote sensing data (i.e. direct mapping of canopy and forest structural changes or indirect mapping through modelling approaches), with the combination of techniques providing the best options. Developing countries frequently lack consistent historical field data for assessing past forest degradation, and so must rely more on remote sensing approaches mixed with current field assessments of carbon stock changes. Historical degradation estimates will have larger uncertainties as it will be difficult to determine their accuracy. However improving monitoring capacities for systematic forest degradation estimates today will help reduce uncertainties even for historical estimates.  相似文献   

10.
ABSTRACT

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.  相似文献   

11.
Informal small-scale mining is spread in many countries and provides livelihood to numerous families in rural areas yet often with devastating social and environmental impacts. The alluvial gold mining process in Colombia, also known as placer mining, involves excavations using heavy machinery and creates large footprints of bare soil and mining ponds. The very dynamic nature of this extractive activity and its spread in rural and remote areas make its mapping and monitoring very challenging. The use of freely available satellite data of the Copernicus programme provides great new possibilities to study these activities and provides stakeholders integrated data to better understand the spatial and temporal extent of the activities and mitigate affected areas. The objective of this work is to assess the potential of Sentinel-2 data to identify mining areas and to understand the dynamics in landcover change over a study area located at the border of the municipalities of El Bagre and Zaragoza in Bajo Cauca, Colombia. The study utilizes a classification approach followed by post-processing using field knowledge on a set of images from 2016 to 2019. Sequential pattern mining of classified images shows the likelihood of certain annual and seasonal changes in mining-impacted landcover and in the natural vegetation. The results show a slight reduction in the detected mining areas from 2016 to 2019. On the other hand, there are more mining activities in the dry season than in the wet season. Excavated areas of bare soil have a 50% chance to remain in excavation over the considered period or they transition to non-vegetated areas or mining ponds. Vegetation loss due to the extractive activities corresponds to about 35% while recovered vegetated areas are 7% of the total excavated areas in June 2019. An analysis of abandoned sites using NDVI shows that it takes a much longer period than the one considered in this paper for potential natural recovery of vegetation. Finally, the work was disseminated among stakeholders and the public on MapX (https://mapx.org), an online open platform for mapping and visualizing geospatial data on natural resources. It is a pilot study the will be the basis of the analysis of more regions in the department of Antioquia.  相似文献   

12.
Land and Sea Surface Temperatures (LST and SST) are both recognized as Essential Climate Variables, and are routinely retrieved by a wealth of satellites. However, for validated approaches, the latest data are usually not available to the general public. We offer to bridge this gap, by using Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI), with its 15 min temporal resolution. Here, we present generic algorithms for the retrieval of both LST and SST, valid for the SEVIRI instrument onboard MSG platforms 8–11, which we validate using hourly data of 4 ground stations and 11 buoys in Spain over the years 2015 to 2018. These validations show that in the best conditions of surface homogeneity (cloud-free summer nights), errors in our LST estimation are below 1.5 K for stations with good thermal homogeneity. Comparison with LSA-SAF (Land Surface Analysis - Satellite Application Facility) LST shows differences below 2 K for most of SEVIRI disk, with higher differences in arid areas and during daytime. As for SST retrieval, the average error amount to 0.67 K for cloud-free buoy data. These algorithms have been implemented in a near-real time processing chain, which provide actualized LST and SST maps every 15 min within 5 min of image reception. These maps, along with other products, can be freely consulted from a dedicated webpage (https://www.uv.es/iplsat).  相似文献   

13.
"数字地球"战略及其制高点   总被引：40，自引：0，他引：40  
陈述彭 《遥感学报》1999,3(4):247-253
科学技术本来就是双刃剑。“数字地球”战略可能有助于全球信息资源的共享,全球化经济贸易的繁荣,同时也威胁着发展中国家的权益与安全。中国既要积极参与,更要独立自主。实现国家信息化建设是基础;应付“数字地球”的挑战是对策,不能混淆,不能等同。各国不是站在同一起跑线上出发的。必须扬长避短,发挥优势,及时占据制高点。  相似文献   

14.
Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.  相似文献   

15.
The discovery of and access to capacity building resources are often essential to conduct environmental projects based on Earth Observation (EO) resources, whether they are Earth Observation products, methodological tools, techniques, organizations that impart training in these techniques or even projects that have shown practical achievements. Recognizing this opportunity and need, the European Commission through two FP7 projects jointly with the Group on Earth Observations (GEO) teamed up with the Committee on Earth observation Satellites (CEOS). The Global Earth Observation CApacity Building (GEOCAB) portal aims at compiling all current capacity building efforts on the use of EO data for societal benefits into an easily updateable and user-friendly portal. GEOCAB offers a faceted search to improve user discovery experience with a fully interactive world map with all inventoried projects and activities. This paper focuses on the conceptual framework used to implement the underlying platform. An ISO19115 metadata model associated with a terminological repository are the core elements that provide a semantic search application and an interoperable discovery service. The organization and the contribution of different user communities to ensure the management and the update of the content of GEOCAB are addressed.  相似文献   

16.
ABSTRACT

In recent years, researchers of different communities have increased their efforts in formalizing a set of measurements regularly collected for analysing changes in Drivers, States, Impacts and Responses of a given discipline. In some cases, different actors have converged in a minimum set of Essential Variables (EVs), such as for Climate, Biodiversity or Oceans. The definition of such EVs is an ongoing evolution and in extension (e.g. EVs for water) although some communities have not even started (e.g. agriculture and energy). This paper characterizes the Earth Observation (EO) networks and creates a graph representation of their relations. Secondly, this graph is enriched with the EVs produced by each network creating a knowledge base. Finally, an effort has been done to identify links between EVs and Sustainable Development Goals (SDG) indicators in a way that they indirectly connect the EO. An analysis to detect gaps in EO variables due to a lack of observational networks is performed. Several suggestions for improving SDG indicators framework by considering EVs are exposed, as well as proposing new necessary EVs and suggesting new EO based indicators. The complete graph is available in the ENEON website (http://www.eneon.net/graph-ev-sdg/).  相似文献   

17.
In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7–12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS2, and highest for the benchmark data set, PS0. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS3; all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly developed PS is available for Germany via DOI: https://doi.org/10.15489/1ccmlap3mn39.  相似文献   

18.
Climate variation and land transformations related to exploitative land uses are among the main drivers of vegetation productivity decline and ongoing land degradation in East Africa. We combined analysis of vegetation trends and cumulative rain use efficiency differences (CRD), calculated from 250-m MODIS NDVI time-series data, to map vegetation productivity loss over eastern Africa between 2001 and 2011. The CRD index values were furthermore used to discern areas of particular severe vegetation productivity loss over the observation period. Monthly 25-km Tropical Rainfall Measuring Mission (TRMM) data metrics were used to mask areas of rainfall declines not related to human-induced land productivity loss. To provide insights on the productivity decline, we linked the MODIS-based vegetation productivity map to land transformation processes using very high resolution (VHR) imagery in Google Earth (GE) and a Landsat-based land-cover change map. In total, 3.8 million ha experienced significant vegetation loss over the monitoring period. An overall agreement of 68% was found between the rainfall-corrected MODIS productivity decline map and all reference pixels discernable from GE and the Landsat map. The CRD index showed a good potential to discern areas with ‘severe’ vegetation productivity losses under high land-use intensities.  相似文献   

19.
ABSTRACT

Measuring the achievement of a sustainable development requires the integration of various data sets and disciplines describing bio-physical and socio-economic conditions. These data allow characterizing any location on Earth, assessing the status of the environment at various scales (e.g. national, regional, global), understanding interactions between different systems (e.g. atmosphere, hydrosphere, biosphere, geosphere), and modeling future changes. The Group on Earth Observations (GEO) was established in 2005 in response to the need for coordinated, comprehensive, and sustained observations related to the state of the Earth. GEO’s global engagement priorities include supporting the UN 2030 Agenda for Sustainable Development, the Paris Agreement on Climate, and the Sendai Framework for Disaster Risk Reduction. A proposition is made for generalizing and integrating the concept of EVs across the Societal Benefit Areas of GEO and across the border between Socio-Economic and Earth systems EVs. The contributions of the European Union projects ConnectinGEO and GEOEssential in the evaluation of existing EV classes are introduced. Finally, the main aim of the 10 papers of the special issue is shortly presented and mapped according to the proposed typology of SBA-related EV classes.  相似文献   

20.
Abstract

Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.  相似文献   

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