Geo-Wiki: An online platform for improving global land cover

Fritz, Steffen; McCallum, Ian; Schill, C.; Perger, Christoph; Schepaschenko, Dmitry; Velde, Marijn van der; Kraxner, Florian; Obersteiner, Michael

doi:10.1016/j.envsoft.2011.11.015

Cited by 281 publications

(215 citation statements)

References 26 publications

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“…However, such datasets do not currently exist at the global level, and in the interim, the broad relationship between field size and necessary spatial resolution is sufficient to allocate fine, moderate, and coarse spatial resolution data acquisitions. A research group at the International Institute for Applied Systems Analysis have deployed an online collaborative tool called "GEO-WIKI" [1,74,75] to gather "crowd-sourced" information on field size. Volunteers from around the world visually interpret high-resolution imagery on GEO-WIKI's Google Earth platform and use visual interpretation to estimate field size.…”

Section: Input Datasets: Where To Image?mentioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation's (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10-100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of "reasonably clear" are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9-4.8 days (depending on the month), 3.0-4.1 days for 80% clear, OPEN ACCESSRemote Sens. 2015, 7 1462 2.2-3.3 days for 90% clear, and 1.7-2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.

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Section: Input Datasets: Where To Image?mentioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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“…For example, several studies have investigated the potential of VGI to support 2D and 3D mapping and characterizing buildings (e.g., [91][92][93][94]), land use and land cover (e.g., [26,[95][96][97]) and for cataloging spatial features, such as small fish ponds near homes [14]. Given the increasing digital engagement of non-expert mappers, and the wikification of geographic information [98], VGI is now enabling users to capture what they perceive as important , both tangible and intangible.…”

Section: Discussionmentioning

confidence: 99%

Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya

Mahabir

Stefanidis

Croitoru

et al. 2017

IJGI

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Abstract:With volunteered geographic information (VGI) platforms such as OpenStreetMap (OSM) becoming increasingly popular, we are faced with the challenge of assessing the quality of their content, in order to better understand its place relative to the authoritative content of more traditional sources. Until now, studies have focused primarily on developed countries, showing that VGI content can match or even surpass the quality of authoritative sources, with very few studies in developing countries. In this paper, we compare the quality of authoritative (data from the Regional Center for Mapping of Resources for Development (RCMRD)) and non-authoritative (data from OSM and Google's Map Maker) road data in conjunction with population data in and around Nairobi, Kenya. Results show variability in coverage between all of these datasets. RCMRD provided the most complete, albeit less current, coverage when taking into account the entire study area, while OSM and Map Maker showed a degradation of coverage as one moves from central Nairobi towards rural areas. Furthermore, OSM had higher content density in large slums, surpassing the authoritative datasets at these locations, while Map Maker showed better coverage in rural housing areas. These results suggest a greater need for a more inclusive approach using VGI to supplement gaps in authoritative data in developing nations.

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“…Table 4 presents the articles considered for this review. Compared to previously discussed variables, the contribution of citizens to land cover maps generation has been already proved as a concept (Albrecht et al, 2014;Fritz et al, 2012), nowadays being researched further for quality of data (Salk et al, 2016) and fusion of maps (Lesiv et al, 2016). 10…”

Section: Land Cover/land Usementioning

confidence: 99%

Citizen observations contributing to flood modelling: opportunities and challenges

Assumpção¹,

Popescu²,

Jonoski³

et al. 2017

Preprint

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Abstract. Citizen contributions to science have been successfully implemented in many fields -and water resources is one of them. Through citizens, it is possible to collect data and obtain a more integrated decision-making process. Specifically, data scarcity has always been an issue in flood modelling, which has been addressed in the last decades by remote sensing and is already being discussed in a citizen science scenario. In this context, this article aims to review the literature on the 10 topic and analyse the opportunities and challenges that lie ahead. The literature on monitoring, mapping and modelling, was evaluated according to the flood-related variable citizens contributed to. Pros and cons of the collection/analysis methods were summarised. Then, pertinent publications were mapped into the flood modelling cycle, considering how citizen data properties (spatial and temporal coverage, uncertainty and volume) are related to its integration into modelling. It was clear that the number of studies in the area is rising. There are positive experiences reported in collection and analysis methods, 15 for instance with velocity and land cover, and also when modelling is concerned, for example by using social media mining.However, matching the data properties necessary for each part of the modelling cycle with citizen generated data is still challenging. Nevertheless, the concept that citizen contributions can be used for simulation and forecasting is proved and further work lies in continuing developing and improving not only methods for collection and analysis but certainly for integration into models as well. Finally, in view of recent automated sensors and satellite technologies, it is through studies 20 as the ones analysed in this article that the value of citizen contributions is demonstrated.

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Geo-Wiki: An online platform for improving global land cover

Cited by 281 publications

References 26 publications

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

Authoritative and Volunteered Geographical Information in a Developing Country: A Comparative Case Study of Road Datasets in Nairobi, Kenya

Citizen observations contributing to flood modelling: opportunities and challenges

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