Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics

Schepaschenko, Dmitry; See, Linda; Lesiv, Myroslava; McCallum, Ian; Salk, Carl; Moltchanova, Elena; Perger, Christoph; Shchepashchenko, Maria; Shvidenko, А.; Kovalevskyi, Sergii; Gilitukha, Dmytro; Albrecht, Franziska; Kraxner, Florian; Bun, A.; Maksyutov, Shamil; Sokolov, Alexander; Dürauer, Martina; Obersteiner, Michael; Karminov, Viktor; Ontikov, Petr

doi:10.1016/j.rse.2015.02.011

Cited by 104 publications

(89 citation statements)

References 42 publications

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“…See et al [14] underline that current land cover products are not accurate enough for many applications and that better and more accessible validation data are needed to improve them. The Geo-Wiki platform, by Fritz et al [15], is successfully providing a large quantity of crowd-sourced samples for global land cover mapping [16] or accuracy assessment of a global forest mask [17]. In other studies, VGI was combined with satellite images for mapping [18,19], to augment image time series [20] or to assist forest monitoring in the context of REDD+ [18,[21][22][23] (Reducing Emissions from Deforestation and forest Degradation in developing countries and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks).…”

Section: Introductionmentioning

confidence: 99%

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine (R 2 = 0.75, RMSE = 5.33 m 2 /ha), spruce (R 2 = 0.75, RMSE = 6.73 m 2 /ha) and birch (R 2 = 0.71, RMSE = 4.98 m 2 /ha), with total relative RMSE(%) = 29.66%. In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine (R 2 = 0.88, RMSE = 32.46 m 3 /ha) and total stem volume (R 2 = 0.87, RMSE = 35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

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Section: Introductionmentioning

confidence: 99%

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

et al. 2016

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“…Figure 4 shows the land cover map generated in this way using data from all contributors. This land cover mapping is generated in the same way the operational data described by See et al (2015) and Schepaschenko et al (2015). Figure 4 provides a baseline against which to compare the impacts of only using data from specific groups ( Figure 5 and Figure 6).…”

Section: Resultsmentioning

confidence: 99%

“…These preliminary results for a North and South American case study suggest that the well known differences in what is perceived to be there by different groups matters, even with a very simple 10 class nomenclature. This has profound implications for a number of on-going research activities that are using crowdsourced data to generate hybrid global land cover datasets from existing (but uncertain) global datasets and from crowdsourced data Schepaschenko et al, 2015). These researches have not considered the impacts of contributor cultural or national background but their datasets they are creating are being suggested as suitable and improved inputs to global climate change models.…”

Section: Discussionmentioning

confidence: 99%

“…However, the geographically weighted approach to analysing Geo-Wiki data is at the core of many current activities which are using the data to construct land cover maps for operational usage Schepaschenko et al, 2015). The research presented in this paper suggests that, as well thinking about developing measures of crowd-sourced data quality (Comber, 2013a;Foody et al 2014), there is a critical need to consider some of the 'COSIT' considerations related to spatial cognition 6 and who the volunteers are, where they come from, what their background (cultural and professional) is and so on.…”

Section: Discussionmentioning

confidence: 99%

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COMPARING NATIONAL DIFFERENCES IN WHAT PEOPLE PERCEIVE TO BE THERE: MAPPING VARIATIONS IN CROWD SOURCED LAND COVER

Comber

Mooney

Purves

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper describes a simple comparison of the distributions of land cover features identified from volunteered data contributed by different social groups -in this case comparing two groups of Geo-Wiki campaigns. Understanding the impacts on analyses of citizen science data contributed by different groups is critical to ensure robust scientific outputs and to fully realise the potential benefits to formal scientific research. It is well known that different people, with different backgrounds and subject to different cultural factors, hold varying landscape conceptualisations. This paper analyses volunteered geographical information on land cover to generate land cover maps. It uses a geographically weighted approach to generate land cover mappings. The mappings generated by different groups (in this case a from a specific unnamed country) are compared and the results show how the predicted land cover distributions vary, with large differences in some classes (e.g. Barren land, Shrubland, Wetland) and little difference in others (e.g. Tree cover). This suggests that for some landscape features cultural and national differences matter when it comes to using crowdsourced data in formal scientific analyses and highlights the potential problems of not considering contributor backgrounds in citizen science. This is important because such data re now routinely being used to develop global land cover data, to generate uncertainty estimates of existing global land cover products and to generate global forest inventories. These in turn are being suggested as suitable inputs to such things as global climate models. A number of critical research directions arising from these findings are discussed.

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“…Crowd-based reference data collection efforts have also enabled an increase in the number of sample sites used for accuracy assessment, e.g., through initiatives such as the Degree Confluence Project, which collects reference data based on photographs and site descriptions gathered by volunteers who visit confluence points of latitude and longitude [24], and the Geo-Wiki platform, which collects global-and regional-scale reference data for LC through interpretation of satellite data or photographs by volunteers [25]. Reference data for a large number of sample sites have been collected via Geo-Wiki, which have then been used to assess global-scale maps and generate hybrid LC maps [8,26,27]. More recently, the Geo-Wiki platform has been extended to LACO-Wiki, which is an online land cover and land use validation platform and aims at creating a community around the sharing of reference datasets globally [28].…”

Section: Independent Map Validation Has Become Commonplacementioning

confidence: 99%

Towards an Integrated Global Land Cover Monitoring and Mapping System

et al. 2016

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Global land cover mapping has evolved in a number of ways over the past two decades including increased activity in the areas of map validation and inter-comparison, which is the main focus of this Special Issue in Remote Sensing. Here we describe the major trends in global land cover mapping that have occurred, followed by recent advances as exemplified by the papers in the Special Issue. Finally, we consider what the future holds for global land cover mapping.Keywords: validation; land cover; land use; data fusion; global land cover mapping; map inter-comparison Trends in Global Land Cover MappingGlobal-scale land cover provides essential information for policy development and scientific applications such as climate modeling, food security, carbon assessment, biodiversity and environmental modeling [1]. Global-scale land cover mapping has therefore been of interest to many researchers over the last two decades. After the initial attempts at producing a global land cover (GLC) map at a one degree resolution using remote sensing [2], a number of different medium-resolution (300-1000 m) GLC maps have been developed [3][4][5][6]. This has, in turn, led to the production of integrated or hybrid maps [7,8], which are based on exploiting the strengths of individual GLC maps. With continued advancements in remote sensing data and technology, more GLC maps are currently being produced. Here we summarize the trends in available GLC maps with respect to spatial, thematic and temporal properties, along with their accuracy assessments and user considerations.In the last five years, at least 14 GLC maps have been produced, which accounts for more than half of the currently available GLC products. Figure 1 summarizes the trends in the production of GLC maps over the last two decades.

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Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics

Cited by 104 publications

References 42 publications

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

COMPARING NATIONAL DIFFERENCES IN WHAT PEOPLE PERCEIVE TO BE THERE: MAPPING VARIATIONS IN CROWD SOURCED LAND COVER

Towards an Integrated Global Land Cover Monitoring and Mapping System

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