Production of Global Land Cover Data – GLCNMO2008

Tateishi, Ryosuke; Hoan, Nguyen Thanh; Kobayashi, Toshiyuki; Alsaaideh, Bayan; Tana, Gegen; Phong, Dong Xuan

doi:10.5539/jgg.v6n3p99

Cited by 120 publications

(105 citation statements)

References 9 publications

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“…With the recent launch of new high spatial resolution data such as Landsat 8 and Sentinel 1 and 2, together with the development of cloud computing technologies such as Google Earth Engine, the availability of global land cover datasets is expected to increase considerably [30]. However, based on the large discrepancies between the datasets found and despite the increasing frequency and spatial resolution of land cover classification input data, we can conclude that land cover assessments, similar to the one proposed in this paper, still need to highlight the differences and document the accuracy of existing and future datasets.…”

Section: Resultsmentioning

confidence: 99%

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Comparison of Global Land Cover Datasets for Cropland Monitoring

et al. 2017

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Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating response planning. In this paper, we compared some of the most widely used global land cover datasets to examine their comparative advantages for cropland monitoring. (1) highlighting discrepancies in the extent and spatial distribution of cropland, (2) comparing the areas with FAO agricultural statistics at the country level, and (3) providing accuracy assessment through freely available reference datasets. Recommendations for crop monitoring at the country level are based on a priority ranking derived from the results obtained from analyses 2 and 3. Our results revealed that cropland information varies substantially among the analyzed land cover datasets. FAO-GLCshare and Globeland30 generally provided adequate results to monitor cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter. The recently launched LC-CCI datasets (i.e., LC-CCI2015) show a higher potential for cropland monitoring uses than the previous version (i.e., LC-CCI2010).

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Section: Resultsmentioning

confidence: 99%

“…Actually, the number of composites on the western coast of central Africa for LC-CCI2010 is reported to be low due to a limited number of valid and cloud-free weekly images [30].…”

Section: Spatial Agreement and Discrepanciesmentioning

confidence: 99%

Comparison of Global Land Cover Datasets for Cropland Monitoring

et al. 2017

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“…To benefit from previous efforts on reference data collection, the GLC map validation community has made some reference datasets accessible to the public through the GOFC-GOLD and International Steering Committee for Global Mapping [32,33]. The notion of making the best use of available reference datasets has been highlighted by Olofsson et al [34] while the reusability of available reference datasets has been assessed in [35].…”

Section: Reference Data Collection: Community and Crowd Togethermentioning

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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“…The land cover layer of the GLCNMO is available as a cluster dataset divided into 20 items according to the land cover classification system (LCCS) developed by the Food and Agriculture Organization of the United Nations (FAO). The landcover legend includes evergreen broad-leaf forest, deciduous broadleaf forest, evergreen needle-leaf forest, deciduous needle-leaf forest, mixed forest, grassland, fields, wet rice paddies, mangroves, swamps, bare ground (sand), and bare ground (pebbles and stone) (Tateishi et al, 2014). This research uses the first revision of the 1-km resolution data.…”

Section: Glcmnomentioning

confidence: 99%

Determination of Methane Sourcex Globally by Sciamachy

Park

2016

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

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Commission, WG VIII/3 KEY WORDS: Methane, Methane Sources, Emission concentration, SCIAMACHY, Broadleaf evergreen forest ABSTRACT:Since the beginning of the industrial revolution, the atmospheric concentration of carbon dioxide (CO2) has increased by nearly 30%, and the Methane (CH4) concentration has more than doubled. CH4 is the second most important greenhouse gas, after CO2. Emissions, extrapolated from measurements of actual gas flux from wetlands, vary from place to place, even within the same wetland. This high variability makes large-scale estimates difficult and means that average emissions levels include a large degree of estimated uncertainty. The SCIAMACHY instrument on the European Space Agency satellite ENVISAT measured greenhouse gases in the troposphere and stratosphere. In this study, the CH4 source area is extracted by estimating the concentrations of methane emissions from time-series satellite data. Contamination of the data by cloud is interpolated both spatially and temporally. It is assumed that methane emission is negligible over ocean and that the concentration in the ocean area is due to advection from land. Background CH4 concentration on land was defined as the ocean CH4 concentration at the same latitude. Land CH4 emission concentrations show that areas of concentrated high CH4 emission are not in paddy fields only but also in broadleaf evergreen areas in South America and Central Africa.

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Production of Global Land Cover Data – GLCNMO2008

Cited by 120 publications

References 9 publications

Comparison of Global Land Cover Datasets for Cropland Monitoring

Comparison of Global Land Cover Datasets for Cropland Monitoring

Towards an Integrated Global Land Cover Monitoring and Mapping System

Determination of Methane Sourcex Globally by Sciamachy

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