Production of Global Land Cover Data – GLCNMO2013

Abstract

Global land cover products have been created for global environmental studies by several institutions and organizations. The Global Mapping Project coordinated by the International Steering Committee for Global Mapping (ISCGM) has been periodically producing global land cover datasets as one of the eight basic global datasets. It has produced a new fifteen-second (approximately 500 m resolution at the equator) global land cover dataset -GLCNMO2013 (or GLCNMO version 3). This paper describes the method of producing GLCNMO2013. GLCNMO2013 has 20 land cover classes, and they were mapped by improved methods from GLCNMO version 2. In GLCNMO2013, five classes, which are urban, mangrove, wetland, snow/ice, and water were independently classified. The remaining 15 classes were divided into 4 groups and mapped individually by supervised classification. 2006 polygons of training data collected for GLCNMO2008 were used for supervised classification. In addition, about 3000 polygons of new training data were collected globally using Google Earth, MODIS Normalized Difference Vegetation Index (NDVI) seasonal change patterns, existing regional land cover maps, and existing four global land cover products. The primary data of this product were Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013. GLCNMO2013 was validated at 1006 sampled points. The overall accuracy of GLCNMO2013 was 74.8%, and the overall accuracy for eight aggregated classes was 90.2%. The accuracy of the GLCNMO2013 was not improved compared with the GLCNMO2008 at heterogeneous land covers. It is necessary to prepare the training data for mosaic classes and heterogeneous land covers for improving the accuracy.

Rice fields along the East Asian-Australasian flyway are important habitats for an inland wader’s migration

“…Because plover E had limited data until the GPS logger stopped (see Table 1), only the 95% kernel density estimate is shown. The map was modified from the Global Land Cover data version 3 56 developed by national mapping organizations (GLCNMO) which is constituted from Geospatial Information Authority of Japan, Chiba University and collaborating organizations. We modified some land cover categories from original version (see Methods).…”

“…The base map of the Fig. 2 was modified from the Global Land Cover data version 3 56 developed by national mapping organizations (GLCNMO) which is constituted from Geospatial Information Authority of Japan, Chiba University and collaborating organizations. Although original version has 20 land cover categories 56 , we reclassified them, with the exception of snow/ ice category, into 12 categories to show environments of the points that plovers were recorded; original 6 forest types 56 integrated into "Forest", original 2 herbaceous types integrated into "Herbaceous", two types of bare area into "Bare area".…”

“…2 was modified from the Global Land Cover data version 3 56 developed by national mapping organizations (GLCNMO) which is constituted from Geospatial Information Authority of Japan, Chiba University and collaborating organizations. Although original version has 20 land cover categories 56 , we reclassified them, with the exception of snow/ ice category, into 12 categories to show environments of the points that plovers were recorded; original 6 forest types 56 integrated into "Forest", original 2 herbaceous types integrated into "Herbaceous", two types of bare area into "Bare area". In addition, to identify the detail habitat use of the six plovers during migration and wintering, we determined the land cover types of each position by using land cover data of the countries along the migration routes after 2000 [57][58][59][60][61][62] , aerial photographs from Google Earth Pro, and a field survey in Taiwan in November 2018.…”

Estimation of Land Use Changes in Tan Rai Bauxite Mine by Multi-Variants Change Vector Analysis (MCVA) on Multi-Temporal Remote Sensing Data

“…Chiba University in Japan, which had the cooperation with a number of the organizations, developed GLCNMO (The Global Land Cover by National Mapping Organizations) version 1 using MODIS 1 km data (Tateishi, Uriyangqai, Al-Bilbisi, & Ghar, 2011) in 2003. The GLCNMO version 2 were built in 2008 by using MODIS 500 m data (Tateishi, Hoan, Kobayashi & Alsaaideh, 2014) and the GLCNMO version 3 in 2013 by using MODIS 500 m data (Kobayashi, Tateishi, Alsaaideh, Sharma, Wakaizumi, Miyamoto et al, 2017). However, the accuracy of these global land cover maps were only around 80%, which were unsuitable for detecting and creating the fluctuation maps with the purpose of environmental observation and natural resource management (Bayan, Hoan, & Tateishi, 2014).…”

Characterization of Vegetation Physiognomic Types Using Bidirectional Reflectance Data

“…The classification of vegetation physiognomic types using satellite remote sensing data over a large region is a challenging field. For example, extant maps such as MODIS Land Cover Type Product (MCD12Q1, [26]) and Global Land Cover by National Mapping Organizations (GLCNMO, [27]), from which the vegetation physiognomic information can be obtained, have not correctly classified the vegetation physiognomic types over a region as large and diverse as all of Japan [28,29]. With a focus on ground truth data and mapping at the national scale, more accurate vegetation physiognomic maps have been produced in Japan [28,29].…”