Meta-discoveries from a synthesis of satellite-based land-cover mapping research

Yu, Le; Lu, Liang; Wang, Jie; Zhao, Yuanyuan; Cheng, Qu; Hu, Luanyun; Liu, Shuang; Liang, Yu; Wang, Xiaoyi; Zhu, Peng; Li, Xueyan; Xu, Yue; Li, Congcong; Fu, Wei; Xuecao, Li; Li, Wenyu; Liu, Caixia; Cong, Na; Zhang, Han; Sun, Fei; Bi, Xinfang; Xin, Qinchuan; Li, Dandan; Yan, Donghui; Zhu, Zhiliang; Goodchild, Michael F.; Gong, Peng

doi:10.1080/01431161.2014.930206

Cited by 151 publications

(117 citation statements)

References 33 publications

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“…For example, in the study by Yu et al [53], they assembled a database of 6771 research papers published before 2013 on land cover maps. LACO-Wiki could provide a research tool for accuracy assessment of land cover maps developed as part of ongoing research.…”

Section: Scientific Researchmentioning

confidence: 99%

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

et al. 2017

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Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement.

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Section: Scientific Researchmentioning

confidence: 99%

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

et al. 2017

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“…To evaluate the potential impact of land cover on the Ta-LST relationship, we adopted a relatively new land-cover product FROM-GLC (Finer Resolution Observation and Monitoring of Global Land Cover), provided by the Center for Earth System Science (CESS) at Tsinghua University [51,52]. FROM-GLC was generated at the initial spatial resolution of 30 m using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data circa 2009 and 2010, which is consistent with the time period of our study.…”

Section: Land-cover Mapmentioning

confidence: 99%

Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery

et al. 2017

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Abstract:Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

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“…Land-cover mapping, which is one of the most widely studied applications in remote sensing, provides fundamental information to a large number of research fields (Yu et al, 2014a). In biodiversity conservation research, land-cover maps can be used as a surrogate for habitat type and a proxy to estimate and monitor locally expected biodiversity and ecological processes (Gould, 2000;Shi et al, 2005;Tuanmu and Jetz, 2014).…”

Section: Introductionmentioning

confidence: 99%

Land cover mapping and data availability in critical terrestrial ecoregions: A global perspective with Landsat thematic mapper and enhanced thematic mapper plus data

Shi

Gong

2015

Biological Conservation

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Meta-discoveries from a synthesis of satellite-based land-cover mapping research

Cited by 151 publications

References 33 publications

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery

Land cover mapping and data availability in critical terrestrial ecoregions: A global perspective with Landsat thematic mapper and enhanced thematic mapper plus data

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