Towards operational validation of annual global land cover maps

Many investigators use global land cover (GLC) maps for different purposes, such as an input for global climate models. The current GLC maps used for such purposes are based on different remote sensing data, methodologies and legends. Consequently, comparison of GLC maps is difficult and information about their relative utility is limited. The objective of this study is to analyse and compare the thematic accuracies of GLC maps (i.e., IGBP-DISCover, UMD, MODIS, GLC2000 and SYNMAP) at 1 km resolutions by (a) re-analysing the GLC2000 reference dataset, (b) applying a generalized GLC legend and (c) comparing their thematic accuracies at different homogeneity levels. The accuracy assessment was based on the GLC2000 reference dataset with 1253 samples that were visually interpreted. The legends of the GLC maps and the reference datasets were harmonized into 11 general land cover classes. There results show that the map accuracy estimates vary up to 10-16% depending on the homogeneity of the reference point (HRP) for all the GLC maps. An increase of the HRP resulted in higher overall accuracies but reduced accuracy confidence for the GLC maps due to less number of accountable samples. The overall accuracy of the SYNMAP was the highest at any HRP level followed by the GLC2000. The overall accuracies of the maps also varied by up to 10% depending on the definition of agreement between the reference and map categories in heterogeneous landscape. A careful consideration of heterogeneous landscape is therefore recommended for future accuracy assessments of land cover maps.

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Analyzing the Uncertainty of Degree Confluence Project for Validating Global Land-Cover Maps Using Reference Data-Based Classification Schemes

Remote Sensing ◽

10.3390/rs12162589 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2589

Author(s):

Tana Qian ◽

Tsuguki Kinoshita ◽

Minoru Fujii ◽

Yuhai Bao

Keyword(s):

Land Cover ◽

Reference Data ◽

Data Uncertainty ◽

Validation Data ◽

Classification Schemes ◽

Reference Information ◽

Global Land Cover ◽

Global Land ◽

Land Cover Maps ◽

The Impact

Global land-cover products play an important role in assisting the understanding of climate-related changes and the assessment of progress in the implementation of international initiatives for the mitigation of, and adaption to, climate change. However, concerns over the accuracies of land-cover products remain, due to the issue of validation data uncertainty. The volunteer-based Degree Confluence Project (DCP) was created in 1996, and it has been used to provide useful ground-reference information. This study aims to investigate the impact of DCP-based validation data uncertainty and the thematic issues on map accuracies. We built a reference dataset based on the DCP-interpreted dataset and applied a comparison for three existing global land-cover maps and DCP dataset-based probability maps under different classification schemes. The results of the obtained confusion matrices indicate that the uncertainty, including the number of classes and the confusion in mosaic classes, leads to a decrease in map accuracy. This paper proposes an informative classification scheme that uses a matrix structure of unaggregated land-cover and land-use classes, and has the potential to assist in the land-cover interpretation and validation processes. The findings of this study can potentially serve as a guide to select reference data and choose/define appropriate classification schemes.

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Spatial statistical analysis of three global land cover maps

IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120) ◽

10.1109/igarss.2000.858212 ◽

2002 ◽

Author(s):

E. Morisette ◽

J. Privette ◽

D. Olson ◽

P. Davis

Keyword(s):

Statistical Analysis ◽

Land Cover ◽

Spatial Statistical Analysis ◽

Global Land Cover ◽

Global Land ◽

Land Cover Maps

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REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2021-175-2021 ◽

2021 ◽

Vol XLIII-B4-2021 ◽

pp. 175-182

Author(s):

G. Bratic ◽

A. Vavassori ◽

M. A. Brovelli

Keyword(s):

High Resolution ◽

Land Cover ◽

Spatial Resolution ◽

Accuracy Assessment ◽

General Information ◽

Tree Canopy ◽

Global Land Cover ◽

Tree Canopy Cover ◽

Global Land ◽

Land Cover Maps

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

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Towards an open and synergistic framework for mapping global land cover

PeerJ ◽

10.7717/peerj.11877 ◽

2021 ◽

Vol 9 ◽

pp. e11877

Author(s):

Jiyao Zhao ◽

Le Yu ◽

Han Liu ◽

Huabing Huang ◽

Jie Wang ◽

...

Keyword(s):

Land Cover ◽

Google Earth ◽

Single Type ◽

Impervious Surfaces ◽

Sources Of Information ◽

Computing Platform ◽

Global Land Cover ◽

Global Land ◽

Cloud Computing Platform ◽

Land Cover Maps

Global land-cover datasets are key sources of information for understanding the complex inter-actions between human activities and global change. They are also among the most critical variables for climate change studies. Over time, the spatial resolution of land cover maps has increased from the kilometer scale to 10-m scale. Single-type historical land cover datasets, including for forests, water, and impervious surfaces, have also been developed in recent years. In this study, we present an open and synergy framework to produce a global land cover dataset that combines supervised land cover classification and aggregation of existing multiple thematic land cover maps with the Google Earth Engine (GEE) cloud computing platform. On the basis of this method of classification and mosaicking, we derived a global land cover dataset for 6 years over a time span of 25 years. The overall accuracies of the six maps were around 75% and the accuracy for change area detection was over 70%. Our product also showed good similarity with the FAO and existing land cover maps.

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