Producing global land cover maps consistent over time to respond the needs of the climate modelling community

2011 ◽  
Author(s):  
Bontemps Sophie ◽  
Defourny Pierre ◽  
Van Bogaert Eric ◽  
Herold Martin ◽  
Kooistra Lammert ◽  
...  
Keyword(s):  
Land Cover ◽  
Climate Modelling ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps ◽  
Over Time

scholarly journals Utilizing the Global Land Cover 2000 reference dataset for a comparative accuracy assessment of 1 km global land cover maps

2015 ◽  
Vol XL-7/W3 ◽  
pp. 503-510 ◽  
Author(s):  
M. Schultz ◽  
N. E. Tsendbazazr ◽  
M. Herold ◽  
M. Jung ◽  
P. Mayaux ◽  
...  
Keyword(s):  
Land Cover ◽  
Climate Models ◽  
Accuracy Assessment ◽  
Careful Consideration ◽  
Global Climate Models ◽  
Reference Dataset ◽  
Heterogeneous Landscape ◽  
Global Land Cover ◽  
Global Land ◽  
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.

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

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.

scholarly journals REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

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.

scholarly journals Towards an open and synergistic framework for mapping global land cover

PeerJ ◽  
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.

A scale-up method for reference data for validation of global land cover maps using ALOS/AVNIR-2 satellite data

2015 ◽  
Author(s):  
Noriko Soyama ◽  
Kanako Muramatsu ◽  
Itsuko Ohashi ◽  
Motomasa Daigo ◽  
Fumio Ochiai ◽  
...  
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Reference Data ◽  
Scale Up ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps

scholarly journals Highlighting continued uncertainty in global land cover maps for the user community

2011 ◽  
Vol 6 (4) ◽  
pp. 044005 ◽  
Author(s):  
Steffen Fritz ◽  
Linda See ◽  
Ian McCallum ◽  
Christian Schill ◽  
Michael Obersteiner ◽  
...  
Keyword(s):  
Land Cover ◽  
User Community ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps

scholarly journals Copernicus Global Land Cover Layers—Collection 2

2020 ◽  
Vol 12 (6) ◽  
pp. 1044 ◽  
Author(s):  
Marcel Buchhorn ◽  
Myroslava Lesiv ◽  
Nandin-Erdene Tsendbazar ◽  
Martin Herold ◽  
Luc Bertels ◽  
...  
Keyword(s):  
Land Cover ◽  
Classification Scheme ◽  
Processing System ◽  
Satellite System ◽  
Global Scale ◽  
Forest Monitoring ◽  
Validation Data ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation.

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