Accuracy assessment of global land cover products derived from satellite data

2003 ◽  
Author(s):  
R Latifovic ◽  
I Olthof
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Accuracy Assessment ◽  
Global Land Cover ◽  
Global Land

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.

Accuracy assessment of seven global land cover datasets over China

2017 ◽  
Vol 125 ◽  
pp. 156-173 ◽  
Author(s):  
Yongke Yang ◽  
Pengfeng Xiao ◽  
Xuezhi Feng ◽  
Haixing Li
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Global Land Cover ◽  
Global Land

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.

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 Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing

2017 ◽  
Vol 9 (11) ◽  
pp. 1105 ◽  
Author(s):  
Uttam Kumar ◽  
Sangram Ganguly ◽  
Ramakrishna R. Nemani ◽  
Kumar S Raja ◽  
Cristina Milesi ◽  
...  
Keyword(s):  
Land Cover ◽  
High Performance Computing ◽  
Satellite Data ◽  
High Performance ◽  
Learning Algorithms ◽  
Global Land Cover ◽  
Global Land ◽  
Performance Computing

scholarly journals Spatial Accuracy Assessment and Integration of Global Land Cover Datasets

2015 ◽  
Vol 7 (12) ◽  
pp. 15804-15821 ◽  
Author(s):  
Nandin-Erdene Tsendbazar ◽  
Sytze de Bruin ◽  
Steffen Fritz ◽  
Martin Herold
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Spatial Accuracy ◽  
Global Land Cover ◽  
Global Land

scholarly journals Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

2021 ◽  
Vol 13 (15) ◽  
pp. 2950
Author(s):  
Yoshie Ishii ◽  
Koki Iwao ◽  
Tsuguki Kinoshita
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Validation Dataset ◽  
Validation Data ◽  
Global Land Cover ◽  
Spatial Uniformity ◽  
Global Land ◽  
Land Cover Map ◽  
Map Validation ◽  
Land Cover Maps

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.

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