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.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

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 Production of Global Land Cover Data – GLCNMO2013

2017 ◽  
Vol 9 (3) ◽  
pp. 1 ◽  
Author(s):  
Toshiyuki Kobayashi ◽  
Ryutaro Tateishi ◽  
Bayan Alsaaideh ◽  
Ram C. Sharma ◽  
Takuma Wakaizumi ◽  
...  
Keyword(s):  
Land Cover ◽  
Supervised Classification ◽  
Vegetation Index ◽  
Google Earth ◽  
Training Data ◽  
Primary Data ◽  
Global Land Cover ◽  
Global Land ◽  
Global Mapping ◽  
Land Covers

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 asone 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 waterwere 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.

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 A Global Land Cover Climatology Using MODIS Data

2014 ◽  
Vol 53 (6) ◽  
pp. 1593-1605 ◽  
Author(s):  
Patrick D. Broxton ◽  
Xubin Zeng ◽  
Damien Sulla-Menashe ◽  
Peter A. Troch
Keyword(s):  
Land Cover ◽  
Google Earth ◽  
Global Distribution ◽  
Quality Data ◽  
Land Cover Type ◽  
Cover Type ◽  
Modis Data ◽  
Global Land Cover ◽  
Global Land ◽  
Better Than

AbstractGlobal land cover data are widely used in weather, climate, and hydrometeorological models. The Collection 5.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) product is found to have a substantial amount of interannual variability, with 40% of land pixels showing land cover change one or more times during 2001–10. This affects the global distribution of vegetation if any one year or many years of data are used, for example, to parameterize land processes in regional and global models. In this paper, a value-added global 0.5-km land cover climatology (a single representative map for 2001–10) is developed by weighting each land cover type by its corresponding confidence score for each year and using the highest-weighted land cover type in each pixel in the 2001–10 MODIS data. The climatology is validated by comparing it with the System for Terrestrial Ecosystem Parameterization database as well as additional pixels that are identified from the Google Earth proprietary software database. When compared with the data of any individual year, this climatology does not substantially alter the overall global frequencies of most land cover classes but does affect the global distribution of many land cover classes. In addition, it is validated as well as or better than the MODIS data for individual years. Also, it is based on higher-quality data and is validated better than the Global Land Cover Characteristics database, which is based on 1 year of Advanced Very High Resolution Radiometer data and represents a widely used first-generation global product.

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
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