scholarly journals Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

2019 ◽  
Vol 11 (19) ◽  
pp. 2286
Author(s):  
Libo Wang ◽  
Paul Bartlett ◽  
Darren Pouliot ◽  
Ed Chan ◽  
Céline Lamarche ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Land Surface ◽  
Forest Cover ◽  
Reference Sample ◽  
Tree Cover ◽  
Initial Surface ◽  
Forest Cover Change ◽  
Global Land Cover ◽  
Global Land

Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.

scholarly journals Representation of tree cover in global land cover products: Finland as a case study area

2021 ◽  
Vol 193 (3) ◽  
Author(s):  
Titta Majasalmi ◽  
Miina Rautiainen
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Forest Cover ◽  
Surface Fluxes ◽  
Tree Cover ◽  
Deciduous Tree ◽  
Spatial Mapping ◽  
Deciduous Species ◽  
Global Land Cover ◽  
Global Land

AbstractForest extent mapping is required for climate modeling and monitoring changes in ecosystem state. Different global land cover (LC) products employ simple tree cover (referred also as “forest cover” or even “vegetation cover”) definitions to differentiate forests from non-forests. Since 1990, a large number of forest extent maps have become available. Although many studies have compared forest extent data, they often use old data (i.e., around the year 2000). In this study, we assessed tree cover representations of three different annual, global LC products (MODIS VCF (MOD44B, Collection 6 (C6)), MCD12Q1 (C6), and CCI LC (v.2.1.1)) using the Finnish Multi-Source National Forest Inventory (MS-NFI) data for the year 2017. In addition, we present an intercomparison approach for analyzing spatial representations of coniferous and deciduous species. Intercomparison of different LC products is often overlooked due to challenges involved in non-standard and overlapping LC class definitions. Global LC products are used for monitoring changes in land use and land cover and modeling of surface fluxes. Given that LC is a major driver of global change through modifiers such as land surface albedo, more attention should be paid to spatial mapping of coniferous and deciduous species. Our results show that tree cover was either overestimated or underestimated depending on the LC product, and classification accuracy varied between 42 and 75%. Intercomparison of the LC products showed large differences in conifer and deciduous species spatial distributions. Spatial mapping of coniferous and deciduous tree covers was the best represented by the CCI LC product as compared with the reference MS-NFI data.

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 Integrating global land cover products for improved forest cover characterization: an application in North America

2013 ◽  
Vol 7 (9) ◽  
pp. 709-724 ◽  
Author(s):  
Xiao-Peng Song ◽  
Chengquan Huang ◽  
Min Feng ◽  
Joseph O. Sexton ◽  
Saurabh Channan ◽  
...  
Keyword(s):  
North America ◽  
Land Cover ◽  
Forest Cover ◽  
Global Land Cover ◽  
Global Land

scholarly journals Comparison of Water-Related Land Cover Types in Six 1-km Global Land Cover Datasets

2012 ◽  
Vol 13 (2) ◽  
pp. 649-664 ◽  
Author(s):  
Tosiyuki Nakaegawa
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Human Impacts ◽  
Open Water ◽  
Land Cover Classification ◽  
Wetland Classification ◽  
Land Cover Types ◽  
Global Land Cover ◽  
Global Land ◽  
Snow And Ice

Abstract Land cover classification is a fundamental and vital activity that is helpful for understanding natural dynamics and the human impacts of land surface processes. Available multiple 1-km global land cover datasets have been compared to identify classification accuracy and uncertainties for vegetation land cover types, but they have not been adequately compared for water-related land cover types. Six 1-km global land cover datasets were comprehensively examined by focusing on three water-related land cover types (snow and ice, wetlands, and open water). The global mean per-pixel agreement measured by the class-specific consistency is high for snow and ice, medium for open water, and low for wetlands. The agreement is low for snow and ice in low latitudes and high for open water and snow and ice in high latitudes. Areas classified as wetlands in a pixel in one dataset are rarely classified as wetlands in the same pixel in the other five datasets. These areas are most often classified as forest, wetland, or shrub. Areas of snow and ice and open water in some regions are not always chronologically consistent among the datasets because nonsatellite data and different algorithms are used to determine the areas. Further research is necessary to reduce uncertainty in the water-related land cover classification and to develop an advanced classification algorithm that can detect water under a vegetation canopy for improvement in wetland classification. Chronological inconsistency between 1-km land cover datasets and satellite observation periods must also be addressed.

scholarly journals Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4452 ◽  
Author(s):  
Chunying Ren ◽  
Bai Zhang ◽  
Zongming Wang ◽  
Lin Li ◽  
Mingming Jia
Keyword(s):  
Land Cover ◽  
Temporal Resolution ◽  
Northeast China ◽  
Forest Cover ◽  
Forest Area ◽  
Ground Truth Data ◽  
Biogeochemical Models ◽  
Global Land Cover ◽  
Object Based ◽  
Global Land

Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing’an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.

scholarly journals COMPARING NATIONAL DIFFERENCES IN WHAT PEOPLE PERCEIVE TO BE THERE: MAPPING VARIATIONS IN CROWD SOURCED LAND COVER

2015 ◽  
Vol XL-3/W3 ◽  
pp. 71-75 ◽  
Author(s):  
A. Comber ◽  
P. Mooney ◽  
R. S. Purves ◽  
D. Rocchini ◽  
A. Walz
Keyword(s):  
Land Cover ◽  
Citizen Science ◽  
Global Climate Models ◽  
Geographical Information ◽  
Tree Cover ◽  
Barren Land ◽  
Global Land Cover ◽  
National Differences ◽  
Uncertainty Estimates ◽  
Global Land

This paper describes a simple comparison of the distributions of land cover features identified from volunteered data contributed by different social groups – in this case comparing two groups of Geo-Wiki campaigns. Understanding the impacts on analyses of citizen science data contributed by different groups is critical to ensure robust scientific outputs and to fully realise the potential benefits to formal scientific research. It is well known that different people, with different backgrounds and subject to different cultural factors, hold varying landscape conceptualisations. This paper analyses volunteered geographical information on land cover to generate land cover maps. It uses a geographically weighted approach to generate land cover mappings. The mappings generated by different groups (in this case a from a specific unnamed country) are compared and the results show how the predicted land cover distributions vary, with large differences in some classes (e.g. Barren land, Shrubland, Wetland) and little difference in others (e.g. Tree cover). This suggests that for some landscape features cultural and national differences matter when it comes to using crowdsourced data in formal scientific analyses and highlights the potential problems of not considering contributor backgrounds in citizen science. This is important because such data re now routinely being used to develop global land cover data, to generate uncertainty estimates of existing global land cover products and to generate global forest inventories. These in turn are being suggested as suitable inputs to such things as global climate models. A number of critical research directions arising from these findings are discussed.

scholarly journals Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product

2010 ◽  
Vol 114 (8) ◽  
pp. 1805-1816 ◽  
Author(s):  
Sangram Ganguly ◽  
Mark A. Friedl ◽  
Bin Tan ◽  
Xiaoyang Zhang ◽  
Manish Verma
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Land Surface Phenology ◽  
Global Land Cover ◽  
Land Cover Dynamics ◽  
Global Land

scholarly journals Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO

2018 ◽  
Vol 10 (11) ◽  
pp. 1846 ◽  
Author(s):  
Ting Hua ◽  
Wenwu Zhao ◽  
Yanxu Liu ◽  
Shuai Wang ◽  
Siqi Yang
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Global Environmental Change ◽  
Global Scale ◽  
Surface Conditions ◽  
Global Land Cover ◽  
Spatial Consistency ◽  
Global Environmental ◽  
Global Land ◽  
Process Simulations

Numerous global-scale land-cover datasets have greatly contributed to the study of global environmental change and the sustainable management of natural resources. However, land-cover datasets inevitably experience information loss because of the nature of the uncertainty in the interpretation of remote-sensing images. Therefore, analyzing the spatial consistency of multi-source land-cover datasets on the global scale is important to maintain the consistency of time and consider the effects of land-cover changes on spatial consistency. In this study, we assess the spatial consistency of five land-cover datasets, namely, GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO, at the global and continental scales through climate and elevation partitions. The influencing factors of surface conditions and data producers on the spatial inconsistency are discussed. The results show that the global overall consistency of the five datasets ranges from 49.2% to 67.63%. The spatial consistency of Europe is high, and the multi-year value is 66.57%. In addition, the overall consistency in the EF climatic zone is very high, around 95%. The surface conditions and data producers affect the spatial consistency of land-cover datasets to different degrees. CCI LC and GLCNMO (2013) have the highest overall consistencies on the global scale, reaching 67.63%. Generally, the consistency of these five global land-cover datasets is relatively low, increasing the difficulty of satisfying the needs of high-precision land-surface-process simulations.

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