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.

scholarly journals Annual Dynamics of Global Land Cover and its Long-term Changes from 1982 to 2015

2019 ◽  
Author(s):  
Han Liu ◽  
Peng Gong ◽  
Jie Wang ◽  
Nicholas Clinton ◽  
Yuqi Bai ◽  
...  
Keyword(s):  
Land Cover ◽  
Environmental Change ◽  
Global Environmental Change ◽  
Global Scale ◽  
Google Earth ◽  
Data Set ◽  
Global Land Cover ◽  
Global Environmental ◽  
Global Land

Abstract. Land cover (LC) is an important terrestrial variable and key information for understanding the interaction between human activities and global change. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale is rare. Using the latest version of GLASS (The Global Land Surface Satellite) CDRs (Climate Data Records) from 1982 to 2015, we built the first set of CDRs to record the annual dynamics of global land cover (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global LC products, GLASS-GLC is characterized by high consistency, more detailed classes, and longer temporal coverage. The average overall accuracy is 85 %. We implemented a systematic uncertainty analysis at the global scale. In addition, we carried out a comprehensive spatiotemporal pattern analysis. Significant changes and patterns at various scales were found, including deforestation and agricultural land expansion in the tropics, afforestation and forest expansion in northern high latitudes, land degradation in Asian grassland and reclamation in northeast China, etc. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable Earth greening. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling in order to facilitate research on global carbon and water cycling, vegetation dynamics and climate change. The data set presented in this article is published in the Tagged Image File Format (TIFF) at https://doi.org/10.1594/PANGAEA.898096. The data set includes 34 TIFF files and one instruction doc file.

scholarly journals Annual dynamics of global land cover and its long-term changes from 1982 to 2015

2020 ◽  
Vol 12 (2) ◽  
pp. 1217-1243 ◽  
Author(s):  
Han Liu ◽  
Peng Gong ◽  
Jie Wang ◽  
Nicholas Clinton ◽  
Yuqi Bai ◽  
...  
Keyword(s):  
Land Cover ◽  
Environmental Change ◽  
Global Environmental Change ◽  
Google Earth ◽  
Spatiotemporal Pattern ◽  
Barren Land ◽  
Global Land Cover ◽  
Global Environmental ◽  
Global Land

Abstract. Land cover is the physical material at the surface of the Earth. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale are rare. With the latest version of GLASS (Global Land Surface Satellite) CDRs (climate data records) from 1982 to 2015, we built the first record of 34-year-long annual dynamics of global land cover (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global land cover (LC) products, GLASS-GLC is characterized by high consistency, more detail, and longer temporal coverage. The average overall accuracy for the 34 years each with seven classes, including cropland, forest, grassland, shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431 test sample units. We implemented a systematic uncertainty analysis and carried out a comprehensive spatiotemporal pattern analysis. Significant changes at various scales were found, including barren land loss and cropland gain in the tropics, forest gain in the Northern Hemisphere, and grassland loss in Asia. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable vegetation gain, especially for forest. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling to facilitate research on global carbon and water cycling, vegetation dynamics, and climate change. The GLASS-GLC data set presented in this article is available at https://doi.org/10.1594/PANGAEA.913496 (Liu et al., 2020).

scholarly journals Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 236
Author(s):  
Ling Zhu ◽  
Guangshuai Jin ◽  
Dejun Gao
Keyword(s):  
Land Cover ◽  
Semantic Similarity ◽  
Mixed Forest ◽  
Global Scale ◽  
Semantic Mapping ◽  
Global Land Cover ◽  
Resolution Observation ◽  
Local Accuracy ◽  
Global Land ◽  
Broad Leaf

Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.

The SYPR Integrative Assessment Model: Complexity in Development

2004 ◽  
Author(s):  
Steven Manson
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Change ◽  
Land Cover Change ◽  
Integrated Assessment ◽  
Global Environmental Change ◽  
Global Scale ◽  
Assessment Model ◽  
Integrated Assessment Modeling ◽  
Global Environmental

Be it global environmental change or environment and development, landuse and land-cover change is central to the dynamics and consequences in question in the southern Yucatán peninsular region. Designing policies to address these impacts is hampered by the difficulty of projecting land use and land cover, not only because the dynamics are complex but also because consequences are strongly place-based. This chapter describes an integrated assessment modeling framework that builds on the research detailed in earlier chapters in order to project land-use and land-cover change in a geographically explicit way. Integrated assessment is a term that describes holistic treatments of complex problems to assess both science and policy endeavors in global environmental change (Rotmans and Dowlatabadi 1998). The most common form of integrated assessment is computer modeling that combines socioeconomic and biogeophysical factors to predict global climate. Advanced in part by the successes of these global-scale models, integrated assessment has expanded to structure knowledge and set research priorities for a large range of coupled human–environment problems. Increasing recognition is given to the need for integrated assessment models to address regionalscale problems that are masked by global-scale assessments (Walker 1994). Such models must address two issues to project successfully land-use and land-cover change at the regional scale. First, change occurs incrementally in spatially distinct patterns that have different implications for global change (Lambin 1994). Second, a model must account for the complexity of, and relationships among, socio-economic and environmental factors (B. L. Turner et al. 1995). The SYPR integrated assessment model, therefore, has a fine temporal and spatial grain and it places land-use and landcover change at the intersection of land-manager decision-making, the environment, and socio-economic institutions. What follows is a description of an ongoing integrated assessment modeling endeavor of the SYPR project (henceforth, SYPR IA model). The depth and breadth of the SYPR project poses a challenge to the integrated assessment modeling effort since some unifying framework must reconcile a broad array of issues, theories, and data. The global change research community offers a general conception of how environmental change results from infrastructure development, population pressure, market opportunities, resource institutions, and environmental or resource policies (Stern, Young, and Drukman 1992).

scholarly journals GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

2016 ◽  
Vol XLI-B7 ◽  
pp. 219-222 ◽  
Author(s):  
Kiyonari Fukue ◽  
Haruhisa Shimoda
Keyword(s):  
Land Cover ◽  
Time Domain ◽  
Land Cover Classification ◽  
Global Scale ◽  
Surface Reflectance ◽  
Classification Feature ◽  
Global Land Cover ◽  
Global Land ◽  
Multi Temporal ◽  
Occurrence Matrix

The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for timedomain co-occurrence matrix, which performs multi-dimensional pattern matching for time-domain co-occurrence matrices of a classification target pixel and each classification classes. The global land cover classification experiments have been conducted by applying the proposed classification method using 46 multi-temporal(in one year) SR(Surface Reflectance) and NBAR(Nadir BRDF-Adjusted Reflectance) products, respectively. IGBP 17 land cover categories were used in our classification experiments. As the results, SR and NBAR products showed similar classification accuracy of 99%.

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 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 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.

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
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