Integrating Land-Cover Products Based on Ontologies and Local Accuracy

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.

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GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PROSUCTS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-219-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 219-222 ◽

Cited By ~ 1

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%.

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Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

International Journal of Remote Sensing ◽

10.1080/01431161.2012.748992 ◽

2012 ◽

Vol 34 (7) ◽

pp. 2607-2654 ◽

Cited By ~ 718

Author(s):

Peng Gong ◽

Jie Wang ◽

Le Yu ◽

Yongchao Zhao ◽

Yuanyuan Zhao ◽

...

Keyword(s):

Land Cover ◽

Landsat Tm ◽

Global Land Cover ◽

Resolution Observation ◽

Global Land

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Copernicus Global Land Cover Layers—Collection 2

Remote Sensing ◽

10.3390/rs12061044 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1044 ◽

Cited By ~ 23

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

Remote Sensing ◽

10.3390/rs10111846 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1846 ◽

Cited By ~ 10

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

10.5194/essd-2019-23 ◽

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.

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