scholarly journals Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets

2017 ◽  
Author(s):  
Ketut Wikantika ◽  
Anjar Dimara Sakti ◽  
Wataru Takeuchi
Keyword(s):  
Land Cover ◽  
National Level ◽  
Sources Of Information ◽  
Change Analysis ◽  
Total Size ◽  
Area Change ◽  
Cropland Area ◽  
Global Land Cover ◽  
Area Distribution ◽  
Global Land

Global cropland monitoring is important when considering tactical strategies for achieving food sustainability. Different global land cover (GLC) datasets providing cropland information have already been published and they are used in many applications. The different data input methods, classification techniques, class definitions and production years among the different GLC datasets make them all independently useful sources of information. This study attempted to produce a cropland agreement level (CAL) analysis based on the integration of several cropland datasets to more accurately estimate cropland area distribution. Estimating cropland area and how it has changed on a national level was done by converting the level of cropland agreement into percentages with an existing cropland fraction map. A pre-analysis showed that the four GLC datasets used in the 2005 and 2010 groups had similar year input data acquisitions. Therefore, we placed these four datasets (GlobCover, MODIS LC, GLCNMO and ESACCI LC) into 2005 and 2010 year-groups and selected them to process dataset integration through a CRISP approach. The results of this process proposed four agreement levels for this CAL analysis, and the model correlation was converted into percentage values. The cropland estimate results from the CAL analysis were observed along with FAO data statistics and showed the highest accuracy, with a 0.70 and 0.71 regression value for 2005 and 2010 respectively. In the cropland area change analysis, this CAL change analysis had the highest level of accuracy when describing the total size of cropland area change from 2005 and 2010 when compared to other individual original GLC datasets

scholarly journals Large Uncertainty on Forest Area Change in the Early 21st Century among Widely Used Global Land Cover Datasets

2020 ◽  
Vol 12 (21) ◽  
pp. 3502
Author(s):  
He Chen ◽  
Zhenzhong Zeng ◽  
Jie Wu ◽  
Liqing Peng ◽  
Venkataraman Lakshmi ◽  
...  
Keyword(s):  
Land Cover ◽  
21St Century ◽  
Forest Area ◽  
Validation Dataset ◽  
Area Change ◽  
Early 21St Century ◽  
Global Land Cover ◽  
Global Land ◽  
Earth’S System ◽  
The Early 21St Century

Forests play an important role in the Earth’s system. Understanding the states and changes in global forests is vital for ecological assessments and forest policy guidance. However, there is no consensus on how global forests have changed based on current datasets. In this study, five global land cover datasets and Global Forest Resources Assessments (FRA) were assessed to reveal uncertainties in the global forest changes in the early 21st century. These datasets displayed substantial divergences in total area, spatial distribution, latitudinal profile, and annual area change from 2001 to 2012. These datasets also display completely divergent conclusions on forest area changes for different countries. Among the datasets, total forest area changes range from an increase of 1.7 × 106 km2 to a decrease of 1.6 × 106 km2. All the datasets show deforestation in the tropics. The accuracies of the datasets in detecting forest cover changes were evaluated by a global land cover validation dataset. The spatial patterns of accuracies are inconsistent among the datasets. This study calls for the development of a more accurate database to support forest policies and to contribute to global actions against climate change.

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 Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping

2020 ◽  
Vol 12 (3) ◽  
pp. 426 ◽  
Author(s):  
Peter Potapov ◽  
Matthew C. Hansen ◽  
Indrani Kommareddy ◽  
Anil Kommareddy ◽  
Svetlana Turubanova ◽  
...  
Keyword(s):  
Land Cover ◽  
Resource Assessment ◽  
Free Access ◽  
Change Analysis ◽  
University Of Maryland ◽  
Global Land Cover ◽  
Global Land ◽  
Multi Temporal ◽  
Natural Resource Assessment ◽  
The University

The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.

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.

scholarly journals Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0

2021 ◽  
Vol 258 ◽  
pp. 112364
Author(s):  
Han Liu ◽  
Peng Gong ◽  
Jie Wang ◽  
Xi Wang ◽  
Grant Ning ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Data Cubes ◽  
Global Land Cover ◽  
Global Land

A remote sensing based vegetation classification logic for global land cover analysis

1995 ◽  
Vol 51 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Steven W. Running ◽  
Thomas R. Loveland ◽  
Lars L. Pierce ◽  
R.R. Nemani ◽  
E.R. Hunt
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Classification ◽  
Global Land Cover ◽  
Global Land
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