scholarly journals Consistency Analysis of Remote Sensing Land Cover Products in the Tropical Rainforest Climate Region: A Case Study of Indonesia

2020 ◽  
Vol 12 (9) ◽  
pp. 1410 ◽  
Author(s):  
Junmei Kang ◽  
Zhihua Wang ◽  
Lichun Sui ◽  
Xiaomei Yang ◽  
Yuanzheng Ma ◽  
...  
Keyword(s):  
Land Cover ◽  
Tropical Rainforest ◽  
Accuracy Evaluation ◽  
Mapping Accuracy ◽  
Climate Zones ◽  
Global Land Cover ◽  
Spatial Consistency ◽  
Global Land ◽  
Land Types

Land cover changes in tropical rainforest climate zones play an important role in global climate change and the functioning of the Earth’s natural system. Existing research on the consistency of different land cover products has mainly focused on administrative divisions (continental or national scales). However, the ongoing production of large regional or global land cover products with higher resolutions requires us to have a better grasp of confusing land types and their geographical locations for different zoning (e.g., geographical zoning) in order to guide the optimization of strategies such as zoning and sample selection in automated land cover classification. Therefore, we selected the GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC2015 global land cover products with a 30-m resolution covering Indonesia, which has a tropical rainforest climate, as a case study, and then analyzed these products in terms of areal consistency, spatial consistency, and accuracy evaluation. The results revealed that (a) all three land cover products revealed that forest is the main land cover type in Indonesia. The area correlation coefficient of any two products is better than 0.89; (b) the areas that are completely consistent among the three products account for 58% of the total area of Indonesia, mainly distributed in the central and northern parts of Kalimantan and Papua, which are dominated by forest land types. The spatial consistency of the three products is low, however, due to the complex surface types and staggered distributions of grassland, shrub, cultivated land, artificial surface, and other land cover types in Java, eastern Sumatra, and the eastern, southern, and northwestern sections of Kalimantan, where the elevation is less than 200 m. Given these results, land cover producers should take heed of the classification accuracy of these areas; (c) the absolute accuracy evaluation demonstrated that the GLC_FCS30-2015 product has the highest overall accuracy (65.59%), followed by the overall accuracy of the GlobeLand30-2010 product (61.65%), while the FROM_GLC2015 exhibits the lowest overall accuracy (57.71%). The mapping accuracy of the three products is higher for forests and artificial surfaces. The cropland mapping accuracy of the GLC_FCS30-2015 product is higher than those of the other two products. The mapping accuracy of all products is low for grassland, shrubland, bareland, and wetland. The classification accuracy of these land cover types requires further improvement and cannot be used directly by land cover users when conducting relevant research in tropical rainforest climate zones, since the utilization of these products could lead to serious errors.

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 Synergistic Modern Global 1 Km Cropland Dataset Derived from Multi-Sets of Land Cover Products

2019 ◽  
Vol 11 (19) ◽  
pp. 2250 ◽  
Author(s):  
Chengpeng Zhang ◽  
Yu Ye ◽  
Xiuqi Fang ◽  
Hansunbai Li ◽  
Xueqiong Wei
Keyword(s):  
Spatial Distribution ◽  
Land Cover ◽  
Expert Knowledge ◽  
Global Changes ◽  
Moderate Amount ◽  
Global Land Cover ◽  
Spatial Consistency ◽  
Sensing Technology ◽  
Global Land ◽  
Pixel Scale

The quality of global cropland products could affect our understanding of the impacts of cropland reclamation on global changes. With the advancement of remote sensing technology, several global land cover products and synergistic datasets have been developed in recent decades. However, there are still some disagreements among the global cropland datasets. In this paper, we proposed a new synergistic method that integrates the reliability of spatial distribution and cropland fraction on a pixel scale, and developed a modern (around 2000 C.E.) fractional cropland dataset with a 1 km × 1 km spatial resolution on the basis of the spatial consistency of cropland reclamation intensity derived from multi-sets of global land cover products. The main conclusions are shown as follows: (1) The accuracy of spatial distribution assessed by validation samples in this synergistic dataset reaches 87.6%, and the dataset also has a moderate amount of cropland pixels when compared with other products. (2) The reliability of cropland fraction on the pixel scale had been highly improved, and most cropland pixel has a higher fraction (over 90%) in this dataset. The “L” shape of the histogram of pixel numbers with different reclamation intensities is reasonable because it is consistent with the up-scaling results derived from satellite-derived products with high spatial resolutions and the expert knowledge on cultivation. (3) The cropland areas in this non-calibrated result are generally closer to that of FAOSTAT on scales from global to national when compared to other non-calibrated synergistic datasets and original satellite-derived products. (4) The reliability of the synergistic result developed by this method might be decreased to some degree in the regions with high discrepancies among the original multi-sets of cropland datasets.

Integrating global land cover products to refine GlobeLand30 forest types: a case study of conterminous United States (CONUS)

2020 ◽  
Vol 42 (6) ◽  
pp. 2105-2130
Author(s):  
Ling Zhu ◽  
Guangshuai Jin ◽  
Xiaohong Zhang ◽  
Ruoming Shi ◽  
Yixuan La ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Forest Types ◽  
Global Land Cover ◽  
Global Land

scholarly journals Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China

2017 ◽  
Vol 16 (2) ◽  
pp. 298-311 ◽  
Author(s):  
Xiao-yu CHEN ◽  
Ya LIN ◽  
Min ZHANG ◽  
Le YU ◽  
Hao-chuan LI ◽  
...  
Keyword(s):  
Land Cover ◽  
Shaanxi Province ◽  
Global Land Cover ◽  
Global Land

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
Sign in / Sign up

Export Citation Format

Share Document