scholarly journals Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

2017 ◽  
Vol 191 ◽  
pp. 328-341 ◽  
Author(s):  
James Wickham ◽  
Stephen V. Stehman ◽  
Leila Gass ◽  
Jon A. Dewitz ◽  
Daniel G. Sorenson ◽  
...  
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Thematic Accuracy

scholarly journals Analysis of the Impact of Positional Accuracy When Using a Single Pixel for Thematic Accuracy Assessment

2020 ◽  
Vol 12 (24) ◽  
pp. 4093
Author(s):  
Jianyu Gu ◽  
Russell G. Congalton
Keyword(s):  
Land Cover ◽  
Spatial Scale ◽  
Classification Scheme ◽  
Accuracy Assessment ◽  
Land Cover Mapping ◽  
Sensing Applications ◽  
Assessment Unit ◽  
Thematic Accuracy ◽  
The Impact ◽  
Single Pixel

The primary goal of thematic accuracy assessment is to measure the quality of land cover products and it has become an essential component in global or regional land cover mapping. However, there are many uncertainties introduced in the validation process which could propagate into the derived accuracy measures and therefore impact the decisions made with these maps. Choosing the appropriate reference data sample unit is one of the most important decisions in this process. The majority of researchers have used a single pixel as the assessment unit for thematic accuracy assessment, while others have claimed that a single pixel is not appropriate. The research reported here shows the results of a simulation analysis from the perspective of positional errors. Factors including landscape characteristics, the classification scheme, the spatial scale, and the labeling threshold were also examined. The thematic errors caused by positional errors were analyzed using the current level of geo-registration accuracy achieved by several global land cover mapping projects. The primary results demonstrate that using a single-pixel as an assessment unit introduces a significant amount of thematic error. In addition, the coarser the spatial scale, the greater the impact on positional errors as most pixels in the image become mixed. A classification scheme with more classes and a more heterogeneous landscape increased the positional effect. Using a higher labeling threshold decreased the positional impact but greatly increased the number of abandoned units in the sample. This research showed that remote sensing applications should not employ a single-pixel as an assessment unit in the thematic accuracy assessment.

Thematic accuracy assessment of the NLCD 2016 land cover for the conterminous United States

2021 ◽  
Vol 257 ◽  
pp. 112357
Author(s):  
James Wickham ◽  
Stephen V. Stehman ◽  
Daniel G. Sorenson ◽  
Leila Gass ◽  
Jon A. Dewitz
Keyword(s):  
United States ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Thematic Accuracy

scholarly journals VALIDATION OF THE GLOBAL HIGH-RESOLUTION GLOBELAND30 LAND COVER MAP IN EUROPE USING LAND COVER FIELD SURVEY DATABASE - LUCAS

2018 ◽  
Vol XLII-4 ◽  
pp. 51-59
Author(s):  
G. Bratic ◽  
M. E. Molinari ◽  
M. A. Brovelli
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Field Survey ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Small Region ◽  
Gis And Remote Sensing ◽  
High Level ◽  
Thematic Accuracy ◽  
Land Cover Maps

<p><strong>Abstract.</strong> High-resolution land cover maps are one of the technological innovations driving improvements in many fields influenced by Geographic Information Systems (GIS) and Remote Sensing. In particular, the GlobeLand30 (GL30), global LC map with spatial resolution of 30<span class="thinspace"></span>m, is thought to be one of the highest quality high-resolution products. However, these LC maps require validation to determine their suitability for a particular purpose. One of the best ways to provide useful validation reference data is to do a high-level accuracy field survey, but this is time consuming and expensive. Another option is to exploit already available datasets. This study assesses thematic accuracy of GL30 in Europe using LUCAS as a validation reference, because it is a free and open field survey database. The results were generally not good, and very bad for some classes. Analysis was then restricted to a small region (Lombardy, Italy) where LC data of higher resolution than those of GL30 were available. LUCAS was also found to be incoherent with this product. Further comparisons of LUCAS with other independent sources confirmed that the LC attributes of LUCAS are inconsistent with expectations. Although these findings may not be generalized to other regions, the results warn against the suitability of LUCAS as ground truth for LC validation. The paper discusses the process of thematic accuracy assessment of the GL30 and the applicability of LUCAS for high-resolution global LC validation.</p>

scholarly journals PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON

2018 ◽  
Vol 24 (2) ◽  
pp. 250-269 ◽  
Author(s):  
João Arthur Pompeu Pavanelli ◽  
João Roberto dos Santos ◽  
Lênio Soares Galvão ◽  
Maristela Xaud ◽  
Haron Abrahim Magalhães Xaud
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Brazilian Amazon ◽  
Landsat 8 ◽  
Array Type ◽  
Complex Landscape ◽  
Hybrid Classification ◽  
L Band ◽  
Thematic Accuracy ◽  
Better Than

Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.

scholarly journals Utilizing the Global Land Cover 2000 reference dataset for a comparative accuracy assessment of 1 km global land cover maps

2015 ◽  
Vol XL-7/W3 ◽  
pp. 503-510 ◽  
Author(s):  
M. Schultz ◽  
N. E. Tsendbazazr ◽  
M. Herold ◽  
M. Jung ◽  
P. Mayaux ◽  
...  
Keyword(s):  
Land Cover ◽  
Climate Models ◽  
Accuracy Assessment ◽  
Careful Consideration ◽  
Global Climate Models ◽  
Reference Dataset ◽  
Heterogeneous Landscape ◽  
Global Land Cover ◽  
Global Land ◽  
Land Cover Maps

Many investigators use global land cover (GLC) maps for different purposes, such as an input for global climate models. The current GLC maps used for such purposes are based on different remote sensing data, methodologies and legends. Consequently, comparison of GLC maps is difficult and information about their relative utility is limited. The objective of this study is to analyse and compare the thematic accuracies of GLC maps (i.e., IGBP-DISCover, UMD, MODIS, GLC2000 and SYNMAP) at 1 km resolutions by (a) re-analysing the GLC2000 reference dataset, (b) applying a generalized GLC legend and (c) comparing their thematic accuracies at different homogeneity levels. The accuracy assessment was based on the GLC2000 reference dataset with 1253 samples that were visually interpreted. The legends of the GLC maps and the reference datasets were harmonized into 11 general land cover classes. There results show that the map accuracy estimates vary up to 10-16% depending on the homogeneity of the reference point (HRP) for all the GLC maps. An increase of the HRP resulted in higher overall accuracies but reduced accuracy confidence for the GLC maps due to less number of accountable samples. The overall accuracy of the SYNMAP was the highest at any HRP level followed by the GLC2000. The overall accuracies of the maps also varied by up to 10% depending on the definition of agreement between the reference and map categories in heterogeneous landscape. A careful consideration of heterogeneous landscape is therefore recommended for future accuracy assessments of land cover maps.

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