scholarly journals Accuracy assessment of Globeland30: a case study of Ontario,Canada

2021 ◽  
Author(s):  
Rishita Rangarh
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Landsat Imagery ◽  
Global Scale ◽  
Data Set ◽  
Error Matrix ◽  
Successful Model ◽  
Land Cover Data ◽  
Made In

GlobeLand30 is the world’s first 30m high resolution land cover data set (Chen et al. 2014) and has been a successful model of Big-Data mining from a host of Landsat imagery, thereby contributing to and enhancing the existing global geospatial knowledge base (GlobeLand30 2014). As there is a lot of uncertainty and errors in the global land cover data, therefore it becomes very difficult to validate land cover on a global scale. Efforts on validating Globeland30 data have been made in various parts of the world in the past and will continue to be done. The objective of this project is to validate GlobeLand30 data set by carrying out a case study in Ontario, Canada. The adopted methodology for doing validation is by using cell-to-cell benchmarking (Maria et al. 2015), thereby deriving Error Matrix, and its derivatives, which includes overall accuracy, user accuracy, producer accuracy and kappa coefficient. The results show that an overall accuracy of 84.14% is obtained for GlobeLand30 data with consideration of shadows, which is relatively a high percentage number indicating that the GlobeLand30 data classification is highly accurate for Ontario, Canada. Keywords: land cover; GlobeLand30; accuracy assessment; Ontario

scholarly journals Accuracy assessment of Globeland30: a case study of Ontario,Canada

2021 ◽  
Author(s):  
Rishita Rangarh
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Landsat Imagery ◽  
Global Scale ◽  
Data Set ◽  
Error Matrix ◽  
Successful Model ◽  
Land Cover Data ◽  
Made In

GlobeLand30 is the world’s first 30m high resolution land cover data set (Chen et al. 2014) and has been a successful model of Big-Data mining from a host of Landsat imagery, thereby contributing to and enhancing the existing global geospatial knowledge base (GlobeLand30 2014). As there is a lot of uncertainty and errors in the global land cover data, therefore it becomes very difficult to validate land cover on a global scale. Efforts on validating Globeland30 data have been made in various parts of the world in the past and will continue to be done. The objective of this project is to validate GlobeLand30 data set by carrying out a case study in Ontario, Canada. The adopted methodology for doing validation is by using cell-to-cell benchmarking (Maria et al. 2015), thereby deriving Error Matrix, and its derivatives, which includes overall accuracy, user accuracy, producer accuracy and kappa coefficient. The results show that an overall accuracy of 84.14% is obtained for GlobeLand30 data with consideration of shadows, which is relatively a high percentage number indicating that the GlobeLand30 data classification is highly accurate for Ontario, Canada. Keywords: land cover; GlobeLand30; accuracy assessment; Ontario

scholarly journals The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey

2014 ◽  
Vol II-4 ◽  
pp. 35-40 ◽  
Author(s):  
X. Ji ◽  
X. Niu
Keyword(s):  
Land Cover ◽  
Accuracy Assessment ◽  
Classification Result ◽  
Error Matrix ◽  
National Geographic ◽  
Land Cover Data ◽  
Object Based ◽  
Data Production ◽  
Traditional Approaches

With the widespread national survey of geographic conditions, object-based data has already became the most common data organization pattern in the area of land cover research. Assessing the accuracy of object-based land cover data is related to lots of processes of data production, such like the efficiency of inside production and the quality of final land cover data. Therefore，there are a great deal of requirements of accuracy assessment of object-based classification map. Traditional approaches for accuracy assessment in surveying and mapping are not aimed at land cover data. It is necessary to employ the accuracy assessment in imagery classification. However traditional pixel-based accuracy assessing methods are inadequate for the requirements. The measures we improved are based on error matrix and using objects as sample units, because the pixel sample units are not suitable for assessing the accuracy of object-based classification result. Compared to pixel samples, we realize that the uniformity of object samples has changed. In order to make the indexes generating from error matrix reliable, we using the areas of object samples as the weight to establish the error matrix of object-based image classification map. We compare the result of two error matrixes setting up by the number of object samples and the sum of area of object samples. The error matrix using the sum of area of object sample is proved to be an intuitive, useful technique for reflecting the actual accuracy of object-based imagery classification result.

scholarly journals UNCERTAINTY ASSESSMENT OF GLOBELAND30 LAND COVER DATA SET OVER CENTRAL ASIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1313-1317 ◽  
Author(s):  
Bo Sun ◽  
Xi Chen ◽  
Qiming Zhou
Keyword(s):  
Land Cover ◽  
Central Asia ◽  
Image Interpretation ◽  
Google Earth ◽  
Large Sample Size ◽  
Data Set ◽  
Error Matrix ◽  
Continental Scale ◽  
Land Cover Data ◽  
Bare Land

GlobeLand30, the world’s first 30m-resolution global land cover data set, has recently been issued for research on global change at a fine resolution. Given the accuracy of GlobeLand30 data may show significant variation in different parts of the world and data quality at continental scale has not been validated yet, this study aims to evaluate the uncertainty of the data over Central Asia. Since it is difficult to get long-term historical ground references, GlobeLand30 data at the most recent epoch (<i>i.e.</i>, GlobeLand30-2010) was assessed. In the test, a large sample size was adopted, and more than 25 thousand samples were selected by a random sampling scheme and interpreted manually as ground references based on higher resolution imagery at the same epoch, such as images from ZY-3 (China Resources Series) satellite and Google earth. Cross validation of image interpretation by three well-trained interpreters was adopted to make the references more reliable. Error matrix and <i>Kappa</i> coefficient were utilized to quantify data accuracies in terms of classification accuracy. Results show that the GlobeLand30-2010 data presents an overall accuracy of 46% in the study area. As for specific land cover types, bare land illustrates a high user’s accuracy but a lower producer’s accuracy. At the same time, the accuracies of grassland and forest are significantly lower than other types. The majority of misclassification types come from bare land. It implies a difficulty of distinguishing grassland or forest from bare land in the study area. In addition, the confusion between shrub land and grassland also results in the misclassification. The results serve as a useful reference of data accuracy for further analysis of land cover change in Central Asia as well as the applications of GlobeLand30 data at a regional or continental scale.

scholarly journals UNCERTAINTY ASSESSMENT OF GLOBELAND30 LAND COVER DATA SET OVER CENTRAL ASIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 1313-1317
Author(s):  
Bo Sun ◽  
Xi Chen ◽  
Qiming Zhou
Keyword(s):  
Land Cover ◽  
Central Asia ◽  
Image Interpretation ◽  
Google Earth ◽  
Large Sample Size ◽  
Data Set ◽  
Error Matrix ◽  
Continental Scale ◽  
Land Cover Data ◽  
Bare Land

GlobeLand30, the world’s first 30m-resolution global land cover data set, has recently been issued for research on global change at a fine resolution. Given the accuracy of GlobeLand30 data may show significant variation in different parts of the world and data quality at continental scale has not been validated yet, this study aims to evaluate the uncertainty of the data over Central Asia. Since it is difficult to get long-term historical ground references, GlobeLand30 data at the most recent epoch (<i>i.e.</i>, GlobeLand30-2010) was assessed. In the test, a large sample size was adopted, and more than 25 thousand samples were selected by a random sampling scheme and interpreted manually as ground references based on higher resolution imagery at the same epoch, such as images from ZY-3 (China Resources Series) satellite and Google earth. Cross validation of image interpretation by three well-trained interpreters was adopted to make the references more reliable. Error matrix and <i>Kappa</i> coefficient were utilized to quantify data accuracies in terms of classification accuracy. Results show that the GlobeLand30-2010 data presents an overall accuracy of 46% in the study area. As for specific land cover types, bare land illustrates a high user’s accuracy but a lower producer’s accuracy. At the same time, the accuracies of grassland and forest are significantly lower than other types. The majority of misclassification types come from bare land. It implies a difficulty of distinguishing grassland or forest from bare land in the study area. In addition, the confusion between shrub land and grassland also results in the misclassification. The results serve as a useful reference of data accuracy for further analysis of land cover change in Central Asia as well as the applications of GlobeLand30 data at a regional or continental scale.

scholarly journals The Use of Geographic Databases for Analyzing Changes in Land Cover—A Case Study of the Region of Warmia and Mazury in Poland

2020 ◽  
Vol 9 (6) ◽  
pp. 358
Author(s):  
Iwona Cieślak ◽  
Andrzej Biłozor ◽  
Anna Źróbek-Sokolnik ◽  
Marek Zagroba
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Landscape Fragmentation ◽  
Water Bodies ◽  
Corine Land Cover ◽  
Land Cover Data ◽  
Land Use Types ◽  
Over Time

This article analyzes the applicability of spatial data for evaluating and monitoring changes in land use and their impact on the local landscape. The Coordination of Information on the Environment (CORINE) Land Cover database was used to develop a procedure and an indicator for analyzing changes in land cover, and the continuity of different land use types. Changes in land use types were evaluated based on land cover data. The results were analyzed over time to track changes in the evaluated region. The studied area was the Region of Warmia and Mazury in Poland. The preservation of homogeneous land cover plays a particularly important role in areas characterized by high natural value and an abundance of forests and water bodies. The study revealed considerable changes in land cover and landscape fragmentation in the analyzed region.

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