Abstract
The finer resolution observation and monitoring of the global land cover (FROM-GLC) product makes it the first 30 m resolution global land cover product from which one can extract a global water mask. However, two major types of misclassification exist with this product due to spectral similarity and spectral mixing. Mountain and cloud shadows are often incorrectly classified as water since they both have very low reflectance, while more water pixels at the boundaries of water bodies tend to be misclassified as land. In this paper, we aim to improve the accuracy of the 30 m FROM-GLC water mask by addressing those two types of errors. For the first, we adopt an object-based method by computing the topographical feature, spectral feature, and geometrical relation with cloud for every water object in the FROM-GLC water mask, and set specific rules to determine whether a water object is misclassified. For the second, we perform a local spectral unmixing using a two-endmember linear mixing model for each pixel falling in the water-land boundary zone that is 8-neighborhood connected to water-land boundary pixels. Those pixels with big enough water fractions are determined as water. The procedure is automatic. Experimental results show that the total area of inland water has been decreased by 15.83% in the new global water mask compared with the FROM-GLC water mask. Specifically, more than 30% of the FROM-GLC water objects have been relabeled as shadows, and nearly 8% of land pixels in the water-land boundary zone have been relabeled as water, whereas, on the contrary, fewer than 2% of water pixels in the same zone have been relabeled as land. As a result, both the user’s accuracy and Kappa coefficient of the new water mask (UA = 88.39%, Kappa = 0.87) have been substantially increased compared with those of the FROM-GLC product (UA = 81.97%, Kappa = 0.81).
Highlights
Land surface water cover information is critical to studies such as climate change, flood monitoring and crop yield prediction at the global scale [1,2]
The proposed object-based method is applied to the FROM-GLC water mask to modify the misclassified mountain- or cloud-shadow objects
The FROM-GLC water mask is improved by first applying an object-based method to remove the commission errors, and performing local spectral unmixing at water-land boundary areas
Summary
Land surface water cover information is critical to studies such as climate change, flood monitoring and crop yield prediction at the global scale [1,2]. The recent advancement of remote sensing technology makes it possible to have sufficient satellite data that provide continuous coverage of the Earth’s surface with finer spatial resolution and quality. Some of these data have been used to automatically classify global land cover at 30 m resolution [3]. The quality of water cover in general purpose land-cover classification using remotely sensed data is often contaminated by cloud shadows and land background of shallow water surfaces. General purpose global land-cover maps such as IGBP
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
