Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing them with Google Earth images from different parts of Singapore, asking survey-takers to classify these images according to their understanding by the IGBP definitions provided. We also conducted interviews with experts from major governmental agencies working with satellite imagery, which highlighted the need for a detailed land classification for Singapore. In addition to the qualitative analysis of the IGBP land classification scheme, we carried out a validation of the MCD12Q1-1 remote sensing product against SPOT-5 imagery for our study area. The user study revealed that survey-takers were able to correctly classify urban areas, as well as densely forested areas. Misclassifications between Cropland
and Mixed Forest
classes were highest and were attributed by users to the broad terminology of the IGBP of the two land cover class definitions. For the accuracy assessment, we obtained validation points using weighted and unweighted stratified sampling. The overall classification accuracy for all 17 IGBP land classes is 62%. Upon selecting only the four most occurring IGBP land classes in Singapore, the classification accuracy improved to 71%. Validation of the MCD12Q1-1 against ground truth for Singapore revealed less-common land classes that may be of importance in a global context but are sources of error when the same product is applied at a smaller scale. Combining the user study with the accuracy assessment gives a comprehensive overview of the challenges associated with using global-level land cover data to derive localized land cover information specifically for smaller land masses like Singapore.
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