Effect of Using Different Amounts of Multi-Temporal Data on the Accuracy: A Case of Land Cover Mapping of Parts of Africa Using FengYun-3C Data

Abstract

Regional or continental-scale land cover mapping requires various amounts of months of multi-temporal satellite data to pick phenological variation in vegetation, enhancing differentiability among surface cover types and improving accuracy. However, little has been addressed about the number of months/multi-temporal images needed to obtain the best result and the impact of using different amounts of these data on the accuracy of individual classes. This work aimed to analyze these effects by utilizing the various amounts of months of time series FengYun-3C (FY-3C) data within one year for land cover mapping of parts of Africa using a random forest classifier. The study area covers roughly one-third of Africa, including eastern, central, and northern parts of the continent. One-year FY-3C ten-day composite images consisting of eleven-band each with 1-km spatial resolution were divided into seven input datasets that comprise stacked images of 1-month, 3-month, 6-month, consecutive 9-month, 12-month, selected images from 12 months using band/feature importance, and selected 9-month. Comparisons of these datasets on independent test samples revealed that overall accuracy, kappa coefficient, and the accuracy of the individual classes generally increase significantly with increasing the number of data/months. However, the highest accuracy and kappa coefficient, 0.86 and 0.83, were obtained by processing selected 9-month imageries. The second maximum accuracy and kappa (0.85 and 0.82,) were found by manipulating 12-month scenes which are the same as the results obtained by applying feature reduction. Although 4% and 5% higher accuracy were achieved by manipulating 3-month and 6-month data relative to 1-month imageries, no variation of accuracy was observed between six- and nine-months of consecutive data, both yielding equal accuracy and kappa value (0.84 and 0.81) indicating redundancy of information. Overall, the high accuracy results show the feasibility of FY-3C data for land cover mapping of Africa.

Keywords

• land cover classification
• machine learning
• random forest
• feature selection/reduction
• FengYun-3C
• Africa