Abstract
Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.
Highlights
IntroductionNumerous studies have shown promising outputs using Moderate Resolution Imaging Spectroradiometer (MODIS) data in land cover classification at large scales
Land cover mapping at a regional scale provides essential information for monitoring and dynamic assessment of the environment, as well as for economic and social impact assessment [1].Moderate-Resolution Imaging Spectroradiometer (MODIS) Earth Observation data with daily coverage, multi-bands and moderate spatial resolution are suitable datasets for such applications.Numerous studies have shown promising outputs using Moderate Resolution Imaging Spectroradiometer (MODIS) data in land cover classification at large scales
We explored the Random Forests (RF)’s variable importance feature to value the contribution of every band at different time frame of Canada Centre for Remote Sensing (CCRS) 10-day clear-sky time-series MODIS composites to land cover classification, and analyzed the optimal subsets for efficient usage of the dataset, which forms the major goal of the study
Summary
Numerous studies have shown promising outputs using MODIS data in land cover classification at large scales. Friedl et al [2] have used MODIS data for global land cover mapping, and Knight et al [3] characterized regional scale land cover with MODIS imagery. MODIS data have been widely used for agricultural land cover mapping and land surface information retrieval. Wardlow and Egbert [4] mapped large-area crop with MODIS Normalized Difference Vegetation Index (NDVI); Alcantara et al [5] conducted abandoned agriculture mapping using MODIS satellite data; Guindin-Garcia et al [6] evaluated MODIS 8- and 16-day composite products for maize green leaf area index monitoring; and Xiao et al [7] proposed a framework for consistent estimation of multiple
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