Abstract
Land cover information depicting the complex interactions between human activities and surface change is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results, which hinders its applications in regional studies requiring more accurate land cover data. Informing these issues, here we developed a robust framework to improve the mapping results of 10 m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multisource remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), machine learning algorithms, and a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that random forest models achieved better performance than classification and regression trees, minimum distance, and support vector machine models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps but also provide certain guidelines for future land cover mapping from regional to global scales.
Highlights
L AND cover, as a key element for earth system science, provides fundamental information for understanding the complex interactions between human activities and surface change
We found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations
The random forest (RF) model achieved the best performance for land cover classification, followed by minimum distance (MD) and classification and regression trees (CART) models with slightly lower overall accuracies of 81.58% and 80.21% (Kappa coefficients of 0.79 and 0.77), respectively
Summary
L AND cover, as a key element for earth system science, provides fundamental information for understanding the complex interactions between human activities and surface change. Land cover maps play an important role in natural resources management, including biodiversity conservation, carbon cycling, climate change, ecosystem protection, and hydrological process [1]–[6]. They are essential to studies of public health, sustainable development, and urban planning [7]–[10]. The relatively coarse resolution and considerably low accuracies of these land cover datasets make it difficult to provide sufficient details of the Earth’s surface [19]–[21] They are far from satisfactory for many sophisticated applications such as the identification of cropping types, evaluation of disasters, and management of urban transportation
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