Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine

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 (RF) models achieved better performance than classification and regression trees (CART), minimum distance (MD), and support vector machine (SVM) 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.

Spatial Stratification Method for the Sampling Design of LULC Classification Accuracy Assessment: A Case Study in Beijing, China

“…Given the true land class was unknown, we selected 30 m CLUDs as target data, used 500 m MCD12Q1 as ancillary data to carry out the spatial stratification, and employed 10-m FROM-GLC10, which had a higher spatial resolution and more reliable global/regional accuracy [31,32], as reference data to measure the sampling efficiency in the case study. However, in application of the proposed spatial stratification method, products with different resolutions should be used in the stratification as much as possible, and reference data, e.g., FROM-GLC10, can be also used for spatial stratification.…”

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

“…Among the various supervised and unsupervised classification methods, Machine Learning (ML) methods have become a prominent focus of RS for wetland mapping [22]. It has been widely reported that among different ML classification techniques, RF achieves better performance for wetland mapping [8,[23][24][25].…”

“…GEE is a cloud-based computing platform that includes massive amounts of open access earth observation datasets, image processing, and classification algorithms [25,30]. The availability of many datasets and ready-to-use image-driven products, easy downloading and uploading of data in various formats, and access to many image processing and ML algorithms are some of the features that have contributed to the widespread use of GEE [30][31][32][33].…”

A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery

“…The fusion of optical and SAR data is an active area of research and it has proven successful for different remote sensing tasks [7,8]. In this regard, S1 and S2 complement each other perfectly due to their different characteristics, facilitating the mapping tasks [9], although few works have considered using them together for pixel-wise labeling purposes [10,11]. In fact, to the best of our knowledge, there is no prior work combining S1 and S2 for building and road detection.…”