Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform

Hanyu Ji,Wei Liu,Xinchun Wei,Lianpeng Zhang,Xing Li,Lijuan Wang

doi:10.3390/rs12172832

Abstract

Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data using the Google Earth Engine (GEE) platform, we proposed an efficient framework with good transferability for mapping rural settlements in the Yangtze River Delta. To avoid the time-consuming selection of a large number of training samples in the whole study area, we employed four random forest models obtained from the training samples in respective training municipal districts in four different regions to classify other municipal districts in their corresponding region. We found that different features play diverse vital roles in the extraction of rural settlements in various regions. Compared to results only using optical data, accuracies obtained by the proposed method were significantly improved. The average user’s accuracy, producer’s accuracy, overall accuracy, and Kappa coefficient increased by 16.75%, 17.75%, 11.50%, and 14.50% in the four training municipal administrative areas, respectively. The overall accuracy and Kappa coefficient were 96% and 0.84, respectively. By contrast, our classification results are superior to other public datasets. The final mapping results provided a detailed spatial distribution of the rural settlements in the Yangtze River Delta and revealed that the total area of rural settlements is approximately 32,121.1 km2, accounting for 17.41% of the total area. The high-density rural settlements are mainly distributed in the Northern Plain and East Coast, while the low-density rural settlements are located in the Central Hills and Southern Mountain.

Highlights

Rural settlements—that is, settlement areas for rural residents to produce and live in—are closely related to population distribution and economic growth in rural areas [1]
We found that our trained random forest models with a large ntree are non-transferable to other municipal districts without training samples
We proposed a framework for rural settlement mapping at 10 m spatial resolution using multi-source remote sensing datasets based on the Google Earth Engine (GEE) platform

Summary

Introduction

Rural settlements—that is, settlement areas for rural residents to produce and live in—are closely related to population distribution and economic growth in rural areas [1]. Factors including rural residents entering cities for work, the construction of various types of rural infrastructure, and the evolution of rural industrial structure have collectively led to the large-scale expansion of rural settlements [6], resulting in problems such as “hollow villages” and “unbalanced development of the human-land relationship” in rural settlements in many areas of China [7]. The current urban-centered development strategy in China has overlooked rural development, leaving rural settlements in a disorderly state of development for a long period [8]. Due to their large number and scattered distribution, rural settlements have substantially impacted cultivated land resources and the environment [5]. A comprehensive understanding of the spatial distribution and structural scale of rural settlements is required for both scientific management and sustainable development to ensure reasonable development and utilization of rural land resources in China

Methods

Results

Discussion

Conclusion