Abstract
Mapping urban areas at global and regional scales is an urgent and crucial task for detecting urbanization and human activities throughout the world and is useful for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. DMSP-OLS stable nighttime lights have provided an effective way to monitor human activities on a global scale. Threshold-based algorithms have been widely used for extracting urban areas and estimating urban expansion, but the accuracy can decrease because of the empirical and subjective selection of threshold values. This paper proposes an approach for extracting urban areas with the integration of DMSP-OLS stable nighttime lights and MODIS data utilizing training sample datasets selected from DMSP-OLS and MODIS NDVI based on several simple strategies. Four classification algorithms were implemented for comparison: the classification and regression tree (CART), k-nearest-neighbors (k-NN), support vector machine (SVM), and random forests (RF). A case study was carried out on the eastern part of China, covering 99 cities and 1,027,700 km2. The classification results were validated using an independent land cover dataset, and then compared with an existing contextual classification method. The results showed that the new method can achieve results with comparable accuracies, and is easier to implement and less sensitive to the initial thresholds than the contextual method. Among the four classifiers implemented, RF achieved the most stable results and the highest average Kappa. Meanwhile CART produced highly overestimated results compared to the other three classifiers. Although k-NN and SVM tended to produce similar accuracy, less-bright areas around the urban cores seemed to be ignored when using SVM, which led to the underestimation of urban areas. Furthermore, quantity assessment showed that the results produced by k-NN, SVM, and RFs exhibited better agreement in larger cities and low consistency in small cities.
Highlights
The urban areas on Earth’s land surface have experienced rapid expansion rates through the last three decades [1]
We present four machine learning methods, including classification and regression tree (CART), k-nearest neighbors (k-NN), random forests (RF), and support vector machine (SVM), to extract urban areas by using DMSP-OLS and MODIS NDVI data, attempting to develop a new approach to derive urban areas or human settlement from DMSP-OLS
We proposed a per-pixel classification method to extract urban areas from DMSP-OLS nighttime lights (NTL) and MODIS NDVI data; a simple threshold strategy and probability of selection were used to select the training sets, and the training sets were input into machine learning classifiers to classify the unknown pixels
Summary
The urban areas on Earth’s land surface have experienced rapid expansion rates through the last three decades [1]. Remote sensing-based techniques have provided an efficient approach for mapping urban areas at multiple scales. Urban areas or human settlements can be mapped at different scales utilizing remote sensing data with different spatial resolution. High (
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