A Methodology to Monitor Urban Expansion and Green Space Change Using a Time Series of Multi-Sensor SPOT and Sentinel-2A Images

Abstract

Monitoring urban expansion and greenspace change is an urgent need for planning and decision-making. This paper presents a methodology integrating Principal Component Analysis (PCA) and hybrid classifier to undertake this kind of work using a sequence of multi-sensor SPOT images (SPOT-2,3,5) and Sentinel-2A data from 1996 to 2016 in Hangzhou City, which is the central metropolis of the Yangtze River Delta in China. In this study, orthorectification was first applied on the SPOT and Sentinel-2A images to guarantee precise geometric correction which outperformed the conventional polynomial transformation method. After pre-processing, PCA and hybrid classifier were used together to enhance and extract change information. Accuracy assessment combining stratified random and user-defined plots sampling strategies was performed with 930 reference points. The results indicate reasonable high accuracies for four periods. It was further revealed that the proposed method yielded higher accuracy than that of the traditional post-classification comparison approach. On the whole, the developed methodology provides the effectiveness of monitoring urban expansion and green space change in this study, despite the existence of obvious confusions that resulted from compound factors.

Multitemporal Analysis of Soil Sealing and Land Use Changes Linked to Urban Expansion of Salamanca (Spain) Using Landsat Images and Soil Carbon Management as a Mitigating Tool for Climate Change

“…Multitemporal evaluation has been widely used to study changes in land cover, especially using Landsat image analyses. Recently, some studies have incorporated higher resolution Sentinel images for researching the most recent changes, as more accurate results can be obtained [91,92]. In this work, we maintain Landsat Regarding the model, we find a part that analyzes the evolution of coverage, and another that evaluates past impacts, and that also serves to restrict future urbanization.…”

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

“…In this study, the textural features (i.e., angular second moment, contrast, dissimilarity, and entropy) were extracted by computing Gray Level Co-Occurrence Matrix (GLCM) that contain rich information on spatial structure and landscapes [57]. The near-infrared (NIR) band from the Sentinel image was used for computing the spectral and textural features, as it contains a negative correlation between built-up and vegetation [58][59][60]. Moreover, density features from POI help to present the differences in spatial patterns among different types of parcels.…”

Remote Sensing of Urban Forests

“…In the panorama of remote sensing, LiDAR point-cloud data represent one of the best choices for describing vertical forest structure; however, multispectral satellite data are more cost-effective and consistent over time when investigating canopy cover and, more generally, land use/land cover distribution. In the study conducted by Deng et al [10], the authors developed a method to detect land use change in the Hangzhou area (China) by integrating images acquired by Satellite Pour l'Observation de la Terre (SPOT) and Sentinel-2A satellites spanning the past 20 years. Their methods, which include multi-date Principal Component Analysis (PCA) and a hybrid classifier, resulted in higher accuracies compared to traditional land use change detection approaches.…”

“…Given the increasing availability of satellite images from different sensors, the spread of LiDAR data and growing potential of cloud-based services (i.e., Google Earth Engine or Amazon Web Services), there is a need for innovative research focusing on advanced remote sensing applications for monitoring and assessing urban forest areas and associated ESS. This Special Issue includes research studies focusing on the temporal dynamics of urban forests [9,10] and their distribution in space through the application of advanced semantic segmentation techniques [11] and in relationship with green space accessibility [12], the implementation of laser scanner for improving allometry-based forest biometrics [13], and a review investigating the state of the art of remote sensing in urban forestry [14] (see Table 1).…”