Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

No abstract

ABSTRACT:The Globeland30 dataset is the most highly spatial resolution global land cover mapping product, which developed by the National Geomatics Center of China (NGCC) in 2015. It plays a significant role in environmental monitoring, climate change, and ecosystem assessment, etc. In this study, Jiangxi province was selected as our study area, the 1:100000 land use data in 2010 was employed as the reference data. We aim to examine the accuracy of the Globeland30 from three methods, including area error analysis, shape consistency analysis and confusion matrix. The results show as follows: The land cover types in the study area are primarily occupied by the cultivated land and forest, and secondarily by grassland, water bodies and artificial surfaces. The area error of cultivated land, forest and water bodies are all less than 13%; The general conformance of the shape consistency reaches to 67%, but the shape consistency of every land type differs to a large degree, the best shape consistency of forests is up to 75%; The confusion matrix is obtained in two cases of different class boundary with buffer and no buffer area. It is found that the overall accuracy and kappa coefficient of GlobeLand30 are improved with buffer area. The value of overall accuracy is higher than 78%, the value of kappa coefficient is higher than 0.52.

Impervious surface area (ISA) mapping at the global scale has entered a new era. Currently, the number of highresolution global ISA products is gradually increasing; however, a literature review that systematically investigates these ISA products is still lacking, which limits the application of these products. Thus, we provide a comprehensive analysis of the existing high-resolution global ISA products, concentrating on the aspects of the data sources, training samples, features, and methods. Moreover, we evaluate these products at multi-temporal and multi-spatial scales, using a series of independent test samples. The results demonstrate that the multi-temporal accuracy of the ISA products presents an increasing trend, due to the increase of the available sensors. Among the continuous time-series products (e.g., the updated new global impervious surface area (GISA 2.0), the global impervious surface area (GISA), global annual urban dynamics (GAUD), Global Human Settlement Layer (GHSL), and global artificial impervious areas (GAIA)), the accuracy of the GISA 2.0 outperforms the others at global, continental, and regional scales. However, the mapping performance of these products in small towns and arid and rural regions needs to be enhanced. In particular, we focus on the spatio-temporal disagreement of the ISA products. We show that the high disagreement regions are predominantly concentrated in eastern Asia, western Europe, and eastern North America. In addition, the high disagreement regions are characterized by low ISA density, high vegetation coverage, and high albedo bare ground coverage. Additionally, this paper concludes with some remarks about the future directions of global ISA mapping.

Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth’s subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 m spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 m land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R2 is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NS is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.