DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

Abstract

Implying the prosperity and development of the city, impervious surface area (ISA) is playing an increasingly important role in ecological processes, microclimate, material and energy flows, and urban flood. The free sub-meter resolution Google Earth image, which is integrated by several high spatial resolution data, appears to have potential for high-resolution ISA extraction, where present study is rare and performances remain to be improved. Due to the high spatial and spectral variation of the urban environment as well as confusion between ISA and soil, the accurate delineating of ISA with traditional (direct) methods can be costly and time-consuming, which is in a word resource-intensive. However, this paper presents a novel indirect ISA extraction conceptual model and a new detecting and masking background land cover (DMBLC) approach that: uses a freely available, high-resolution dataset; requires a reduced set of training samples; and consists of relatively simple, common, and feasible image processing steps. The key characteristic of DMBLC is to detect the background of ISA (vegetation, soil, and water) accurately and obtain the ISA by masking the background. The approach relies on background detection to avoid the predicaments of direct ISA extraction. Water can be directly gained by water body vector data, in DMBLC; mixture tuned matched filtering (MTMF) is exploited to detect vegetation and soil, image segmentation is used to mitigate the spectral variation problem within the same land cover, and segment rectangularity reduces the confusion between ISA and soil. From experiments in a core area of Fuzhou, China, the DMBLC approach reached high performance and outperformed the powerful traditional support vector machines (SVM) method (overall accuracy of 94.45% and Kappa coefficient of 0.8885, compared to 86.44% and 0.7329, respectively). From the comparison of different levels of complexity within the inner processing steps, it is confirmed that the DMBLC approach is a powerful and flexible changed framework for indirect ISA extraction, which can be improved by using more advanced inner methods.

Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan

“…Qualitative performance appraisals were adopted on the basis of this technique. The PA, UA, OA, and kappa coefficient (KC) values were calculated with a confusion matrix from the detected results, and the ground truth data were collected from different locations in the study area [22,59]. The PA is the error emission in the classified images, and the UA is the reliability; the probability of a pixel on the map represents that of the same category on the ground.…”

“…The driving forces of unplanned impervious surface expansion are divided into two categories: potential and direct factors. Direct factors include development in terms of infrastructure, settlements, and industrial development, as mentioned by Lu et al and Huang et al [22,23]; and potential factors include technological, economic, population, policy, and natural factors [24][25][26]. Due to constraints in the availability of continuous statistical data, quantitative analyses are frequently used for impervious surface analyses [27].…”

High-resolution and Multitemporal Impervious Surface Mapping in the Lancang-Mekong Basin with Google Earth Engine

“…8. This is because improved spatial resolution can minimize the mixed-pixel phenomenon and provide abundant information in the spatial domain, thus potentially increasing the accuracy and thematic details of IS mapping products (Huang et al, 2018). Moreover, the proposed framework was implemented on the GEE cloud platform, which provides almost all Sentinel-1/2 archive images across the entire time series and study area.…”

Digital Image Classification: a Comparison of Classic Methods for Land Cover and Land Use Mapping

“…The use of Google Earth® platforms and SASPlanet® has been recurrent in several studies (Oliveira et al 2009;Schneider 2012;Hu et al 2013;Ghaffarian & Ghaffarian 2014;Jacobson et al 2015;Abdelaty 2016;Malarvizh, Kumar & Porchelvan 2016;Wibowo et al 2016;Huang et al 2018). In this sense, Hu et al (2013) revealed that no significant difference was found between these two classification results by adopting the Z Test, which strongly proved the potential of Google Earth® images in land cover and land use mapping.…”