A maximum entropy method to extract urban land by combining MODIS reflectance, MODIS NDVI, and DMSP-OLS data

Abstract

Researchers often encounter difficulties in obtaining timely and detailed information on urban growth. Modern remote-sensing techniques can address such difficulties. With desirable spectral resolution and temporal resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) products have significant advantages in tackling land-use and land-cover change issues at regional and global scales. However, simply based on spectral information, traditional methods of remote-sensing image classification are barely satisfactory. For example, it is quite difficult to distinguish urban and bare lands. Moreover, training samples of all land-cover types are needed, which means that traditional classification methods are inefficient in one-class classification. Even support vector machine, a current state-of-the-art method, still has several drawbacks. To address the aforementioned problems, this study proposes extracting urban land by combining MODIS surface reflectance, MODIS normalized difference vegetation index (NDVI), and Defense Meteorological Satellite Program Operational Linescan System data based on the maximum entropy model (MAXENT). This model has been proved successful in solving one-class problems in many other fields. But the application of MAXENT in remote sensing remains rare. A combination of NDVI and Defense Meteorological Satellite Program Operational Linescan System data can provide more information to facilitate the one-class classification of MODIS images. A multi-temporal case study of China in 2000China in , 2005China in , and 2010 shows that this novel method performs effectively. Several validations demonstrate that the urban land extraction results are comparable to classified Landsat TM (Thematic Mapper) images. These results are also more reliable than those of MODIS land-cover type product (MCD12Q1). Thus, this study presents an innovative and practical method to extract urban land at large scale using multiple source data, which can be further applied to other periods and regions.

Simulating and Predicting the Impacts of Light Rail Transit Systems on Urban Land Use by Using Cellular Automata: A Case Study of Dongguan, China

“…Numerous studies have indicated that the urban land area in this country exhibited an exponential growth pattern during the past two decades [3][4][5]. This unprecedented phenomenon is accompanied by massive rural-urban migration [6,7].…”

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

“…More importantly, problems related to the same objects using different spectra and different objects using similar spectra remain unresolved, which increases the difficulty of distinguishing between urban and bare lands simply based on the spectral information obtained from several bands of surface reflectance data. Furthermore, exploring regional or global areas is labor intensive and time consuming because of the massive data volumes involved [36]. Thus, to precisely and reliably extract urban areas, DMSP-OLS data are used as the main data source of the data integration, and spectral information from the surface reflectance data is only used as an auxiliary source.…”

Forest Tree Species Distribution Mapping Using Landsat Satellite Imagery and Topographic Variables With the Maximum Entropy Method in Mongolia

“…Other approaches for species distribution modelling and classification include the Maximum Entropy algorithm (MaxEnt) developed by Phillips et al (2006), which requires a series of occurrence locations and a set of explanatory variables defining features that potentially influence the suitability of a species or an event. MaxEnt has been extensively applied for species distribution mapping (Kumar et al 2014) for one-class classification purposes (Lin et al 2014) and probability distribution in various other fields (Petrov and Wessling 2014). Because MaxEnt has shown higher predictive accuracy for classification problems and distribution modelling than other methods such as SVM and artificial neural networks (ANN) (Lin et al 2014;Phillips et al 2006), it was selected for this study.…”

“…MaxEnt has been extensively applied for species distribution mapping (Kumar et al 2014) for one-class classification purposes (Lin et al 2014) and probability distribution in various other fields (Petrov and Wessling 2014). Because MaxEnt has shown higher predictive accuracy for classification problems and distribution modelling than other methods such as SVM and artificial neural networks (ANN) (Lin et al 2014;Phillips et al 2006), it was selected for this study.…”