Investigating the application of pixel-level and product-level image fusion approaches for monitoring surface water changes

The aim of this paper was to investigate the suitability of the pixel-level and product-level image fusion approaches to detect surface water changes. In doing so, firstly, the principal component analysis technique was applied to Landsat TM 2010 multispectral image to generate the PC components. Several pixel-level image fusion techniques were then performed to merge the Landsat ETM+ 2000 panchromatic with the PC1PC2PC3 band combination of Landsat TM 2010 imagery to highlight the surface water changes between the two images. The suitability of the resulting fused images for surface water change detection was evaluated quantitatively and visually. Finally, the support vector machine (SVM) technique was applied to the qualified fused images to map the highlighted changes. Furthermore, a product level fusion (PLF) approach based on various satellite-derived indices was employed to detect the surface water changes between ETM+ 2000 and TM 2010 images. The accuracy of the resulting change maps was assessed based on a reference change map produced using visual interpretation. The results demonstrated the effectiveness of the proposed approaches for surface water change detection, especially using the Gram Schmidt-SVM, PLF-NDWI, and PLF-NDVI methods which improved the accuracy of change detection over 99.70

Investigating the impact of Pan Sharpening on the accuracy of land cover mapping in Landsat OLI imagery

Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose

Normalized difference vegetation change index: a technique for detecting vegetation changes using Landsat imagery

Vegetation indices have been developed to characterize and extract the Earth's vegetation cover from space using satellite images. For detection of vegetation changes, temporal images are usually independently analyzed or vegetation index differencing is implemented. In this study, a vegetation change index, named normalized difference vegetation change index (NDVCI), was developed to directly detect vegetation changes between two different time images with improved accuracy. The effectiveness of the proposed method to detect vegetation changes was evaluated in comparison with that of enhanced vegetation index (EVI) differencing and normalized difference vegetation index (NDVI) differencing methods at seven test sites under different environmental conditions in Iran, Malaysia, and Italy. Landsat imagery as one of the most widely used sources of data in remote sensing was used for this purpose. Overall accuracy, kappa coefficient, and omission and commission errors were calculated to assess the accuracy of the resulting change maps. The results demonstrated superiority and higher performance of NDVCI compared to EVI and NDVI differencing for detection of vegetation changes. In five out of the seven test sites, the classification accuracy of NDVCI was higher compared to that of the other methods. In contrast, the results revealed lower accuracy of EVI differencing for vegetation change detection at all the test sites, while NDVI differencing was superior at two of the test sites. In conclusion, the study demonstrated great performance of NDVCI for monitoring vegetation changes at different environmental conditions. Accordingly, this technique may improve the vegetation change detection in future studies

Comparative analysis of ASTER DEM, ASTER GDEM, and SRTM DEM based on ground-truth GPS data

This study aims to compare the accuracies of ASTER DEM, ASTER GDEM, and SRTM DEM for the area of Universiti Teknologi Malaysia (UTM) and surrounding. In doing so, a number of Ground Control Points (GCPs) were collected using GPS technology and used to generate an absolute DEM using the ASTER stereo imagery. Moreover, two well-known DEMs including ASTER GDEM and SRTM DEM were obtained for the same area with ASTER image. Subsequently, several high accuracy ground-truth points were established around UTM using dual frequency GPS and used to assess the accuracies of the obtained DEMs. The results indicate that an elevation Root Mean Square Error (RMSE) of ±14.86m is achieved for the generated ASTER DEM, which is less than the 15m pixel size of ASTER image. The results further show that the elevation RMSEs of the ASTER GDEM and SRTM DEM are respectively ±4.52m and ±4.14m for the study area. The results illustrate although the resolution of SRTM DEM is much lower than ASTER GDEM, it could provide higher elevation accuracy. Finally, although the accuracy of the ASTER DEM in this study is not high in comparison with the accuracies of ASTER GDEM and SRTM DEM, based on the selected number of check points and resolution of ASTER image, it could be useful for various geoinformation applications

Identifying the optimum locations for food industries in Qaemshahr-Iran, using GIS and image processing techniques

Determining optimum locations for an industry is a critical and complex decision for manufacturers. To select a potential site to establish food industries, various criteria should be considered regarding accessibility, delivery, market, and environmental factors. In this study, the weighted overlay method was employed to develop an up-to-date geodatabase providing the potential areas for establishing food industries to assist urban development in Qaemshahr city, Iran. The required data were acquired through various sources including remote sensing, Global Positioning System (GPS), and Geographical Information System (GIS). To achieve the aim, firstly the criteria of food industries settlement in Qaemshahr city have been measures, and the corresponding maps have been prepared. Using weighted overlay method all weighted maps were aggregated and final suitability map was established. Finally, the suitable sites for food industries in Qaemshahr were determined. The results have been validated by experts practicing Qaemshahr urban development. The results showed that around 172 sites are suitable for establishing the food industries in Qaemshahr. Accuracy assessment analysis proved the effectiveness of the achieved results with the accuracy level of 90%. The study concluded that the geodatabase model prepared in this study could be useful for future food industry development plans in Qaemshahr city, Ira

A new approach for surface water change detection: integration of pixel level image fusion and image classification techniques

Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000-2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection