Food industry site selection using geospatial technology approach

Food security has been an ongoing concern of governments and international organizations. One of the main issues in food security in Developing and Sanctioned Countries (DSCs) is establishment of food industries and related distributions in appropriate places. In this respect, geospatial technology offers the most up-to-date Land Cover (LC) information to improve site selection for assisting food security in the study area. Currently food security issues are not comprehensively addressed, especially in DSCs. In this research, ASTER L1B and LANDSAT satellite data were used to derive various LC biophysical parameters including build-up area, water body, forest, citrus, and rice fields in Qaemshahr city, Iran using different satellite-derived indices. A Product Level Fusion (PLF) approach was implemented to merge the outputs of the indices to prepare an improved LC map. The suitability of the proposed approach for LC mapping was evaluated in comparison with Support Vector Machine (SVM) and Artificial Neural Network (ANN) classification techniques. For implementing site selection, the outcomes of satellite-derived indices, as well as the city, village, road, railway, river, aqueduct, fault, casting, abattoir, cemetery, waste accumulation, wastewater treatment, educational centre, medical centre, military centre, asphalt factory, cement factory, and slope layers were obtained using Global Positioning System (GPS), on-screen digitizing, and image processing were used as input data. The Fuzzy Overlay and Weighted Linear Combination (WLC) methods were adopted to perform site selection process. The outcomes were then classified and analyzed based on the accessibility to main roads, cities and raw food materials. Finally, the existing industrial zones in the study area were evaluated for establishing food industries based on site selection results of this study. The results indicated higher performance of PLF method to provide up-to-date LC information with an overall accuracy and Kappa coefficient values of 95.95% and 0.95, respectively. The site selection result obtained using WLC method with the accuracy of 90% was superior, thus it was selected for further analyses. Based on the achieved results, the study has proven the applicability of current satellite data and geospatial technology for food industry site selection to resolve food security issues. In conclusion, site selection using geospatial technology provides a great potential for a reliable decision-making in food industry planning, as a significant issue in agro-based food security, especially in sanctioned countries

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

Separation of different vegetation types in ASTER and landsat satellite images using satellite‐derived vegetation indices

Separation of different vegetation types in satellite images is a critical issue in remote sensing. This is because of the close reflectance between different vegetation types that it makes difficult segregation of them in satellite images. In this study, to facilitate this problem, different satellite derived vegetation indices including: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index 2 (EVI2) were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat-5 TM data. The obtained NDVI, EVI, and EVI2 images were then analyzed and interpreted in order to evaluate their effectiveness to discriminate rice and citrus fields from ASTER and Landsat data. In doing so, the Density Slicing (DS) classification technique followed by the trial and error method was implemented. The results indicated that the accuracies of ASTER NDVI and ASTER EVI2 for citrus mapping are about 75% and 65%, while the accuracies of Landsat NDVI and Landsat EVI for rice mapping are about 60% and 65%, respectively. The achieved results demonstrated higher performance of ASTER NDVI for citrus mapping and Landsat EVI for rice mapping. The study concluded that it is difficult to detect and map rice fields from satellite images using satellite-derived indices with high accuracy. However, the citrus fields can be mapped with the higher accuracy using satellite-derived indices

Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping

Increasing the accuracy of thematic maps generated using satellite imagery is a crucial task in remote sensing. In this study, a product-level fusion (PLF) approach based on integration of different land-type maps generated using various satellite-derived indices including normalized difference water index (NDWI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) is proposed to improve the accuracy of land cover mapping. The suitability of the proposed approach for land cover mapping is evaluated in comparison with two high-performance image classification techniques including support vector machine (SVM) and artificial neural network (ANN). The results show that the overall accuracy and kappa values of about 95.95 % and 0.95, 94.91 % and 0.94, and 85.32 % and 0.82 are achieved for the PLF, SVM, and ANN approaches, respectively. The results indicate superiority of the PLF approach than SVM and ANN techniques for land cover classification of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery, especially for the extraction of forest, rice, and citrus classes. However, SVM technique also provided reliable result for land cover mapping

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

Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010) in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), and Automated Water Extraction Index (AWEI) were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs) was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously