Methodologies for Analyzing Impact of Urbanization on Irrigation Districts Rio Grande Basin Initiative

The region of Texas along the Mexican border has been experiencing rapid urban growth. This has caused fragmentation of many irrigation districts who are struggling to address the challenges resulting from urbanization. A previous paper provided an analysis of the growth of urban area and its impact on water distribution networks in five Texas border counties over the ten year period, 1996 to 2006. In this paper, we discuss alternative procedures developed to assess such impacts, and we evaluate their effectiveness in identifying critical areas, growth patterns, etc. Urbanized areas were identified starting from aerial photographs using two different approaches: manual, and automatic based on the analysis of radiometric and structural image information. The resulting urbanization maps were then overlapped with distribution network density maps, and critical impact areas were identified. This paper compares the results obtained between the different methods, and evaluates if the analysis can be further improved by categorizing the urban area with the Morphological Segmentation Method. INTRODUCTION Texas is predicted to have the fastest population growth in the USA between 2010 and 2060, and the Rio Grande Regional Water Planning Area (Region M), which is one of the 16 Texas Water Planning Regions established by Senate Bill 1 in 1997, is predicted to have the highest growth in Texas, with +182% (Texas Water Development Board, 2012). Within Region M, Hidalgo and Cameron are the most populated counties, with an expected growth of +103 and +164%, respectively between 2010 and 2060 (Rio Grande Regional Water Planning Group, 2010). Urbanization in South Texas is causing the fragmentation and loss of agricultural land, with detrimental effects on normal operation and maintenance of districts (Gooch and Anderson, 2008, and Gooch, 2009). In particular, districts have to abandon structures and invest in new ones to ensure proper operation, change how to operate systems when canals become oversized, and increase rates to address the challenge of reduced revenues from water sales. Districts in this region primarily operate their systems manually, with a canal rider personally moving from site to site. As a consequence, urbanization can create access to and maintenance of facilities difficult or more time consuming. Transfer of water rights from agricultural to other uses reduces the total amount of water flowing through the water distribution networks, which typically decreases 1 Extension Associate, Department of Biological and Agricultural Engineering, 2117 Texas A&M University, College Station, Texas 77843‐2117; gbonaiti@ag.tamu.edu 2 Professor and Extension Agricultural Engineer, Department of Biological and Agricultural Engineering, 2117 Texas A&M University, College Station, Texas 77843‐2117; g-fipps@tamu.edu conveyance efficiency and increases losses. Finally, the increasing presence of subdivisions and industrial areas in the vicinity of the delivery network increase the liability for canal breaks and flooding. Most districts in the region do very little analysis of the effects of urbanization on their operation and management procedures, or incorporate urbanization trends into planning for future infrastructure improvements. Therefore, there is a need for identification of critical areas. There would be several benefits from such analysis, for example identify priority areas for conversion from open canal to pipeline (Lambert, 2011). The objective of this paper is to compare alternative procedures and techniques to assess urbanization impacts on irrigation districts and to evaluate their effectiveness in identifying critical areas. Literature review Several methodologies have been used to identify urban area extent and growth. Many studies use satellite archive imagery as source of data (e.g., Landsat) which are becoming more readily available, are characterized by a multi-spectral data, and have good spatial resolution for landscape scale analysis. When analysis is carried out on smaller areas, results can be more accurate using aerial photographs, which provide more detail on geometric information. Analysis of imagery data for interpretation of land use and land cover dynamics can be performed with manual (Bonaiti and Fipps, 2011) or automatic procedures. The most utilized automatic approaches are Pixel-Oriented (PO) and Object-Oriented (OO) analysis. In the last decade, several studies demonstrated that the OO method can give more accurate results compared to PO (Pakhale and Gupta, 2010). Urbanization maps identify only the location of urban areas. To interpret the evolution of spatial patterns, Ritters, et al. (2000) proposed a model which distinguishes different types of forest fragmentation through an automatic pixel analysis of aerial photography. Ritters’ analysis is used to determine the progressive intrusion of urbanization, classified into categories: edge, perforated, transition and patched. Vogt, et al. (2007) and Soille and Vogt (2009) proposed an improvement in Ritters method by analyzing the fragmentation on the base of image convolution, called the Morphological Segmentation Method. This method helps to prevent misclassifications of fragmentation and can be easily applied using a free software (Soille and Vogt, 2009, GUIDOS, 2008). Impact on districts can be measured not only with the size or the type of urbanization intrusion in their service area, but also with a specific analysis of the interaction between water distribution network and urban expansion. Little attention has been given to this aspect (Gooch, 2009) MATERIALS AND METHODS Study area Six counties along the Texas-Mexico border have irrigation districts with Texas Class A irrigation water rights. Our analysis was carried out on the three southern counties of the basin: Cameron, Hidalgo, and Willacy (Fig. 1). These counties contain 28 irrigation districts with a total service area of 759,200 acres, and a canal system 3,174 miles long. Figure 1. Location of the study area Urbanization Maps and Network Fragmentation Index Methods for obtaining Manual Urbanization Maps (MUM), Buffered Manual Urbanization Map (B-MUM), Network Fragments (NF) and fragmentation indexes (Network Fragmentation Index, NFI, and District Fragmentation Index, DFI) have been discussed in Bonaiti and Fipps (2011). The creation of automatic urbanization maps was done using the eCognition software, which is based on an object-based image analysis method. We called them Automatic Urbanization Maps (AUM). Since the preparation of aerial photography is time consuming, we applied the methodology only to the South Eastern portion of the Brownsville Irrigation District (BID) for the year 2006. The method was also applied to the area inside the city limits. This method is Study area Falcon reservoir faster and gives higher detail compared to MUM, but since it is based on a slightly different approach (e.g., all houses are included) consistency between the two methods must be evaluated. Similarly to what done with MUM, we added 0.03-mile buffer to AUM to create a Buffered Automatic Urbanization Map (B-AUM). Then we overlapped it with open canals and pipelines and we identified “automatic” Network Fragments (NFa). Finally, we applied the Kernel density to NFa and we obtained the “automatic” Network Fragmentation Index (NFIa). Morphological Segmentation Method In order to add information to the urbanization maps, we categorized them using the Morphological Segmentation Method. The categories that are defined by the procedure are: Core, Edge, Perforation, Bridge, Loop, Branch, Islet. We used the GUIDOS 1.3 software (Vogt, 2010). In particular, the software implements the Morphological Spatial Pattern Analysis (MSPA) and allows modification of four (4) parameters as described in the MSPA Guide (Vogt, 2010): • Foreground Connectivity: for a set of 3 x 3 pixels the center pixel is connected to its adjacent neighboring pixels by having either a) a pixel border and a pixel corner in common (8-connectivity) or, b) a common pixel border only (4-connectivity). The default value is 8 • Edge Width: this parameter defines the width or thickness of the non-core classes in pixels. The actual distance in meters corresponds to the number of edge pixels multiplied by the pixel resolution of the data. The default value is 1 • Transition: transition pixels are those pixels of an edge or a perforation where the core area intersects with a loop or a bridge. If Transition is set to 0 (↔ hide transition pixels) then the perforation and the edges will be closed core boundaries. Note that a loop or a bridge of length 2 will not be visible for this setting since it will be hidden under the edge/perforation. The default value is 1 • Intext: this parameter allows distinguishing internal from external features, where internal features are defined as being enclosed by a Perforation. The default is to enable this distinction which will add a second layer of classes to the seven basic classes. All classes, with the exception of Perforation, which by default is always internal, can then appear as internal or external (default value equal to 1) We applied the methodology to B-MUM, B-AUM, and AUM. We used default values for the four parameters except for the Edge Width with AUM, which was set to 10 to account for the smaller pixel size of this map. To be suitable for the software, the original files (shapefiles) had to be first converted to raster. To do that, we chose a cell size that looked reasonable for the type of detail of the original map. Therefore we used a cell size of 310 for B-MUM and B-AUM, and a cell size of 31 for AUM. Based on the idea that network fragmentation has a different impact on districts operation according to the category that overlaps it, we also set up a procedure to correct the NFI using a categorization map. Using the 1996 B-MUM, we gave the following weights to categories: 1, 2, 3