An Internet-based Agricultural Land Use Trends Visualization System (AgLuT)

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An Internet-based Agricultural Land Use Trends Visualization System (AgLuT) Prepared for Missouri Department of Natural Resources Missouri Department of Conservation 07-01-2000-12-31-2001 Submitted by Ramanathan Sugumaran and Daniel Zerr Center for Agricultural, Resource and Environmental Systems College of Agriculture, Food and Natural Resources University of Missouri - Columbia December 2001.

1. Goal The goal of this study is to develop and implement an Internet-based Agricultural Land Use Trends Visualization System (AgLuT) that allows the user to: (1) understand the quality, quantity and spatial distribution of agricultural land lost since 1983; and (2) generate spatially interactive what if scenarios regarding the quality, quantity and distribution of future agricultural land being converted to other land uses - based on user-delineated areas of potential conversion or different economic and policy scenarios. The target users in this study are resource professionals and the citizens of Missouri. 2. Objectives: Objective 1. Determine the quality, quantity and spatial distribution of Missouri s agricultural land lost using remote sensing, GIS, and other information technologies. Objective 2. Based on past trends and statistical models, identify and record land currently in agricultural use that may be converted to residential, commercial or industrial use. Objective 3. Develop and implement an Internet-based Agricultural Land Use Trends Visualization System. 3. Research approach An Internet-based Agricultural Land Use Trends Visualization System (AgLuT) will be developed based on three major objectives mentioned above. The research approach for each objective is given below. a). Determining the quantity and spatial distribution of Missouri's land cover (including agricultural land). In order to measure the quantity and spatial distribution of lost agricultural lands, high resolution remotely sensed imagery (Landsat Thematic Mapper) from 1983, 1991 and 2000 would be used. Datasets including socio-economic and demographic data, Missouri agricultural statistics, the National Resources Inventory, and other existing land cover data sets may be incorporated to generate a final digital product. Remotely sensed imagery data from the Landsat Thematic Mapper (TM) satellite were chosen to monitor land cover because the 1983 and 2000 satellite imagery can be compared to the 1991 imagery that is already available from MoRAP (Missouri Resource Assessment Partnership). The different change detection methods will be tested with algorithms such as image differencing, post-classification comparison, and change vector analysis for the development of the end product. b). Agricultural land cover conversion prediction. Using the information developed in objective 1, the second objective will assign a probability for conversion of agricultural land to other uses. Other information, such as demographic and regional economic trends, local amenities and services, local land use policies, and anticipated changes in regional transportation (and other) infrastructure will be included in the analysis. Using statistical analysis of historical rates of land conversion, all agricultural lands in the state will be assigned a high, medium or low probability for conversion.

c). Developing the Internet-based Agricultural Land Use Trends Visualization System. The goal of this objective will be the use and delivery of the results of the first two objectives. The advantages of using an Internet-based visualization system are that the Internet is an efficient and affordable way to distribute information to the public and other resource professionals. The system will include historic land use data as well as the probability for conversion for any section of land in the state. Tools within the system will allow users to summarize the quantity, quality and spatial distribution of agricultural land converted for either a predefined area (i.e. a watershed) or a user defined area. Additional tools will allow the potential economic impacts of land use conversions to be determined and to recalculate the conversion probability based on new user input (i.e. a new highway). 4. CARES progress: Progress will be addressed here regarding each objective and subdivided into stages of the objective. Objective 1: a) 1985 Land Cover Map The land cover map for the state of Missouri for 1984-85 is complete (see figure 1 below). Approximate total acreage for the five land cover classes of water, woodland, cropland, grassland and urban are listed below in table 1. Table 1. Approximate Total Acreage of Land Cover Classes 1985 Land Cover Class Acreage Water 606,400 Woodland 15,553,000 Cropland 12,487,400 Grassland 15,276,100 Urban/Built Up 679,900 The land cover map was completed in December 2001 and is currently being subjected to an accuracy assessment. The accuracy assessment involves using known areas of land cover from the aerial photos described in earlier reports. These known areas are delineated and submitted to the software, which then compares these areas pixel by pixel to the classification output. A table is then produced showing the accuracy of the classification pixelby-pixel, as well as statistical coefficients to help the user understand the overall confidence of the assessment.

Figure 1. Missouri Land Cover - 1985 b) 2000 Land Cover Map Co registration of the Landsat images acquired for 2000 is underway with approximately one-third of the 30 images already co registered to the same images from the 1990s that were used for co registration of the 1985 data (this is necessary to maintain geographic consistency over the three time periods being studied). Once the accuracy of the 1985 land cover map is determined to be satisfactory, classification of the 2000 data will begin. No color infrared aerial photos such as those used in the classification of the 1985 images were available for the 2000 data, so a different methodology will be used in the classification process. Currently training sites from around the state are being collected to be used in the classification process. Training sites are simply areas visually inspected for actual land cover, and referenced to the Landsat images from 2000. These sites will be augmented by the use of USGS Digital Ortho Quarter Quad (DOQQs) photographs. The DOQQs are black and white aerial photos that were mostly acquired in 1997 and are available for the entire state.

Objective 2: In order to begin the process of analyzing the land use trends over the three time periods, small test areas were prepared. Staff at CARES has produced land cover maps of Boone and St. Charles counties for the three time periods of interest. Figure 2 shows the land cover change map produced for Boone County using multi-temporal Landsat TM of 1984, 1992 and ETM 2000. These land cover maps have been delivered to Tom Johnson and the staff at CPAC. These will be used to begin testing the various statistical approaches that may be incorporated to study land use trends and build prediction models. Once the final land cover maps for the entire state have been completed and assessed, the test methods used on these smaller areas can be applied to the entire state of Missouri. 1984 1992 2000 Figure 2 Land cover change map of Boone County Objective 3: In order to develop an Internet-based Agricultural Land Use Trends Visualization System, the following items were attempted in the third half-yearly period. Agricultural Land Use Trends Visualization System (AgLUT) website was modified and all the reports and interactive visualization system are available online. The URL is http://www.cares.missouri.edu/aglut. AgLUT interactive page is moved from ArcView based Internet Map Server to ArcIMS. An interactive Agricultural Land Use Trends Visualization System is currently available at http://ims.missouri.edu/website/aglut/ and has raster layers such as 1985 and 1992 (MoRAP) land cover maps and vector layers including state boundary, county boundaries etc. (Figure 3). Future directions include adding 2000 land cover map and additional vector layers.

Figure 3 AgLUT website