Model Maker Asignment

modelling The Potential for agricultural land use expansion in the Lake lanier region of georgia usa using remore sensing technology

Padraig QuinnStudent Number:11125900

Padraig Quinn11125900

Executive Summary

The Lake Lanier Region in Gainsville Georgia USA is a primarily rural location. The state as a whole relies heavily on agriculture for employment and industry, where livestock and dairy farming are high on the list. The Lake Lanier region is composed of rich forested land that has the potential to be transformed into high quality agricultural land. Through the use of remote sensing technology, digital image processing and modelling techniques were implemented in order to produce high quality, class defined land suitability imagery for the Lake Lanier region using specified criteria and restraints. The results proved to be positive, with several locations considered suitable for sustainable agriculture, without having serious detrimental effects on the surrounding landscape and the prosperity and survival of the remaining woodland areas.

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EXECUTIVE SUMMARY..................................................................................................................... 1

TABLE OF FIGURES........................................................................................................................... 3

TABLE OF TABLES............................................................................................................................. 4

1. INTRODUCTION AND CONTEXT....................................................................................................5

1.1 Introduction........................................................................................................................................51.2 Context................................................................................................................................................5

2. METHODOLOGY........................................................................................................................... 6

2.1 Selecting Image Layers........................................................................................................................62.2 Altering the Primary Image Layers.....................................................................................................7

2.2.1 Activating Modelmaker...............................................................................................................72.2.2 Recoding the Insoils Image Data..................................................................................................82.2.3 Altering the Inlandc Data.............................................................................................................9

2.3 Modelling the Altered Layers...........................................................................................................112.4 Overlaying Suitability Classes on Landsat Image..............................................................................13

2.4.1 Combining NIR Image with Suitability Classes...........................................................................132.4.2 Combining NDVI Model and Existing Model..............................................................................15

3. RESULTS.................................................................................................................................... 18

3.1 Modelling Results.............................................................................................................................183.2 Accuracy Assessment........................................................................................................................183.3 Impact Assessment...........................................................................................................................19

4. DISCUSSION AND CONCLUSION..................................................................................................20

4.1 Discussion.........................................................................................................................................204.2 Conclusion.........................................................................................................................................20

BIBLIOGRAPHY.............................................................................................................................. 21

5. APPENDIX I................................................................................................................................ 22

5.1 Instructions for Model Maker...........................................................................................................22

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Table of FiguresFIGURE 1: LANIER TM LANDSAT IMAGE...........................................................................................................5FIGURE 2: INSOILS (LEFT) AND INLANDC (RIGHT) IMAGES.....................................................................................5FIGURE 3: TOOL PALETTE..............................................................................................................................6FIGURE 4: SIMPLE MODEL.............................................................................................................................7FIGURE 5: BEGINNING OF SUITABILITY MODEL..................................................................................................7FIGURE 6: RECODED IMAGES.........................................................................................................................8FIGURE 7: INLANDC EITHER FUNCTION.............................................................................................................9FIGURE 8: DECIDUOUS MODEL.....................................................................................................................10FIGURE 9: DECIDUOUS IMAGE......................................................................................................................10FIGURE 10: LAND USE AND SOIL SUITABILITY MODEL.......................................................................................11FIGURE 11: CONDITIONAL FUNCTION............................................................................................................11FIGURE 12: SUITABILITY CLASSES IMAGE........................................................................................................12FIGURE 13: NIR AND SUITABILITY CLASSES MODEL..........................................................................................13FIGURE 14: NIR FUNCTION.........................................................................................................................13FIGURE 15: NIR AND SUITABILITY FUNCTION..................................................................................................14FIGURE 16: NDVI AND SUITABILITY CLASSES MODEL.......................................................................................14FIGURE 17: NDVI EITHER FUNCTION............................................................................................................15FIGURE 18: GLOBAL MIN FUNCTION.............................................................................................................15FIGURE 19: NDVI AND SUITABILITY CLASSES FUNCTION...................................................................................15FIGURE 20: NDVI AND NIR IMAGES.............................................................................................................16FIGURE 21: MODELLING ACCURACY ASSESSMENT............................................................................................18FIGURE 22: UN-MODELLED FORESTRY (LEFT) AND SUITABLE MODELLED FORESTRY (RIGHT).....................................18

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Table of TablesTABLE 1: SELECTED CLASSIFIED ATTRIBUTES FOR INSOILS IMAGE...........................................................................6TABLE 2: CLASSIFIED ATTRIBUTES FOR INLANDC IMAGE.......................................................................................7TABLE 3: RECODED VALUES...........................................................................................................................9TABLE 4: INLANDC ATTRIBUTE CATEGORIES....................................................................................................10TABLE 5: FINAL IMAGE CLASSIFICATION.........................................................................................................18

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1. Introduction and Context

1.1 IntroductionThere is an ever increasing demand for meat and dairy products worldwide, perhaps due to more of the world’s population having access to a disposable income, an ever growing population and also an increased meat intake in diets within society. This in turn puts pressure on farmers and agricultural industries to produce increased quantities of sustainable outputs. Meat and dairy production in particular has steadily increased throughout the twentieth century in North America at a rapid industrial rate (Pew Commission on Industrial Farm Animal Production, 2008). For these reasons, it is important for decision makers to monitor and implement the best methods and strategies available to help combat and relieve such issues. Remote sensing is a powerful tool that can be unobtrusively incorporated into real life decision making processes and strategies at global, national and regional scales (Gibson, 2000). Through the use of digital image processing and modelling techniques it is possible to view, interrogate, manipulate and combine various separate criteria into a single suitability model to aid in decision making. This report incorporates several selected criteria and constraints relating to optimal land suitability for agricultural use, and combines them into a single class defined suitability output image, using remote sensing digital image processing and modelling techniques. The report begins with a context section outlining the topic, followed by a detailed methodology section divided into subheadings and sections that explain each fundamental step, supported with screen shots for visual representation. Furthermore, this section will be followed by a results division, detailing the outcome of the processing system. The final segment will comprise of a discussion and conclusion section that further explains and summarises the findings in the context of this report. Detailed instructions of how to operate the model are attached to this report as appendices.

1.2 ContextThe region of interest in this report was the Lake Lanier region of Gainsville Georgia in the United States. Gainsville is a region that is surrounded by rich forestland which has the potential to be exploited for agricultural use. Increasing meat and dairy production in Georgia, would create more employment, generate extra money within local rural communities and allow the region to be more competitive at a national scale (Kane, et al., 2010). The agricultural industry is already worth seventy four billion to its economy with one in seven people working in agriculture, forestry or a related field (Farm Bureau Georgia, 2016).This report aimed to produce a suitability model for areas within the Lanier region of Gainsville Georgia, which were suitable for livestock and dairy farming. The modelling was based on a Landsat TM satellite image of the region. Various digital image processing and modelling techniques were applied under specific criteria and constraints to produce a class defined land suitability image. The criteria and restraints were based on detailed research from dependable sources, so as to limit or eradicate subjectivity where possible.

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2. Methodology

2.1 Selecting Image LayersThe image layers chosen for this report were the Lanier TM Landsat satellite image, the Insoils image and the Inlandc image. The insoils image was a soil image of the same region that had been categorised and colour coded based on individual soil types. The inlandc image was a land use image of the same area that also had been categorised based on the different land uses in the region. The images are represented in Figures 1 and 2 below. The classified attributes for the insoils and inlandc images are shown in Table1 and Table 2 respectively.

Figure 1: Lanier TM Landsat Image

Figure 2: Insoils (left) and Inlandc (right) Images

Table 1: Selected Classified Attributes for Insoils Image

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Table 2: Classified Attributes for Inlandc Image

These images were used as the basis for all of the queries regarding land type and soil quality for the Lake Lanier region, concerning the land suitability model. The attributes for the insoils and inlandc images needed to be further modified so that they were correlated with the criteria and constraints of this modelling report.

2.2 Altering the Primary Image Layers2.2.1 Activating ModelmakerBefore the images were altered, the modelmaker option first had to be activated from the toolbox menu on the main screen within the Erdas toolbar. This opened a new window where the modelmaker procedures were implemented. From within the Tool Palette, new raster layers were added by clicking on the raster icon, followed by the function icon, then an output raster object and finally connected using the connect inputs icon, all are outlined below.

Figure 3: Tool Palette

Figure 4 serves as a simple example of a model and its layout structure containing three inputs, a function and an output image.

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Place Raster Object

Function

Connect Inputs


Figure 4: Simple Model

2.2.2 Recoding the Insoils Image Data In order to incorporate the images in a way that suited the requirements of the model, the attribute data of the inlandc image insoils image needed to be altered. Firstly, the insoils data was divided into several different colour coded categories based on the individual soil types the layer was composed of. For the purpose of this report, these multiple soil types were divided into three categories; excellent, good and fair. This was because the new land to be exploited needed to have fertile soil that could be used for grazing and rearing animals, so poor quality soils were not included. To implement this, the insoils layer was placed in the model three separate times at the beginning, see below.

Figure 5: Beginning of Suitability Model

The data for each image needed to be recoded separately. This was achieved by double clicking the image, clicking the recode data option, followed by the setup recode selection. For the first insoil image, soils classed as excellent were given the

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new value of 3 so that the most suitable areas had the highest values. All other values were classified as 1 and the background and water as zero, as illustrated below.

Table 3: Recoded Values

The same procedure was followed for the remaining two images, however, for the second image the soils classed as good were given the value of 2 and for the third image, the fair classes were assigned values of 1 with the remaining values for both images assigned zeros. This ensured that the areas with the best soil types had the highest class values. Also, the letters RC were assigned to the end of the name of the modified image to indicate that the images had been recoded, as depicted in Figure 6.

Figure 6: Recoded Images

A conditional function was then applied to the images, which is explained in full in section 2.3.

2.2.3 Altering the Inlandc DataAs with the insoils layer, the inlandc layer needed to be altered for the modelling process. This layer had 10 land use categories, and the land use of interest was the deciduous category, see Table 4. This was because deciduous trees offered better soil types than that of the other forested areas, namely pine. Pinewood or conifer trees are said to make the soil more acidic and generally grow in poorer quality soils (Collier &Farrell, 2007). Deciduous make the soil more fertile due to the decomposition of leaves at the surface (Nature's Web, 2007), making them more suitable for pastures for rearing livestock. For these reasons, the areas that consisted of deciduous trees were of interest to the modelling. As illustrated in Table 4, the remaining land use

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categories were not of interest to the model. Although there is an agricultural land section, it was not relevant as the purpose of this model was to locate land that was not being already used for agriculture, and had the potential for agricultural use, thus expand on the areas already used for agriculture.

Table 4: Inlandc Attribute Categories

In order to extract only the areas of deciduous forest, a simple model with an either function from within the conditional setting needed to be carried out, so that it could later be combined with the recoded insoils layers. The function is described below in figure 7. All areas that were of interest (deciduous) to the model were assigned zero (black) and the opposite occurred for areas that were not of interest (white).

Figure 7: Inlandc Either Function

This created an output image that only contained areas that consisted of deciduous forest, and was named Deciduous. See Figure 8 and 9 for the model and output image respectively.

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Figure 8: Deciduous Model

Figure 9: Deciduous Image

2.3 Modelling the Altered LayersIn order to model the altered layers together, they needed to be combined by a function and an output image. This was achieved by inserting a function icon into the model and joining the relevant layers to it, as represented below. The output image was named suitability classes and the areas with the highest degrees of suitability had the highest class values.

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Figure 10: Land Use and Soil Suitability Model

The function used to achieve this was a conditional function and is described below. It stated that each layer in the function had to be deciduous and also had to contain the value of its category in order to be assigned to its class. The excellent soil category served as an example for this explanation. The sn3_Insoils_RC layer (excellent soil), had to be equal to the value of the recoded value (3) and also it had to be deciduous, where the value for deciduous was zero in the newly created deciduous image. This AND function was incorporated from within the Boolean menu. Then after meeting these specific conditions, the output was assigned the third class, the class of highest suitability.

Figure 11: Conditional Function

The output image was a colour classified image illustrated in figure 12. Areas of fair suitability were assigned red, good suitability blue, excellent suitability areas were assigned green and non-applicable (non-deciduous forest, water and background) assigned black.

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Figure 12: Suitability Classes Image

2.4 Overlaying Suitability Classes on Landsat ImageOriginally the suitability classes image was combined with a Landsat image (Lanier) of the region viewed through the near infrared (NIR) range of the electromagnetic spectrum. NIR appears panchromatic, but displays crisp, sharp boundaries between vegetation and water (Gibson & Power, 2000), which aided in clearer representation of the model. This was later changed to combining the suitability classes image with an NDVI Normalised Difference Vegetation Index model in order to produce a clearer well defined output image that better distinguished between topographical/land use features, called second_classes_ndvi_lanier_three_val. An NDVI image is essentially a ratio image, which suppresses the topographical albedo effect, but enhances gradient changes in the spectral reflectivity curves of different materials and can emphasise the differences between them (Gibson & Power, 2000). It is a ratio between NIR and Red bands of the electromagnetic spectrum. Red has low reflectivity with healthy vegetation and NIR has high reflectivity. Therefore a ratio between the two showed areas of high vegetation as bright and bare soil or low vegetation as dark. This was important when analysing the accuracy of the final output classified image, as areas of higher suitability should be in areas with higher densities of vegetation.

2.4.1 Combining NIR Image with Suitability ClassesIn order to complete this process a simple model had to be incorporated into the existing model. It consisted of the original Lanier image, a function and an output image called ir_lanier. See model below.

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Figure 13: NIR and Suitability Classes Model

The function needed for this model was implemented by simply clicking the lanier (4) option from the available inputs section, as this is the NIR band and let the model run.

Figure 14: NIR Function

Then it was simply linked to the existing model using an either function. The function also added a scalar integer tab that was assigned a value of 4. The value of 4 offset the values of the satellite image by 4, which prevented the model from overwriting the lower number class values where the highest value was 3. Also a stretch was employed to make the output image more distinguishable and easier to identify the features. The function is displayed below.

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Figure 15: NIR and Suitability Function

2.4.2 Combining NDVI Model and Existing ModelThis process was undertaken in the same way as the NIR image, however, this time a readily available NDVI model from within the Erdas system was incorporated into the model. The model is outlined below. The NDVI functions were IR+R (bands 3 + 4) and IR – R (bands 3 – 4) and then divide the temporary outputs using the either function evident in Figure 17. The resulting temporary layer was further modified by using the global min function, which computed the minimal values from the image, shown in Figure 18 and produced an output image that could be incorporated into the suitability classes model.

Figure 16: NDVI and Suitability Classes Model

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Figure 17: NDVI Either Function

Figure 18: Global Min Function

The either function implemented to join the two separate models was the same as the preceding NIR function previously explained. However, this time the output Lanier_ndvi image was stretched for display purposes. See below for representation.

Figure 19: NDVI and Suitability Classes Function

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Both the NDVI image and the NIR image are evident below with the overlain land suitability classes.

Figure 20: NDVI and NIR Images

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3. Results

3.1 Modelling ResultsThe final output results from both the NDVI and NIR models showed similar results as evident in Figure 20, however, the NDVI model was deemed to be most suitable for visualisation and accuracy assessment purposes. The classified land suitability areas are clearly visible and easy to interpret. The red areas are areas with fair quality soils and deciduous forest, the blue areas are good quality soils and deciduous forest and the green areas are excellent quality soils and deciduous forest. The excellent soils category was given a class value of 3, which was the highest, meaning it contained areas of highest suitability for agricultural development. The good soils category was given a class value of two, meaning that it contained land and soils of medium suitability. The final category fair, was given a class value of 1, where it was seen as having the lowest degree of suitability out of the three classes. The background and water land uses were given a class value of zero, as they were non-applicable to the modelling and were assigned the colour black. See Table 6 for details.

Table 5: Final Image Classification

3.2 Accuracy AssessmentAs previously stated the accuracy and representation of the NDVI model was deemed to be superior to that of the IR model. The NDVI image was a ratio image between the NIR and Red bands on the multispectral scanner on-board the satellite, and limited the albedo effect and emphasised the difference between spectral differences relating to the land cover, as previously explained in section 2.4. The NDVI image corresponded with the deciduous image, illustrating that deciduous areas were also areas with high reflectivity in the NDVI image. Also all of the land suitability classes appeared in deciduous areas/high reflective NDVI areas, further bolstering the accuracy of the model in relation to the criteria. None of the darker areas in the model contained any of the class suitability areas, as these areas contained low reflectivity values as they most likely contained urban areas, infrastructure, water and also areas already used for agriculture. These areas had exposed soil and no vegetation and were not selected by the model, which suggests that the accuracy of the model was a success. A selection of these features are highlighted in yellow below.

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Figure 21: Modelling Accuracy Assessment

3.3 Impact AssessmentThe impact of redeveloping forest land for agriculture appeared to be less harmful than one might have expected. The region as a whole is predominantly composed of forest land. This forest land is categorised as deciduous, mixed (pine and deciduous) and pine. The findings illustrated that the areas suitable for development in the context of this report were relatively small in size compared to the forested area as a whole. Additionally, there were still large quantities of mixed forest land that contained deciduous forestland, meaning the forest could be sustainably exploited for agriculture, as much of it remained unharmed and particular tree type distinctions (deciduous) remained in other locations within the region. Figure 22 outlines the remaining areas of natural forest that were not incorporated into the modelling (left) compared with the areas that were included in the final model (right). The darker green colour is pine and deciduous mixed and the lighter green is pine. The class defined final image (right) concerns areas that are deciduous only.

Figure 22: Un-modelled Forestry (left) and Suitable Modelled Forestry (right)

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4. Discussion and Conclusion

4.1 DiscussionThe results generated in this study delivered valuable insight into the available land that could be utilised for livestock and dairy farming. The complex analysis provided a high quality assessment of the land use around the region combined with the quality of the underlying soils. The best possible locations were distinctly identifiable and furthermore were clearly categorised in terms of their degree of suitability. As recent studies have identified the growing output from the meat industry has exerted considerable pressure on farmers and decision makers to come up with sustainable solutions (Pew Commission on Industrial Farm Animal Production, 2008), Remote Sensing modelling techniques similar to this report, may prove to be a valuable asset for future operations and strategies.The modelling procedures permitted the user to combine various criteria and constraints from separate images into a single output image. The model only extracted areas that met these particular criteria and restraints, which produced high quality exceptionally specified output imagery. Previous studies outlined the benefits the region would enjoy if it could become more self-sufficient in its farming methods, and how expansion of production could generate local employment and compete at a national scale (Kane, et al., 2010). This study highlighted the potential than the Lake Lanier region possesses for agricultural expansion, without depleting vast areas of forested land. As farming is such a valuable commodity in the area (Farm BureauGeorgia, 2016), remote sensing modelling techniques can be considered as being a valuable asset for unobtrusively providing plausible solutions and outlooks into expansive sustainable agriculture, whilst identifying and protecting the needs of the surrounding people and landscape.

4.2 ConclusionThis report provided valuable insight into the complexity of identifying suitable land that could be utilised for livestock agriculture, but also the complexities and challenges faced as how to best represent the outcomes as an image. Several issues needed to be addressed before the analysis could even begin. Issues such as; recoding data, creating models and combining models and applying functions that extracted and presented the data relative to the study. Several separate analysis techniques were implemented to reduce as much of the inconsistencies as possible and to make the modelling outcomes as plausible as possible. These complex techniques combined, contrasted and compared all the relevant variables as a single, colour coded, class defined land suitability image. For future improvements in the modelling procedure, but outside the scope of this research, a cost analysis of transforming the forested land into agricultural land may have been useful. This could be utilised as to identify whether or not it would be economically viable to undertake such a project, or to identify a timescale as to if and when it may become viable. Conclusively, this research highlighted the importance and flexibility of remote sensing techniques for producing genuine models from real life data. Additionally, these techniques can be incorporated into a diversity of scenarios and have proven to be a valuable research and development tool.

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BibliographyCollier, M. & Farrell, E., 2007. The Environmetal Impact of Platning Broad Leaved Trees on Acid-Sensitive Soils, Dublin: COFORD.

Farm Bureau Georgia, 2016. Agriculture - Georgia's $74 Billion Dollar Industry. [Online] Available at: http://www.gfb.org/aboutus/georgia_agriculture.html[Accessed 28 March 2016].

Gibson, P., 2000. Introductory Remote Sensing principals and Concepts. London: Routledge.

Gibson, P. & Power, C., 2000. Introductory Remote Sensing: Digital Image Processing and Applications. London: Routledge.

Kane, S., Wolfe, K., Jones, M. & McKissic, J., 2010. The Local Food Impact: What if Georgians Ate Georgia Meat and Dairy, Athens: University of Georgia.

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Nature's Web, 2007. Trees - Deciduous and Evergreen. Nature's Web, p. 6.

Pew Commission on Industrial Farm Animal Production, 2008. Putting Meat on the Table: Industrial Farm Animal Production in America, Kansas: Bloomberg School of Public Health.

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5. Appendix I

5.1 Instructions for Model Maker1. Images needed: Lanier.img, Insoils.img, Inlandc.img.2. To implement the model the insoils image needs to be loaded three

times .Then the data needs to be recoded. For the first insoils image all excellent soils are recoded to 3. For all good soils, recoded to 2.Fair soils = 1.

3. To alter the inland data, the image needs to be loaded and an either function needs to be applied EITHER 0 IF ( $n14_lnlandc_RC==1 ) OR 1 OTHERWISE. The output is saved as deciduous. The saved model for this image is called deciduous.gmd.

4. These images are combined using a conditional function CONDITIONAL { ($n5_lnsoils_RC==3 AND $n16_deciduous==0) 3 , ($n2_lnsoils_RC==2 AND $n16_deciduous==0) 2, ($n6_lnsoils_RC==1 AND $n16_deciduous==0) 1, ($n5_lnsoils_RC==0) 0 } and the output was saved as suitability_classes and the model as suitability_classes.gmd.

5. Open the veg_NDVI model (veg_NDVI.gmd) from the available models within ERDAS. Copy and paste the model into the suitability classes model.

6. Place scaler output in the model and assign a value of 4.7. Link the two models with a conditional function EITHER

$n8_suitability_classes IF ( $n8_suitability_classes ) OR STRETCH ( $n25_lanier_ndvi , 2 , 0 , 250 ) + $n12_Integer OTHERWISE (Figure 19).

8. Output – second_classes_ndvi_lanier_three_val.9. Model saved as second_classes_onndvi_lanier_three_val.

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