Remote Sensing of Wooded Species Prior to their Removal for Watershed Remediation

Remote Sensing of Wooded Species Prior to their Removal for Watershed Remediation Daniel M. Lupton Remote Sensing Fall 2008 Term Project December 8, 2008 1

Abstract Juniperus ashei (Ashe Juniper) has overtaken the Central Texas Hill Country. It has been theorized that this particular plant can have a significant impact on the water resources in an area. In an attempt to transition their property back into a sustainable ecosystem, researchers at the C.L. Browning have initiated an Ashe Juniper removal program with the overall goal of understanding the impacts on groundwater and surface water resources for the ranch. The goal of this remote sensing project is to assess the amount of infestation with respect to wooded species on the ranch. TNRIS DOQQ s in combination with aerial photography was processed using ENVI v. 9.4 and Arc GIS v. 9.3. Three different techniques were used and one was selected for its most appealing results. The resulting data was processed in Arc GIS and an overall wooded to non wooded species ratio was derived for each watershed. Results were encouraging for this stage in the process. Issues with the analysis centered on the inability to spectrally differentiate between Ashe Juniper and Live Oak species of wooded plants. Methods for continued research, specifically differentiating between Ashe Juniper and Live Oak, will be discussed. Introduction The continued spread of wooded plant species, specifically Juniperus ashei (Ashe Juniper), across central Texas is of increasing concern to land owners with respect to hydrologic resources. Ashe Juniper in central Texas grows mainly on karstified limestone terrain with shallow soil horizons (Wilcox and others, 2006). The wooded plant is thought to consume an inordinate amount of water from its resident ecosystem. The Trinity Hill Country Aquifer underlies most of central Texas. The topmost layer of this aquifer system is the Glen Rose Limestone which has been observed to act as a karstified limestone layer. The study area for the project is the C.L. Browning Ranch which is approximately 3 miles North of Johnson City, Texas (Fig. 1). The Browning Ranch in Johnson City, Texas is a ranch dedicated to developing a methodology for analyzing scientific information in a manner that provides a useful base of knowledge from which to build increasingly competent ways of stewarding land not only in Texas, but elsewhere as well (C. L. Browning Ranch Homepage). The Browning Ranch displays characteristic central Texas Geology including karstified Glen Rose limestone with shallow soil horizons and moderate to high relief topography. The ranch is underlain by the Glen Rose Limestone on the majority of the upland recharge area (Fig. 2). In regards to Ashe Juniper, a study is being conducted where the invasive species (mainly Ashe Juniper) will be taken off of 2 of the 4 watersheds on the ranch. With the use of water level monitoring equipment, a full hydrologic characterization will take place before, during, and after the removal of Ashe Jumiper from the watershed. This level of monitoring will allow researchers to make quantitative evaluations of the change in the water budget for the ranch as a function of invasive species removal. A project of this caliber has yet to be executed and, if done according to plan, the project will have far reaching effects on how invasive species are dealt with in central Texas and other areas. Land owners in central Texas are ever increasingly removing Ashe Juniper from their property. Accompanying the Asher Juniper removal has, in some cases, been an increase in 2

spring-flow (Bamburger Ranch website) which would lead one to believe that the removal of invasive species like Ashe Juniper is a key to increasing recharge to the underlying groundwater systems. Drawing these conclusions incorporates a series of assumptions that could potentially nullify the current hypothesis that talking out all of the phreatophytes will automatically increase recharge to the underlying groundwater system. Wilcox and others (2006) have concluded that it is important to factor in the scale of the remediation project before trying to predict resulting increases to the water budget. In regards to this study, the scale of study will be called Small catchment scale with springs Wilcox and others (2006). Wright (1996) worked on a 3 hectare watershed in Seco Creek Watershed of central Texas and measured an increase in springflow from.007 cfs for the 2year pre-treatment period to.0084 cfs following the partial removal of Ashe Juniper. Although the Browning Ranch is much larger than 3 ha (approx 365 ha), the scale should be appropriate in order to hypothesize changes in springflow as a result of removing the Ashe Juniper. It is important to have a viable mechanism for assessing the amount of infested area of Ashe Juniper on the ranch. Past ways of assessing infested area include: 1) GPSing the outline of the of the infested areas and subsequently creating polygons in an Arc GIS environment and 2) tracing the outline of infested areas in an Arc GIS environment and subsequently creating polygons of the infested areas. Both methods were time consuming and could potentially be done with a higher level of accuracy. The aim of this research is to perform an image analysis with the goal of separating out Ashe Juniper using readily available Digital Orthophoto Quadrangle (DOQQ) images of the area taken from the Texas Natural Resource Information System (TNRIS) Homepage: http://www.tnris.state.tx.us/stratmap.aspx?layer=126. The DOQQs are three band images (Band 1= Near Infrared, Band 2= Red, and Band 3= Green). Ashe Juniper can be spectrally distinguished from other wooded species during the spring and summer by using the near-infrared band. The internal and external structure of plants and its interaction with the electromagnetic spectrum during photosynthesis is what gives plants their spectral characteristics (Jenson, 2007). Green plants such as Ashe Juniper absorb most of their energy from the visible light portion of the electromagnetic spectrum (.35-.75µm) because the incident blue and red light is used for photosynthesis. The 0.54 µm (green) portion of the electromagnetic spectrum is where there is an absorption trough and corresponding reflectance peak (Jenson, 2007). This natural phenomenon is the reason that humans perceive certain types of vegetation as green. In regards to the near-infrared portion of the electromagnetic spectrum (which is thought to be characteristically unique for the Ashe Juniper in certain times of the year (Everitt and others 2006)), plants cannot absorb this portion with the same vigor that they do the visible portion of the spectrum. Therefore, the plants will reflect the near-infrared energy back into space. Reflection of the energy will be characteristic for each species of plant. The goal of this study is to find and extract that unique signature provided by the Ashe Juniper and use it to evaluate Ashe Juniper infestation on the ranch s watersheds. Methodology Four Digital Orthophoto Quarter Quadrangles(DOQQ) were downloaded from the TNRIS website (http://www.tnris.state.tx.us/stratmap.aspx?layer=126 ). A digital orthophoto is a raster image of remotely sensed data in which displacement in the image due to sensor 3

orientation and terrain relief have been removed (DOQQ metadata file). Images used were sensed using a 3 band array: B1- Near Infrared, B2- Red, and B3-Green. The dates for the creation of the DOQQ s are as follows: 01/09/1995, 07/07/2004, 09/17/2005, and 12/18/2007. Restrictions arose on ability to use all of the images because of ideal timeframe for distinguishing Ashe Juniper from Live Oak. Therefore, due to this limitation and others imposed by the quality of the DOQQs, only one image was able to be used for this particular study: 07/07/2004 (Fig. 3). The DOQQ was subsequently loaded into ENVI v. 4.5 for image processing. The first image analysis technique performed was a Normalized Differential Vegetation Index (NDVI) analysis. A band math equation was set up as follows: (NIR-RED)/(NIR+RED). By using this equation, most of the inorganic pixels were excluded (Fig. 4). Once the NDVI was complete, a mask was made for all pixels with a non negative values ie; organic pixels. The mask was used for all further image processing of the DOQQ. The first of three methods attempted to make the best representation of the Ashe Juniper on the ranch was a Band Threshold to ROI. The tool allows the user to set a threshold for the pixel values of the NDVI image. The general trend is that wooded species will have a higher pixel value than grasslands or other vegetation because of their higher reflectance values. Therefore, after experimenting with a multitude of threshold values, a value of 0.19 was settled on and the image showed agreement with the aerial photo (Fig. 5). This value represents the threshold between the grassy and the wooded vegetation. The second of the three methods attempted was an Iso Unsupervised Classification scheme. The classification was applied using the NDVI as a mask. The classification limit was set to categorize 5 classes. After the classification was complete GPSed locations of real world Ashe Juniper and Live Oak trees were used to analyze results (Fig. 6). The third and final technique used to try and remotely sense Ashe Juniper was a Parallel Piped Supervised Classification. Known real world coordinated for Ashe Juniper were made into ROIs (Regions of Interest). Subsequently, the analysis ran in an attempt to try and categorize all like values to the ones contained within the user made ROI. The results of this analysis are shown in Fig.7. Upon qualitatively comparing the three methods of classification it was concluded that the most ideal was the Band Threshold to ROI. All following analyses were performed using Arc GIS v. 9.3. Evaluation involved juxtaposing the ranch aerial image and the band threshold to ROI and superimposing the watershed on top of both images (Fig. 8). Next, a model was made in Arc GIS that would process all of the images and extract raster data using each watershed as a mask (Fig.9). Once the model ran, statistics on all band threshold to ROI images were run so that a quantitative estimate of infested area as a % of the whole watershed could be analyzed (Fig. 10). Results Of all three analysis techniques used, the most appealing was the Band threshold to ROI (Fig. 5). The other two techniques have significant potential to provide good data but, there was insufficient information for the input. This point will be further addressed in the Discussion 4

portion of the paper. By visually comparing the processed image to the aerial photo of the ranch, it is obvious that the band threshold used was adequate to represent the Ashe Juniper on the ranch. Unfortunately, there is no distinguishing between the Ashe Juniper and Live Oak. Attempts to correct this issue centered on adjusting the band threshold value but, the values for the two wooded species were not spectrally unique enough to segregate them at this point in the project. This leads to the conclusion that, when using the above band threshold value, it is for all wooded vegetation. This only poses minor issues for the resulting analyses of this portion of the project because Ashe Juniper predominates on the upland reaches of the ranch. Therefore, most imaging of the infested areas on the watersheds should represent real world trends of Ashe Juniper density. Results from clipping the band threshold and aerial photo to the watershed boundaries were visually appealing. Upon looking at the band threshold to ROI watershed image compared to the aerial image, it is shown that there is substantial agreement between the two with regards to imaging wooded species (Fig 10a and b). Once the new raster images were made for each watershed, a % of infested area was calculated by simply looking at the attribute table of the raster image and finding the ratio in % of black pixels to green pixels. Although this was a simple technique, this method could provide significant data in support of looking at watershed change through time as the Ashe Juniper is removed and the watersheds are converted into grasslands. Discussion This analysis technique has provided a quick and accurate way to image wooded species using a TNRIS derived DOQQ. By using this technique, an accurate quantification of % of wooded species canopy compared to the entire watershed was derived. The resulting ratios of the pixels representing wooded species, mainly Ashe Juniper, appear to be a good representation of not only the ranch s watersheds but also the general trends in Central Texas. Further analyses of different watersheds in the area would have to be conducted to validate this hypothesis. TNRIS will continue to take DOQQs of the area at regular intervals. As these images come in, the ongoing removal of Ashe Juniper can be visually and quantitatively represented using the technique presented in this paper. Additionally, with continued research into further spectrally segregating wooded species, the analysis technique will continue to be refined. In the beginning of this project, the main focus of the paper was to try and download a series of DOQQs from the TNRIS website and process them with the goal of separating out the Ashe Juniper and look at change through time. In an attempt to follow Everitt and other 2006, the images would have to have been attained in spring or summer. This is due to the unique spectral reflectance values in the Near Infrared portion of the electromagnetic spectrum exhibited by the Ashe Juniper. Ideally, by processing the applicable DOQQs with the aforementioned consideration, the result would be a 0,1 raster image of the ranch with Ashe Juniper represented by the value of 1 and everything else represented by the value of 0. Unfortunately, there were a number of issues that thwarted the previously mentioned processing steps; only one DOQQ fit into the ideal timeframe, the pixel values were coded and a mechanism for transforming the 5

values into spectral reflectance values was not recognized, and the pixels representing Ashe Juniper were not spectrally unique enough to differentiate them from other wooded species. Given these constraints, further analysis techniques need to be perused in order to maximize the potential to remotely sense Ashe Juniper. The first technique would be to go to the Browning Ranch and take 100 GPS points; 50 Ashe Juniper and 50 Live Oak. The trees would ideally be mature so that they can be identified as far back as possible on the DOQQs. These GPSed points would be loaded into ENVI and a series of ROI s would be made of 50 of the data points (25 Ashe Juniper and 25 Live Oak). Subsequently, the ROIs would be loaded into a Parallel Piped Supervised Classification and the image would be processed for the two different pixel value arrays created by the two different groups of ROIs. The resulting data would then be checked for accuracy using the remaining 50 GPS locations (25 Ashe Juniper and 25 Live Oak). An error matrix would be set up like the one shown in Fig 11. and an overall quantitative measurement of accuracy would be derived. The second technique would involve using the spectroradiometer, owned by the Laboratory for Remote Sensing and Geoinformatics, to derive reflectance values for Ashe Junipers and Live Oaks on the ranch. The process would involve going out to the ranch x2 each month and using the instrument to sense reflectance values for Ashe Juniper trees and Live Oak Trees. The reflectance values for the year would be compiled with highly accurate weather data taken from the weather station on the ranch. By culminating all of this data, a chart could be made that would show the spectral signatures for Ashe Juniper and Live Oak as a function of time of year and weather conditions. This would be a significant contribution because it would provide values for remotely sensing Ashe Juniper regardless of the time of the year. DOQQ s could then be used regardless of the time of year they were created. With the removal of Ashe Juniper as important of a topic as it is today, this research could have far reaching ramifications. 6

References Afinowicz, J. D., Munster, C. L., Wilcox, B.P., and Lacey, R.E. 2005. A Process for Assessing Wooded Plant Cover by Remote Sensing. Range ecology and Management 58(2). Bamberger Ranch Homepage [Internet]. Blanco County, Texas. (Cited 2008 Dec 5] Available from: http://www.bambergerranch.org/ C. L. Browning Ranch Homepage [Internet]. Johnson City, TX. [Cited 2008 Oct 31] Available from: http://clbrowningranch.org/index.php?t=mission Everitt, J.H., Yang, Johnsom, H.B. Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation. 2006. Environ. Monit. Assess. 130:403-413. Jenson, J. R. 2007. Remote Sensing of the Planet: An Earth Resource Perspective. Upper Saddle River, NJ: Pearson Education, Inc. 592p. Texas Natural Resource Information System (TNRIS) Homepage [Internet] Austin, TX [Cited 2008 Oct 31] Available from: http://www.tnris.state.tx.us/stratmap.aspx?layer=126 Wilcox, B.P., Owens, M.K., Williams, D.A., Ueckert, D.N., Hart, C.R. Shrubs, Streamflow, and the Paradox of Scale. 2006. Hydrol. Process 20. 3245-3259. Wright PN. 1996. Spring enhancement of the Sego Creek water quality demonstration project. Seco Creek Water Quality Water Demonstration Project. U.S.D.A. Natural Resource Conservation Service: Temple, Texas. 7

Figures Johnson City Fig.1 The C.L. Browning Ranch lies in the southern portion of the Pedernale s River watershe d approxim ately 3 miles north of Johnson City in Blanco County. 8

Fig.2 Geologic Map of the Browning Ranch. Of note is that all light blue in the upper reaches of the ranch represents the Glen Rose Limestone. The Hensel Sandstone (Green) represents a sandstone that has been cemented with a carbonate derived cement. Other layers on the map are carbonate units with potential karstic qualities but, none are as inundated with Ashe Juniper as the Glen Rose and Hensel layers. Fig. 3 Digital Orthophoto Quadrangle (DOQ) image of the C. L. Browning Ranch. The image was created on 7/7/2004. 9

Fig. 4 NDVI analysis performed on the DOQQ image of the ranch (Fig. 3). Black values represent inorganic pixels such as roads, buildings, rock terrain, etc. This image was made into a mask that allowed all further image analyses to be performed only on the pixels that are represented by organic matter. Fig. 5 Band Threshold to ROI analysis performed on the NDVI image of the ranch (Fig. 4). The teal values represent pixels that are higher than 0.19 and are assumed to be wooded vegetation. 10

Fig. 6 Check of Iso Unsupervised classification. Live Oak and Ashe Juniper trees were GPS at the ranch and classification techniques were checked against real world features. Different colors for the classification represent different categories created by the classification. 11

Fig. 7 Parallel piped classification showing all values categorized as Ashe Juniper by the classification scheme. 12

Fig. 8 Qualitative analysis of NDVI band threshold to ROI compared to aerial image of the ranch. Visual inspection focused around known Ashe Juniper clumps and degree to which the NDVI band threshold to ROI represented the Ashe Juniper. 13

Fig. 9 Arc GIS model used to extract raster data for each of the watersheds. 14

Fig 10a. Image on the left is the aerial image while the image on the right represents all wooded vegetation as was derived from the band threshold to ROI analysis using the NDVI as the base image. Percentage of green to black represents the ratio of pixels represented by wooded species to all other pixels. As is common in the Texas Hill Country, wooded vegetation, mainly Ashe Juniper, represents a significant portion of the pixel values making up the watershed image. 15

Fig 10b. Image on the left is the aerial image while the image on the right represents all wooded vegetation as was derived from the band threshold to ROI analysis using the NDVI as the base image. Percentage of green to black represents the ratio of pixels represented by wooded species to all other pixels. As is common in the Texas Hill Country, wooded vegetation, mainly Ashe Juniper, represents a significant portion of the pixel values making up the watershed image. 16

Fig. 11 Error matrix (taken from Everitt and others 2006) that can be used as an example for future error matrices created in support of quantitative evaluation of accuracy of Ashe Juniper imaging. 17