APUNTES SOBRE LOS REFERENTES TEÓRICO-CONCEPTUALES

The first step of the process is to obtain pre- and post-diversion Landsat images to delineate the area affected by the freshwater diversion. Once the study area is defined, it can be categorized into wetlands and “other terrain,” which includes open water,

developed land, or any landform type other than a wetland. By examining satellite images that were taken over a specific time interval and comparing the area of wetlands in each of them, it should be possible to determine whether the wetlands are increasing or decreasing, and by how much.

As long as there is a rigorous control baseline defined for the area under study that can be applied across all of the images, it should be possible to resolve any change the

freshwater diversion is causing in the wetlands areas. Ideally, at the end of the study we will have some precise quantifiable values for wetland changes to the areas of concern. Since we know the total cost of the Caernarvon structure, we will be able to calculate the cost of wetland restoration per acre. In that manner, we will have a sound methodology to make determinations as to the efficacy of the diversion structures, and we will be able to compare that to other wetland restoration processes being used or proposed for the Mississippi Delta.

In order to determine the effectiveness of the diversion structures as a method of

addressing wetland loss, this research uses satellite images to measure any gain or loss of wetlands in the areas directly impacted by these diversion structures. Only the

enough (16 years) to monitor its effect on wetlands, so this was used to measure what, if any, effect diversion has had on wetland loss.

Two study areas are used, one that includes the entire Breton Sound, approximately 10.5 km2 (Figure 8), and another much smaller area directly below the diversion structure, approximately 3 km2 (Figure 9). The two areas are used because in the course of the project it became clear that the further reaches of Breton Sound were not noticeably affected by the freshwater diversion. Reducing the area of study would hopefully measure changes that were a direct result of the diversion.

Figure 9. Area close to freshwater discharge (USGS 2002).

Six satellite images were used, one Multispectral Scanner (MMS) image from 1974, and five Thematic Mapper (TM) images from 1983, 1988, 1991, 2002, and 2006. The images chosen were all taken during the fall/winter season in an attempt to reduce seasonal variation between images. Images with adverse atmospheric effects were avoided, and only cloud-free images were used. However, some variation in quality among the images is inevitable.

The Multispectral Scanner and the Thematic Mapper are two instruments used aboard the early NASA remote sensing satellites. The Multispectral Scanner was the older of the two, and its spectral range covered the entire spectra from green through red to infrared. The Thematic Mapper narrowed the bandwidth it received to only those spectra that experience showed were less affected by the atmosphere. This improved researcher’s ability to focus on actual ground radiance and reduce atmospheric interference. The following is a list of the spectral bands used by the two satellite platforms and their wavelengths (Table 2):

Thematic Mapper Multispectral Scanner

Band Wavelength Common name Band Wavelength Common name 3 0.63 – 0.69 μm Red (R) 5 0.60 – 0.70 μm Red (R)

4 0.76 – 0.90 μm Near-IR (NIR) 6 0.70 – 0.80 μm Near-IR (NIR) 5 1.55 – 1.75 μm Mid-IR (MIR) 7 0.80 – 1.10 μm Mid-IR (MIR)

Table 2. Satellite sensor bandwidths.

Seven spectral enhancement procedures were used (Table 3):

Index Formula Normalized Diff Vegetation Index (NDVI): (Band 4 – Band 3) / (Band 4 + Band 3)

Transfomed NDVI (T-NDVI): Sqrt((Band 4 – Band 3 / Band 4 + Band 3) + 0.5) Vegetation Index (VI): Band 4 – Band 3

Infrared / Red (NIR/R): Band 4 / Band 3 Square of Infrared / Red (SqrtNIR/R): Sqrt(Band 4 / Band 3)

Normalized Diff Water Index (NDWI): (Band 3 – Band 5) / (Band 3 + Band 5) Principal Component Analysis (PCA) (Bands determined by analysis)

Table 3. Spectral bands used by studied indicies (continued).

The Normalized Difference Vegetation Index (NDVI) was developed in the early days of the remote sensing satellite programs, particularly from data collected by the Earth Resources Technology Satellite (ERTS, which would eventually be renamed Landsat-1). Differences in solar zenith angle across the large swath image captured with each orbital pass were making it difficult to quantify rangeland vegetation. By taking the ratio of the differences between the red and infrared wavelengths over their sum, they were able to “normalize” these solar zenith angle effects (Rouse et al. 1973). It was an effective technique that was soon widely applied in many vegetation classification efforts. The Transformed NDVI (T-NDVI) is a slight variation on the standard NDVI. Taking the square-root variation of the difference-sum ratio will sharpen the contrast between vegetation and non-vegetation in some images.

In addition to the indices discussed above, Vegetation Index (VI), NIR/Red, and Sqrt NIR/IR (Table 3) were also used in this study. While they are very simple indices, they nonetheless were very useful when used to determine biomass by measuring green leaf area (Tucker 1979). When used to separate vegetation from water, the high reflectivity of leaf biomass in the NIR and Red wavelengths is balanced by their near total absorption by water.

The Normalized Difference Water Index (NDWI) index uses the same formula as the NDVI, but substitutes TM Band 3 (0.63 – 0.69 μm) for TM Band 4 (0.76 – 0.90 μm) and TM Band 5 (1.55 – 1.75 μm) for TM Band 3 (0.63 – 0.69 μm). This index was chosen because it has been demonstrated to be useful in the delineation of water from other landforms, including vegetation (Gao, 1996).

There are literally hundreds of indices in use today, but these six were chosen because of their potential to measure wetland change. All of the indices but one use Landsat Bands 3 and 4 (0.63 – 0.69 μm and 0.76 – 0.90 μm, respectively). These bands are ideal because they display a large contrast between water and vegetation as the result of high IR absorption of water and high NIR reflectance by the vegetation. Vegetation absorbs energy in the red wavelengths (TM Band 3), but its reflectance in the NIR wavelengths (TM Band 4) is high.

In addition to vegetation indices discussed above, Principal Component Analysis (PCA) was also used in the data analysis. PCA attempts to enhance the original data by

condensing redundant components within correlated variables into a reduced set of uncorrelated variables, called principal components. These principal components are then used to differentiate between water and vegetation.

While each of the procedures returns an image in which the water and vegetation

procedure is more accurate, and perhaps even impossible. The images were taken under different conditions over a period of fifteen years, and some procedures return better results than others given the conditions of that particular day. While the images were chosen to reduce as many differences as possible, variation is inevitable.

By using seven different procedures to classify the images, the researcher intended that some differences between images will be averaged out and allow an overall trend to emerge. While not strictly a meta-analysis, as the different factors are not weighted according to their confidence level, the procedure does allow a trend to emerge, especially when taking the relatively long timeline (15 years) into consideration.

After obtaining the spectrally enhanced images, they were classified as water or vegetation using an unsupervised classification procedure. The amount of water and vegetation in the area of interest (AOI) of each image was determined and its percent of the total image calculated. By comparing the percentage of vegetation to open water in each image, it was possible to determine what effect the diversion structure has had on the wetland vegetation (Figure 10 and Figure 11).

Amount of vegetation compared to water is a very rough approximation of wetland extent, but will function well enough for the purposes of this research. While it would be desirable to measure many more aspects of the environment, such as specific plant species and their distribution, as well as amount of bare soil, human impacts, etc, the spatial resolution restrictions of using Thematic Mapper (30m) and Multispectral Scanner (80m) preclude such detailed investigations. On the other hand, these images have the decided advantage of being historical records of long-term changes in the wetlands, and change is exactly what this research seeks to determine.