Remote Sensing s1

Remote Sensing:

VEGETATION INDICES

Karen Marais Student Number : 2657211 September 2006

The aim of this assignment is to evaluate all the different vegetation indices available in IDRISI using one set of images and to find out which of the models works best for these specific images. Once an index has been chosen, the resulting images from two different dates will then be compared for any change in the vegetation.

LITERATURE REVIEW:

The vegetation indices (VI) available in IDRISI can be divided into three groups: the Slope-based VI’s, the Distance-based VI’s and the Orthogonal Transformation VI’s. I will only look at the Slope-based and Distance-based Vegetation Indices as the data available does not allow for Orthogonal Transformation VI’s as only the red and near-infrared bands are available.

1. SLOPE-BASED VEGETATION INDICES:

In IDRISI there are 7 slope-based vegetation indices available. The simplest of the slope-based indices is the Simple Ratio Vegetation Index (Ratio), which was proposed by Rouse et al. (1974).

RATIO = NIR / RED (where NIR = near-infrared band and RED = red band)

This index simply divides the reflectance values in the near infrared band by those in the red band. As green vegetation absorbs red light (low reflectance value) but strongly scatters or reflects light from the near infrared band (high reflectance value), high index values would indicate high vegetation biomass. This index has several flaws, one being division by zero errors. Rouse et al. (1974) then also proposed the Normalized Difference Vegetation Index (NDVI), to make up for the errors in RATIO.

NDVI = (NIR-RED) / (NIR+RED) This vegetation index is the most commonly used index. Calculations per pixel always result in a range between -1 and +1, where “zero” indicates the approximate value of no vegetation, +1 the densest possible vegetation cover and negative values indicate bare soil or rocks.

The Transformed Vegetation Index (TVI) was introduced by Deering et al. (1975). They added a constant of 0.5 to all the values and then taking the square root of the results. This was done to reduce the probability of getting negative values and the square root was intended to “correct” the NDVI’s approximate Poisson distribution to a normal distribution. (This would mean however that the minimum input NDVI values need to be more than -0.5, otherwise negative values would remain.) This equation does however not enhance the vegetation detection ability, compared to the NDVI.

TVI = √(NIR-RED) / (NIR+RED) +0.5

Perry and Lautenschlager (1984) then proposed the Corrected Transformed Vegetation Index (CTVI) to correct for the possibility of negative values in the TVI.

CTVI = NDVI+0.5 / absolute(NDVI + 0.5) x √absolute(NDVI + 0.5)

According to Thiam (1997) this equation could however overestimated the greenness. He came up with the Thiam’s Transformed Vegetation Index (TTVI) which simply takes the square root of absolute value of the NDVI + 0.5.

TTVI = √absolute(NDVI + 0.5)

The Ratio Vegetation Index (RVI) is the reverse of the RATIO and was suggested by Richardson and Wiegand (1977).

RVI = RED / NIR

Lastly, Baret and Guyot (1991) suggested the Normalized Ratio Vegetation Index (NRVI). This equation is supposed to be similar in effect to the NDVI, in that it creates a normal distribution and also reduces topographic, illumination and atmospheric effects.

NRVI = (RVI – 1) / (RVI + 1)

2. DISTANCE-BASED VEGETATION INDICES:

This group of vegetation indices was developed specifically for arid and semi-arid environments. Here vegetation is sparse and many pixels would contain a mixture of green vegetation and soil background. The main aim of this group of vegetation indices is thus to reduce the effect of soil brightness in areas of sparse vegetation. All indices in this group are derived from the Perpendicular Vegetation Index (PVI) (Richardson and Wiegand 1977). A soil line is produces through linear regression of the near-infrared band and the red band for a sample of bare soil pixels. First of all bare soil pixels needs to be identified, so that a mask image can be produced, where 1 = bare soil and 0 = other. Bare soil pixels can be identified on an image by on-screen digitization, if one knows the area well. Otherwise one can use a NDVI image, assuming everything including and below zero is soil and everything above zero is vegetation (see Image 1 below for soil line).

IMAGE 1 Example of a soil line calculated through linear regression using the red band as dependent variable (Y) and the near-infrared band as the independent variable (X). y = a + bx , where a = intercept and b = slope. Pixels close to the soil line are assumed to be soil and pixels far from the soil line vegetation (or water).

Once a soil line has been produced the PVI uses the perpendicular distance from a pixel to the soil line as the vegetation index values. The further the distance, the higher the vegetation index value. The PVI however does not distinguish between pixels falling to the right and left of the soil line. Thus a pixels falling far from the soil line on either side will be assigned a high vegetation index value, resulting in water being assigned high vegetation index values too.

PVI = √(Rgg5 –Rp5)² + (Rgg7 –Rp7)² (where Rgg5 is the Y position (RED) and Rgg7 the X position (NIR) on the soil line and Rp5 the Y position and Rp7 the X position of the pixel from which the perpendicular distance to the soil line is calculated) Three alternate PVI’s were suggested to improve the PVI. PVI1 (Perry and Lautenschlager 1984) assigns negative values to pixels lying to the left of the soil line and thus distinguishing water from vegetation. PVI2 (Bannari et al. 1996) weights the red band with the intercept of the soil line and PVI3 that multiplies the intercept with the near-infrared band and then subtracts the red band multiplied by the slope.

The Difference Vegetation Index (DVI) (Richardson and Wiegand 1977) multiplies the slope with the near-infrared band and then subtracts the red band. Like the PVI1 it results in positive values indicating vegetation, zero bare soil and negative values water.

The Ashburn Vegetation Index (AVI) (Ashburn 1978) simply subtracts the red band from the near-infrared band.

The Soil Adjusted Vegetation Index (SAVI) (Huete 1988) incorporates a constant soil adjustment factor “L”, which varies depending on the vegetation density.

There are two Transformed Soil-Adjusted vegetation Indices, which were proposed because SAVI is only accurate if the intercept is zero and the slope one. TSAVI1 is supposed to be a good candidate for the use in semi-arid regions as it was specifically designed for such regions. TSAVI2 has a correction factor to counter for background soil brightness.

The Modified Soil-Adjusted Vegetation Indices use a modified L factor. MSAVI1 is used for semi-arid regions and MSAVI2 is used for areas with a high vegetation density.

Lastly the Weighted Difference Vegetation Index (WDVI) weights the red band with the slope of the soil line, which maximizes the vegetation signal in the near- infrared band and minimizes the soil brightness.

STUDY AREA:

The data used for this assignment are images from a section of the Richtersveld (Northern Cape) from two bands from the Landsat 5 satellite, namely the red band (band 3) and the near-infrared band (band 4). They were obtained from the Satellite Application Centre. Date: April and September 2004 Reference System: UTM Reference Units: meters Minimum X: 673805 Maximum X: 742875 Minimum Y: 6791745 Maximum Y : 6898935 Resolution :30m RESULTS :

1. SLOPE-BASED INDICES

TABLE 1 SLOPE-BASED VEGETATION INDICES, indicating minimum, maximum values, the mean and the standard deviation, as well as the direction of the index, i.e. if a higher value means more or less vegetation. The results are only for the April 2004 images, using the red band (band 3) and the near-infrared band (band 4) of Landsat5 from a section of the Richtersveld, Northern Cape.

Vegetatio Display Display mean Standard direction n index minimum maximum deviation Ratio 0 1E36 up NDVI -1 +1 0.0168 0.0731 up TVI -1 1.2247 0.7173 0.0532 up CTVI -0.7071 1.2247 0.7173 0.0511 up TTVI 0 1.2247 0.7174 0.048 up RVI 0 50 0.8377 0.471 down NRVI -1 0.9608 -0.1547 0.343 down

TABLE 2 SUMMARY OF RESULTS FROM A PCA OF ALL THE SLOPE- BASED VEGETATION INDICES AVAILABLE IN IDRISI. (using absolute values) Component One indicates indices that performed well in general, picking up greenness and soil brightness. Component Two indicate indices that correct for soil background and Component Three describes soil moisture. Thiam and Amadou (1997).

Component One Component Two Component Three CTVI 0.965750 Ratio 0.983360 TVI 0.169059 NDVI 0.963359 TTVI 0.952255 TVI 0.955136

LOADING C 1 C 2 C 3 C 4 C 5 C 6 C 7

ratio_apr_2004_stretch -0.165436 0.983360 0.013798 0.071359 -0.011822 0.006847 0.012703 ndvi_apr_2004_stretch -0.963359 -0.238252 -0.091477 0.045949 0.067305 -0.012625 0.002130 tvi_apr_2004_stretch -0.955136 -0.240036 0.169059 0.010400 -0.019852 -0.031779 0.000695 ctvi_apr_2004_stretch -0.965750 -0.243101 0.075457 0.016642 0.002256 0.047408 -0.002628 ttvi_apr_2004_stretch -0.952255 -0.255764 -0.153500 -0.031578 -0.056851 -0.002054 0.002291 rvi_apr_2004_stretch 0.525757 -0.838356 -0.018312 0.141023 -0.022711 -0.000047 -0.000549 nrvi_apr_2004_stretch 0.358504 -0.930706 0.033362 -0.061748 0.010891 0.006887 0.013249 IMAGE 2 and 3. CTVI and NDVI vegetation index images using band three and band four of the April 2004 Landsat5 images of a section of the Richtersveld, Northern Cape. Images were firstly converted to byte binary by using a linear stretch and then a histogram equalization was applied, to get a visually better image.

From the slope based vegetation indices, it was difficult to decide which one performed the best for the study area as the Principal Component Analysis gave negative values for Component One for the indices that performed best visually. I therefore used the absolute values in the loadings table from the PCA results to evaluate which of the indices performed best. According to the loadings for Component One, the CTVI had the highest loading, closely followed by the NDVI and then the TTVI and TVI. The indices that have a reversed value for vegetation (lower values for vegetation and higher values for soil) fared best for Component Two, with the RATIO coming out tops and for Component Three it was the TVI that picked up moisture the best.

Because the majority of pixels were clustered around the mean with a few far outliers, the images had to be stretched to get a visually better image. As IDRISI cannot use real or integer values to produce an image with histogram equalization or a linear stretch with saturation, I first had to convert the image data to byte binary, using the simple linear stretch module. I then used the histogram equalization on the new stretched image. Images 2 and 3 are examples of the visually improved images. They both show up the agricultural area very well as well as the river and the mountains. This was confirmed as being fairly accurate by comparing Google Earth images of the same area. Click on Richtersveld.kmz below to take you to the Google Earth Image:

( Richtersveld.kmz )

I then did a simple change analysis for two of the indices. I simply subtracted the September image from the April image for each of the two indices, namely the CTVI and the NDVI. I then reclassed the new images and gave the value of one to negative change (more than 2 times the standard deviation to the left of the mean), two to no change (every pixel that falls within two times the standard deviation) and the value three to a positive change. See Table 3 for results. A percentage value for each class was obtained through the Histo module in IDRISI, choosing a numeric output.

TABLE 3 Change in vegetation cover detected between seasons using the two slope-based vegetation indices CTVI and NDVI. The April image was subtracted from the September image and the resulting images reclassed. Percentage values were obtained by using the module Histo and choosing the numeric output. Only a change greater than two times the standard deviation was considered as significant.

Vegetation index Percentage Percentage no Percentage negative change change positive change CTVI 0.55 97.67 1.78 NDVI 0.55 96.89 2.56

2. DISTANCE-BASED INDICES

One would expect the distance based vegetation indices to produce better results as the study area is a very arid region and soil brightness would presumably be a big factor.

I used the image below (IMAGE 4) to identify soil pixels in order to calculate the soil line for the distance based VI’s. I reclassed soil and rock as “one” and the rest as “zero” and used that image as my soil mask image. The soil line was calculated twice, once using the red band as the independent variable (intercept = -4.870582 ; slope = 0.988882) and once the near-infrared band as the independent variable (intercept = 8.746796 ; slope = 0.983467). This was done as some distance based indices use red as independent variable and some NIR.

IMAGE 4 Land use classes for the study area in the Richtersveld.

See Table 4 for results of all the Distance-based Vegetation indices I used in IDRISI.

TABLE 4 DISTANCE-BASED VEGETATION INDICES, indicating minimum, maximum values, the mean and the standard deviation, as well as the direction of the index, i.e. if a higher value means more or less vegetation. The results are only for the April 2004 images, using the red band (band 3) and the near-infrared band (band 4) of Landsat5 from a section of the Richtersveld, Northern Cape. Vegetatio Display Display mean Standard direction n index minimum maximum deviation PVI 0.0041 185.0393 9.9933 9.4084 up PVI1 -71.1131 185.0939 7.8142 11.4297 up PVI2 0.1989 301.2738 154.2053 87.1869 down PVI3 -1494.163 0 -187.4542 441.2104 up DVI -108.488 250.7841 2.2132 16.0309 up AVI -107 255 4.4305 16.3182 up SAVI -1.9773 1.9922 0.0335 0.1459 up TSAVI1 -0.9889 127.4971 65.832 37.3546 down TSAVI2 -0.8726 0.9994 0.171 0.325 up MSAVI2 -12.7004 1 0.0245 0.1721 up WDVI -105.1318 255 6.5745 16.2599 up

I then did a Principal Component Analyses to compare all the distance based vegetation indices. I included 11 indices, although I could have excluded MSAVI2 along with MSAVI1 as according to the IDRISI guide it is not suitable for arid or semi-arid regions. See results from the PCA below and a summary in Table 5.

LOADING C 1 C 2 C 3 C 4 C 5 C 6 C 7 C 8 C 9 C 10 C 11

pvi_apr_2004_stretch 0.888806 0.017255 -0.303589 -0.097765 0.326989 -0.032433 -0.000116 -0.000182 0.000360 0.000002 -0.000002 pvi1_apr_2004_stretch 0.985048 -0.075756 -0.055796 -0.089967 -0.111956 -0.000670 -0.014461 0.001234 -0.000059 0.000547 -0.001325 pvi2_apr_2004_stretch 0.025724 0.986861 0.125673 -0.088680 0.040639 0.008358 -0.001570 0.004152 -0.007306 0.002366 0.000328 pvi3_apr_2004_stretch -0.164510 -0.973836 -0.116932 0.100622 -0.025005 -0.008088 0.001085 -0.003735 0.007346 0.002435 0.000239 dvi_apr_2004_stretch 0.985057 -0.075759 -0.055741 -0.089967 -0.111955 -0.000673 -0.013158 -0.001122 0.000658 -0.000440 0.001403 avi_apr_2004_stretch 0.988086 0.010279 -0.051769 -0.092900 -0.108739 -0.001072 0.014435 0.014251 0.005389 0.000059 0.000056 savi_apr_2004_stretch 0.936943 -0.089073 0.144105 0.254412 0.097155 0.138930 0.000019 -0.000286 0.000791 0.000001 -0.000004 tsavi1_stretch 0.076837 0.985111 0.128951 -0.079661 0.019649 -0.004649 -0.000476 -0.006906 0.015333 0.000031 -0.000081 tsavi2_stretch 0.076657 -0.833047 0.390672 -0.370403 0.101271 0.009756 0.000370 -0.000200 0.000289 0.000002 -0.000003 msavi2_apr_2004_stretch 0.890733 -0.082800 0.309889 0.298609 0.040238 -0.113569 -0.000026 0.000370 -0.000867 -0.000001 0.000005 wdvi_apr_2004_stretch 0.984914 0.084820 -0.043169 -0.096527 -0.105516 0.000266 0.013482 -0.014485 -0.006099 0.000175 -0.000096

TABLE 5 SUMMARY OF RESULTS FROM A PCA OF ALL THE DISTANCE- BASED VEGETATION INDICES AVAILABLE IN IDRISI. Component One indicates indices that performed well in general, picking up greenness and soil brightness. Component Two indicate indices that correct for soil background and Component Three describes soil moisture. Thiam and Amadou (1997).

Component One Component Two Component Three AVI 0.988086 PVI2 0.986861 TSAVI2 0.390672 DVI 0.985057 TSAVI1 0.985111 PVI1 0.985048 WDVI 0.984914

From these results it seems that the AVI, DVI, PVI1 and WDVI had the highest loadings for Component One. These vegetation indices thus can be described as general vegetation indices, describing greenness and soil background (Thiam and Amadou 1997). According to Thiam and Amadou (1997) the second component is supposed to represent those VI’s that corrected for the soil background. In this case the PVI2 and TSAVI1 had the highest loadings for Component Two and TSAVI2 for Component Three (soil moisture).

Evaluating all the vegetation indices images by comparing them with one another and with the Google Earth images, the indices that had the highest loadings for Component one, are indeed the best ones. See Images 5 and 6, and 7 and 8. Here one can see that the river, the mountains and the agricultural area show up very well. Although the literature states that TSAVI1 is supposed to do well in arid regions, it did not produce good results. See images 9 and 10.

IMAGE 5 and 6 Ashburn Vegetation Index images for Landsat5 images using bands three and four for April and September 2004. Both images were stretched using histogram equalization and are displayed with the ndvi256 palette. The September image shows more vegetation than the April image, especially in the mountainous region as one would expect after good winter rains. IMAGE 7 and 8 Perpendicular Vegetation Index 1 images for Landsat5 images using bands three and four for April and September 2004. A stretch using histogram equalization was used to produce a visually better image using the ndvi256 palette. The September image shows more vegetation than the April image, as one would expect after good winter rains. IMAGE 9 and 10 PVI1 compared with TSAVI1. Vegetation Indices that scored high for Component Two of the PCA were the PVI2 and the TSAVI1. Here one can see that TSAVI1 (Component Two) did not produce a good image compared to PVI1 (Component One). Landsat5 images using bands three and four from April 2004 for a section of the Richtersveld, Northern Cape, using the ndvi256 palette. Take note that TSAVI1 has a reversed index.

PVI2 and TSAVI1 produced very similar results and both were not good. In Image 10 one can see that the agricultural area next to the river on the right hand side of the image can hardly be distinguished from the background. The mountains and the river also do not show up well. These indices can thus not be considered as good indices for this particular region.

I also did a simple change analysis (simple subtraction) for three of the distance- based vegetation indices, namely the three that performed best for Component One of the PCA. The results can be seen in Table 6. They have produced almost identical results. One can see a positive change in vegetation from April to September, with September being greener, especially in the mountainous regions. The negative change seems to have happened on the lower lying areas and on the agricultural lands. (see Images 5,6,7,8 and 11)

I also did the same analysis with PVI2 (Component Two) to show how it differs from the images that had the highest loadings for Component One. TABLE 6 Change in vegetation cover detected between seasons using the three distance-based vegetation indices AVI, DVI and PVI1. The April image was subtracted from the September image and the resulting image reclassed. Only a change greater than two times the standard deviation was considered as significant. PVI2 was also done to demonstrate the difference. Vegetation index Percentage Percentage no Percentage negative change change positive change AVI 0.33 96.67 2.91 DVI 0.35 96.73 2.92 PVI1 0.34 96.69 2.97 PVI2 1.81 95.78 2.46

IMAGE 11 Indicating the change from April to September, where 1 indicate negative change, 2 indicates no change and 3 positive change. CONCLUSION:

The best slope-based and distance-based indices were not that much different, although the distance-based indices do seem to give the best results visually. The best indices were indeed those that had the highest loadings for Component One of the Principal Component Analysis for both the slope- and the distance- based indices. Thus AVI, DVI and PVI1 are the indices that performed the best in this exercise.

There is also a definite seasonality change in vegetation between April and September, with September showing a definite increase in vegetation visually and statistically. The percentage change seems small, but considering that I took two times the standard deviation as the threshold for change to be significant, there is a significant positive change.

References:

Ashburn P. 1978. The vegetative index number and crop identification. The LACIE Symposium Proceedings of the Technical Session. 843-850.

Bannari A, Huete AR, Morin D and Zagolski. 1996. Effets de la Couleur et de la Brillance du Sol Sur les Indices de Végétation. International Journal of Remote Sensing 17(10): 1885-1906.

Baret F, Guyot G and Major D. 1989. TSAVI: A Vegetation Index Which Minimizes Soil Brightness Effects on LAI and APAR Estimation.12th Canadian Symposium on Remote Sensing and IGARSS’90, Vancouver, Canada. 4.

Baret F and Guyot G. 1991. Potentials and Limits of Vegetation Indices for LAI and APAR Assessment. Remote Sensing and the Environment 35: 161-173.

Deering DW, Rouse JW, Haas RH and Schell JA. 1975. Measuring “Forage Production” of Grazing Units From Landsat MSS Data. Proceedings of the 10th International Symposium on Remote Sensing of Environment II. 1169-1178.

Huete AR. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing and the Environment 25: 53-70.

Perry C Jr. and Lautenschlager LF. 1984. Functional Equivalence of Spectral Vegetation Indices. Remote Sensing and the Environment 14: 169-182.

Qi J, Chehbouni A, Huete AR, Kerr YH and Sorooshian S. 1994. A Modified Soil Adjusted Vegetation Index. Remote Sensing and the Environment 48: 119-126. Richardson AJ and Wiegand CL. 1977. Distinguishing Vegetation From Soil Background Information. Photogramnetric Engineering and Remote Sensing 43(12): 1541-1552.

Rouse JW Jr., Haas RH, Schell JA and Deering DW. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Earth Resources Technology Satellite-1 Symposium. Goddard Space Flight Center, Washington D.C. 309- 317

Rouse JW Jr. Haas RH, Deering DW, Schell JA and Harlan JC. 1974. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. NASA/GSFC Type III Final Report, Greenbelt, MD., 371.

Thiam AK. 1997. Geographic Information Systems and Remote Sensing Methods for Assessing and Monitoring Land Degradation in the Sahel: The Case of Southern Mauritania. Doctoral Dissertation, Clark University, Worcester Massachusetts. .