Using Landsat TM Imagery to Estimate LAI in a Eucalyptus Plantation Rebecca A. Megown, Mike Webster, and Shayne Jacobs

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1 Using Landsat TM Imagery to Estimate LAI in a Eucalyptus Plantation Rebecca A. Megown, Mike Webster, and Shayne Jacobs Abstract The use of remote sensing in relation to determining parameters of the forest industry is increasing world wide. In South Africa, there has been limited success with using remote sensing imagery to predict ground based leaf area index (LAI) measurements. This study investigates two commonly used vegetation indices, NDVI and NDVIc. The NDVI does not correlate well with ground based methods of estimated LAI; LiCor 2000 (r 2 = ) and destructive sampling (r 2 = 0.123). The NDVIc, which takes into account the midinfrared band, has shown very promising results; LiCor 2000 (r 2 = ) and destructive sampling (r 2 = 0.915). Future work needs to be done in southern Africa to determine the usefulness of remote sensing for forestry as ground based methods of determining forestry parameters is becoming increasing expensive, time consuming, and unsafe in many parts of the region. Introduction LAI and Remote Sensing Remotely sensed leaf area index (LAI) values provide the opportunity to gain spatial information on various plant biophysical attributes that can be used in process based growth models as well as for simple spatial growth indices. LAI can be determined remotely relatively cheaply and easily using imagery from various satellites. The most commonly used satellites for determining LAI are SPOT, NOAA AVHRR and Landsat TM, all with differing spatial and spectral resolutions. The inherent problem with satellite imagery is that the understory exerts quite a large influence on the spectral bands, especially from plantation forests with open canopies (Nemani et al. 1993). There are several ways of correcting for this, which will be discussed in the next section. Various Vegetations Indices (VIs) use different ratios of bands in the visible and infrared regions to derive forestry parameters, such as LAI and Intercepted Photosynthetically Active Radiation (fipar) from satellite imagery. Vegetation indices Megown et al. (1999) reviewed the methods associated with transforming remotely sensed images into LAI estimates. This review documents the recent advances in VIs that can be used to determine LAI. The most basic VI is the Normalised Difference Vegetation Index (NDVI), which is derived from the ratio of the near infrared and the red bands:

2 Equation 1. NDVI NIR RED NDVI = or NIR + RED NIR VIS NIR + VIS Where: NIR = Reflectance in the Near-Infrared Band RED = Reflectance in the RED Band VIS = Reflectance in the Visible Band The correlation between LAI and NDVI is generally strong enough to accurately estimate LAI, however the estimation becomes problematic when the LAI is over 3-4 (Baret and Guyot, 1991, Olthof, 1997). There are also differences between spectral reflectances for coniferous and broadleaved forests. Different VIs with differing band ratios can be used to optimise the relationship between VI and LAI for varying plant forms (Fassnacht et al. 1997). Therefore when the VI reaches infinity (canopy closure), a different VI may be used to more accurately estimate LAI (Megown et al. 1999). For instance, NDVI is related to green LAI, green biomass, canopy chlorophyll and foliar N levels as well as to fipar, if corrected for greenness. The relationship between NDVI and LAI determined using Landsat TM imagery can be improved upon by including a mid infrared band into the NDVI thereby correcting for greenness and creating a new VI entitled Corrected Normalised Difference Vegetation Index (NDVIc) (Nemani et al. 1993): Equation 2. NDVI C NIR - RED MIR - MIRmin NDVI C = 1- NIR + RED MIRmax MIRmin Where: NIR = Reflectance in the Near-Infrared Band RED = Reflectance in the RED Band MIR = Reflectance in the Mid-Infrared Band MIRmin = Minimum reflectance value in MIR band, complete canopy closure MIRmax = Maximum reflectance value in MIR band, complete open canopy The MIR then acts as a scalar for canopy closure in order to eliminate or reduce the contribution of the ground vegetation. Other indices, like SAVI and TSAVI, correct for soil brightness (Baret and Guyot, 1991), ARVI adjusts for air interference (Huete et al. 1994) and SARVI, corrects for both air and soil interference (Huete et al. 1994; Huete et al. 1997), have been developed and show potential for use instead of NDVI in South African conditions. SARVI in particular, determined from Landsat TM images, shows promise where NDVI is unsuitable. For instance, high LAI values cause NDVI estimates to mimic the red reflectances and becomes saturated, while SARVI follows variations in NIR reflectances (Huete et al. 1997). This was shown to

3 follow a trend over a large variety of forest ecosystems. SARVI was developed for use on MODIS-EOS and is calculated according to the following formulae: Equation 3. SARVI NIR * RB * SARVI = NIR * + RB * + L Where: NIR* = Reflectance in the Near-Infrared Band RB* = Adjusts for air interference L = A constant (0.5) that adjusts for soil brightness Heute et al. (1994) found that SARVI and ARVI predict LAI to within 0.4% of actual LAI (15% cover), while the NDVI could only predict LAI to within 0.8% (30% cover). Despite the shortcomings of NDVI, it is still the most widely used VI and relationships have been established for a wide variety of ecosystems and plant forms. Other indices, however, have been developed recently and relationships with biophysical and ecological attributes will have to be tested and validated before they can be used widely. Environmental Factors Influencing LAI and VI The leaf area of a tree is not only determined by the age of the tree, but also by the quality of the site. Leaf area to sapwood area generally decreases from mesic to xeric environments, possibly to avoid excessive transpiration in dry conditions (Waring et al. 1982). In addition, this relationship is influenced by nutrient availability (Brix and Mitchell, 1983). These trends suggest a controlling influence of site on the LAI and the specific leaf area of the trees. The relationship between sapwood area and leaf area has been described by Olbrich (1994) in Eucalyptus grandis near Sabie, Mpumalanga. This has, however, not been validated for sites of differing quality. Nemani et al. (1993) found that LAI varies with microclimate and soil water condition in a mixed conifer forest in Montana. Hilltops had lower LAI, owing to less available water and therefore more stress, while the bottoms of hill slopes had more water, less stress, higher temperatures, possibly more nutrients and showed higher LAI values. Vegetation indices in general are poor indicators of total canopy biomass (Gamon et al. 1995). However, NDVI is very sensitive to measures of canopy greenness, such as green biomass, green LAI, chlorophyll, and foliar N content at low canopy cover in a mixed forest/woodland, but cannot distinguish between these attributes at higher canopy cover i.e % (Gamon et al. 1995). Thus, NDVI is relatively insensitive to small seasonal changes in canopy characteristics caused by stress beyond a certain LAI, generally above 3.

4 There is a strong non-linear relationship between NDVI and fipar across different landscapes and plant forms if the latter is corrected for greenness (Gamon et al. 1995). While there exist a strong relationship between NDVI and Net Photosynthetic Productivity (NPP), this is merely an integrator of past photosynthetic activity (PA), and cannot be used directly for future predictions of PA, especially if the percentage greenness is not known (Gamon et al. 1995). Site quality is a function of temperature, radiation, moisture and nutrients as well as species (McLeod and Running, 1987). LAI may be used as a site quality index to quantify potential productivity in plantations (McLeod and Running, 1987). This is related to the close correlation in coniferous stands between Site Index (SI) and annual stem volume increment, and LAI and annual stem volume increment. Stand LAI accounted for much of the variation in productivity in these stands, suggesting that LAI is an integrator for factors impacting on growth. In this case, soil available water also correlated well with annual stem volume increase, suggesting that water may be the main limiting factor on these sites. However, LAI varied with stand density, limiting its use as a practical measure of site quality. LAI and SI increased with stand density, which is in contrast to the results of Knight et al. (1981) for pines and Battaglia et al. (1997) for Eucalyptus, where it was found that LAI does not vary much between stands ranging in density from 900 to 1400 stems/ha. The inclusion of LAI in a simple ecosystem process model might provide a site quality index that accounts for biophysical aspects of growth. A further complication of the use of LAI as a site quality index based on volume growth is that there seems to be a theoretical maximum LAI beyond which volume is unresponsive to increases in soil fertility (Vose and Allen, 1988). Leaf area of a tree determines the amount of light intercepted by the canopy; hence it is a determinant of the photosynthetic capacity of the tree (Landsberg & Gower, 1997). The leaf area is also a determinant of transpiration rate and is thus a primary input into several process-based growth models such as the 3PG model (Waring and Landsberg, 1997). The LiCOR-2000 is an easy to use, portable plant canopy analyser that has been used successfully in other studies of LAI (Battaglia et al., 1998; Gong et al., 1995; Olthof and King 1997). The main drawback of the system is that branches and stems are also included in the final figure and as such that should be referred to as plant area index (PAI). To make this figure useful for LAI studies, a calibration curve for every species needs to be determined. This can be accomplished by using the LiCOR-2000 and destructive sampling of trees in parallel (Battaglia et al. 1997). Although it has been suggested that a generic calibration curve can be used for all eucalypts, this has not been validated for E. grandis, or for local growing conditions and the local range of site quality. Several other methods exist to determine LAI (Megown et al. 1999). These include:

5 the modular estimation technique, based upon measuring true LAI of a few clumps (modules) in a eucalypt tree foliage and scaling up, the theoretical approach using leaf water and energy balance, the direct beam (remote sensing), and digital cameras (remote sensing). The primary aim of this study is to determine the correlation between ground estimates of LAI and estimates calculated from satellite imagery using VIs. The establishment of these relationships will avoid the need for costly and timeconsuming fieldwork in future when LAI of Eucalyptus grandis needs to be determined in future. This can also be used to test the validity of a generic calibration curve for eucalypt species (Battaglia et al. 1997). The second aim of this project is to outline the methodology of developing the LAI estimates, both on the ground and derived from satellite imagery. Methodolog Study site locations The Eucalyptus grandis sites are situated in the Sabie/White River and Barberton areas in Mpumalanga (Table 1 and Figure 1). Three different age classes were chosen; 3-, 6- and 9-year-old. Within each age class, three sites of differing site quality were chosen, so that the influence of site on leaf area could be examined. Sites with site index of and at age 10 years (SI 10 ) were chosen. In total, there were five sites representing a cross-section of site indices (SI 10 ) and ages of Eucalyptus grandis. Due to the scarcity of the resource, no site could be found to fit the requirements for a 9-year-old site with a SI 10 of Table 1. Site descriptions. Sites 3 year old 6 year old 9 year old SI Class 20 SI Class 35 SI Class 20 SI Class 35 SI Class 20 Age SI Current stocking (stems ha -1 ) Average DBH(cm) Average height(m) Plantation White River Venus Glenthorpe Venus Glenthorpe Compartment WR A57 F2 A18 F14

6 Latitude 25º18' º0' º45' 25º0' º46'6.977 Longitude 30º59'18 30º57' º51' º55' º52'6.154 Altitude (metres above sea level) Geology Granite Dolorite Granite/ Quartzite Dolorite Granite Figure 1. Study site locations in Mpumalanga Province, South Africa. Field Methodology Diameters at breast height (DBH) and heights of a 25m x 25m plot of trees were taken to determine mean DBH and height of the trees for each site. Plant area index measurements were carried out using a LiCOR-2000 portable leaf canopy analyser. Five trees, representative of the stand in terms of diameter at breast height measurements were felled and the height measured with a measuring tape. All the leaves were stripped off by hand. The leaves were collected and put in plastic bags and the fresh weight was taken with a spring balance. A small leaf sample, representative of the whole canopy, was taken, the fresh weight determined and taken back to the laboratory for leaf area determinations. The leaf area of the sampled leaves was taken with a Delta-T areameter. The sample was then dried to constant mass in an oven at 60ºC and the specific leaf area determined. These results were used to determine the leaf area of the entire canopy and LAI of the site. The scaling up involved taking the DBH of the 25m x 25m plot and scaling up using the DBH of the felled trees as base.

7 Field points locations: The field sites were located on South Africa 1: sheets (topographical maps). X and Y coordinates were determined in the Geographic projection using a sliding scale. These points were entered into ArcInfo and used to generate a point coverage. Information pertaining to each point (i.e. LAI, mean DBH and mean tree height) was attached to the relevant point. Remote Sensing Methodology The Landsat TM bands used in this project were: Red, Near-Infrared, and the Mid-Infrared bands. These bands are the most useful for vegetation discrimination and VI calculations. ERDAS Imagine Software was used to interpret three images that covered the extent of the study area. The forested areas were determined and extracted from the imagery. Figure 2 shows an example of the false colour imagery. Figure 2. False Colour Satellite image for White River site. Two vegetation indices, NDVI and NDVIc, were applied to all three images (Figure 3) and values of the indices were determined for each of the five study sites.

8 Figure 3. NDVIc Model applied to White River image. Results On visual inspection, both the NDVI and the NDVIc show higher LAI estimates in forestry compared to non-forested areas (represented by lighter grey tones vs. darker grey tones respectively). However, visual inspection is not enough to determine the accuracies of the satellite based estimates. The LAI estimate values from NDVI, NDVIc, LAI from destructive sampling and LAI from the LiCor 2000 are found in Table 2. The NDVI and NDVIc values are in digital numbers that are converted to meaningful numbers based on the equations determined through regression analysis. The LiCor 2000 estimates tend to overestimate LAI compared with destructive sampling methods. Table 2. LAI from destructive sampling and from LiCor LAI-2000 measurements, together with NDVI and NDVIc measurements. SITE Age SI Class X_COORD Y_COORD NDVI NDVIC LAI LAI-2000 A A F F WR NDVI produced higher digital numbers, meaning lower LAI estimates, than did NDVIc (Figure 4). NDVIc showed an increasing trend in digital number over age

9 whereas NDVI didn t show a trend. The two ground based methods produced similar results, although the LiCor 2000 tended to underestimate LAI (Figure 5). In general, LAI estimates tended to decrease as age increased over these sites. Vegetation Indices by SI over Age Digital Number Age NDVI SI 20 NDVI SI 35 NDVIc SI 20 NDVIc SI 35 Figure 4. Vegetation indices by site index over age. Ground LAI Estimates by SI over Age 4 LAI LAI Dest SI 20 LAI Dest SI 35 LAI-2000 SI 20 LAI-2000 SI Age Figure 5. Ground based LAI estimates by site index over age.

10 Table 3. Diameter at breast height of the 25mx25m plots on the five sites, measured in cm. 3 year old 6 year old 9 year old SI Class 20 SI Class 35 SI Class 20 SI Class 35 SI Class 20 Average StandardError Minimum Maximum There were very little correlation between the bands (Table 4). This means that the information supplied by one band is not being repeated by another. The highest correlation was found to be between the red and m-ir bands (0.865). Table 4. Correlation matrix for the different bands of the image representing the White River site. Red n-ir m-ir Red 1 n-ir m-ir The correlations between the vegetation indices and the ground based methods of collecting LAI estimates show promise (Table 5). The correlations between NDVI and LAI collected from the LiCor 2000 and from destructive sampling are relatively poor (-0.5 and 0.4 respectively). However, the NDVIc correlates well with ground based methods of LAI estimation (-0.99 and 0.96). Table 5. Correlation matrix comparing NDVI, NDVIc, LAI-2000 and LAI determined from destructive sampling. NDVI 1 NDVI NDVIC LAI_DESTRU LAI-2000 NDVIC LAI_DESTRU LAI Through regression analysis, NDVI proved to be a poor estimator of LAI, for both the LiCor 2000 (Figure 6; r 2 = ) and destructive sampling (Figure 7; r 2 = 0.123).

11 NDVI vs LAI_ R 2 = Figure 6. Linear regression of NDVI and LAI estimates from the LiCor NDVI vs LAI_DESTR R 2 = Figure 7. Linear regression of NDVI and LAI estimates from destructive sampling. The NDVIc proved to be much more valuable in terms of estimating LAI when compared with both the LiCor 2000 results (Figure 8; r 2 = ) and the destructive sampling (Figure 9; r 2 = 0.915). The r 2 value can be slightly improved for the NDVIc vs. destructive sampling by using a polynomial regression (r 2 = 0.933), however this increase in r 2 was not thought to outweigh the loss of power of the model and was not considered further.

12 NDVIc vs LAI_ R 2 = Figure 8. Linear regression of NDVIc and LAI estimates from the LiCor NDVIc vs LAI_DESTRU R 2 = Figure 9. Linear regression of NDVIc and LAI estimates from destructive sampling. Finally, the model determined from the NDVIc vs. LiCor 2000 regression was used on the NDVIc image. This produced a map of adjusted LAI values (Figure 10). However, the area modelled includes all forestry and not just the species that was modelled for (Eucalyptus grandis).

13 Figure 10. NDVIc image superimposed by the model determined from the NDVIc vs. LiCor 2000 regression. Discussion Field based methods of estimating LAI and biomass of forestry plantations are expensive and time consuming. The ability to predict LAI, biomass and eventually water use over a large area remotely is sought after by the forestry companies. As the technology improves, so does the desire to apply the technology to new areas of scientific research. The use of LAI in studies of site quality is hampered by the fact that it also takes into account senescing and dead leaves, thus overestimating the photosynthetic capacity of the stand. Thus the stressed vegetation cannot be accurately determined using LAI alone. One way of bypassing this limitation of NDVI is to determine APAR and FPAR, which are both independent of the LAI, which instead provides an estimate of the photosynthetically active foliage. Techniques for remote sensing of these indices are currently being developed. The NDVIc shows great promise in South Africa for estimating LAI values of Eucalyptus grandis, and possibly other Eucalyptus species and hybrids. More research needs to be done to determine whether this relationship still holds between different species and over different geographic regions.