Spatial Analysis of Biom biomass, Vegetable Density and Surface Water Flow

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1 Remote Sensing and Hvdrology 2000 (Proceedings of a symposium held at Santa Fe, New Mexico, USA, April 2000). IAHS Pubi. no. 267, Image and in situ data integration to derive sawgrass density for surface flow modelling in the Everglades, Florida, USA JOHN W. JONES US Geological Survey, Reston, Virginia 20192, USA e-mai I : iwiones@usgs.gov Abstract The US Geological Survey is building models of the Florida Everglades to be used in managing south Florida surface water flows for habitat restoration and maintenance. Because of the low gradients in the Everglades, vegetation structural characteristics are very important and greatly influence surface water flow and distribution. Vegetation density is being evaluated as an index of surface resistance to flow. Digital multispectral videography (DMSV) has been captured over several sites just before field collection of vegetation data. Linear regression has been used to establish a relationship between normalized difference vegetation index (NDVI) values computed from the DMSV and field-collected biomass and density estimates. Spatial analysis applied to the DMSV data indicates that thematic mapper (TM) resolution is at the limit required to capture land surface heterogeneity. The TM data collected close to the time of the DMSV will be used to derive a regional sawgrass density map. Key words biomass; Florida Everglades, USA; multispectral; NDVI; sawgrass; spatial autocorrelation; vegetation quadrats INTRODUCTION The US Geological Survey is building hydrological models of the Florida Everglades (USA) to be used in managing south Florida surface water flows and storage for habitat restoration and maintenance in the Everglades National Park (ENP) and Water Conservation Areas. Various remote sensing and in situ data are being collected and integrated to characterize south Florida's topography and land cover. These characterizations are used to parameterize hydrodynamic, water quality and ecological models. Because of the low gradients in the Everglades, the structural characteristics of aquatic vegetation greatly influence surface water flow and distribution. At present however, it is not clear which aspects of vegetation structure have the greatest influence on surface water velocity. Using a variety of measurement techniques and a flume planted with sawgrass, scientists are conducting field and laboratory experiments to quantify resistance caused by major Everglades vegetation types to surface flows of varying depth (Lee & Carter, 1997). Measurements of biomass, vegetation density, and vegetation type, are all being correlated with surface flow resistance. In preparation for formalizing these relationships, the use of remotely sensed and in situ data to derive fields that may be used to index flow resistance is being investigated. This paper documents technique development for mapping biomass and density for sawgrass, a dominant Everglades vegetation type.

2 508 John W. Jones VEGETATION DENSITY AND VEGETATION INDEXES Field and airborne data collection Details of the field data collection are provided in Carter et al. (1999). Information regarding the digital multispectral video (DMSV) instrument, data collection and postprocessing is provided by Anderson et al. (1997). Only procedures important to the density mapping effort are discussed here. Although vegetation sampling was conducted over several field campaigns beginning in 1996, only the spring 1996 collection was nearly coincident with a DMSV overflight. At that time, field data were collected at two sites (designated "P33" and "NESRS3") that also serve as meteorological data collection points for évapotranspiration modelling (German, 1996). At each site, a m 2 area was measured and bounded by four bright-white 1 m 2 panels. These areas were then further divided into cells within which vegetation quadrats were randomly sampled. Vegetation was characterized by type (e.g. sawgrass or periphyton) and biomass (live and dead) measured as a function of height above the soil surface. Figure 1 provides an example of the biomass distribution that can be constructed for each quadrat. Once the results of field and flume research regarding vegetation resistance to flow are available, vegetation biomass ranges can be grouped into density classes to which resistance coefficients are assigned. a live biomass dead biomass periphyton Fig. 1 Plot of biomass distribution vs height above the soil for one quadrat. For airborne data collection, several images of 0.5-m spatial resolution and four 25-nm wide spectral bands, centred at 450, 550, 650 and 770 nm were collected for each site. These DMSV data were spatially averaged to match the vegetation quadrat size (1 m). Because the grid-corner panels are visible in the DMSV imagery, they were used to replicate the site grid and locate vegetation quadrat values in the DMSV data. Vegetation index/field biomass comparison Figure 2 illustrates the relationship between the DMSV-derived Normalized Difference Vegetation Index (NDVI) and field-measured total biomass across all quadrats. The NDVI

3 Image and in situ data integration to derive sawgrass density for surface flow modelling 509 CM 5000 E S 4000 in 3000 in I 2000 _o ~ I au it 1 m A», NDVI from DMSV A NESRS3 points P33 points Fig. 2 NDVI from DMSV vs. total biomass for all sample quadrats. [(near-infrared - red)/(near-infrared + red)] is a spectral vegetation index that has been related to vegetation characteristics such as leaf area index, biomass and fractional vegetation cover (Jones, 1996). Using NDVI as the independent variable, several linear regressions were performed against live and total biomass for each site separately and both sites combined. The results are shown in Table 1. NDVI exhibits a significant correlation with live biomass for the combined dataset and for NESRS3 samples in particular. The weaker relationship at the P33 site is understandable given its relatively low biomass and high water level. Additionally, quadrats containing vegetation types other than sawgrass, while present at P33, were not removed from the analysis. The relatively strong relationship between NDVI and total biomass across all samples is noteworthy. This is presumably due to the strong linear relationship between live biomass above the water surface and total biomass below. Regression of these two variables resulted in a r 2 of 0.65 (Fig. 3). This finding is important because both live and dead biomass will retard the flow of surface water. Table 1 Linear regression results for various sample combinations and biomass types. Samples NDVI us total biomass NDVI vs live biomass JustNESRS3 (» = 12) 7-2 = 0.79 p = r = 0.68 p = Just P33 (n = 14) r 2 = 0.13 p = ? = 0.27 p = NESRS3 and P33 (n = 26) r 2 = 0.55 p = r 2 = 0.63 p = sr 9oo E j </> IS 600 I 500!5 fj 400 Ï 300 I 200 m? l lo a below water total biomass (gdw/m2) Fig. 3 Live biomass above the water vs total biomass below the water (all quadrats).

4 510 John W. Jones NDVI SPATIAL ANALYSIS DMSV data cannot be efficiently collected for the entire area of interest because the area covered by each image is no greater than 0.1 km 2. Therefore, practical techniques must use readily available satellite data. First, we must assess whether the spatial resolution of satellite NDVI is adequate to represent the local-scale heterogeneity of biomass. Measures of spatial autocorrelation of NDVI can be used to estimate the appropriate resolution for ground and remotely sensed data collection (Curran, 1988). The high resolution of the DMSV data can be exploited to answer this question. In addition to the two DMSV images analysed previously, three images were captured over évapotranspiration study sites during the same timeframe. All were analysed using the Geary's c index: c,y = (z,- - zjf, where cy is a measure of dissimilarity of fs and fs (i.e. two pixels') attributes and z/ and zj are the values of interest at each point (in this case the NDVI) (Warner & Shank, 1997). The c index is summed over all point pairs in the image and scaled to the image variance (Goodchild, 1986). When a high degree of spatial autocorrelation is present, pixels close to one another have similar attribute values, and thus the index value is close to 0. As values become unrelated, c tends toward 1. Values close to 2 are indicative of high negative spatial correlation. The NDVI image was computed from the 0.5-m DMSV. Resampling these data (nearest neighbour algorithm) produced imagery at a range of sample distances, or lags. A rooks-case, or four- (cardinal) direction c value was then calculated for each image (i.e. at each lag). Plots of Geary's c as a function of lag are termed variograms (Goodchild, 1986) and are diagnostic of spatial structure (Legendre & Legendre, 1998). Figure 4 provides the variograms for each of the five DMSV study sites. The greatest lag depicted (64 m) is approximately one-fourth the overall length of any DMSV image. Values for longer lags are not shown because confidence in index values calculated with so few observations would be low (Webster, 1985). The indexes typically fail to reach a value of 1, indicating that some spatial autocorrelation is present at all lengths covered by the DMSV data. The rate of increase in c across all sites begins to level off at a range of approximately 50 m. Corresponding to the "sill" often noted in semi-variogram analysis (Schowengerdt, 1997), this is interpreted as the length beyond which vegetation biomass amounts (inferred from NDVI) are poorly correlated. Because the 30 m ground sample distance of thematic mapper (TM) data is below this length, it should capture spatial variation in vegetation density across the region (Curran, 1988). However, the variograms also indicate that spatial autocorrelation decreases rapidly with distance (lag) up to the sill. This may indicate that comparison of individual (1 m) quadrats with 30 m TM measurements may prove problematic especially when the root mean squared error associated with TM georeferencing is typically, at best, ±15 m. REGIONAL DENSITY MAPPING A TM scene was collected over South Florida on 21 March 1996, approximately two weeks prior to the vegetation and DMSV data collection campaign. Unfortunately, no information is available to calibrate the DMSV data to at-sensor spectral reflectance.

5 Image and in situ data integration to derive sawgrass density for surface flow modelling lag (m)»-lox -HJ-F4 -&-Cmp23 X NESRS3 -*-P33 Old I. [ Fig. 4 Geary's c (all sites) vs distance between samples. However, great similarity of the vegetation and water conditions at the time of both the TM and DMSV collections presents an unusual opportunity. The average NDVI from the DMSV for NESRS3 and P33 (0.173 and , respectively) and the NDVI for the TM pixels centred on the field study grids (0.176 and 0.032, respectively) exhibit striking similarity. The P33 area is representative of sparse biomass with an overall average of gdw m" 2. NESRS3 has moderate-to-high biomass, gdw m" 2 average. Therefore, the relationship derived from the DMSV for NDVI and total biomass was simply applied to the TM-derived NDVI to yield a regional map of sawgrass biomass. Additional field-measured biomass data collected independently of the P33 and NESRS3 samples undergoing final processing. These will be used to assess and refine the biomass extrapolation. CONCLUSIONS/FUTURE RESEARCH Other techniques for deriving nominal vegetation density classes are being developed in parallel with this effort. However, this technique has the advantage of yielding a ratio-scale variable (gdw nf 2 ) that can be further divided into density ranges once vegetation density-flow resistance relationships have been formalized. Ultimately, the value of this input field will be assessed through its use in assigning resistance coefficients to cells in the hydrodynamic model. Acknowledgements Funding was provided by the US Geological Survey South Florida Place Based Studies Program, with support from the US National Park Service and the US Army Corps of Engineers. Vegetation data were diligently collected and generously provided by Virginia Carter, Justin Reel, Henry Ruhl and Nancy Rybicki. REFERENCES Anderson, J. E., Desmond, G. B., Lemeshewsky, G. P. & Morgan, D. R. (1997) Reflectance calibrated digital multispectfal video: a test-bed for high spectral and spatial resolution remote sensing. Phologram. Engng & Remote Sens. 63,

6 512 John W. Jones Carter, V., Rybicki, N. B., Reel, J. T., Ruhl, H. A., Stewart, D. W. & Jones, J. W. (1999) Classification of vegetation for surface-water flow models in Taylor Slough, Everglades National Park. In: Proc, Third Int. Symposium on Ecohydraulics (12-16 July 1999, Salt Lake City, Utah). CD-Rom, Int. Assoc. Hydraulic Research. Curran, P. J. (1988). The semivariogram in remote sensing: an introduction. Remote Sens. Environ. 24, German, E. R. (1996) Regional Evaluation of Evapotranspiration in the Everglades. US Geological Survey Fact Sheet FS US Geological Survey, Reston, Virginia, USA. Goodchild, M. F. (1986) Spatial Autocorrelation. CATMOG 47. University of Western Ontario, London, Ontario, Canada. Jones, J. W. (1996) Relationships between vegetation index values and hydrologie fluxes at macro scales. In: Applications of Remote Sensing in Hydrology (ed. by G. W. Kite, A. Pietroniro & T. J. Pultz) (Proc. Third Int. Workshop, October 1996, Greenbelt, Maryland), National Hydrology Research Institute, Saskatchewan, Canada. Lee, J. K. & Carter, V. (1997) Vegetation Resistance to Flow in the Florida Everglades. US Geological Survey, Reston, Virginia, USA. Legendre, P. & Legendre, L. (1998) Numerical Ecology. Elsevier, Amsterdam, The Netherlands. Schowengerdt, R. A. (1997) Remote Sensing: Models and Methods for Image Processing. Academic Press, San Diego, USA. Warner, T. A. & Shank, M. C. (1997) Spatial autocorrelation analysis of hyperspectral imagery for feature selection. Remote Sens. Environ. 60, Webster, R. (1985) Quantitative spatial analysis of soil in the field. Adv. Soil Sci. 3, 1-70.