Remote Sensing and GIS Accuracy Assessment - Chapter 4

CHAPTER 4 In Situ Estimates of Forest LAI for MODIS Data Validation John S. Iiames, Jr., Andrew N. Pilant, and Timothy E. Lewis CONTENTS 4.1 Introduction.............................................................................................................................41 4.1.1 Study Area.................................................................................................................43 4.2 Background.............................................................................................................................43 4.2.1 TRAC Measurements.................................................................................................43 4.2.2 Hemispherical Photography Measurements...............................................................45 4.2.3 Combining TRAC and Hemispherical Photography .................................................45 4.2.4 Satellite Data ..............................................................................................................46 4.2.5 MODIS LAI and NDVI Products..............................................................................46 4.3 Methods..................................................................................................................................47 4.3.1 Sampling Frame Design.............................................................................................47 4.3.2 Biometric Mensuration...............................................................................................48 4.3.3 TRAC Measurements.................................................................................................50 4.3.4 Hemispherical Photography .......................................................................................51 4.3.5 Hemispherical Photography Quality Assurance ........................................................52 4.4 Discussion...............................................................................................................................52 4.4.1 LAI Accuracy Assessment .........................................................................................52 4.4.2 Hemispherical Photography .......................................................................................52 4.4.3 Satellite Remote Sensing Issues.................................................................................54 4.5 Summary.................................................................................................................................54 Acknowledgments............................................................................................................................55 References........................................................................................................................................55 4.1 INTRODUCTION Satellite remote sensor data are commonly used to assess ecosystem conditions through synoptic monitoring of terrestrial vegetation extent, biomass, and seasonal dynamics. Two commonly used vegetation indices that can be derived from various remote sensor systems include the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI). Detailed knowledge of vegetation 41 © 2004 by Taylor & Francis Group, LLC 42 REMOTE SENSING AND GIS ACCURACY ASSESSMENT N S Virginia Roanoke 3 Beach 2 4 1 VA NC Miles 0 50 Albemarle-Pamlico Basin 5 Raleigh Kilometers 0 50 Figure 4.1 LAI field validation site locations within the Albemarle-Pamlico Basin in southern Virginia and northern North Carolina. (1) Hertford; (2) South Hill; (3) Appomattox; (4) Fairystone; (5) Duke FACE; (6) Umstead. index performance is required to characterize both the natural variability across forest stands and the intraannual variability (phenology) associated with individual stands. To assess performance accuracy, in situ validation procedures can be applied to evaluate the accuracy of remote sensor-derived indices. A collaborative effort was established with researchers from the U.S. Environmental Protection Agency (EPA), National Aeronautics and Space Administration (NASA), academia, and state and municipal governmental organizations, and private forest industry to evaluate the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and LAI products across six validation sites in the Albemarle-Pamlico Basin (APB), in North Carolina and Virginia (Figure 4.1). The significance of LAI and NDVI as source data for process-based ecological models has been well documented. LAI has been identified as the variable of greatest importance for quantifying energy and mass exchange by plant canopies (Running et al., 1986) and has been shown to explain 80 to 90% of the variation in the above-ground forest net primary production (NPP) (Gholz, 1982; Gower et al., 1992; Fassnacht and Gower, 1997). LAI is an important biophysical state parameter linked to biological productivity and carbon sequestration potential and is defined here as one half the total green leaf area per unit of ground surface area (Chen and Black, 1992). NPP is the rate at which carbon is accumulated by autotrophs and is expressed as the difference between gross photosynthesis and autotrophic respiration (Jenkins et al., 1999). NDVI has been used to provide LAI estimates for the prediction of stand and foliar biomass (Burton et al., 1991) and as a surrogate to estimate stand biomass for denitrification potential in forest filter zones for agricultural nonpoint source nitrogenous pollution along riparian waterways (Verchot et al., 1998). Interest in tracking LAI and NDVI changes includes the role forests play in the sequestration of carbon from carbon emissions (Johnsen et al., 2001) and the formation of © 2004 by Taylor & Francis Group, LLC IN SITU ESTIMATES OF FOREST LAI FOR MODIS DATA VALIDATION 43 tropospheric ozone from biogenic emissions of volatile organic compounds naturally released into the atmosphere (Geron et al., 1994). The NDVI has commonly been used as an indicator of biomass (Eidenshink and Haas, 1992) and vegetation vigor (Carlson and Ripley, 1997). NDVI has been applied in monitoring seasonal and interannual vegetation growth cycles, land-cover (LC) mapping, and change detection. Indirectly, it has been used as a precursor to calculate LAI, biomass, the fraction of absorbed photosynthetically active radiation (fAPAR), and the areal extent of green vegetation cover (Chen, 1996). Direct estimates of LAI can be made using destructive sampling and leaf litter collection methods (Neumann et al., 1989). Direct destructive sampling is regarded as the most accurate approach, yielding the closest approximation of “true” LAI. However, destructive sampling is time-consuming and labor-intensive, motivating development of more rapid, indirect field optical meth-ods. A subset of field optical techniques include hemispherical photography, LiCOR Plant Canopy Analyzer (PCA) (Deblonde et al., 1994), and the Tracing Radiation and Architecture of Canopies (TRAC) sunfleck profiling instrument (Leblanc et al., 2002). In situ forest measurements serve as both reference data for satellite product validation and as baseline measurements of seasonal vegetation dynamics, particularly the seasonal expansion and contraction of leaf biomass. The development of appropriate ground-based sampling strategies is critical to the accurate specification of uncertainties in LAI products (Tian et al., 2002). Other methods that have been implemented to assess the MODIS LAI product have included a spatial cluster design and a patch-based design (Burrows et al., 2002). Privette et al. (2002) used multiple parallel 750-m TRAC sampling transects to assess LAI and other canopy properties at scales approaching that of a single MODIS pixel. Also, a stratified random sampling (SRS) design element provided sample intensi-fication for less frequently occurring LC types (Lunetta et al., 2001). 4.1.1 Study Area The study area is the Albemarle-Pamlico Basin (APB) of North Carolina and Virginia (Figure 4.1). The APB has a drainage area of 738,735 km2 and includes three physiographic provinces: mountain, piedmont, and coastal plain, ranging in elevation from 1280 m to sea level. The APB subbasins include the Albemarle-Chowan, Roanoke, Pamlico, and Neuse River basins. The Albe-marle-Pamlico Sounds compose the second-largest estuarine system within the continental U.S. The 1992 LC in the APB consisted primarily of forests (50%), agriculture (27%), and wetlands (17%). The forest component is distributed as follows: deciduous (48%), conifer (33%), and mixed (19%) (Vogelmann et al., 1998). 4.2 BACKGROUND 4.2.1 TRAC Measurements The TRAC sunfleck profiling instrument consists of three quantum PAR sensors (LI-COR, Lincoln, NE, Model LI-190SB) mounted on a wand with a built-in data logger (Leblanc et al., 2002) (Figure 4.2). The instrument is hand-carried along a linear transect at a constant speed, measuring the downwelling solar photosynthetic photon flux density (PPFD) in units of micromoles per square meter per second. The data record light–dark transitions as the direct solar beam is alternately transmitted and eclipsed by canopy elements (Figure 4.3). This record of sunflecks and shadows is processed to yield a canopy gap size distribution and other canopy architectural param-eters, including LAI and a foliage element clumping index. From the downwelling solar flux recorded along a transect, the TRACWin software (Leblanc et al., 2002) computes the following derived parameters describing forest canopy architecture: (1) © 2004 by Taylor & Francis Group, LLC 44 REMOTE SENSING AND GIS ACCURACY ASSESSMENT A B Figure 4.2 Photograph of (A) TRAC Instrument (length ~ 80 cm) and (B) PAR detectors (close-up). 1300 10 m 00 50 100 Position along transect (m) Figure 4.3 TRAC transect in loblolly pine plantation (site: Hertford). Peaks (black spikes) are canopy gaps. Computed parameters for this transect were gap fraction = 9%; clumping index (W ) = 0.94; PAI = 3.07; Le = 4.4 (assuming g = 1.5, a = 0.1, and mean element width = 50 mm). canopy gap size (physical dimension of a canopy gap), (2) canopy gap fraction (percentage of canopy gaps), (3) foliage element clumping index, W (q), (4) plant area index (LAI, which includes both foliage and woody material), and (5) LAI with clumping index (W ) incorporated. Note that in each case the parameters are for the particular solar zenith angle q at the time of data acquisition, defining an inclined plane slicing the canopy between the moving instrument and the sun. Parameters entered into the TRACWin software to invert measured PPFD to the derived output parameters include the mean element width (the mean size of shadows cast by the canopy), the needle-to-shoot area ratio (g) (within-shoot clumping index), woody-to-total area ratio (a), lati-tude/longitude, and time. Potential uncertainties were inherent in the first three parameters and will be assessed in future computational error analyses. Solar zenith and azimuth influence data quality. Optimal results are achieved with a solar zenith angle q between 30 and 60 degrees. As q approaches the horizon (q > 60˚), the relationship between LAI and light extinction becomes increasingly nonlinear. Similarly, best results are attained when TRAC sampling is conducted with a solar azimuth perpendicular to the transect azimuth. Sky condition is a significant factor for TRAC measurements. Clear, blue sky with unobstructed sun is optimal. Overcast conditions are unsuitable; the methodology requires distinct sunflecks and shadows. © 2004 by Taylor & Francis Group, LLC IN SITU ESTIMATES OF FOREST LAI FOR MODIS DATA VALIDATION 45 N q N E W W E r a S S A B Figure 4.4 Illustration of (A) a hemispherical coordinate system. Such a system is used to convert a hemi-spherical photograph into a two-dimensional circular image (B), where the zenith () is in the center, the horizon at the periphery, east is to the left, and west is to the right. In a equiangular hemispherical projection, distance along a radius (r) is proportional to zenith angle (Rich, 1990). The TRAC manual (Leblanc et al., 2002) lists the following as studies validating the TRAC instrument and approach: Chen and Cihlar (1995), Chen (1996), Chen et al. (1997), Kucharik et al. (1997), and Leblanc (2002). TRAC results were compared with direct destructive sampling, which is generally regarded as the most accurate sampling technique. 4.2.2 Hemispherical Photography Measurements Hemispherical photography is an indirect optical method that has been used in studies of forest light transmission and canopy structure. Photographs taken upward from the forest floor with a 180˚ hemispherical (fish-eye) lens produce circular images that record the size, shape, and location of gaps in the forest overstory. Photographs can be taken using 35-mm film cameras or digital cameras. A properly classified fish-eye photograph provides a detailed map of sky visibility and obstructions (sky map) relative to the location where the photograph was taken. Various software programs, such as Gap Light Analyzer (GLA), were available to process film or digital fish-eye camera images into a myriad of metrics that reveal information about the light regimes beneath the canopy and the productivity of the plant canopy. These programs rely on an accurate projection of a three-dimensional hemispherical coordinate system onto a two-dimensional surface (Figure 4.4). Accurate projection requires calibration information for the fish-eye lens that is used and any spherical distortions associated with the lens. GLA used in this analysis was available for download at http://www.ecostudies.org/gla/ (Frazer et al., 1999). The calculation of canopy metrics depends on accurate measures of gap fraction as a function of zenith angle and azimuth. The digital image can be divided into zenith and azimuth “sky addresses” or sectors (Figure 4.5). Each sector can be described by a combined zenith angle and azimuth value. Within a given sector, gap fraction is calculated with values between zero (totally “obscured” sky) and one (totally “open” sky) and is defined as the proportion of unobscured sky as seen from a position beneath the plant canopy (Delta-T Devices, 1998). 4.2.3 Combining TRAC and Hemispherical Photography LAI calculated using hemispherical photography or other indirect optical methods does not account for the nonrandomness of canopy foliage elements. Hence, the term effective leaf area index (Le) is used to refer to the leaf area index estimated from optical measurements including hemi-spherical photography. Le typically underestimates “true” LAI (Chen et al., 1991). This underesti-mation is due in part to nonrandomness in the canopy (i.e., foliage “clumping” at the scales of tree © 2004 by Taylor & Francis Group, LLC ... - tailieumienphi.vn