Field Measurement of Effective Leaf Area Index using Optical Device in Vegetation Canopy

Leaf area index (LAI) is an essential canopy variable describing the amount of foliage in an ecosystem. The parameter serves as the interface between green components of plants and the atmosphere, and many physiological processes occur there, primarily photosynthetic uptake, respiration, and transpiration. LAI is also an input parameter for many models involving carbon, water, and the energy cycle. Moreover, ground-based in situ measurements serve as the calibration method for LAI obtained from remote sensing products. Therefore, straightforward indirect optical methods are necessary for making precise and rapid LAI estimates. The methodological approach, advantages, controversies, and future perspectives of the newly developed LP 110 optical device based on the relation between radiation transmitted through the vegetation canopy and canopy gaps were discussed in the protocol. Furthermore, the instrument was compared to the world standard LAI-2200 Plant Canopy Analyzer. The LP 110 enables more rapid and more straightforward processing of data acquired in the field, and it is more affordable than the Plant Canopy Analyzer. The new instrument is characterized by its ease of use for both above- and below-canopy readings due to its greater sensor sensitivity, in-built digital inclinometer, and automatic logging of readings at the correct position. Therefore, the hand-held LP 110 device is a suitable gadget for performing LAI estimation in forestry, ecology, horticulture, and agriculture based on the representative results. Moreover, the same device also enables the user to take accurate measurements of incident photosynthetically active radiation (PAR) intensity.Postprin

Seasonal changes in canopy leaf area index and MODIS vegetation products for a boreal forest site in central Finland

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Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index

In situ leaf area index (LAI) measurements are essential to validate widely-used large-area or global LAI products derived, indirectly, from satellite observations. Here, we compare three common and emerging ground-based sensors for rapid LAI characterisation of large areas, namely digital hemispherical photography (DHP), two versions of a widely-used commercial LAI sensor (LiCOR LAI-2000 and 2200), and terrestrial laser scanning (TLS). The comparison is conducted during leaf-on and leaf-off conditions at an unprecedented sample size in a deciduous woodland canopy. The deviation between estimates of these three ground-based instruments yields differences greater than the 5% threshold goal set by the World Meteorological Organization. The variance at sample level is reduced when aggregated to plot scale (1 ha) or site scale (6 ha). TLS shows the lowest relative standard deviation in both leaf-on (11.78%) and leaf-off (13.02%) conditions. Whereas the relative standard deviation of effective plant area index (ePAI) derived from DHP relates closely to us in leaf-on conditions, it is as large as 28.14-29.74% for effective wood area index (eWAI) values in leaf-off conditions depending on the thresholding technique that was used. ePAI values of TLS and LAI-2x00 agree best in leaf-on conditions with a concordance correlation coefficient (CCC) of 0.796. In leaf-off conditions, eWAI values derived from DHP with Ridler and Calvard thresholding agrees best with TLS. Sample size analysis using Monte Carlo bootstrapping shows that TLS requires the fewest samples to achieve a precision better than 5% for the mean +/- standard deviation. We therefore support earlier studies that suggest that TLS measurements are preferential to measurements from instruments that are dependent on specific illumination conditions. A key issue with validation of indirect estimates of LAI is that the true values are not known. Since we cannot know the true values of LAI, we cannot quantify the accuracy of the measurements. Our radiative transfer simulations show that ePAI estimates are, on average, 27% higher than eLAI estimates. Linear regression indicated a linear relationship between eLAI and ePAI-eWAI (R-2 = 0.87), with an intercept of 0.552 and suggests that caution is required when using LAI estimates

A comparison of different methods for assessing leaf area index in four canopy types

The agreement of Leaf Area Index (LAI) assessments from three indirect methods, i.e. the LAI–2200 Plant Canopy Analyzer, the SS1 SunScan Canopy Analysis System and Digital Hemispherical Photography (DHP) was evaluated for four canopy types, i.e. a short rotation coppice plantation (SRC) with poplar, a Scots pine stand, a Pedunculate oak stand and amaize field. In the SRC and in the maize field, the indirect measurements were compared with direct measurements (litter fall and harvesting). In the low LAI range (0 to 2) the discrepancies of the SS1 were partly explained by the inability to properly account for clumping and the uncertainty of the ellipsoidal leaf angle distribu tion parameter. The higher values for SS1 in the medium (2 to 6) to high (6 to 8) ranges might be explained by gap fraction saturation for LAI–2200 and DHP above certain values. Wood area index –understood as the woody light blocking elements from the canopy with respect to diameter growth– accounted for overestimation by all indirect methods when compared to direct methods in the SRC. The inter-comparison of the three indirect methods in the four canopy types showed a general agreement for all methods in the medium LAI range (2 to 6). LAI–2200 and DHP revealed the best agreement among the indirect methods along the entire range of LAI (0 to 8) in all canopy types. SS1 showed some discrepancies with the LAI–2200 and DHP at low (0 to 2) and high ranges of LAI (6 to 8

Multiple approaches for assessing mangrove biophysical and biochemical variables using in situ and remote sensing techniques

Mangrove forests are important ecosystems and play a key role in maintaining the equilibrium in coastal lagoons and estuaries. However, in recent years, there has been a considerable loss of mangrove extension due to anthropogenic activities. Recent studies suggest that multiple in situ and remote sensing approaches must be carried out to understand the dynamics in these complex ecosystems. Therefore, the objective for this PhD dissertation is to develop multiple techniques for monitoring the seasonal biophysical and biochemical conditions of the mangrove forests. Particular objectives will include: i. Test the feasibility of using a Chlorophyll Content Index from a CCM-200 unit as an estimator of the variation of leaf pigments (chlorophyll-a, chlorophyll-b) content for a range of mangrove species. ii. Assess changes in chlorophyll-a, leaf area, leaf length, and Leaf Area Index between the dry and rainy seasons in a variety of mangrove classes. iii. Assess the seasonal importance of in situ hyperspectral measurements (e.g. 450-1000 nm) for chlorophyll-a determination in a variety of mangrove species. And finally, iv. Determine whether an object-based image analysis approach can provide an accurate classification of mangroves from spaceborne Synthetic Aperture Radar data. The results from these studies could provide reliable information regarding seasonal ecological assessments of mangrove forests using in situ and remote sensing methods

Assessing the accuracy of the MODIS LAI 1-km product in southeastern United States loblolly pine plantations: Accounting for measurement variance from ground to satellite

Leaf area index (LAI), defined here as one-half of the total leaf area per unit ground surface area (Chen, 1996), has been estimated at a global scale from spectral data processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard two NASA EOS-AM spacecraft, Terra (launched in 1999) and Aqua (launched in 2002). The MOD15A2 LAI product is a 1 km global data product composited over an 8-day period and is derived from a three-dimensional radiative transfer model driven by an atmosphere corrected surface reflectance product (MOD09), a land cover product (MOD12) and ancillary information on surface characteristics. The United States Environmental Protection Agency (US EPA) initiated validation research (2002) in the evergreen needle leaf biome, as defined in the MOD12 classification, in a regional study located in the southeastern United States. The validation effort was prompted by the potential use of MODIS LAI inputs into atmospheric deposition and biogenic emission models developed within the US EPA Office of Research and Development. The MODIS LAI validation process involves the creation of a high spatial resolution LAI surface map, which when scaled to the MOD15A2 resolution (1 km) allowed for comparison and analysis with the 1 km MODIS LAI product. Creation of this LAI surface map involved: (1) the collection of in situ LAI measurements via indirect optical measurements, (2) the correlation of land cover specific LAI estimates with spectral values retrieved from high resolution imagery (20 m--30 m), and (3) the aggregation of these 30 m cells to 1 km spatial resolution, matching the resolution of the MODIS product and enabling a comparison of the two LAI values (Morisette et al. 2006). This research assessed the uncertainty associated with the creation of the high-resolution LAI reference map, specifically addressing uncertainty in the indirect in situ optical measurements of LAI and the uncertainty in the land cover classification process. Also addressed was the influence of vegetative understory on satellite-derived vegetation indices from the IKONOS sensor