The potential for increasing rubber production by matching tapping intensity to leaf area index

Understanding resource capture can help design appropriate species combinations, planting designs and management. Leaf area index (LAI) and its longevity are the most important factors defining dry matter production and thus growth and productivity. The ecophysiological modifications and yield of rubber (Hevea spp.) in an agroforestry system (AFS) with beans (Phaseolus vulgaris L.) were studied. The experiment was established in Southeast-Brazil, with three rubber cultivars: IAN 3087, RRIM 600 and RRIM 527. The AFS comprised double rows of rubber trees along with beans sown in autumn and winter seasons in 1999. There was about 50% higher rubber yield per tree in the AFS than the rubber monoculture. Trees within the AFS responded to higher solar radiation availability with higher LAI and total foliage area, allowing its greater interception. All three cultivars had higher LAI in the AFS than monoculture, reaching maximum values in the AFS between April and May of 3.17 for RRIM 527; 2.83 for RRIM 600 and 2.28 for IAN 3087. The maximum LAI values for monocrop rubber trees were: 2.65, 2.62 and 1.99, respectively, for each cultivar. Rubber production and LAI were positively correlated in both the AFS and monoculture but leaf fall of rubber trees in the AFS was delayed and total phytomass was larger. It is suggested that trees in the AFS were under exploited and could yield more without compromising their life cycle if the tapping system was intensified. This shows how knowledge of LAI can be used to manage tapping intensity in the field, leading to higher rubber yield.

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and interannually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R(2) = 0.80 and R(2) = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages. (C) 2010 Elsevier Inc. All rights reserved.

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km2 area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.

Preliminary leaf area index estimates from airborne small footprint full-waveform LiDAR data

This study has compared preliminary estimates of effective leaf area index (LAI) derived from fish-eye lens photographs to those estimated from airborne full-waveform small-footprint LiDAR data for a forest dataset in Australia. The full-waveform data was decomposed and optimized using a trust-region-reflective algorithm to extract denser point clouds. LAI LiDAR estimates were derived in two ways (1) from the probability of discrete pulses reaching the ground without being intercepted (point method) and (2) from raw waveform canopy height profile processing adapted to small-footprint laser altimetry (waveform method) accounting for reflectance ratio between vegetation and ground. The best results, that matched hemispherical photography estimates, were achieved for the waveform method with a study area-adjusted reflectance ratio of 0.4 (RMSE of 0.15 and 0.03 at plot and site level, respectively). The point method generally overestimated, whereas the waveform method with an arbitrary reflectance ratio of 0.5 underestimated the fish-eye lens LAI estimates.

Specific leaf area and leaf area index in developing stands of Fagus sylvatica L. and Picea abies Karst

European beech (Fagus sylvatica L.) and Norway spruce (Picea abies Karst.) are two of the most ecologically and economically important forest tree species in Europe. These two species co-occur in many locations in Europe, leading to direct competition for canopy space. Foliage characteristics of two naturally regenerated pure stands of beech and spruce with fully closed canopies were contrasted to assess the dynamic relationship between foliage adaptability to shading, stand LAI and tree growth. We found that individual leaf size is far more conservative in spruce than in beech. Individual leaf and needle area was larger at the top than at the bottom of the canopy in both species. Inverse relationship was found for specific leaf area (SLA), highest SLA values were found at lowest light availability under the canopy. There was no difference in leaf area index (LAI) between the two stands, however LAI increased from 10.8 to 14.6 m2m-2 between 2009 and 2011. Dominant trees of both species were more efficient in converting foliage mass or area to produce stem biomass, although this relationship changed with age and was species-specific. Overall, we found larger foliage plasticity in beech than in spruce in relation to light conditions, indicating larger capacity to exploit niche openings.

An improved estimate of leaf area index based on the histogram analysis of hemispherical photographs

Leaf area index (LAI) is a key parameter that affects the surface fluxes of energy, mass, and momentum over vegetated lands, but observational measurements are scarce, especially in remote areas with complex canopy structure. In this paper we present an indirect method to calculate the LAI based on the analyses of histograms of hemispherical photographs. The optimal threshold value (OTV), the gray-level required to separate the background (sky) and the foreground (leaves), was analytically calculated using the entropy crossover method (Sahoo, P.K., Slaaf, D.W., Albert, T.A., 1997. Threshold selection using a minimal histogram entropy difference. Optical Engineering 36(7) 1976-1981). The OTV was used to calculate the LAI using the well-known gap fraction method. This methodology was tested in two different ecosystems, including Amazon forest and pasturelands in Brazil. In general, the error between observed and calculated LAI was similar to 6%. The methodology presented is suitable for the calculation of LAI since it is responsive to sky conditions, automatic, easy to implement, faster than commercially available software, and requires less data storage. (C) 2008 Elsevier B.V. All rights reserved.

TANZANIA GRASS STRUCTURE GRAZED BY GOATS MANAGED WITH DIFFERENT RESIDUAL LEAF AREA INDEX UNDER INTERMITTENT STOCKING

The objective of this study was to evaluate the structure of Tanzania grassland grazed by goats managed with different residue leaf area index (RLAI) under intermittent stocking. The experiment was carried out from February to August, 2008. The treatments consisted of three different targets RLAI (0.8, 1.6 and 2.4) and 95% light interception (LI) criterion determined the rest period. Forage samples were collected at average height sampling points and weighed. Subsequently, a smaller sample was removed to separate the morphological components (leaf, stem and dead material) and to determine the structural and productive features. The canopy architecture was evaluated by the method of inclined point quadrat. The pre-grazing height in the paddocks were significantly different among treatments. RLAI influenced dry matter contents of green forage, leaf, stem and total, with the exception of dry matter of dead material, where the lowest values were observed for 0.8 RLAI. Thus, RLAI modifies canopy structure and is sensitive to canopy height changes throughout the year. Pasture regrowth is not compromised by residual leaf area indexes between 0.8 and 2.4, when climatic factors are not limiting.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI mayor que 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.

Sugarcane leaf area estimate obtained from the corrected normalized difference vegetation index (NDVI)

v. 46, n. 2, p. 140-148, apr./jun. 2016.

Improved methods for predicting individual leaf area and leaf senescence in maize (Zea mays)

The ability to predict leaf area and leaf area index is crucial in crop simulation models that predict crop growth and yield. Previous studies have shown existing methods of predicting leaf area to be inadequate when applied to a broad range of cultivars with different numbers of leaves. The objectives of the study were to (i) develop generalised methods of modelling individual and total plant leaf area, and leaf senescence, that do not require constants that are specific to environments and/or genotypes, (ii) re-examine the base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence, and (iii) assess the method of calculation of individual leaf area from leaf length and leaf width in experimental work. Five cultivars of maize differing widely in maturity and adaptation were planted in October 1994 in south-eastern Queensland, and grown under non-limiting conditions of water and plant nutrient supplies. Additional data for maize plants with low total leaf number (12-17) grown at Katumani Research Centre, Kenya, were included to extend the range in the total leaf number per plant. The equation for the modified (slightly skewed) bell curve could be generalised for modelling individual leaf area, as all coefficients in it were related to total leaf number. Use of coefficients for individual genotypes can be avoided, and individual and total plant leaf area can be calculated from total leaf number. A single, logistic equation, relying on maximum plant leaf area and thermal time from emergence, was developed to predict leaf senescence. The base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence were 8, 34, and 40 degrees C, and apply for the whole crop-cycle when used in modelling of leaf senescence. Thus, the modelling of leaf production and senescence is simplified, improved, and generalised. Consequently, the modelling of leaf area index (LAI) and variables that rely on LAI will be improved. For experimental purposes, we found that the calculation of leaf area from leaf length and leaf width remains appropriate, though the relationship differed slightly from previously published equations.

Predicting plant leaf area production: shoot assimilate accumulation and partitioning, and leaf area ratio, are stable for a wide range of sorghum population densities

Predicting plant leaf area production is required for modelling carbon balance and tiller dynamics in plant canopies. Plant leaf area production can be studied using a framework based on radiation intercepted, radiation use efficiency (RUE) and leaf area ratio (LAR) (ratio of leaf area to net above-ground biomass). The objective of this study was to test this framework for predicting leaf area production of sorghum during vegetative development by examining the stability of the contributing components over a large range of plant density. Four densities, varying from 2 to 16 plants m(-2), were implemented in a field experiment. Plants were either allowed to tiller or were maintained as uniculm by systematic tiller removal. In all cases, intercepted radiation was recorded daily and leaf area and shoot dry matter partitioning were quantified weekly at individual culm level. Up to anthesis, a unique relationship applied between fraction of intercepted radiation and leaf area index, and between shoot dry weight accumulation and amount of intercepted radiation, regardless of plant density. Partitioning of shoot assimilate between leaf, stem and head was also common across treatments up to anthesis, at both plant and culm levels. The relationship with thermal time (TT) from emergence of specific leaf area (SLA) and LAR of tillering plants did not change with plant density. In contrast, SLA of uniculm plants was appreciably lower under low-density conditions at any given TT from emergence. This was interpreted as a consequence of assimilate surplus arising from the inability of the plant to compensate by increasing the leaf area a culm could produce. It is argued that the stability of the extinction coefficient, RUE and plant LAR of tillering plants observed in these conditions provides a reliable way to predict leaf area production regardless of plant density. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

(Table 3) Leaf area and leaf nitrogen for deciduous, evergreen, forb and graminoid species at Barrow, Svalbard and Zackenberg

Arctic vegetation is characterized by high spatial variability in plant functional type (PFT) composition and gross primary productivity (P). Despite this variability, the two main drivers of P in sub-Arctic tundra are leaf area index (LT) and total foliar nitrogen (NT). LT and NT have been shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-scaling by allowing quantification of the main drivers of P from remotely sensed LT. Our objective was to test the LT-NT relationship across multiple Arctic latitudes and to assess LT as a predictor of P for the pan-Arctic. Including PFT-specific parameters in models of LT-NT coupling provided only incremental improvements in model fit, but significant improvements were gained from including site-specific parameters. The degree of curvature in the LT-NT relationship, controlled by a fitted canopy nitrogen extinction co-efficient, was negatively related to average levels of diffuse radiation at a site. This is consistent with theoretical predictions of more uniform vertical canopy N distributions under diffuse light conditions. Higher latitude sites had higher average leaf N content by mass (NM), and we show for the first time that LT-NT coupling is achieved across latitudes via canopy-scale trade-offs between NM and leaf mass per unit leaf area (LM). Site-specific parameters provided small but significant improvements in models of P based on LT and moss cover. Our results suggest that differences in LT-NT coupling between sites could be used to improve pan-Arctic models of P and we provide unique evidence that prevailing radiation conditions can significantly affect N allocation over regional scales.