Canopy Analyzer Research Articles

Leaf Area Index (LAI) in different type of agroforestry systems based on hemispherical photographs in Cidanau Watershed

Ecological functions of agroforestry systems have perceived benefit to people around Cidanau Watershed, especially in the protection of water quality. The main causes of the problems encountered in the Cidanau Watershed are associated with the human factors, especially encroachment and conversion of forest into farmland. The encroachment has made most forest in Cidanau Watershed become bare land. To preserve the ecological function of agroforestry systems in Cidanau Watershed, monitoring of the condition of the vegetation canopy in agroforestry systems is really needed. High intensity thinning of crown density due to deforestation can change stand leaf area index dramatically. By knowing LAI, we can assess the condition of the vegetation canopy in agroforestry systems. LAI in this research was obtained from Hemispherical Photographs analysis using the threshold method in HemiView Canopy Analysis Software. Our research results indicate that there are six types of agroforestry in Cidanau Watershed i.e. Sengon Agroforestry, Clove Agroforestry, Melinjo Agroforestry, Chocolate Agroforestry, Coffee Agroforestry, and Complex Agroforestry. Several factors potentially contribute to variations in the value of LAI in different types of agroforestry. The simple assumptions about differences ranges of LAI values on six types of agroforestry is closely related to leaf area and plant population density.

LEAF AREA DYNAMICS AND ABOVEGROUND BIOMASS OF SPECIFIC VEGETATION TYPES OF A SEMI-ARID GRASSLAND IN SOUTHERN ETHIOPIA

Leaf Area Index (LAI) dynamics and aboveground biomass of a semi-arid grassland region in Southern Ethiopia were determined over a long rain season. The vegetation was categorized into four distinct vegetation types namely Grassland (G), Tree-Grassland (TG), Bushed-Grassland (BG) and Bush-Tree grassland (BT). LAI was measured using a Plant Canopy Analyzer (LAI2000). Biomass dynamics of litter and herbaceous components were determined through clipping while the above ground biomass of trees and shrubs were estimated using species-specific allometric equations from literature. LAI showed a seasonal increase over the season with the maximum recorded in the BG vegetation (2.52). Total aboveground biomass for the different vegetation types ranged from 0.61 ton C/ha in areas where trees were non-existent to 8.80 ± 3.81ton C/ha in the Tree-Grassland vegetation in the study site. A correlation of LAI and AGB yielded a positive relationship with an R<sup>2</sup> value of 0.55. The results demonstrate the importance of tropical semi-arid grasslands as carbon sinks hence their potential in mitigation of climate change.

A Novel Approach for Retrieving Tree Leaf Area from Ground-Based LiDAR

Leaf area is an important plant canopy structure parameter with important ecological significance. Light detection and ranging technology (LiDAR) with the application of a terrestrial laser scanner (TLS) is an appealing method for accurately estimating leaf area; however, the actual utility of this scanner depends largely on the efficacy of point cloud data (PCD) analysis. In this paper, we present a novel method for quantifying total leaf area within each tree canopy from PCD. Firstly, the shape, normal vector distribution and structure tensor of PCD features were combined with the semi-supervised support vector machine (SVM) method to separate various tree organs, i.e., branches and leaves. In addition, the moving least squares (MLS) method was adopted to remove ghost points caused by the shaking of leaves in the wind during the scanning process. Secondly, each target tree was scanned using two patterns, i.e., one scan and three scans around the canopy, to reduce the occlusion effect. Specific layer subdivision strategies according to the acquisition ranges of the scanners were designed to separate the canopy into several layers. Thirdly, 10% of the PCD was randomly chosen as an analytic dataset (ADS). For the ADS, an innovative triangulation algorithm with an assembly threshold was designed to transform these discrete scanning points into leaf surfaces and estimate the fractions of each foliage surface covered by the laser pulses. Then, a novel ratio of the point number to leaf area in each layer was defined and combined with the total number of scanned points to retrieve the total area of the leaves in the canopy. The quantified total leaf area of each tree was validated using laborious measurements with a LAI-2200 Plant Canopy Analyser and an LI-3000C Portable Area Meter. The results showed that the individual tree leaf area was accurately reproduced using our method from three registered scans, with a relative deviation of less than 10%. Nevertheless, estimations from only one scan resulted in a deviation of >25% in the retrieved individual tree leaf area due to the occlusion effect. Indeed, this study provides a novel connection between leaf area estimates and scanning sensor configuration and supplies an interesting method for estimating leaf area based on PCD.

An automated method to quantify crop height and calibrate satellite-derived biomass using hypertemporal lidar

Crop biomass information is of great importance for a variety of applications, ranging from supporting farm management decisions to modeling the crop-environment system. Dimensionless spectral vegetation index values derived from satellite imagery are commonly used to derive crop biomass. However, the highly empirical nature of spectrally derived biomass estimates requires frequent and costly calibration with manually collected ground data. Recently, low cost, autonomously operating terrestrial laser scanners (ATLSs) have become available for near-surface applications. In contrast to the dimensionless nature of spectral index values, autonomous light detection and ranging (lidar) technology measures physical vegetation structure by recording the x, y, z coordinates of canopy components at very high spatial (<10cm), and temporal (<2days) resolution. The objective of this study was to assess the suitability of an ATLS to i) monitor crop growth dynamics and ii) calibrate satellite imagery for estimating crop biomass. Wheat (Triticum aestivum spp.) growth was monitored by acquiring hypertemporal (every 28h for a full growing season) ATLS data at three different field sites across a range of experimentally manipulated crop growth conditions. The ATLS-derived crop height explained nearly three-quarters of the variability in destructively sampled wheat biomass (r2=0.74, RMSE=514.20kgha−1), showing a slightly stronger correlation to crop biomass than did leaf area index (LAI) measurements collected in the field using a LAI-2000 Plant Canopy Analyzer (r2=0.71, RMSE=546kgha−1). Satellite-based crop biomass estimates calibrated with ATLS data captured the variability in wheat biomass throughout a farm field with a biomass error of 730.96 and 727.60kgha−1 (RMSE) during the jointing (Development stage: Zadoks 37) and heading (Development stage: Zadoks 50) growth stages, respectively. These findings suggest that the hypertemporal lidar information provided via ATLS technology could constitute a major step forward in operational monitoring and mapping of crop biomass.

Evaluation of cultivation environment and management based on LAI measurement in farmers’ paddy fields in Pursat province, Cambodia

Rice is by far the most important crop in Cambodia. The improvement of its productivity is strongly recommended, but information about cultivation environment, management and their effects on rice production in farmers’ paddy fields is quite limited. This study periodically measured the leaf area index (LAI) by using a plant canopy analyzer and conducted yield, water, soil and management investigations in 77 farmers’ paddy fields in the Bakan district, Pursat province, Cambodia in 2014. Because the LAI showed linear growth in almost all farmers’ fields, the LAI growth rate was determined by linear regression. The grain yield ranged from 70.8gm−2 to 499.0gm−2 and was positively correlated with the maximum LAI. The variability in the maximum LAI was mainly explained by the LAI growth rate, while the growth period had a significant correlation with the maximum LAI only in the broadcast fields, where earlier planting led to larger LAI. The result of the ANCOVA shows that the LAI growth rate was affected by seeding date, water status and soil condition (C content and C/N ratio) while the effect of planting method (transplanting/broadcasting) and N fertilizer was non-significant. The non-significance of effect of N fertilizer may imply the timing or method of application is improper. The results suggest that the key means to improve rice productivity are effective application of fertilizer and selection of planting methods dependent on the soil fertility, together with water-saving irrigation method.

Performance of LAI-2200 Plant Canopy Analyzer on Leaf Area Index of Jatropha Nut Estimation

Leaf Area Index (LAI) of crops can be determined by many different methodologies with direct or indirect measurements. The use of sensors to estimate LAI has the advantage of fast and easy measurements, however, it is necessary to test and calibrate in order to verify its reliability. The objective of this study was to test and calibrate the LAI-2200 plant canopy analyzer on the estimative of LAI of jatropha nut trees. Methodology: The study was conducted in the experimental area of Biosystem Engineering, Department of ESALQ/USP, in Piracicaba, Sao Paulo, Brazil. The test was performed with 9 jatropha nut plants, by comparing estimated LAI data with LAI-2200 equipment with real LAI by destructive sampling. Results: The isolate plant measurement overestimated around 30% of real LAI and the pattern measurements underestimated around 10%. Conclusion: Both models were considered adequate according to the statistical parameters results and they can be used to correct estimated LAI in order to present better result of LAI for jatropha nut.

TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

Abstract. Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

Measuring leaf area index from colour digital image of wheat crop

Leaf area index (LAI) is an important physiological trait that determines solar radiation interception and thus biomass. In this study leaf area index (LAI) was estimated from vertical gap fraction derived from top-of-canopy digital colour photography ofwheat canopies. An improved vegetation index, Excess Green minus Excess Red (ExG-ExR) was compared to the commonly used Excess Green (ExG), Excess Red (ExR) and normalized difference (NDI) indices. A histogram-based threshold technique was used to separate green vegetation tissues from background soil in order to derive the canopy vertical gap fraction. LAI derived from the ExG-ExR, ExG indexed image was comparable to the LAI measured using the commercial plant canopy analyzer (LAI-2200,LI-CORInc., USA) (R2 = 0.68 and 0.66 for ExG-ExRand ExG, respectively) with RMSE of 0.63 and 0.79, respectively.However, NDI was overestimated while Ex Rwas found to be under estimated LAI as compared with that measured using the commercial plant canopy analyzer(R2 = 0.47 and 0.35 for NDI and ExR, respectively) with RMSE of 4.09 and 2.19, respectively. Thus, digital photography based ExG-ExRmethod can be used as low cost, non-destructive high through put method for assessing LAI, early vigour and gap fraction of wheat and potentially other cereal crops.

Evaluation of Indirect Measurement Method of Seasonal Patterns of Leaf Area Index in a High-Density Short Rotation Coppice Culture of Poplar

Leaf area index (LAI) is an important determinant of biomass production and yield of short rotation bio-energy plantation. An accurate measurement of LAI is critical for quantifying light interception and penetration within the canopy, and subsequently understanding its influence on the stand carbon and energy balance. The aim of the current study is validation of the Sunscan Plant Canopy Analyzer which serves as an indirect method for the evaluation of the seasonal patterns of LAI, relation between LAI and above ground woody dry biomass and to determine the specific leaf area in short-rotation poplar hybrid clone J-105 (Populus nigra × P. maximowiczii) in uncoppiced (1st rotation) and coppiced (2nd rotation), respectively. LAI was measured in uncoppiced and coppiced by two different methods using indirect (SunScan Plant Canopy Analyzer) and direct (litterfall collection). Sunscan Plant Canopy Analyzer was compared against litterfall collection (only way to retrieve the actual LAI). Simple regression (R2 = 0.82) model was fitted to validate indirect measurement method and a very good agreement (82%) was observed in LAI values estimated from SunScan Plant Canopy Analyzer and from litterfall collection. Seasonal variability of LAI in a short rotation coppice (SRC) culture of poplar clone J-105 was evaluated over six years period (2008–2013), for uncoppiced (2008 and 2009) and coppiced (2010, 2011, 2012 and 2013) culture. The maximum canopy LAI (LAImax) reached 7.3 (uncoppiced) and 9.5 (coppiced). The linear regression (R2 = 0.93) for average LAI and above ground woody dry biomass was determined, and it was found that LAI acts an indicator of biomass productivity. Specific leaf area (SLA) was estimated in both uncoppiced and coppiced culture of poplar. The maximum SLA was found to be 138.9 cm2g−1 in uncoppiced and 126.9 cm2g−1 in coppiced. To conclude, the evaluated indirect LAI measurement method is portable, reliable and faster than direct LAI measurement in high density poplar short-rotation coppice culture.

Study of the Remote Sensing Model of FAPAR over Rugged Terrains

Mountainous areas with rugged terrains are widely distributed around the world. Remotely sensed values of the fraction of absorbed photosynthetically active radiation (FAPAR) suffer from the effect of rugged terrain. In this study, the effect of rugged terrain was incorporated into the FAPAR model based on recollision probability (FAPAR-P), which was improved in two aspects: calculating the sky viewing factor to correct for the fraction of diffuse sky radiation to the total radiation, and correcting the interception probability according to the slope and aspect of each pixel. The newly developed model is called FAPAR-PR (FAPAR-P Model for Rugged Terrain Area). Two study areas were chosen to validate the proposed model: the Dayekou watershed in Gansu Province, and Weichang in Hebei Province, China. The FAPAR values derived from the models were compared with FAPAR values measured in situ using photon flux sensors and the SunScan canopy analysis system (Delta-T Devices Ltd., Cambridge, UK). The validation results show that the FAPAR-PR model is applicable to rugged terrain areas, and it achieves a high level of accuracy. The FAPAR retrieval at different scales was also conducted to estimate the effect of terrain on the FAPAR-P and FAPAR-PR models. In our chosen study area, the effect of rugged terrain was significant in fine resolution pixels, but it was not obvious at larger scales, as the effects of slope and aspect were partly eliminated by the upscaling of the digital elevation model.

Specular reflection in the signal of LAI-2000 plant canopy analyzer

Abstract In a vegetation canopy part of leaves reflect sky radiation specularly to the LAI-2000 sensor. This fraction is smaller in a planophile canopy and higher in an erectophile canopy. As the specularly reflected radiation does not enter the leaf nor it is absorbed by leaf pigments and depending on the incidence angle on the leaf the reflection coefficient may be very high. Depending on the leaf area index (LAI) and leaf angle distribution the scattered sky radiation may cause an overestimation of the gap fraction and consequently an underestimation of LAI up to 20–25% if the LAI measurements with plant canopy analyzer LAI-2000 are carried out under ideal perfectly overcast conditions. Similar biases are present in measurements of gap fraction and LAI with hemispherical photos if these measurement procedures are calibrated against LAI-2000. Considering the scattered sky radiation in optical techniques (LAI-2000, hemispherical photos) of measuring gap fraction in vegetation canopies explains at least part of the observed discrepancies in gap fraction estimated with terrestrial laser scanner and hemispherical photos. The scattered sky radiation in the LAI-2000 signal introduces bias both in the estimated LAI and foliage clumping index.

Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances.

Comparison of optical LAI measurements under diffuse and clear skies after correcting for scattered radiation

Abstract Leaf area index (LAI) is a forest canopy variable that is closely related to forest growth and health. The LAI-2000 Plant Canopy Analyzer is widely used for indirect measurement of LAI; however, use of the instrument is limited to diffuse sky conditions, greatly restricting sampling. A new bidirectional transmission model allows measurements to be obtained under clear sky conditions. This is accomplished by calculating a correction factor to reduce the impact of scattered light from canopy elements on gap fraction estimates. In this study we evaluate this technique by contrasting LAI measurements taken under diffuse and clear skies in a Pinus radiata D. Don plantation. We also evaluate the importance of obtaining accurate needle optical properties to parameterise the scattering correction model. Clear sky LAI estimates calculated with (a) measured optical values of P. radiata needles, (b) default values from the instrument software, and (c) maximum published values were compared to diffuse sky LAI estimates. Agreement was strongest where measured optical properties were used ( R 2 = 0.87), with the relationship weakening under the default ( R 2 = 0.78), and maximum value ( R 2 = 0.67) scenarios. Under these three scenarios average clear sky LAI exceeded diffuse sky LAI by 16%, 17%, and 22%, respectively. The disagreement was due in part to erroneous measurements from the outer sensor ring under diffuse skies. With these data removed agreement improved markedly under all scenarios ( R 2 = 0.94, 0.89, and 0.75, respectively), and the mean differences under each scenario declined to 8%, 9%, and 15%, respectively. Measurement of LAI under clear skies appeared to reduce error in the outer ring, greatly reduced logistical constraints, and reduced errors associated with sky variability.

Radiometric calibration of a dual-wavelength terrestrial laser scanner using neural networks

ABSTRACTThe Salford Advanced Laser Canopy Analyser (SALCA) is a unique dual-wavelength full-waveform terrestrial laser scanner (TLS) designed to measure forest canopies. This article has two principle objectives, first to present the detailed analysis of the radiometric properties of the SALCA instrument, and second, to propose a novel method to calibrate the recorded intensity to apparent reflectance using a neural network approach. The results demonstrate the complexity of the radiometric response to range, reflectance, and laser temperature and show that neural networks can accurately estimate apparent reflectance for both wavelengths (a root mean square error (RMSE) of 0.072 and 0.069 for the 1063 and 1545 nm wavelengths, respectively). The trained network can then be used to calibrate full hemispherical scans in a forest environment, providing new opportunities for quantitative data analysis.

Parameterization of leaf growth in rice (Oryza sativa L.) utilizing a plant canopy analyzer

Monitoring leaf growth is a useful method for evaluating crop growth. In this study, frequent measurements of the rice canopy were conducted with a plant canopy analyzer, and parameters were estimated by applying several equations to evaluate leaf growth. The applicability of this evaluation method is discussed based on the statistical analysis of parameters. Experiments were conducted for 6 rice cultivars under 5 treatments in 2 years. Parameters describing the dynamics of leaf growth were obtained by applying 4 mathematical growth models in which the independent variable was the effective accumulated temperature. The parameters numerically represented the dynamics of leaf growth in each cultivar and treatment. In particular, maximum LAI and interception growth rates (MLGR and MIGR) which were obtained by applying logistic function to leaf area index (LAI) and diffuse non-light intercepted (DIFN), respectively, showed no interaction between the cultivar and environment. The lack of interaction may indicate that these parameters typically characterize cultivar and growth environments. The differences in the parameters could also be used to quantify the effect of basal fertilizer application on leaf growth enhancement and that of additional fertilizer on the extension of this enhancement. These results suggest that the applied method for parameterizing leaf growth based on frequent measurements with a canopy analyzer is suitable for evaluating the effects of genotype, management and the environment because it facilitates measurements in many plots.

Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar

ABSTRACTAirborne discrete-return light detection and ranging (lidar) can be used to estimate leaf area index (LAI) with relatively high accuracy. This capacity was explored with regard to assessing the capability of estimating LAI at different heights at the plot level, in the presence of understorey vegetation, within intensively managed Loblolly pine forest in North Carolina, USA. Field measurements utilized the LI-COR LAI-2200 plant canopy analyser for field-based estimates of effective LAI at three elevations within each plot; these were on the ground (0.0 m) and 1.0 m and 2.5 m above the ground within the various understorey heights and densities. A number of new and previously existing lidar metrics and indices were calculated from the distribution of return heights, which have been identified as potentially strong predictors of LAI. A bivariate and stepwise regression approach was then applied to create models for the estimation of LAI from lidar-derived height distribution metrics. The results show that specific logarithm transformed laser penetration indices calculated using a height threshold (e.g. the number of returns below 2.5 m ratioed against all returns) as close to field LAI measurement height (e.g. 2.5 m) was more effective than other lidar metrics. LAI can be estimated for each of the three measurement heights within the understorey component explaining 67 to 76% of the variance (root mean square error 0.42–0.57). The indices that produced the highest correlations and which were selected in stepwise regression analysis were calculated using all returns. The results indicate that LAI can be estimated accurately using lidar data in pine plantation forest over a variety of stand conditions.

Spectroscopic Analysis of Green, Desiccated and Dead Tamarisk Canopies

AbstractDefoliation by the northern tamarisk beetle (Diorhabda cari- nulata) causes changes in the reflectance of tamarisk (Tama- rix spp.) canopies. Cross correlogram spectral matching was used to examine spectral separability of green, yellow desiccated, brown desiccated, and dead tamarisk canopy types. Using a feature selection technique (the instability index), four spectral regions were identified as important for canopy type discrimination, including one red (645-693 nm), one near infrared (735-946 nm), and two shortwave infrared regions (1,960-2,090 nm and 2,400-2,478 nm). The random forests decision tree algorithm was used to compare classification performances of full-range and feature-selected hyperspectral spectra as well as simulated WorldView-2 spectra. Classification results indicated that the process of feature selection can reduce data redundancy and computation time while improving accuracy of tamarisk canopy type classification.

English

Leaf area is one of the most important biophysical characteristics of a plant, as it determines how much photosynthetic radiation is intercepted so that the photosynthesis can occur. The presented research aim was to generate numerical indicators of leaf area and to study the behavior of black wattle (Acacia mearnsii De Wild.) throughout a plantation cycle (7 years), relating them with environmental variables. The study was conducted in commercial stands located in agroecological regions with high densities of black wattle plantations in the state of Rio Grande do Sul, Brazil. The leaf area was obtained by measuring leaf biomass and leaf area with an integrator (3000 Canopy Analyser, Li-Cor). Leaf area was found to be strongly related to leaf biomass, and is not influenced by planting site, but rather by stand age. Values ​​of individual leaf area vary depending on stand age and plantation area. The leaf area is related to soil properties and particularly with the phosphorus content. It is also related to meteorological conditions, most notably accumulated solar radiation. The obtained model, which involves global accumulated solar radiation, phosphorus and clay content, adequately explain variations in leaf area. &nbsp; Key words: Global solar radiation, phosphorus, leaf biomass, black wattle, plantation cycle.

Leaf area allometrics and morphometrics in baldcypress

Leaf area relationships are important physiologically and ecologically but are not well studied in baldcypress (Taxodium distichum (L.) Rich. var. distichum). Tree leaf area (LA) and leaf area index (LAI) were measured in a wetland in southern Louisiana by dissecting crowns of felled trees and by scaling stand-level measurements with allometry. Branchlet morphology ranged from flat and open with high specific leaf area (87.2 ± 30.7 cm2·g−1; mean ± SD) to scaled with appressed leaves and low specific leaf area (22.1 ± 11.6 cm2·g−1). Leaves were more appressed higher in the canopy. Tree LA was strongly related to sapwood basal area (SBA), and SBA was related to diameter; these allometric relationships enabled estimating LA from diameters. At the plot level, LAI estimated by allometric relationship (ranging from 1.8 to 10.2) was not linearly related to output from an optical canopy analyzer measuring light extinction; ratios of allometric to optical methods were 0.8 for the sparsest plot and 2.4 for the densest plot. LAI was less in deeper flooded plots (3.6 ± 0.6) than in transiently flooded plots (8.4 ± 0.6), but it is unclear whether this represents a difference in maximum LAI or delayed attainment of maximum LAI in lower areas.