Calculation Of Leaf Area Index Research Articles

Computation of evapotranspiration using crop simulation models and comparison with leaf area index from multiple sources

The study demonstrated the computation of evapotranspiration (ET) in cultivation of groundnut (Arachis hypogaea) through the application of crop simulation models, alongside a comparative analysis with leaf area index (LAI) from various sources. The cultivation period for groundnut was conducted during the calendar year 2023, during which 2 distinct growth patterns were noted, attributed to variations in environmental conditions. The study analyses the estimated evapotranspiration from AquaCrop model, utilizing crop coefficient (Kc) values in comparison with evapotranspiration data derived from satellite observations provided by the MOD16A2v061 product. Furthermore, LAI was measured through 3 methodologies: an empirical equation based on field data, the Li-Cor 2200 plant canopy analyzer and LAI calculations derived from cloud-free normalized difference vegetation index (NDVI). LAI obtained from Li-Cor 2200 instrument exhibited a higher degree of consistency in correlation with empirical LAI derived from ground observations. Conversely, LAI values calculated using the normalized difference vegetation index (NDVI) demonstrated a greater degree of variability, especially during times of cloud cover. The study emphasizes the relationship between LAI and ET and magnitude of LAI in amount of total evapotranspiration.

Correcting for the clumping effect in leaf area index calculations using one-dimensional fractal dimension

The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from −13.5% to 9.9% for DCP images, from −3.0% to 19.7% for DHP images, and from −3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI.

A comparative study for obtaining effective Leaf Area Index from single Terrestrial Laser Scans by removal of wood material

Leaf Area Index (LAI) is a dimensionless parameter that has a significant impact on forestry applications. With conventional methods, LAI can be calculated with destructive sample collection or with a relatively new non-destructive method called hemispherical photography. With the engagement of surveying instruments in forestry, obtaining LAI value for large areas in a short time has recently become more prominent and possible with the use of Terrestrial Laser Scanners (TLS). Although promising, TLS data evaluation techniques for LAI calculation are still subject to development. This paper aims to make a comparative evaluation of existing novel techniques with newly proposed methods and incorporates the use of neural networks and connected component analysis for segmentation purposes. The in-situ measurements, as a case study, were conducted in Istanbul- University-Cerrahpasa research forest – a part of Belgrad forest – Istanbul, Turkey. The Results obtained from the study show that segmentation and removal of wood materials from forest point cloud data, by using neural network algorithms and connected component analysis methods, albeit time and resource consuming, have a promising future on the calculation of effective LAI values of large areas.

Impact of Environmental Conditions on Grass Phenology in the Regional Climate Model COSMO-CLM

Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) at three locations in Germany covering the period 1999 to 2015 in order to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the annually-recurring standard phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model show a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI improves the representation of vegetation in years with extremely warm winter/spring (e.g., 2007) or extremely dry summer (e.g., 2003) and shows a more realistic growth period. The effect of the newly implemented phenology on atmospheric variables is small but tends to be positive. It should be used in future applications with an extension on more plant functional types.

Comparison of LiDAR-based Models for True Leaf Area Index and Effective Leaf Area Index Estimation in Young Beech Forests

The leaf area index (LAI) is one of the most common leaf area and canopy structure quantifiers. Direct LAI measurement and determination of canopy characteristics in larger areas is unrealistic due to the large number of measurements required to create the distribution model. This study compares the regression models for the ALS-based calculation of LAI, where the effective leaf area index (eLAI) determined by optical methods and the LAI determined by the direct destructive method and developed by allometric equations were used as response variables. LiDAR metrics and the laser penetration index (LPI) were used as predictor variables. The regression models of LPI and eLAI dependency and the LiDAR metrics and eLAI dependency showed coefficients of determination (R2) of 0.75 and 0.92, respectively; the advantage of using LiDAR metrics for more accurate modelling is demonstrated. The model for true LAI estimation reached a R2 of 0.88.

Direct measurement of leaf area index in a deciduous needle-leaf forest, eastern Siberia

Accurate calculation of leaf area index (LAI) in boreal forests, which are vulnerable to climate change and have insufficient ground-truth data, is an important but challenging task when evaluating the spatio-temporal variability of ecosystem structures and functions (photosynthesis and evapotranspiration). By applying a direct method, we calculated the LAI of multiple forest layers (i.e., overstory of larch; understory of birch, willow, and alder; and floor vegetation) in a deciduous needle-leaf forest with a somewhat open canopy in eastern Siberia. We performed field studies that included logging one overstory larch tree, harvesting leaves of larch and floor vegetation (e.g., Vaccinium vitis-idaea), sampling leaves of understory trees, and measuring the architecture and morphology of overstory and understory trees and leaves. The plot-scale LAIs of larch overstory, understory trees, and floor vegetation were 1.30, 0.50, and 0.51, respectively, for a total of 2.31. Sum of our plot-scale LAI of larch overstory and understory trees was similar to previously reported results based on indirect methods. Our results indicate that LAI of understory trees and forest floor vegetation is important to improve the accuracy of total LAI of a deciduous needle-leaf forest with a somewhat open canopy in eastern Siberia.

Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

Calculation of leaf area index in a Canadian boreal forest using adaptive voxelization and terrestrial LiDAR

Leaf Area Index (LAI) is a significant indicator of the forest dynamics and the ecological processes such as, the balance of global carbon exchange, the energy cycle in photosynthesis, evapotranspiration mechanisms, and water/nutrient cycling. LAI can be calculated based on the 3D point cloud data (PCD) collected by Terrestrial Light Detection and Ranging (LiDAR). The methods presented in literature calculate LAI indirectly and by applying the auxiliary models and parameters such as radiative transfer model, gap fraction or the extinction coefficient. Conventionally, the LiDAR PCD is confined to a large cubic box and divided to a 3D array of voxels with uniform and arbitrary size. This generates a large number of empty voxels without any cloud points (OFF voxels). These OFF voxels are counted to calculate the gap fraction with a large error threshold. This paper addresses the mentioned drawbacks and presents a new simple and direct method for LAI calculation. In proposed method, intermediate auxiliary models and parameters are avoided. The LiDAR PCD is voxelized adaptively based on the variable local spatial point density and also the natural characteristics of the canopy. It does not require the large confining box around the tree PCD and counting of OFF voxels. In addition, the effect of varying laser beam diameter is considered in calculations. This method is applied to a Canadian boreal forest and the sensitivity of the calculated LAI to different parameters such as voxel size is analyzed.

The PILOTE-N model for improving water and nitrogen management practices: Application in a Mediterranean context

In this paper we present PILOTE-N, a crop model devoted to the calculation of crop production from the joint water and nitrogen soil status effects. It is the extension of PILOTE, for contexts in which water might not be the sole limiting factor for crop yield, but the same model structure and credo of parsimonious parameterisation have been kept, assuming simplified descriptions of the physical processes at play. One original aspect of PILOTE-N is the calculation of Leaf Area Index (LAI) and cumulative nitrogen plant demand from similar logistic-type functions. LAI is controlled by specific temperature sums, shape parameters and the occurrence of water and nitrogen stresses, while the time average of LAI values over critical phenological periods also affects the predicted harvest index and crop yield. As specific plant parameters are known from PILOTE, calibration was devoted to nitrogen parameters. Which governing the daily-averaged mineralization rate in a first step, then the two shape parameters of the potential nitrogen plant demand (from the dilution curve) in a second step and, at last, which allowing the link between nitrogen uptake and nitrogen of the soil. Model performance was evaluated using multiple initial soil conditions, irrigation and fertilization strategies for corn, durum wheat and sorghum, over a climatic series of 14 years, at the experimental plot of Lavalette (Montpellier, South-East of France), hence in a Mediterranean context characterized by severe, water stresses during summer, typically exceeding nitrogen stresses. Crop yield as well as the dynamics of nitrogen budget were correctly simulated (R2 > 0.94 for grain yield, total dry matter and nitrogen in plant). The robustness of such a simple and easy-to-calibrate tool is expected to facilitate its use and implementation in other agro-pedoclimatic contexts, to decipher the effect of abiotic stresses and improve irrigation and fertilization scenarios when included in dedicated tools.

A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing

Full-waveform LiDAR (FWL) decomposition results often act as the basis for key LiDAR-derived products, for example canopy height, biomass and carbon pool estimation, leaf area index calculation and under canopy detection. To date, the prevailing method for FWL product creation is the Gaussian Decomposition (GD) based on a non-linear Levenberg-Marquardt (LM) optimization for Gaussian node parameter estimation. GD follows a “greedy” approach that may leave weak nodes undetected, merge multiple nodes into one or separate a noisy single node into multiple ones. In this manuscript, we propose an alternative decomposition method called Linearly Approximated Iterative Gaussian Decomposition (LAIGD method). The novelty of the LAIGD method is that it follows a multi-step “slow-and-steady” iterative structure, where new Gaussian nodes are quickly discovered and adjusted using a linear fitting technique before they are forwarded for a non-linear optimization. Two experiments were conducted, one using real full-waveform data from NASA’s land, vegetation, and ice sensor (LVIS) and another using synthetic data containing different number of nodes and degrees of overlap to assess performance in variable signal complexity. LVIS data revealed considerable improvements in RMSE (44.8% lower), RSE (56.3% lower) and rRMSE (74.3% lower) values compared to the benchmark GD method. These results were further confirmed with the synthetic data. Furthermore, the proposed multi-step method reduces execution times in half, an important consideration as there are plans for global coverage with the upcoming Global Ecosystem Dynamics Investigation LiDAR sensor on the International Space Station.

Global Gap-Free MERIS LAI Time Series (2002–2012)

This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002–2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10°), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002–March 2012). Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R2 : 0.88) of agreement on a global scale.

QUANTITATIVE VEGETATION MAPPING OF URBAN AREA USING HIGH-RESOLUTION MULTISPECTRAL SATELLITE IMAGERY

Maintenance of green spaces within the city is very important under current ecological conditions. In the present context, assessment of green spaces held by visually determining indicators plantations clarifying its instrumental measurements. The accuracy of this method strongly depends on the qualifications and professional experience of the employee conducting an assessment. High spatial and temporal resolution satellite systems provide information for operational monitoring of environmental conditions in the city and promote management decision making. Normalized Difference Vegetation Index (NDVI) – index of plant “greenness” or photosynthetic activity – is widely used for vegetation studying. However, a more important phytometric parameter of vegetation is Leaf Area Index (LAI). Actually LAI is defined as the one sided green leaves area per unit of ground area in broadleaf canopies, or as the projected needle leaves area per unit of ground area in needle canopies. Multispectral imagery processing to evaluate the NDVI and LAI provides informative and reliable indicators of vegetation quantity. High-resolution image from Pleiades-1A satellite was used in the study. Complete geoinformation technology for integrated quantitative assessment of vegetation in urbanized territory provides a distribution of LAI within the study area. The first group of operations was used for preprocessing of multispectral satellite image. Output of these operations is a scene surface reflectance. The second group of operations comprises the steps of LAI calculations based on vegetation mask and LAI versus NDVI regression dependence. Final group of operations provides visualization and interpretation of the results. The methodology for calculating the vegetation area within the land registry boundaries gives biased values of green mass and its status. At the beginning of the growing season the regulatory area of green zone is much greater than the actual amount of vegetation. It is possible to determine more accurately the boundaries and the total area of vegetation cover using the NDVI. Also the described LAI-based geoinformation technology provides tuning out the current phase of the phenological cycle. As a result, more accurate estimation of vegetation amount at moment and its changes over time are available too. Further studies aimed at assessing not only the quantity but also the quality of vegetation. The increase in number of ground control points and number of in-situ measurements are planned to clarify the regression confidence. There will also be studying the state of plant communities at different phases of the phenological cycle. High-resolution image from Pleiades-1A satellite was kindly provided by TVIS-INFO LLC (TVIS) Company, which is one of the leading geospatial and GIS solutions provider in Ukraine.

Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., > 1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the creation of the LAI-RM: (1) the uncertainty associated with the indirect in situ optical measurements of southeastern United States needle-leaf LAI and (2) the uncertainty in the process of classifying land cover (LC). Uncertainty within the loblolly pine (Pinus taeda) in situ data collection was highest for the assessment of the plant area index (PAI), Le (27.2%), and the woody-to-total ratio, α, (30.6%). The needle-to-shoot ratio, λE, and the element clumping index, ΩE, contributed 14.9% and 9.3%, respectively, to the uncertainty in the calculation of LAI. Combining LC differences (3.4%) with the uncertainty within the loblolly pine component resulted in doubling the LAI-RM variability (σ = 0.50 to σ = 0.97) at the 1 km2 validation site located in Appomattox, Virginia, USA.

林下植被对遥感估算马尾松LAI的影响研究

PDF HTML阅读 XML下载 导出引用 引用提醒 林下植被对遥感估算马尾松LAI的影响 DOI: 10.5846/stxb201401100078 作者: 作者单位: 南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所,南京大学国际地球系统科学研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技重大专项(30-Y20A01-9003-12/13) Impact of the understory on estimation of leaf area index of Pinus massoniana using remote sensing technology Author: Affiliation: international institute for earth system science,nanjing university,,international institute for earth system science,nanjing university,,,,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:叶面积指数是一项极其重要的描述植被冠层结构的植被特征参量。根据植被物候规律,利用中国环境卫星CCD多光谱影像和野外马尾松样区调查数据,通过建立不同季节和不同郁闭度样区马尾松LAI和影像NDVI经验回归模型,并利用一个新的LAI观测方式定量比较乔木层LAI和生态系统总LAI(包括草本层、灌木层和乔木层)的差异,研究林下植被对马尾松反演的影响程度。结果表明:(1)由于林下植被的物候变化,冬季林下植被对马尾松LAI反演影响最小,马尾松NDVI和LAI线性关系R2维持在0.65;夏季林下植被影响最大,线性关系R2只有0.25;春季和秋季影响居中,NDVI和LAI线性关系R2在0.47附近。但是,受林下植被影响较小的A类样区4个季节内NDVI和LAI线性关系基本都在0.60以上(夏季略低于0.60);(2)乔木层LAI和总LAI差距非常大,最大差距达到2.93,相差的比例最大达到了2.45倍;(3)总LAI和NDVI相关关系显著,其中线性关系R2达到0.66,对数关系R2可达到0.68,而乔木层LAI和NDVI相关关系较差,线性关系R2只有0.30。分别建立冬季和其它季节实测总LAI和NDVI的关系,可以估算出林下植被对马尾松LAI反演的影响程度。 Abstract:Leaf area index (LAI), defined as half the total developed area of green leaves per unit ground horizontal area, is an extremely important vegetation characteristic parameter that describes the construction of vegetation canopy. For the past few years, LAI has been estimated operationally at a regional or even global scale by means of various retrieval methods using remotely sensed optical imagery. Understory vegetation (e.g., grasses, herbs, shrubs, etc.) is the layer of foliage below the forest canopy. As a background signal source in remote sensing, understory has had a serious impact on estimating many forest canopy parameters by remote sensing techniques for two reasons. on the one hand, it has very similar characteristics with those of the forest canopy because they are all vegetation. on the other hand, it has greater spatial-temporal heterogeneity than other backgrounds in remote sensing, e.g., soil, water, rock and litter, etc. The purpose of this paper is to determine the impact of understory on LAI inversion of forests with different canopy closures in different seasons. In this study, data from five field investigations and corresponding Chinese HJ-1 CCD remote sensing images were collected and analyzed to study the impact of understory on LAI inversion of Pinus massoniana during the period September 2012 to October 2013 in Chuzhou, Anhui province. By building empirical models of LAI of Pinus massoniana with different canopy closures and Normalized Difference Vegetation Indices (NDVI) in different seasons based on the phenology of the understory and comparing the difference of LAI of tree layers and the LAI of all layers of the ecosystem using a new way of LAI measurement, the impact of understory on the calculation of LAI of Pinus massoniana with different canopy closures and in different seasons was found. The results show that: (1) Understory had minimal impact on LAI inversion of Pinus massoniana in winter; R2 of the linear relationship between NDVI and LAI was 0.65. Understory had the most serious impact in summer; R2 of the linear relationship was only 0.25. The impact of understory in spring and autumn was greater than in winter and lower than in summer, R2 of linear relationship was about 0.47. However, R2 of linear relationship in the quadrats A, which were less affected by understory, was almost higher than 0.60 in all seasons (slightly less than 0.60 in summer). The reason was that the phenology of the understory caused different impacts in different seasons; (2) A significant difference between tree layer LAI and LAI of all layers in the same season was found, and the biggest gap was 2.93 and the maximum multiple was 2.45; (3) the R2 of the linear relationship between LAI of all layers and NDVI was about 0.66, and R2 of the logarithmic relationship was more than 0.68. However, the correlation between tree layers and NDVI was poor (R2 was only 0.30).These findings indicate that the impact of understory on LAI inversion of Pinus massoniana can be calculated when relationships between the LAI of all layers and NDVI in winter and other seasons are determined. Finally, the difficulties of studying understory impact on forest LAI retrieval are discussed, and several suggestions are proposed for future studies. 参考文献 相似文献 引证文献

Retrieval of Gap Fraction and Effective Plant Area Index from Phase-Shift Terrestrial Laser Scans

The characterization of canopy structure is crucial for modeling eco-physiological processes. Two commonly used metrics for characterizing canopy structure are the gap fraction and the effective Plant Area Index (PAIe). Both have been successfully retrieved with terrestrial laser scanning. However, a systematic assessment of the influence of the laser scan properties on the retrieval of these metrics is still lacking. This study investigated the effects of resolution, measurement speed, and noise compression on the retrieval of gap fraction and PAIe from phase-shift FARO Photon 120 laser scans. We demonstrate that FARO’s noise compression yields gap fractions and PAIe that deviate significantly from those based on scans without noise compression and strongly overestimate Leaf Area Index (LAI) estimates based on litter trap measurements. Scan resolution and measurement speed were also shown to impact gap fraction and PAIe, but this depended on leaf development phase, stand structure, and LAI calculation method. Nevertheless, PAIe estimates based on various scan parameter combinations without noise compression proved to be quite stable.

NON-DESTRUCTIVE ESTIMATION OF LEAF AREA INDEX IN VASE-SHAPE SWEET CHERRY TREES

Knowledge of the Leaf Area Index (LAI) of sweet cherry orchards is important for diagnosis of their production potential. However, measuring LA/tree (for further calculation of LAI) is tedious and therefore seldom performed in commercial orchards. The objective of this research was to develop simple, precise and non-destructive methods for estimating LAI in vase-shape trees. Fifteen ‘Bing’ and 15 ‘Lapins’ trees grafted on Mahaleb were selected as experimental units. At full canopy, all leaves of each tree were counted and 2%, 5% or 10% of the leaves (for small, medium and big trees, respectively) were randomly taken as a sample, from which Mean Leaf Area (MLA; cm2) was estimated. Leaf Area (LA) per tree was calculated multiplying the number of leaves by MLA. At the same time, several parameters were recorded: Trunk Cross-Sectional Area (TCSA; cm2) (5 cm above the graft), a Tree Canopy Volume Estimate (TCVE; m3) (mean height x mean width), Free Between-Row Distance (FBRD; m), the LA of the Main Branch with the closest circumference to the mean (LAMB; m2), the Vertical Optical Porosity (VOP; %) and the Intercepted Photosynthetically Active Radiation (I-PAR; %) at noon. Simple and multiple linear regression models were evaluated using, in all cases, the measured LA/tree (varying between 2.55 and 35.76 m2) as dependent variable. The best models were: Ln LA/tree = -3.42 + 0.50 ln TCVE + 0.57 ln TCSA + 0.66 ln MLA (R2 = 0.98); Ln LA/tree = -0.76 + 0.68 ln TCVE + 0.38 ln TCSA (R2 = 0.95); Ln LA/tree = -0.86 + 0.41 ln MLA + 0.89 ln TCVE (R2 = 0.95) and Ln LA/tree = -6.35 + 1.02 ln MLA + 1.17 ln TCSA (R2 = 0.93). The LAI was calculated by dividing the estimated LA/tree by the tree-allocated area.

Modelling competitive ability of Neotropical savanna grasses: Simulation of shading and drought impacts on biomass production

In this paper, we develop a technique to model the spatial distribution of shoots along vertical and horizontal dimensions of a plant community. We use it to simulate the growth of a tropical savanna near the city of Barinas, Venezuela, to explore the responses of the peak biomass of a plant community to a range of 10-50% reduction of rainfall. We selected three dominant grass species: Elyonurus adustus, Leptocoryphium lanatum, and Andropogon semiberbis in a 4×7m study plot. We estimate parameters values from data measured in the field. The number of shoots for each plant is obtained according to soil water availability and distributed vertically by 10cm levels using a transition matrix. Convolution allows calculation of leaf area index for each cell and vertical level, which is then used to calculate light attenuation and thus the proportion of shaded shoots in each cell and level. With this information, maximum evapotranspiration is determined to calculate soil moisture using daily rain time series. Biomass is calculated for all species based on shoot biomass measured in the field and fire is simulated by removing a fraction of the shoots segments of all species. Modeled biomass fits reasonably well to field data.The model predicts significant differences in the response of each species to drought. A. semiberbis was the most drought resistant of the three species, whereas L. lanatum was the least, and E. adustus was intermediate, in agreement with observations in the literature. Our model suggests that drought resistance increases with the biomass/transpiration ratio for the species considered.

Estimating Daily and 24-Hour Net Radiation for All Sky Conditions through Remote Sensing and Climatic Data

Net radiation is a key variable in hydrological studies. However, measured net radiation data are rarely available and are often subject to error because of equipment calibration or failure. Additionally, point measurements of net radiation do not represent the diversity of the regional net radiation values, which are needed for many physical and biological processes such as climate monitoring and evapotranspiration mapping. The authors present a methodology to estimate daytime net radiation using a combination of remote sensing and climatic data. This paper expands on previous original research by extending the estimation of net radiation to 24 h under all sky conditions. The procedure estimates daytime and nighttime net radiation and combines the results to calculate the 24-h net radiation values. The methodology combines information from satellite and local weather stations to estimate net radiation values. The procedure can estimate net radiation under all sky conditions using measured or estimated solar radiation. Two different methods are presented to estimate net radiation. Comparisons between measured and predicted daytime and 24-h net radiation using the two methods resulted in an average ratio ranging from 0.98–1.0 and a standard error of estimate ranging from 0.83–1.62 MJ m−2 day−1. Satellite data were used for the calculation of leaf area index, albedo, and ground temperature. Although satellite data are scarce, the methodology is not limited to satellite imagery. One can easily use ground level measurements of leaf area index, albedo, and temperature to estimate daytime and 24-h net radiation using the proposed methodology.

Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicators

Abstract Management of agricultural practices such as irrigation by using remotely sensed data requires background data obtained from field experiments carried out under controlled conditions. In this study, spectral and agronomic data from field trials consisting of six different irrigation treatments were used to derive new spectral indicators for estimating growth level and water use status of dwarf green beans. Spectral reflectance (Ref) values were smoothed and first-order derivative spectra (ρ) were calculated. Linear regression and multivariate analysis (cluster and principal component analysis) were done between agronomic indicators and both the smoothed spectral reflectance (R) and ρ of each individual wavelength between 650 and 1100 nm. Based on those calculations, the most appropriate wavelengths were selected for each agronomic indicator and new combinations were calculated by using rationing, differencing, normalized differencing and multiple regression. The ratio between ρ measured at 950 or 960 nm and 1020 nm wavelengths provided estimates in an error band of 2.47 bar for Leaf Water Potential (LWP) and 3.18% for Leaf Water Content (LWC). An equation based on ρ740 and ρ980 was developed to estimate Leaf Relative Water Content (LRWC). In the same manner, the ρ at 820 and 970 nm provided a good estimate of crop water use and the ρ values at 770 and 960 nm were critical for the calculation of Leaf Area Index (LAI) and dry biomass. It was also determined that the ratio of R930 to R670 can be applied to yield estimation.