scholarly journals  Estimation of leaf area index and assessment of its allometric equations in oak forests: Northern Zagros, Iran

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 116-122 ◽  
Author(s):  
S. Khosravi ◽  
M. Namiranian ◽  
H. Ghazanfari ◽  
A. Shirvani
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Allometric Equations ◽  
Oak Forests ◽  
Biophysical Parameters ◽  
Destructive Sampling ◽  
Area Index ◽  
Zagros Forests ◽  
Crown Volume ◽  
Oak Trees

The focus of the present study is the estimation of leaf area index (LAI) and the assessment of allometric equations for predicting the leaf area of Lebanon oaks (Quercus libani Oliv.) in Iran&rsquo;s northern Zagros forests. To that end, 50 oak trees were randomly selected and their biophysical parameters were measured. Then, on the basis of destructive sampling of the oak trees, their specific leaf area (SLA) and leaf area were measured. The results showed that SLA and LAI of the Lebanon oaks were 136.9 cm&middot;g<sup>&ndash;1 </sup>and 1.99, respectively. Among all the parameters we measured, the crown volume exhibited the highest correlation with LAI (r<sup>2</sup> = 0.65). The easily measured tree parameters such as diameter at breast height did not show a high correlation with leaf area (r<sup>2</sup> = 0.36). Our obtained moderate correlations in the allometric equations could be due to the fact that branches of these trees had been pollarded by the local people when the branches were only 3 or 4 years old; therefore, the natural structure of the crowns in these trees might have been damaged. &nbsp;

scholarly journals A PROPOSED METHODOLOGY FOR THE CORRECTION OF THE LEAF AREA INDEX MEASURED WITH A CEPTOMETER FOR PINUS AND EUCALYPTUS FORESTS

2016 ◽  
Vol 40 (5) ◽  
pp. 845-854 ◽  
Author(s):  
Domingos Mendes Lopes ◽  
Nigel Walford ◽  
Helder Viana ◽  
Carlos Roberto Sette Junior
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Correction Factor ◽  
Basal Area ◽  
Allometric Equations ◽  
Nutrient Cycle ◽  
Area Index ◽  
Physiological Processes ◽  
The Difference ◽  
Non Destructive

ABSTRACT Leaf area index (LAI) is an important parameter controlling many biological and physiological processes associated with vegetation on the Earth's surface, such as photosynthesis, respiration, transpiration, carbon and nutrient cycle and rainfall interception. LAI can be measured indirectly by sunfleck ceptometers in an easy and non-destructive way but this practical methodology tends to underestimated when measured by these instruments. Trying to correct this underestimation, some previous studies heave proposed the multiplication of the observed LAI value by a constant correction factor. The assumption of this work is LAI obtained from the allometric equations are not so problematic and can be used as a reference LAI to develop a new methodology to correct the ceptometer one. This new methodology indicates that the bias (the difference between the ceptometer and the reference LAI) is estimated as a function of the basal area per unit ground area and that bias is summed to the measured value. This study has proved that while the measured Pinus LAI needs a correction, there is no need for that correction for the Eucalyptus LAI. However, even for this last specie the proposed methodology gives closer estimations to the real LAI values.

Estimation of the seasonal leaf area index in urban oak forests using Sentinel-2 time series data

2019 ◽  
Author(s):  
A. Kozlova ◽  
I. Piestova ◽  
L. Patrusheva ◽  
M. Lubsky ◽  
A. Nikulina ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Time Series Data ◽  
Series Data ◽  
Oak Forests ◽  
Area Index ◽  
Sentinel 2

scholarly journals Estimation of Biomass and Leaf Area Index in the Western Ghats Forest Ecosystem by the Integrated Analysis of Hyperspectral Data and Space Borne LiDAR Data

2019 ◽  
pp. 1-12
Author(s):  
Indu Indirabai ◽  
M. V. Harindranathan Nair ◽  
Jaishanker R. Nair ◽  
Rama Rao Nidamanuri
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Western Ghats ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Lidar Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
The Western Ghats

The Western Ghats regions of India are characterised by highly complex and biodiverse forest ecosystem with heterogeneous tree species. The integration of LiDAR data with multispectral remote sensing has limitations in the case of spectral information abundance. The objective of this study was to undertake biophysical characterisation in the Western Ghats regions of India by the integration of GLAS ICESat data and AVIRIS-NG hyperspectral data. The methodology of the study includes pre-processing of the hyperspectral and ICESat GLAS data followed by the integration of the two data sets based on pixel based fusion strategy in order to estimate the biophysical parameters of forests. Biomass was estimated by Support Vector Regression method. The structural characteristics extracted from the LiDAR data are integrated with spectral characteristics from the AVIRIS NG imagery based on the pixel level so that biophysical characteristics including canopy height, biomass, Leaf Area Index are estimated. The integrated product on further analysis revealed the applicability of this approach to extract more spectral information and forest parameters. The key findings of the study include biophysical parameters both structural as well as abundant spectral information can be retrieved successfully by the methodology used which have strong correlation with the in situ measurements. The study concluded that biophysical parameters including Leaf Area Index, biomass and canopy height can be effectively estimated by the integration of AVIRIS-NG imagery and GLAS data, which cannot be possible when used independently. It is recommended to have continuous retrieval of LiDAR foot prints instead of discrete, to make modelling of the biophysical parameters a little more effective.

scholarly journals Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan

2020 ◽  
Vol 12 (1) ◽  
pp. 189 ◽  
Author(s):  
Emal Wali ◽  
Masahiro Tasumi ◽  
Masao Moriyama
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Cover ◽  
Polynomial Regression ◽  
Backscattering Coefficient ◽  
Biophysical Parameters ◽  
Area Index ◽  
Dry Biomass ◽  
Green Vegetation

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.

scholarly journals Validation of an Integrated Estimation of Loblolly Pine (Pinus taeda L.) Leaf Area Index (LAI) Using Two Indirect Optical Methods in the Southeastern United States

2008 ◽  
Vol 32 (3) ◽  
pp. 101-110 ◽  
Author(s):  
John S. Iiames ◽  
Russell Congalton ◽  
Andrew Pilant ◽  
Timothy Lewis
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Pinus Taeda ◽  
Loblolly Pine ◽  
Southeastern United States ◽  
Area Ratio ◽  
Allometric Equations ◽  
Area Index ◽  
Whole Tree

Abstract Quality assessment of satellite-derived leaf area index (LAI) products requires appropriate ground measurements for validation. Since the National Aeronautics and Space Administration launch of Terra (1999) and Aqua (2001), 1-km, 8-day composited retrievals of LAI have been produced for six biome classes worldwide. The evergreen needle leaf biome has been examined at numerous validation sites, but the dominant commercial species in the southeastern United States, loblolly pine (Pinus taeda), has not been investigated. The objective of this research was to evaluate an in situ optical LAI estimation technique combining measurements from the Tracing Radiation and Architecture of Canopies (TRAC) optical sensor and digital hemispherical photography (DHP) in the southeastern US P.taeda forests. Stand-level LAI estimated from allometric regression equations developed from whole-tree harvest data were compared to TRAC–DHP optical LAI estimates at a study site located in the North Carolina Sandhills Region. Within-shoot clumping, (i.e., the needle-to-shoot area ratio [γE]) was estimated at 1.21 and fell within the range of previously reported values for coniferous species (1.2–2.1). The woody-to-total area ratio (α = 0.31) was within the range of other published results (0.11–0.34). Overall, the indirect optical TRAC–DHP method of determining LAI was similar to LAI estimates that had been derived from allometric equations from whole-tree harvests. The TRAC–DHP yielded a value 0.14 LAI units below that retrieved from stand-level whole-tree harvest allometric equations. DHP alone yielded the best LAI estimate, a 0.04 LAI unit differential compared with the same allometrically derived LAI.

scholarly journals Growth indices ans productivity in sugarcane

2005 ◽  
Vol 62 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Prediction Models ◽  
Gompertz Model ◽  
Exponential Model ◽  
Index Number ◽  
Total Biomass ◽  
Biophysical Parameters ◽  
Area Index ◽  
Cane Productivity

A knowledge about the temporal development of agronomic variables in sugarcane is a very important aspect for the development of crop yield prediction models using remote sensing, and further studies are still needed. This paper describes the temporal evolution of sugarcane biophysical parameters, such as total biomass, leaf area index, number of plants per meter, and productivity. During two seasons, a commercial field in Araras/SP, planted with variety SP80-1842, on the 4th and 5th cuts, was monitored on eight different dates, and data were obtained for 2 m of sugarcane in three crop rows at 18 sampling points. Linear and multiple regression analyses were used to study growth analysis and to correlate agronomic variables (leaf area index and number of plants per meter) with biomass and productivity. Gompertz model, a sigmoidal curve, was the best adjustment curve for total biomass and yield in relation to days after cutting (r² = 0.8987 and r² = 0.9682, respectively); number of plants and leaf area index showed best fit with a cubic exponential model and a quadratic exponential model, respectively. Total biomass and cane productivity were well correlated with LAI in the first two stages of the sugarcane cycle using linear regression. At the end of the cycle, total biomass and cane productivity were more related to number of plants, and lower r² values than in other stages were obtained by the models.

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