scholarly journals Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

2015 ◽  
Vol XL-7/W3 ◽  
pp. 277-284 ◽  
Author(s):  
A. Ali ◽  
R. Darvishzadeh ◽  
A.-K. Skidmore ◽  
I.-V. Duren ◽  
U. Heiden ◽  
...  
Keyword(s):  
Leaf Area ◽  
Specific Leaf Area ◽  
Leaf Traits ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Transfer Model ◽  
Electromagnetic Spectrum ◽  
Dry Matter Content ◽  
Input Parameters ◽  
Direct Input

Quantification of vegetation properties plays an indispensable role in assessments of ecosystem function with leaf dry mater content (LDMC) and specific leaf area (SLA) being two important vegetation properties. Methods for fast, reliable and accurate measurement of LDMC and SLA are still lacking. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate these two leaf parameters. Inversion of PROSPECT traditionally aims at quantifying its direct input parameters rather than identifying the parameters which can be derived indirectly from the input parameters. The technique has been tested here to indirectly model these parameters for the first time. Biophysical parameters such as leaf area, as well as fresh and dry weights of 137 leaf samples were measured during a field campaign in July 2013 in the mixed mountain forests of the Bavarian Forest National Park, Germany. Reflectance and transmittance of the leaf samples were measured using an ASD field spec III equipped with an integrating sphere. The PROSPECT model was inverted using a look-up table (LUT) approach for the NIR/SWIR region of the spectrum. The retrieved parameters were evaluated using their calculated R<sup>2</sup> and normalized root mean square error (nRMSE) values with the field measurements. Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R<sup>2</sup> values (0.83 for LDMC and 0.89 for SLA) were discovered. The results indicate that the leaf traits studied can be quantified as accurately as the direct input parameters of PROSPECT. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy and in the landscape by using hyperspectral remote sensing data.

scholarly journals Relationships among the leaf traits in temperate forest tree species in Uttarakhand, India

2018 ◽  
Vol 4 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Monika Rawat ◽  
Kusum Arunachalam ◽  
Ayyandar Arunachalam
Keyword(s):  
Leaf Area ◽  
Specific Leaf Area ◽  
Forest Ecosystem ◽  
Temperate Forest ◽  
Dry Matter ◽  
Leaf Traits ◽  
Matter Content ◽  
Dry Matter Content ◽  
Leaf Dry Matter Content ◽  
Temperate Forest Ecosystem

Abstract Background: The primary function of the leaf is the production of the food and interchange the gases between the atmosphere and the plant surface. Establishing the relationship among the leaf traits is essential to understand the ecosystem functioning in the forest ecosystem. Here, the present study proposes a framework for species-level relationships between the traits in the temperate forest ecosystem. Methodology: Three morphological (leaf area, specific leaf area and leaf dry matter content), three chemical (leaf carbon, nitrogen and phosphorous content) and six physiological (chlorophyll, photosynthetic rate, stomatal conductance, intrinsic water use efficiency, transpiration rate, intercellular CO2 concentration) leaf traits were analysed in 10 woody tree species of temperate forest using linear mixed modelling. Results: Results showed that the leaf carbon was the only trait influencing the most to leaf area, specific leaf area and leaf dry matter content and leads to maximum variation in the functioning of the forest ecosystem. Conclusion: The results suggested that consideration of plant traits, and especially the leaf traits, increases the ability to describe variation in the functioning of the forest ecosystem. This study indicated that leaf carbon act as the significant predictor of leaf trait variation among the different species in the temperate forest ecosystem of the Indian Himalayan region.

scholarly journals Physiological plasticity of main tree species of lowland hornbeamoak forest as a results of forest gap regeneration

2016 ◽  
pp. 99-103
Author(s):  
Árpád Szalacsi ◽  
Gergely Király ◽  
Szilvia Veres
Keyword(s):  
Leaf Area ◽  
Specific Leaf Area ◽  
Dry Matter ◽  
Light Condition ◽  
Matter Content ◽  
Plant Production ◽  
Dry Matter Content ◽  
Plant Life ◽  
Sun And Shade ◽  
Opening Process

Specific leaf area (SLA) of English oak (Quercus robur L.) and hornbeam (Carpinus betulus L.) as members of Querco robori-Carpinetum were investigated in two different habitat in terms of gap forest management: in the gap and in the inert forest. The artificial opening process of the forest resulted in more light for growing saplings and need for acclimatization. Photosynthesis is one of the most important ways for plant life and plant production basically influenced by altered light condition resulted in opening process. Efficient photosynthesis is important for plant life, plant production, but species-dependent plasticity of photosynthesis makes one species more tolerant, than others. The specific leaf area is acceptable parameters for characterising plant production, dry matter content and leaf structure. The dry matter content based on known leaf area is higher in oak both sun and shade leaves, than hornbeam. The different place of leaves in the canopy of trees did not influence the values of SLA.

scholarly journals Estimation of Multi-Species Leaf Area Index Based on Chinese GF-1 Satellite Data Using Look-Up Table and Gaussian Process Regression Methods

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2460 ◽  
Author(s):  
Yangyang Zhang ◽  
Jian Yang ◽  
Xiuguo Liu ◽  
Lin Du ◽  
Shuo Shi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Gaussian Process Regression ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Look Up Table ◽  
Band Combinations

Leaf area index (LAI) is an important biophysical parameter, which can be effectively applied in the estimation of vegetation growth status. At present, amounts of studies just focused on the LAI estimation of a single plant type, while plant types are usually mixed rather than single distribution. In this study, the suitability of GF-1 data for multi-species LAI estimation was evaluated by using Gaussian process regression (GPR), and a look-up table (LUT) combined with a PROSAIL radiative transfer model. Then, the performance of the LUT and GPR for multi-species LAI estimation was analyzed in term of 15 different band combinations and 10 published vegetation indices (VIs). Lastly, the effect of the different band combinations and published VIs on the accuracy of LAI estimation was discussed. The results indicated that GF-1 data exhibited a good potential for multi-species LAI retrieval. Then, GPR exhibited better performance than that of LUT for multi-species LAI estimation. What is more, modified soil adjusted vegetation index (MSAVI) was selected based on the GPR algorithm for multi-species LAI estimation with a lower root mean squared error (RMSE = 0.6448 m2/m2) compared to other band combinations and VIs. Then, this study can provide guidance for multi-species LAI estimation.

scholarly journals Radiative Transfer Model parametrization for simulating the reflectance of meadow vegetation

2014 ◽  
Vol 18 (2) ◽  
pp. 5-9 ◽  
Author(s):  
Anna M. Jarocińska
Keyword(s):  
Radiative Transfer ◽  
Field Measurements ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Biophysical Parameters ◽  
Input Parameters ◽  
Vegetation Characteristics ◽  
Field Reflectance ◽  
Lower Water Content

Abstract Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

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