Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices

Tillack, Adina; Clasen, Anne; Kleinschmit, Birgit; Förster, Michael

doi:10.1016/j.rse.2013.10.018

Cited by 111 publications

(78 citation statements)

References 54 publications

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“…These software packages offer diverse options for data processing and different inversion algorithms to calculate LAI. We used the horizontal model of FV2200 to calculate the LAI because it considered as an ideal algorithm for calculating LAI of wide and flat canopies like forest trees [30].…”

Section: Calculating Leaf Area Index From Field Datamentioning

confidence: 99%

“…Specifically, the previous studies have mostly focused on the use of spectral vegetation indices (SVIs) that combined the advent of two or three wavebands as opposed to the use of spectral features at a single waveband on modeling forests LAI [12,[28][29][30]. SVIs are mathematical combinations of different spectral bands, commonly located in the visible and near-infrared (NIR) regions of the electromagnetic spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The groundbased methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R 2 Val = 0.75, RMSE Val = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

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Section: Calculating Leaf Area Index From Field Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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“…The best linear regression equations to generate fine-resolution LAI maps were provided by SR in AREA and by RSR in FEA, with R To verify the accuracy of the regression lines, additional in situ LAI measurements collected on 11 August and 20 September 2012 in AREA and 12 to 15 July 2012 in FEA served as an independent validation dataset. The RMSEs calculated from the validation dataset are 0.55 and 0.79 in AREA and FEA, and the accuracy is acceptable considering the variation of the LAI-VI relationship over time [16]. However, more accurate measurements are required to obtain more statistically-sound results in future studies.…”

Section: Development Of Lai Reference Mapsmentioning

confidence: 87%

“…Empirical approaches rely on the statistical relationships between LAI and RS data. These approaches are computationally efficient, but are site-, species-, time-and sensor-specific [16,17]. Physically-based approaches rely on the inversion of RT models.…”

Section: Introductionmentioning

confidence: 99%

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Yin

Liu

et al. 2015

Remote Sensing

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Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval.If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model's potential to retrieve LAI is limited by the availability of a priori information on RT model OPEN ACCESSRemote Sens. 2015, 7 4605 parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

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“…On the other hand, it has been relatively rare to find quantitative applications for extracting biophysical parameters from HSR images.Although there have been a few cases of using HSR images to extract information regarding the physical conditions of vegetation (Imukova et al, 2015;Pu and Cheng, 2015;Tillack et al, 2014;Sprintsin et al, 2007) and water quality (Choe et al, 2015; Chang et al, 2009) …”

mentioning

confidence: 99%

Atmospheric Correction Problems with Multi-Temporal High Spatial Resolution Images from Different Satellite Sensors

Lee¹,

Lee²

2015

Korean Journal of Remote Sensing

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Since the launch of the IKONOS-1 in 1999 and several other following commercial satellites, High Spatial Resolution (HSR) satellite images have been widely used for topographic mapping, Digital Elevation Model (DEM) data generation, and urban and land cover classifications. On the other hand, it has been relatively rare to find quantitative applications for extracting biophysical parameters from HSR images.Although there have been a few cases of using HSR images to extract information regarding the physical conditions of vegetation (Imukova et al., 2015;Pu and Cheng, 2015;Tillack et al., 2014;Sprintsin et al., 2007) and water quality (Choe et al., 2015; Chang et al., 2009) Department of Geoinformatic Engineering, Inha UniversityAbstract : Atmospheric correction is an essential part in time-series analysis on biophysical parameters of surface features. In this study, we tried to examine possible problems in atmospheric correction of multitemporal High Spatial Resolution (HSR) images obtained from two different sensor systems. Three KOMPSAT-2 and two IKONOS-2 multispectral images were used. Three atmospheric correction methods were applied to derive surface reflectance: (1) Radiative Transfer (RT) -based absolute atmospheric correction method, (2) the Dark Object Subtraction (DOS) method, and (3) the Cosine Of the Uun zeniTh angle (COST) method. Atmospheric correction results were evaluated by comparing spectral reflectance values extracted from invariant targets and vegetation cover types. In overall, multi-temporal reflectance from five images obtained from January to December did not show consistent pattern in invariant targets and did not follow a typical profile of vegetation growth in forests and rice field. The multi-temporal reflectance values were different by sensor type and atmospheric correction methods. The inconsistent atmospheric correction results from these multi-temporal HSR images may be explained by several factors including unstable radiometric calibration coefficients for each sensor and wide range of sun and sensor geometry with the off-nadir viewing HSR images.Key Words : Atmospheric correction, high spatial resolution, calibration coefficients, multi-temporal, surface reflectance, KOMPSAT, IKONOS Atmospheric Correction MethodsAtmospheric correction of optical imagery is mainly to derive purely reflected portion of energy from the atsensor radiance (Lt) by removing the amount of radiance influenced by atmospheric effects. The radiance (Lt) received on-board sensor can be expressed in a simple form of equation (Eq. 1) and atmospheric correction is the process of extracting surface reflectance (ρ) from Lt.where ρ = surface reflectance, E = solar and diffuse irradiance on surface, T = transmittance of atmosphere, and Lp = path radiance.Atmospheric correction methods of optical imagery can be divided into two major categories by means of obtaining the unknown variables of T and Lp in (Eq. 1).Absolute atmospheric correction is based on the calculation of physical processes of radiative trans...

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Estimation of the seasonal leaf area index in an alluvial forest using high-resolution satellite-based vegetation indices

Cited by 111 publications

References 54 publications

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Atmospheric Correction Problems with Multi-Temporal High Spatial Resolution Images from Different Satellite Sensors

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