Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model

Zhang, Xiaoling; Yan, Guangjian; Li, Q.; Li, Z.‐L.; Wan, Huawei; Guo, Zhaoxin

doi:10.1080/01431160600658107

Cited by 52 publications

(34 citation statements)

References 19 publications

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“…However, the positive or negative trend of the predicted biases in Table 3 is consistent with that of the real biases when the values of the absolute deviations that are less than 0.01 are ignored (four cases remain: 10 July and 11 August in Scene 1 and 24 June and 10 July in Scene 2). It should be noted that the scaling effect discussed in the current paper only concerns the transfer from high-resolution NDVI to FVC; specifically, the scaling effect generated from NDVI itself [48,49], which may affect the use of coarse-resolution NDVI, is beyond the scope of this study.…”

Section: Discussionmentioning

confidence: 96%

Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

Song

et al. 2015

Remote Sensing

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Abstract:Validation over heterogeneous areas is critical to ensuring the quality of remote sensing products. This paper focuses on the sampling methods used to validate the coarse-resolution fractional vegetation cover (FVC) product in the Heihe River Basin, where the patterns of spatial variations in and between land cover types vary significantly in the different growth stages of vegetation. A sampling method, called the mean of surface with non-homogeneity (MSN) method, and three other sampling methods are examined with real-world data obtained in 2012. A series of 15-m-resolution fractional vegetation cover reference maps were generated using the regressions of field-measured and satellite data. The sampling methods were tested using the 15-m-resolution normalized difference vegetation index (NDVI) and land cover maps over a complete period of vegetation growth. Two scenes were selected to represent the situations in which sampling locations were sparsely and densely distributed. The results show that the FVCs estimated using the MSN method have errors of approximately less than 0.03 in the two selected scenes. The validation accuracy of the sampling methods varies with variations in the stratified non-homogeneity in the different growing stages of the vegetation. The MSN method, which considers both heterogeneity and autocorrelations between strata, is recommended for use in the determination of samplings prior to the design of an experimental campaign. In addition, the slight scaling bias caused by the non-linear relationship between NDVI and FVC samples is discussed. The positive or negative trend of the biases predicted using a Taylor expansion is found to be consistent with that of the real biases.

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Section: Discussionmentioning

confidence: 96%

Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

Song

et al. 2015

Remote Sensing

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“…Therefore, the bias error in the NDVI caused by the scaling effect will be propagated into such parameters. For instance, a practical case study can be seen in [27,32] for the case of leaf area index and [33] for fraction of vegetation cover.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have examined scaling effects in the calculation of the vegetation index (VI) [16,24,25,30,[38][39][40][41][42][43][44], leaf area index (LAI) [27,32,35,36,45,46], fraction of vegetation cover (FVC) [33], latent [42,47], sensible heat flux [24,30], and other parameters related to surface processes [48][49][50][51][52][53][54][55]. Scaling effects in the calculation of the NDVI have been investigated in the context of empirical investigations [42,44], regression analysis [38,40,41], numerical simulations [39], and analytical studies [24,25,30,43].…”

Section: Introductionmentioning

confidence: 99%

Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution

et al. 2012

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Changes in the spatial distributions of vegetation across the globe are routinely monitored by satellite remote sensing, in which the reflectance spectra over land surface areas are measured with spatial and temporal resolutions that depend on the satellite instrumentation. The use of multiple synchronized satellite sensors permits long-term monitoring with high spatial and temporal resolutions. However, differences in the spatial resolution of images collected by different sensors can introduce systematic biases, called scaling effects, into the biophysical retrievals. This study investigates the mechanism by which the scaling effects distort normalized difference vegetation index (NDVI). This study focused on the monotonicity of the area-averaged NDVI as a function of the spatial resolution. A monotonic relationship was proved analytically by using the resolution transform model proposed in this study in combination with a two-endmember linear mixture model. The monotonicity allowed the inherent uncertainties introduced by the scaling effects (error bounds) to be explicitly determined by averaging the retrievals at the extrema of the resolutions. Error bounds could not be estimated, on the other hand, for non-monotonic relationships. Numerical simulations were conducted to demonstrate the monotonicity of the averaged NDVI along spatial resolution. This study provides a theoretical basis for the scaling effects and develops techniques for rectifying the scaling effects in biophysical Remote Sens. 2012, 4 161 retrievals to facilitate cross-sensor calibration for the long-term monitoring of vegetation dynamics.

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“…For example, when reflectance is directly used as a variable, the minimization process considers the difference (often defined as the root mean square error, RMSE) between the modeled and measured spectrum [8][9][10][11][12][13][14]21,22,24,28,[31][32][33][34][35]. Altering the reflectance into a spectral vegetation index (VI) produces another group of algorithms [1,4,7,25,26,[36][37][38][39]. In this group, several conditions and constraints are imposed to uniquely determine the set of weights.…”

Section: Introductionmentioning

confidence: 99%

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

Obata

Yoshioka

2010

Remote Sensing

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Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC using the spectral vegetation index (VI) as a constraint. Variations in the application of this algorithm arise due to differences in the choice of endmember spectra and VI model assumptions. As a result, the retrieved FVCs from a single spectrum depend on those choices. Therefore, the mechanism underlying this dependency must be understood fully to improve the interpretation of the results. The objective of this study is to clarify the relationships among algorithms based on the LMM. The relationships were derived analytically by limiting both the number of endmembers and the spectral wavelength band to two each. Numerical experiments were conducted to demonstrate and validate the derived relationships. It was found that the relationships between two algorithms of this kind could be characterized by a single parameter that was determined by the endmember spectra and the coefficients of a VI model equation used in the algorithms.

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Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model

Cited by 52 publications

References 19 publications

Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

Evaluation of Sampling Methods for Validation of Remotely Sensed Fractional Vegetation Cover

Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution

Inter-Algorithm Relationships for the Estimation of the Fraction of Vegetation Cover Based on a Two Endmember Linear Mixture Model with the VI Constraint

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