Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China

Ding, Yanling; Zheng, Xingming; Zhao, Kai; Xin, Xiaoping; Liu, Huanjun

doi:10.3390/rs8010029

Cited by 65 publications

(42 citation statements)

References 46 publications

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“…The tree cover estimated in this study also shows a strong correlation with a previous woody cover map by Bucini (Bucini et al 2009;Bucini et al 2010) (Figure 9). Although NDVI values have been reported as being suboptimal for FVC estimation (Jiang et al 2006), some methods that account for soil background contribution in the NDVI have shown good relationships with ground measurements (Moreno-de Las et al 2015;Ding et al 2016;Zeng et al 2000). However, signal contamination, soil background colour, and saturation problems limited the NDVI-FVC relationship (Verger et al 2009;Baumgardner et al 1986).…”

Section: Discussionmentioning

confidence: 99%

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Ibrahim

Balzter

Tansey

et al. 2018

International Journal of Remote Sensing

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Assessments of tree/grass fractional cover in savannahs using remote sensing are challenging due to the heterogeneous mixture of the two plant functional types. Time-series decomposition models can be used to characterize vegetation phenology from satellite data, but have rarely been used for attributing phenological signal components to different plant functional types. Here, tree/ grass dynamics are assessed in savannah ecosystems using timeseries decomposition of 14 years of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index data acquired from 2002 to 2015. The decomposition method uses harmonic analysis and tests the individual harmonic terms for statistical significance. Field data of fractional cover of trees and grasses were collected for 28 plots in Kruger National Park, South Africa. Matching MODIS pixels were analysed for their tree/grass phenological signals. Tree/grass annual and interannual variability were then assessed based on the harmonic models. In most harmonic cycles, grass-dominated sites had higher amplitudes than tree-dominated sites, while the tree green-up started earlier than grasses, before the start of the wet season. While changes in tree phenology are gradual, grasses present higher variability over time. Tree cover showed a significant correlation with the amplitude (r (correlation coefficient) = −0.59, p = 0.001) and phase of the first harmonic term (r = −0.73, p = 0.0001) and the number of cycles of the second harmonic term (r = 0. 56, p = 0.002). Grass cover was also significantly correlated with the amplitude (r = 0. 51, p = 0.005) and phase of the first harmonic term (r = 0.55, p = 0.002) and the number of cycles of the second harmonic term (r = −0.52, p = 0.005). The positive correlation of grass cover with phase and negative correlation with number of cycles is indicating a late greening period and higher variability, respectively. Tree cover estimated from the phase of the strongest harmonic term showed a positive correlation with field-measured tree cover (R 2 (coefficient of determination) = 0.55, p < 0.01, slope = 0.93, root mean square error = 13.26%). The estimated tree cover also had a strong correlation with the woody cover map (r = 0.78, p < 0.01) produced by Bucini. The results show that MODIS time-series data can be used to estimate the fractional tree cover in heterogeneous savannahs from the phase of the plant functional type's phenological behaviour. This study shows that harmonic analysis is able to discriminate between fractional cover by trees and grasses in savannahs. The quantitative analysis of tree/grass phenology from satellite time-series data enables a better understanding of the dynamics of the tree/grass competition and coexistence.

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Section: Discussionmentioning

confidence: 99%

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Ibrahim

Balzter

Tansey

et al. 2018

International Journal of Remote Sensing

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“…Therefore, more remote sensing data from different sensors should be investigated in order to evaluate their performance on FVC estimation in a more complex environment. In addition, only NDVI and EVI were used in the pixel dimidiate model, and more vegetation indices could be used for FVC estimation, such as soil adjusted vegetation index (SAVI) and modified SAVI (MSAVI) [26,60,61]. Therefore, more FVC estimation methods using remote sensing data could be evaluated in future work.…”

Section: Discussionmentioning

confidence: 99%

“…The pixel dimidiate model is a simple and widely used method for FVC estimation using remote sensing data, which assumes that one pixel is only composed of either vegetation or non-vegetation, and the fraction of vegetation composition is considered to be the FVC of this pixel [22][23][24][25][26]. Usually, a vegetation index is used as an indicator in the pixel dimidiate model, and the key issue of the pixel dimidiate model is determining the values of the vegetation index for pure vegetation and pure soil pixels.…”

Section: Introductionmentioning

confidence: 99%

Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data

Jia

Liang

et al. 2017

Remote Sensing

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Abstract:Fractional vegetation cover (FVC), or green vegetation fraction, is an important parameter for characterizing conditions of the land surface vegetation, and also a key variable of models for simulating cycles of water, carbon and energy on the land surface. There are several types of FVC estimation models using remote sensing data, and evaluating their performance over a specific region is of great significance. Therefore, this study firstly evaluated three types of FVC estimation models using Landsat 7 ETM+ data in an agriculture region of Heihe River Basin, China, and then proposed a combination strategy from different individual models to improve the FVC estimation accuracy, which employed the multiple linear regression (MLR) and Bayesian model average (BMA) methods. The validation results indicated that the spectral mixture analysis model with three endmembers (SMA3) achieved the best FVC estimation accuracy (determination coefficient (R 2 ) = 0.902, root mean square error (RMSE) = 0.076) among the seven individual models using Landsat 7 ETM+ data. In addition, the MLR and BMA combination methods could both improve FVC estimation accuracy (R 2 = 0.913, RMSE = 0.063 and R 2 = 0.904, RMSE = 0.069 for MLR and BMA, respectively). Therefore, it could be concluded that both MLR and BMA combination methods integrating FVC estimates from different models using Landsat 7 ETM+ data could effectively weaken the estimation errors of individual models and improve the final FVC estimation accuracy.

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“…Determining FVC from digital photographs is often simpler, faster and more economical than measuring LAI [1,15,17]. FVC values derived from ground and near-ground remotely sensed images for validation of FVC estimated from satellite images is vital in ensuring the quality of FVC estimates derived from satellite images [9,[21][22][23][24][25]. However, there are often significant problems with current FVC estimation methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…Applications include vegetation monitoring [8,9], estimation of LAI [10][11][12], plant nutritional status [6,7,13], fractional vegetation cover measurement [1,[14][15][16][17], growth characteristics [18], weed detection [19] and crop identification [20]. Determining FVC from digital photographs is often simpler, faster and more economical than measuring LAI [1,15,17].…”

Section: Introductionmentioning

confidence: 99%

Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs

et al. 2016

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Abstract:The use of automated methods to estimate fractional vegetation cover (FVC) from digital photographs has increased in recent years given its potential to produce accurate, fast and inexpensive FVC measurements. Wide acceptance has been delayed because of the limitations in accuracy, speed, automation and generalization of these methods. This work introduces a novel technique, the Automated Canopy Estimator (ACE) that overcomes many of these challenges to produce accurate estimates of fractional vegetation cover using an unsupervised segmentation process. ACE is shown to outperform nine other segmentation algorithms, consisting of both threshold-based and machine learning approaches, in the segmentation of photographs of four different crops (oat, corn, rapeseed and flax) with an overall accuracy of 89.6%. ACE is similarly accurate (88.7%) when applied to remotely sensed corn, producing FVC estimates that are strongly correlated with ground truth values.

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Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China

Cited by 65 publications

References 46 publications

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Estimating fractional cover of plant functional types in African savannah from harmonic analysis of MODIS time-series data

Combining Estimation of Green Vegetation Fraction in an Arid Region from Landsat 7 ETM+ Data

Increasing the Accuracy and Automation of Fractional Vegetation Cover Estimation from Digital Photographs

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