A comparison of methods for estimating fractional vegetation cover in arid regions

Jiapaer, Guli; Chen, Xi; Bao, Anming

doi:10.1016/j.agrformet.2011.07.004

Cited by 212 publications

(124 citation statements)

References 32 publications

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“…The ratio of the vertical projected area of vegetation to the total ground area, termed fractional vegetation cover (FVC), is a commonly used indicator for evaluating and monitoring vegetation degradation and desertification [1]. Several methods for retrieval of FVC using remote sensing have been developed including spectral mixture analysis (SMA) [2][3][4], artificial neural networks [5][6][7], fuzzy classifiers [8], maximum likelihood classifiers [9], regression trees [10][11][12], and simple regression based on the Normalized Difference Vegetation Index (NDVI) [13].…”

Section: Introductionmentioning

confidence: 99%

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

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Abstract:Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VI soil and VI veg , with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.

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Section: Introductionmentioning

confidence: 99%

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

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“…Generally, from the methodological point of view, there are three main FVC estimation methods using remotely sensed data: empirical methods, pixel un-mixing modeling, and physical model-based methods [9][10][11]. The empirical methods are based on the statistical relationships between FVC and spectral band reflectance or vegetation indices from airborne or satellite spectra [8,12].…”

Section: Introductionmentioning

confidence: 99%

“…The empirical methods are based on the statistical relationships between FVC and spectral band reflectance or vegetation indices from airborne or satellite spectra [8,12]. Generally, the normalized difference vegetation index (NDVI) derived from the reflectance of the red and near-infrared (NIR) bands is the most frequently used index for regression models development of FVC estimation [11]. Moreover, some other vegetation indices, such as enhanced vegetation index (EVI), visible atmospherically resistant index (VARI) and modified three-band maximal gradient difference vegetation index (MTGDVI), are also used for FVC estimation [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…The pixel un-mixing model assumes that each pixel is composed of several components and considers the fraction of vegetation compositions as the FVC of the pixel [11,16,17]. The dimidiate pixel model, which assumes that the pixels are only composed of vegetation and non-vegetation components and that the spectral information being just a linear synthesis of the two parts, is the simplest and most widely used pixel un-mixing model and has achieved many reliable results at the regional scales [3,11,18].…”

Section: Introductionmentioning

confidence: 99%

“…The dimidiate pixel model, which assumes that the pixels are only composed of vegetation and non-vegetation components and that the spectral information being just a linear synthesis of the two parts, is the simplest and most widely used pixel un-mixing model and has achieved many reliable results at the regional scales [3,11,18]. For example, GF-1 WFV data were used to estimate FVC using dimidiate pixel model in the Beijing-Tianjin-Hebei region [19].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

et al. 2017

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Abstract:Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R 2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R 2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R 2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

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Spatial and temporal change of fractional vegetation cover in North‐western China from 2000 to 2010

Wei

Wang

2017

Geological Journal

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Northwestern China is located at the core area of The Belt and The Road at present and it once has been an essential transportation hub of the ancient Silk Road. However, the north‐western part belongs to arid and semi‐arid region where water resource and vegetation cover are scarce, leading to soil erosion, desertification, and environmental degradation. So it is meaningful to study fractional vegetation cover in North‐western China. Vegetation MODIS data of northwestern China from 2000 to 2010 were collected, and then, the pixel dichotomy model and simple linear regression model were applied in this research. The results were as follows. First, the fractional vegetation cover was decreasing from the margin to the middle and western part and the higher value shown in the north‐east part of Inner Mongolia, the south part of Shaanxi, the north‐west part of Ningxia, the south‐east part of Gansu, and the north‐west part of Xinjiang. Second, the overall trend of fractional vegetation cover was rising during 2000 to 2010, which is clearly shown in northern Shaanxi, south‐eastern Qinghai and Gansu, and also south‐western Inner Mongolia. Third, fractional vegetation cover varied differently with season changes. To be more specific, the index in spring and winter was considerably low; in contrast, the index in summer and autumn was improved dramatically.

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A comparison of methods for estimating fractional vegetation cover in arid regions

Cited by 212 publications

References 32 publications

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Spatial and temporal change of fractional vegetation cover in North‐western China from 2000 to 2010

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