Estimating Vegetation Fraction Using Hyperspectral Pixel Unmixing Method: A Case Study of a Karst Area in China

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.

Section: Spectral Mixture Analysismentioning

confidence: 99%

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

Jia

Liang

et al. 2017

“…Sampling procedure along the diagonal lines has been widely acknowledged to be effective for ground cover measurement of sample plots, wherein vegetation is not distributed in parallel rows [44,45]. In particular, field surveyors were well trained and measurements of each transect for the first 20 sample plots were cross-validated to guarantee reliability of the field reference data.…”

Section: Uncertainties and Sources Of Errormentioning

confidence: 99%

Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data

Zheng

Wang

et al. 2016

Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (f pv ) and NPV (f npv ) using multispectral information. The purpose of this study is to evaluate several spectral unmixing approaches for retrieval of f pv and f npv in the Otindag Sandy Land using GF-1 wide-field view (WFV) data. To deal with endmember variability, pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, and Automated Monte Carlo Unmixing Analysis, AutoMCU) endmember selection approaches were applied. Observed fractional cover data from 104 field sites were used for comparison. For f pv , all methods show statistically significant correlations with observed data, among which AutoMCU had the highest performance (R 2 = 0.49, RMSE = 0.17), followed by MESMA (R 2 = 0.48, RMSE = 0.21), and SMA (R 2 = 0.47, RMSE = 0.27). For f npv , MESMA had the lowest performance (R 2 = 0.11, RMSE = 0.24) because of coupling effects of the NPV and bare soil endmembers, SMA overestimates f npv (R 2 = 0.41, RMSE = 0.20), but is significantly correlated with observed data, and AutoMCU provides the most accurate predictions of f npv (R 2 = 0.49, RMSE = 0.09). Thus, the AutoMCU approach is proven to be more effective than SMA and MESMA, and GF-1 WFV data are capable of distinguishing NPV from bare soil in the Otindag Sandy Land.

“…Recently, an increasing number of digital methods have been developed, such as per-pixel classification [8,9] and subpixel quantitative estimation. As mixed pixels exist abundantly, medium-resolution images, subpixel approaches are widely applied [10,11]; these include spectral mixture analysis (SMA) and spectral index analysis. Linear spectral mixture analysis (LSMA) method has been recently employed to estimate the sub-pixel cover fractions of karst land-surface types [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…As mixed pixels exist abundantly, medium-resolution images, subpixel approaches are widely applied [10,11]; these include spectral mixture analysis (SMA) and spectral index analysis. Linear spectral mixture analysis (LSMA) method has been recently employed to estimate the sub-pixel cover fractions of karst land-surface types [10,11]. For instance, Zhang et al employed a linear spectral unmixing method to retrieve the abundances of vegetation and exposed rock from Hyperion image [11].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Carbonate Rock Fraction in Karst Regions Using Field Spectra in 2.0–2.5 μm

Xie

Tian

et al. 2016

Abstract:Considering the important roles of carbonate rock fraction in karst rocky desertification areas and their potential for indicating damage to vegetation, improved knowledge is desired to assess the application of spectroscopy and remote sensing to characterizing and quantifying the biophysical constituents of karst landscapes. In this study, we examined the spectra of major surface constituents in karst areas for direct evidence of absorption features attributable to carbonate rock fraction. Using spectral feature analysis with continuum removal, we observed that there are overlapping spectral absorption in 2.149-2.398 µm by soils and non-photosynthetic vegetation. These overlapping features complicated the carbonate absorption feature near 2.340 µm in synthetic mixed spectra. To remove the overprint signal, two hyperspectral carbonate rock indices (HCRIs) were developed. Compared to the absorption features including depths, areas, and KRDSIs (karst rocky desertification synthesis indices), linear regression of HCRIs with carbonate rock fraction in linear synthetic mixtures resulted in higher correlations and lower errors. This study demonstrates that spectral variation of the surface constituents spectra in 2.270-2.398 µm region can indicate carbonate rock fraction and be used to quantify them. Still, additional research is needed to advance our understanding of the spectral influences from carbonate petrography relative to carbonate mineralogy, components and physical state of rock surface.