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

Abstract

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.

Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes

“…The three-endmember linear mixture model has already been demonstrated to be valid for the generation of a global linear mixture model from broadband multispectral imagery and is able to represent more than 95% of 30 million globally distributed LANDSAT ETM+ image spectra [44], to be highly correlated (correlation value of 0.95) with the greenness index estimated using Kauth-Thomas tasseled cap transformation [62], and to achieve the best performance for the estimation of FCover when compared to other methods such as multiple linear regression and Bayesian model averaging [63]. LSMA has been demonstrated to produce accurate results for the estimation of global vegetation fraction and exhibits a substantial linear relationship with EVI over a wide range of soils and a relative linear relationship with SAVI for most pixels with f V > 0.2, although a substantial bias is present, and the variance is wide at low values [49].…”

Assessing Land Cover and Ecological Quality Changes in the Forest-Steppe Ecotone of the Greater Khingan Mountains, Northeast China, from Landsat and MODIS Observations from 2000 to 2018

“…FVC is defined as the percentage of the vertically projected area of vegetation on the ground to the total area [50,51]. It is a crucial parameter in climate, hydrology, and soil research [52]. In this study, the dimidiate pixel model was used to extract the FVC in the study area.…”

Whom Do Urban Agriculture Parks Provide Landscape Services to and How? A Case Study of Beijing, China

“…Fractional vegetation cover (FVC) and area were selected as indicators of heterogeneity in UAPs' landscape characteristics and functions. FVC was estimated using the dimidiate pixel model, based on Landsat 8 OLI imagery data in 2017 [33]. The area was determined based on Worldview II remote sensing images (captured on 2 June, 2016).…”