Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

Chai, Guoqi; Wang, Jingpu; Wang, Guangzhen; Kang, Liqiang; Wu, Mengquan; Wang, Zhoulong

doi:10.1080/01431161.2019.1620971

Cited by 3 publications

(6 citation statements)

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“…In addition, they also mapped its spatiotemporal changes and investigated the causes that mainly included population growth, agricultural expansion, and deforestation. Finally, apart from [96], some studies also concentrated on the FVC of senescent vegetation [109][110][111]. Chai et al [109] simulated MODIS spectral bands from ground-based hyperspectral images and then calculated eight vegetation indices, demonstrating that a linear model driven by the dead fuel index (DFI) performed best with R 2 of 0.62.…”

Section: Fvcmentioning

confidence: 99%

“…Finally, apart from [96], some studies also concentrated on the FVC of senescent vegetation [109][110][111]. Chai et al [109] simulated MODIS spectral bands from ground-based hyperspectral images and then calculated eight vegetation indices, demonstrating that a linear model driven by the dead fuel index (DFI) performed best with R 2 of 0.62. Then, Yu et al [110] combined the DFI with the NDVI from MODIS in a linear mixed pixel decomposition model that supposes each pixel is composed of photosynthetic vegetation, senescent vegetation, and bare soil components.…”

Section: Fvcmentioning

confidence: 99%

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Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.

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Section: Fvcmentioning

confidence: 99%

Section: Fvcmentioning

confidence: 99%

Review of Remote Sensing Applications in Grassland Monitoring

Wang

Zhang

et al. 2022

Remote Sensing

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“…The DFI index uses more bands for the calculation, including the Red, NIR SWIR1 and SWIR2. It might reduce the impact of the BS background to some extent and it might further improve NPV detection [4]. Ji [39] similarly concluded that the red-edge and NIR bands of the Sentinel-2 data are effective in improving the accuracy of the f NPV estimates.…”

Section: Discussionmentioning

confidence: 99%

“…In LOOCV, each sample was excluded in turn and the regression model was calculated with all the remnants samples and used to predict that sample. The benefit of LOOCV was its aptitude to detect outliers and its capability of providing nearly unbiased estimations of the prediction error [4,37]. The performance of these models was assessed by the coefficients of determination (R 2 ), root mean square error of leave-one-out cross-validation (RMSECV) and regression significance (p):…”

Section: Pinty Et Al 1992mentioning

confidence: 99%

“…From a functional point of view, vegetation can be divided into two parts: the photosynthetic vegetation (PV) and the non-photosynthetic vegetation (NPV) [3]. The PV mainly refers green leaves, while the NPV mainly includes the vegetation litter (dead leaves, dead branches and stems) and crop residues [4,5]. NPV is important as it not only affects the carbon storage, CO 2 exchange capacity and temperature between the surface and soil [6,7] but also slows down soil erosion, increases the soil organic matter and improves the soil quality [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

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Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

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Estimating fractional cover of non-photosynthetic vegetation for various grasslands based on CAI and DFI

Bai

Zhao

et al. 2021

Ecological Indicators

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Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

Cited by 3 publications

References 37 publications

Review of Remote Sensing Applications in Grassland Monitoring

Review of Remote Sensing Applications in Grassland Monitoring

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimating fractional cover of non-photosynthetic vegetation for various grasslands based on CAI and DFI

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