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

Li, Xiaosong; Zheng, Guoxiong; Wang, Jinying; Ji, Cuicui; Sun, Bin; Gao, Zhong‐Hua

doi:10.3390/rs8100800

Cited by 29 publications

(16 citation statements)

References 39 publications

(34 reference statements)

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“…Scarth et al (2010) also applied similar techniques to Landsat data [16]. Furthermore, Li et al (2016) applied the Automatically Monte Carlo spectral Unmixing model (AutoMCU) to the GF-1 data without SWIR bands for retrieving the PV and NPV cover in the sandy land [17]. The above-mentioned applications show that SMA with MODIS data is an effective mean for retrieving PV and NPV cover simultaneously.…”

Section: Discussionmentioning

confidence: 99%

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

et al. 2017

Data

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Fractional covers of photosynthetic and non-photosynthetic vegetation are key indicators for land degradation surveillance in the dryland of China. However, there are no available, well validated, and multispectral-based products. Aiming for this, we selected the Beijing and Tianjin Sandstorm Source Region as the study area, and utilized the linear spectral mixture model for generating the fractional cover of PV, NPV, and bare soil, with endmember spectra retrieved from the field measured endmember spectral library, SummaryFractional cover of vegetation plays a key role in resisting wind and water erosion, thus it has been widely used as the indicator for land degradation monitoring and assessment. From a functional perspective, vegetation can be categorized as photosynthetic (green leaves) and non-photosynthetic (wood, senescent material, and litter) material [1]. Unlike the well-developed multiple datasets of the fractional cover of PV, the datasets of the fractional cover of NPV are relatively rare, mainly because of the difficulty in differentiating NPV from the soil background, particularly when multispectral sensors are considered. However, NPV is common and widely distributed in the drylands due to the scarce, variable rainfall and low soil fertility. Therefore, simultaneously acquiring the fractional cover of PV and NPV would provide new insights for land degradation surveillance and land management.In this context, an archive of fractional cover of PV and NPV was computed for the Beijing and Tianjin Sandstorm Source Region (BTSSR), with monthly temporal resolution covering 2001

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Section: Discussionmentioning

confidence: 99%

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

et al. 2017

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“…Photosynthetic vegetation (PV) is defined as plant material including chlorophyll (e.g., green leaves and flowers), which is a significant plant factor in arid and semiarid regions. Non-photosynthetic vegetation (NPV) is plant material lacking chlorophyll (e.g., senescent plants, branches, and plant stubble), and it occupies a great part of natural vegetation in arid and semiarid regions [1,2]. PV and NPV are not only important indicators for changes of the ecological environment, but are also essential elements in surveying vegetation status and researching carbon storage in arid regions [3].…”

Section: Introductionmentioning

confidence: 99%

“…Sensor capability generally constrains vegetation indices estimated from multispectral satellite data for PV and NPV mapping, mostly due to available sensors not being specifically designed for NPV calculations. Spectral-mixture analysis (SMA) provides an adequate method to calculate f PV and f NPV [2,17,18]. SMA commonly include two types depending on whether it focuses on multiple scattering or not.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

Huai-dong

et al. 2020

Remote Sensing

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It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract fPV and fNPV through the novel method of spectral-mixture analysis.

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“…From a functional perspective, vegetation can be categorized as photosynthetic (green leaves) and non-photosynthetic (wood, senescent material, and litter) material [ 6 , 7 ]. Undoubtedly, photosynthetic vegetation (PV) is a critical component of desert vegetation, but it is not the only component.…”

Section: Introductionmentioning

confidence: 99%

“…Undoubtedly, photosynthetic vegetation (PV) is a critical component of desert vegetation, but it is not the only component. Non-photosynthetic vegetation(NPV) also plays a key role in carbon and nutrient uptake, fire risk and frequency, and wind and water erosion, the potential for fire risk and wind and water erosion[ 7 ]. Thus, acquiring the fractional cover of PV ( f pv ) and NPV ( f npv ) information simultaneously will be of great value for desert vegetation studies.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China

Ji¹,

Jia²,

Gao³

et al. 2017

PLoS ONE

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Desert vegetation plays significant roles in securing the ecological integrity of oasis ecosystems in western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation cover is necessary to guide management practices on land desertification and research into the mechanisms driving vegetation recession. In this study, nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates are investigated through comparing the performance of linear and nonlinear spectral mixture models with different endmembers applied to field spectral measurements of two types of typical desert vegetation, namely, Nitraria shrubs and Haloxylon. The main results were as follows. (1) The correct selection of endmembers is important for improving the accuracy of vegetation cover estimates, and in particular, shadow endmembers cannot be neglected. (2) For both the Nitraria shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture Model (KNSMM) with nonlinear parameters was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon plots since the nonlinear spectral mixture effects were more obvious for this vegetation type. (3) The vegetation canopy structure (planophile or erectophile) determines the strength of the nonlinear spectral mixture effects. Therefore, no matter for Nitraria shrubs or Haloxylon, the non-linear spectral mixing effects between the photosynthetic / non-photosynthetic vegetation and the bare soil do exist, and its strength is dependent on the three-dimensional structure of the vegetation canopy. The choice of linear or nonlinear spectral mixture models is up to the consideration of computational complexity and the accuracy requirement.

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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

Cited by 29 publications

References 39 publications

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

A 2001–2015 Archive of Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation for Beijing and Tianjin Sandstorm Source Region

Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China

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