Rapid estimation of fractional vegetation cover in grasslands using smartphones

The subtropical vegetation plays an important role in maintaining the structure and function of global ecosystems, and its contribution to the global carbon balance are receiving increasing attention. The fractional vegetation cover (FVC) as an important indicator for monitoring environment change, is widely used to analyze the spatiotemporal pattern of regional and even global vegetation. China is an important distribution area of subtropical vegetation. Therefore, we first used the dimidiate pixel model to extract the subtropical FVC of China during 2001–2018 based on MODIS land surface reflectance data, and then used the linear regression analysis and the variation coefficient to explore its spatiotemporal variations characteristics. Finally, the partial correlation analysis and the partial derivative model were used to analyze the influences and contributions of climate factors on FVC, respectively. The results showed that (1) the subtropical FVC had obvious spatiotemporal heterogeneity; the FVC high-coverage and medium-coverage zones were concentratedly and their combined area accounted for more than 70% of the total study area. (2) The interannual variation in the average subtropical FVC from 2001 to 2018 showed a significant growth trend. (3) In 76.28% of the study area, the regional FVC showed an increasing trend, and the remaining regional FVC showed a decreasing trend. However, the overall fluctuations in the FVC (increasing or decreasing) in the region were relatively stable. (4) The influences of climate factors to the FVC exhibited obvious spatial differences. More than half of all pixels exhibited the influence of the average annual minimum temperature and the annual precipitation had positive on FVC, while the average annual maximum temperature had negative on FVC. (5) The contributions of climate changes to FVC had obvious heterogeneity, and the average annual minimum temperature was the main contribution factor affecting the dynamic variations of FVC.

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Annually Urban Fractional Vegetation Cover Dynamic Mapping in Hefei, China (1999–2018)

Remote Sensing ◽

10.3390/rs13112126 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2126

Author(s):

Yuliang Wang ◽

Mingshi Li

Keyword(s):

Standard Deviation ◽

Vegetation Cover ◽

Vegetation Index ◽

Dynamic Range ◽

Dynamic Change ◽

Estimation Model ◽

Sparse Vegetation ◽

Fractional Vegetation Cover ◽

Dynamic Mapping ◽

Multi Temporal

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

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Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112304 ◽

2021 ◽

Vol 255 ◽

pp. 112304

Author(s):

José Manuel Fernández-Guisuraga ◽

Jochem Verrelst ◽

Leonor Calvo ◽

Susana Suárez-Seoane

Keyword(s):

Radiative Transfer ◽

Spatial Resolution ◽

Vegetation Cover ◽

High Spatial Resolution ◽

Ecological Systems ◽

Radiative Transfer Models ◽

Fractional Vegetation Cover ◽

Reflectance Data ◽

Transfer Models

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New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and leaf area index

Science of Remote Sensing ◽

10.1016/j.srs.2021.100027 ◽

2021 ◽

pp. 100027

Author(s):

Hongliang Fang ◽

Sijia Li ◽

Yinghui Zhang ◽

Shanshan Wei ◽

Yao Wang

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Structural Properties ◽

Vegetation Cover ◽

Area Index ◽

Fractional Vegetation Cover ◽

Clumping Index ◽

Global Vegetation

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Erosion Prediction Model using Fractional Vegetation Cover

Indonesian Journal of Science and Technology ◽

10.17509/ijost.v5i1.21060 ◽

2020 ◽

Vol 5 (1) ◽

pp. 125-132

Author(s):

Nursida Arif ◽

Projo Danoedoro ◽

Hartono Hartono ◽

Andrew Mulabbi

Keyword(s):

Prediction Model ◽

Vegetation Cover ◽

Input Data ◽

Medium Density ◽

Vegetation Density ◽

Erosion Model ◽

Fractional Vegetation Cover ◽

Erosion Prediction ◽

Digital Elevation ◽

Elevation Model

The purpose of this study was to create an erosion prediction model in Serang Watershed, Indonesia. The erosion model used two input data, namely the slope derivied from Digital Elevation Model (DEM) data, and Fractional Vegetation Cover (FVC) from SPOT images. Assessment of the model was carried out using questionnaires and interviews with several experts by presenting the results of the model and its supporting data. Based on the DEM data, the level of slope steepness in the study area is very varied namely; flat (52.77%), sloping (7.62%), and rather steep to very steep (39.59%). Vegetation density according to the FVC results is dominated by medium density. The results of the analysis of the two input models can provide predictions of the level of erosion with an accuracy of 67.92%. Evaluation of the model was done by experts with conclusions that the method was very flexible and can be adapted to similar watersheds elsewhere.

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