Estimating fractional vegetation cover of oasis in Tarim Basin, China, using dimidiate fractional cover model

2015 ◽  
Author(s):  
Xiaoyong Han ◽  
Ling Han
Keyword(s):  
Tarim Basin ◽  
Vegetation Cover ◽  
Fractional Cover ◽  
Fractional Vegetation Cover

scholarly journals Extracting Fractional Vegetation Cover from Digital Photographs: A Comparison of In Situ, SamplePoint, and Image Classification Methods

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7310
Author(s):  
Xiaolei Yu ◽  
Xulin Guo
Keyword(s):  
Image Classification ◽  
Vegetation Cover ◽  
Field Measurements ◽  
Unsupervised Classification ◽  
Classification Methods ◽  
Mean Values ◽  
Fractional Cover ◽  
Fractional Vegetation Cover ◽  
Object Based

Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record.

scholarly journals Spatiotemporal Evolution of Fractional Vegetation Cover and Its Response to Climate Change Based on MODIS Data in the Subtropical Region of China

2021 ◽  
Vol 13 (5) ◽  
pp. 913
Author(s):  
Hua Liu ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
Di’en Zhu ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Distribution Area ◽  
Spatiotemporal Pattern ◽  
Climate Factors ◽  
Important Indicator ◽  
Fractional Vegetation Cover ◽  
Annual Minimum Temperature ◽  
Total Study Area

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.

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

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.

scholarly journals Erosion Prediction Model using Fractional Vegetation Cover

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.

Fractional vegetation cover estimation by using multi-angle vegetation index

2018 ◽  
Vol 216 ◽  
pp. 44-56 ◽  
Author(s):  
Xihan Mu ◽  
Wanjuan Song ◽  
Zhan Gao ◽  
Tim R. McVicar ◽  
Randall J. Donohue ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Fractional Vegetation Cover

Rapid estimation of fractional vegetation cover in grasslands using smartphones

2022 ◽  
Vol 198 ◽  
pp. 104697
Author(s):  
Wanjia Hu ◽  
Zunchi Liu ◽  
Zhicheng Jia ◽  
Thomas Ryan Lock ◽  
Robert L. Kallenbach ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Rapid Estimation ◽  
Fractional Vegetation Cover
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