Extraction of vegetation fraction based on the dimidiate pixel model and vegetation index transform plan

2011 ◽  
Author(s):  
Yaqin Cui ◽  
Youqing Luo ◽  
Lei Wang
Keyword(s):  
Vegetation Index ◽  
Vegetation Fraction ◽  
Dimidiate Pixel Model

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

Study on Vegetation Fraction Cover Retrieval over Chuxiong Prefecture from TM Imagery

2014 ◽  
Vol 638-640 ◽  
pp. 2174-2177
Author(s):  
Wu Jun Xi ◽  
Xue Liang Wang
Keyword(s):  
Vegetation Fraction ◽  
Fractional Cover ◽  
Dimidiate Pixel Model ◽  
The Mean

The paper retrieved vegetation fraction cover over Chuxiong Prefecture from TM Imagery by dimidiate pixel model on February 19h, 2003. The results showed that the mean vegetation fractional cover in Chuxiong prefecture was 0.400117, it’s secondary area mean vegetation fractional cover from large to small were Lufen county, Shuangbai county,Chuxiong city, Yaoan county, Dayao county, Wuding county, Nanhua county, Yongren county, Mouding county, Yuanmou county.

Vegetation Fraction Change Monitoring in Beijing by Remote Sensing

2014 ◽  
Vol 955-959 ◽  
pp. 859-862
Author(s):  
Shi Wei Dong ◽  
Dan Feng Sun ◽  
Hong Li
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Coverage ◽  
Vegetation Fraction ◽  
Important Index ◽  
Dimidiate Pixel Model ◽  
Low Coverage ◽  
Beijing China ◽  
Change Monitoring ◽  
Better Than

Vegetation fraction was a most important index to score the vegetation coverage on the land surface. Improved dimidiate pixel model was applied to calculate and analyze the vegetation fraction change from September 2000 to September 2013 in Beijing, China. The results showed that vegetation coverage of Beijing in 2013 year was much better than 2000 year. The area of low and middle-low coverage of Beijing in 2013 decreased 379 km2 and 591 km2 respectively, and the area of high and middle coverage increased 885 km2 and 85 km2 respectively. The research provided a necessary reference for the related researches of vegetation fraction.

scholarly journals Remote estimation of crop canopy parameters by statistical regression algorithms for winter rapeseed using Sentinel-2 multispectral images

2018 ◽  
Vol 30 ◽  
pp. 75-95 ◽  
Author(s):  
Dessislava Ganeva ◽  
Eugenia Roumenina
Keyword(s):  
Gaussian Processes ◽  
Vegetation Index ◽  
Parametric Models ◽  
Statistical Regression ◽  
Crop Canopy ◽  
Fresh Biomass ◽  
Vegetation Fraction ◽  
Winter Rapeseed ◽  
Sentinel 2 ◽  
Non Parametric

Еstimation of crop canopy parameters is important task for remote sensing monitoring of agriculture and constructing strategies for within-field management. The main objective of this study is to evaluate the retrieval from Sentinel-2 images by parametric and non-parametric statistical models several crop canopy parameters for monitoring before winter and after winter rapeseed crop in real farming conditions of North East Bulgaria. For the calibration of the models in-situ data from three field campaigns is used. For most of the studied parameters models with good accuracy were identified, except for aboveground fresh biomass. The best identified model for vegetation fraction (RMSEcv=0.14%) and plant density (RMSEcv=9 nb/m2) were parametric models with three band vegetation index (3BSI-Tian) and linear fitting function for the first, three band vegetation index (3BSI-Verreslt) and polynomial for the second parameter. For aboveground dry biomass (RMSEcv=52 g/m²), mean plant height (RMSEcv=4cm) and nitrogen concentration in fresh biomass (RMSEcv=2%) the best models were non-parametric, Gaussian Processes Regression for the first parameter and Variational Heteroscedastic variant of the Gaussian Processes Regression for the other two.

scholarly journals Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 281 ◽  
Author(s):  
Jose Manuel Jiménez-Gutiérrez ◽  
Francisco Valero ◽  
Sonia Jerez ◽  
Juan Pedro Montávez
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Spatial Average ◽  
Area Index ◽  
Near Surface ◽  
Vegetation Fraction ◽  
Selection Of

The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ∘ C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ∘ C, that will modify the T2m interannual variability.

scholarly journals Vegetation Monitoring Optimization With Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

2021 ◽  
Vol 3 ◽  
Author(s):  
Shahriar Pervez ◽  
Amy McNally ◽  
Kristi Arsenault ◽  
Michael Budde ◽  
James Rowland
Keyword(s):  
Food Security ◽  
East Africa ◽  
Random Error ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Surface Models ◽  
Vegetation Monitoring ◽  
Vegetation Fraction ◽  
Surface Models

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration (ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

scholarly journals A MODIS-Based Global 1-km Maximum Green Vegetation Fraction Dataset

2014 ◽  
Vol 53 (8) ◽  
pp. 1996-2004 ◽  
Author(s):  
Patrick D. Broxton ◽  
Xubin Zeng ◽  
William Scheftic ◽  
Peter A. Troch
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ground Truth ◽  
Mean Value ◽  
Area Index ◽  
Vegetation Fraction ◽  
Community Land Model ◽  
Green Vegetation ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractGlobal land-cover data are widely used in regional and global models because land cover influences land–atmosphere exchanges of water, energy, momentum, and carbon. Many models use data of maximum green vegetation fraction (MGVF) to describe vegetation abundance. MGVF products have been created in the past using different methods, but their validation with ground sites is difficult. Furthermore, uncertainty is introduced because many products use a single year of satellite data. In this study, a global 1-km MGVF product is developed on the basis of a “climatology” of data of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index and land-cover type, which removes biases associated with unusual greenness and inaccurate land-cover classification for individual years. MGVF shows maximum annual variability from 2001 to 2012 for intermediate values of average MGVF, and the standard deviation of MGVF normalized by its mean value decreases nearly monotonically as MGVF increases. In addition, there are substantial differences between this climatology and MGVF data from the MODIS Continuous Fields (CF) Collection 3, which is currently used in the Community Land Model. Although the CF data only use 2001 MODIS data, many of these differences cannot be explained by usage of different years of data. In particular, MGVF as based on CF data is usually higher than that based on the MODIS climatology from this paper. It is difficult to judge which product is more realistic because of a lack of ground truth, but this new MGVF product is more consistent than the CF data with the MODIS leaf area index product (which is also used to describe vegetation abundance in models).

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