Dynamic change of net primary productivity and fractional vegetation cover in the Yellow River Basin using multi-temporal AVHRR NDVI Data

An exponential relationship between net primary productivity (NPP) and integrated NDVI has been found in this paper. Based on the relationship and using multi-temporal 8 km resolution NOAA AVHRR-NDVI data, the spatial distribution and dynamic change of NPP and fractional vegetation cover in the Yellow River Basin from 1982 to 1999 are analyzed. Finally, the effect of rainfall on NDVI is examined. Results show that mean NPP and fractional vegetation cover have an inclining trend for the whole basin, and rainfall in flood season influences vegetation cover most.

Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data

Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction.

Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.

Estimating Evapotranspiration Using Improved Fractional Vegetation Cover and Land Surface Temperature Space

Vegetation index-land surface temperature （VI-T s ） space has been widely used to estimate evapotranspiration and soil moisture. The limitation of this method is the uncertainty of the observed dry edge, which is usually fitted by scatter plots. Here, a method was used to locate true dry and wet edges based on energy balance formulation, and a simple method to estimate surface energy flux is proposed based on the improved Fractional vegetation cover-Land surface temperature （F v -T s ） space. Seventeen days of MODIS products were selected to estimate evapotranspiration and the estimated sensible heat flux （H） is compared with Large Aperture Scintillometer （LAS） data at a site in Zhengzhou, resulting in a RMSE of 44.06 W m^-2 , bias of 36.99 W m^-2 and R^2 of 0.71. The H scatter plots of estimation versus observation show clearly that most points are around the 1：1 line. Overall, the located true and wet edges are more accurate than the observed true edge. Our results can also be applied to improve the estimation of soil moisture.

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets:Method Development and Validation

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over China by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over China. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

New method for cotton fractional vegetation cover extraction based on UAV RGB images

As the key principle of precision farming,the distribution of fractional vegetation cover is the basis of crop management within the field serves.To estimate crop FVC rapidly at the farm scale,high temporal-spatial resolution imagery obtained by unmanned aerial vehicle(UAV)was adopted.To verify the application potential of consumer-grade UAV RGB imagery in estimated FVC,blue-green characteristic vegetation index(TBVI)and red-green vegetation index(TRVI)were proposed in this study according to the differences of the gray value among cotton vegetation,soil and shadow in the field.First,two new constructed indices and several published indices were used to extract visible light images and generate greyscale images for each of the visible light vegetation indices.Then,the thresholds of cotton vegetation and non-vegetation pixels were established based on the vegetation index threshold method which combines support vector machine classification and vegetation index.Finally,the accuracy difference in vegetation information extraction between the newly constructed and several published indices was compared.The results show that the accuracy of the information extracted by TRVI is higher than that of subdivision index of other visible light(FVC extraction precision in the first bud stage of cotton:R2=0.832,RMSE=2.307,nRMSE=4.405%;FVC extraction precision in the bud stage of cotton:R2=0.981,RMSE=1.393,nRMSE=1.984%;FVC extraction precision in the flowering stage of cotton:R2=0.893,RMSE=2.101,nRMSE=2.422%;FVC extraction precision in the boll stage of cotton:R2=0.958,RMSE=1.850,nRMSE=2.050%).

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).

Tropical forest canopies and their relationships with climate and disturbance： results from a global dataset of consistent field-based measurements

Background： Canopy structure, defined by leaf area index （LAI）, fractional vegetation cover （FCover） and fraction of absorbed photosynthetically active radiation （fAPAR）, regulates a wide range of forest functions and ecosystem services. Spatially consistent field-measurements of canopy structure are however lacking, particularly for the tropics. Methods： Here, we introduce the Global LAI database： a global dataset of field-based canopy structure measurements spanning tropical forests in four continents （Africa, Asia, Australia and the Americas）. We use these measurements to test for climate dependencies within and across continents, and to test for the potential of anthropogenic disturbance and forest protection to modulate those dependences. Results： Using data collected from 887 tropical forest plots, we show that maximum water deficit, defined across the most arid months of the year, is an important predictor of canopy structure, with all three canopy attributes declining significantly with increasing water deficit. Canopy attributes also increase with minimum temperature, and with the protection of forests according to both active （within protected areas） and passive measures （through topography）. Once protection and continent effects are accounted for, other anthropogenic measures （e.g. human population） do not improve the model. Conclusions： We conclude that canopy structure in the tropics is primarily a consequence of forest adaptation to the maximum water deficits historically experienced within a given region. Climate change, and in particular changes in drought regimes may thus affect forest structure and function, but forest protection may offer some resilience against this effect.

The role of glacial gravel in community development of vascular plants on the glacier forelands of the Third Pole

On a deglaciated terrain,glacial gravel is the primary component of the natural habitat for vascular plant colonization and succession.Knowledge regarding the role of glacial gravel in vascular plant growth,however,remains limited.In this study,an unmanned aerial vehicle(UAV)was used to investigate plant family composition,species richness,fractional vegetation cover(FVC),and gravel cover(GC)along elevational gradients on the three glacier forelands(Kekesayi,Jiangmanjiaer,and Koxkar Baxi)of the Third Pole(including the eastern Pamir Plateau and western Tianshan Mountains)in China.We then analyzed the spatial characteristics of vascular plants followed by exploring the effect of glacial gravel on vascular plants.Findings indicated that FVC on these glacier forelands generally decreased as the elevation increased or distance from the current glacier terminus decreased.The shady slope(Kekesayi)was more vegetated in comparison to the sunny slope(Jiangmanjiaer)at the glacier basin scale,and the warm and humid deglaciated terrain(Koxkar Baxi)had the highest FVC at the regional scale.Plant family composition and species richness on the glacier forelands decreased with rising elevation,with the exception of those on the Jiangmanjiaer glacier foreland.The relationships between FVC and GC presented negative correlations;particularly,they exhibited variations in power functions on the Kekesayi and Jiangmanjiaer glacier forelands of the eastern Pamir Plateau and a linear function on the Koxkar Baxi glacier foreland of the western Tianshan Mountains.Glacial gravel was found to be conducive to vegetation colonization and development in the early succession stage up until vascular plants adapted to the cold and arid climatic condition,whereas it is unfavorable to the expansion of vascular plants in the later succession stage.These findings suggested that the spatial difference of plant characteristics had close connections with regional climatic and topographic conditions,as well as glacial gravel distribution.In addition,we concluded that aerial photographs can be an asset for studying the functions of micro-environment in vegetation colonization as well as succession on the glacier forelands.

Triangle Method for Estimating Soil Surface Wetness from Satellite Imagery in Allahabad District, Uttar Pradesh, India

Soil surface wetness is indispensable land surface parameter in agriculture, hydrology and environmental engineering. This paper explores the relationship between surface radiant temperature and fractional vegetation cover derived from satellite imagery to estimate soil surface wetness (triangle method) in Allahabad district. The pixel distributions create triangular shapes because the range of surface radiant temperature decreases as the amount of vegetation cover increases and sufficient number of pixels exists. A very weak correlation is found between the simulated soil surface wetness and ground measured soil moisture at deeper soil layers (R2 < 0.15) on all the dates under investigation. This is because the drying rates at the surface discontinue to be linearly correlated to that at lower levels (depths). The standing water pixels distort the shape of the triangle especially at lower left edge of the triangle. This distortion is removable. The spatial and temporal inhomogeneity of soil surface wetness is examined.

Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change.Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems,although it remains to be tested in areas with dwarf and sparse vegetation,such as the Tibetan Plateau.We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect.We estimated community structural and functional traits,as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices(VIs)derived from hyperspectral images.We quantified the contributions of environmental drivers,VIs,and community traits to community function by structural equation modelling(SEM).Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index,enhanced vegetation index,and simple ratio.Structural equation modelling showed that VIs and community traits positively affected community function,whereas environmental drivers and specific leaf area had the opposite effect.Additionally,VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits.This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes.Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

Assessing the USLE Crop and Management Factor C for Soil Erosion Modeling in a Large Mountainous Watershed in Central China

Due to the impoundment of the Yangtze River, the Three Gorges Dam in China fosters high land-use dynamics. Soil erosion is expected to increase dramatically. One of the key factors in soil erosion control is the vegetation cover and crop type. However, determining these factors adequately for the use in soil erosion modeling is very time-consuming especially for large mountainous areas, such as the Xiangxi （香溪） catchment in the Three Gorges area. In our study, the crop and management factor C was calculated using the fractional vegetation cover （CFvc） based on Landsat-TM images from 2005, 2006, and 2007 and on literature studies （CLIT）. In 2007, the values of CFvc range between 0.001 and 0.98 in the Xiangxi catchment. The mean CFVC value is 0.05. CLIT values are distinctly higher, ranging from 0.08 to 0.46 with a mean value of 0.32 in the Xiangxi catchment. The mean potential soil loss amounts to 120.62 t/ha/a in the Xiangxi catchment when using CLIT for modeling. Based on CFVC, the predicted mean soil loss in the Xiangxi catchment is 11.50 t/ha/a. Therefore, CLIT appears to bemore reliable than the C factor based on the fractional vegetation cover.