A retrieval and validation method for shelterbelt vegetation fraction

Shelterbelts are important in defending against natural disaster and maintaining ecological balances in farmland. Understanding of the shelterbelt vegetation fraction is fundamental to regional research of shelterbelts using remote sensing. We used SPOT5 imagery with 10×10m spatial resolution in combination with knowledge of the characteristics of shelterbelts to develop a method for retrieval of the vegetation fraction of shelterbelts by the pixel un-mixing model. We then used the method to retrieve values for shelterbelts in study area. By combining the parameters of photographic images with characteristics of shelterbelts, we developed a method for measuring the vegetation fraction of shelterbelts based on an advanced photographic method. We then measured the actual values to validate the retrieval result. The multiple correlation coefficients between the retrieved and measured values were 0.715. Our retrieval and measuring methods presented in this paper accurately reflect field conditions. We suggest that this method is useful to describe shelterbelt structure using remote sensing.

Vegetation fractional coverage change in a typical oasis region in Tarim River Watershed based on remote sensing

Vegetation fractional coverage （VFC） is an important index to describe and evaluate the ecological system. The vegetation index is widely used to monitor vegetation coverage in the field of remote sensing （RS）. In this paper, the author conducted a case study of the delta oasis of Weigan and Kuqa rivers, which is a typical saline area in the Tarim River Watershed. The current study was based on the TM/ETM＋ images of 1989, 2001, and 2006, and supported by Geographic Information System （GIS） spatial analysis, vegetation index, and dimidiate pixel model. In addition, VBSl （vegetation, bare soil and shadow indices） suitable for TM/ETM＋ irrlages, constructed with FCD （forest canopy density） model principle and put forward by ITTO （International Tropical Timber Organization）, was used, and it was applied to estimate the VFC. The estimation accuracy was later prow^n to be up to 83.52%. Further, the study analyzed and appraised the changes in vegetation patterns and revealed a pattern of spatial change in the vegetation coverage of the study area by producing the map of VFC levels in the delta oasis. Forest, grassland, and farmland were the three main land-use types with high and extremely-high coverage, and they played an important role in maintaining the vegetation. The forest area determined the changes of the coverage area, whereas the other two land types affected the directions of change. Therefore, planting trees, protecting grasslands, reclaiming farmlands, and controlling unused lands should be included in a long-term program because of their importance in keeping regional vegetation coverage. Finally, the dynamic variation of VFC in the study area was evaluated according to the quantity and spatial distribution rendered by plant cover diigital images to deeply analyze the reason behind the variation.

Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

The green vegetation fraction(GVF)can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction(NWP)models.However,the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well.In this study,a near real-time(NRT)GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model.The annual variability of the GVF over North China is examined in detail.Many differences between the two GVF datasets are found over dryland,grassland,and cropland/grassland mosaic areas.Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity.The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October.Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon.Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state,which in turn helps to improve short-range forecasts in arid and semiarid regions of North China.Moreover,our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.

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.

Analysis of the Diurnal Pattern of Evaporative Fraction and Its Controlling Factors over Croplands in the Northern China

A key issue of applying remotely sensed data to estimate evapotranspiration （ET） for water management is extrapolating instantaneous latent heat flux （LE） at satellite over-passing time to daily ET total. At present, the most commonly used extrapolation methods have the same assumption that evaporative fraction （EF） can be treated as constant during daytime （so-called EF self-preservation）. However, large errors are reported by many documents over various ecosystems with the same approach, which indicates that further analysis of the diurnal pattern of EF is still necessary. The aim of this study is to examine the diurnal pattern of EF under fair weather conditions, then to analyze the dependencies of EF to meteorological and plant factors. Long-term flux observations at four sites over semi-arid and semi-humid climate regions in the northern China are used to analyze the EF diumal pattern. Results show that the EF self-preservation assumption no longer holds over growing seasons of crops. However, the ratio of reference ET to available energy is almost constant during the daytime, which implies the climate factors do not have much effect on the variability of EF. The analysis of diurnal pattern of air temperature, vapor pressure deficiency （VPD）, and relative humidity （RH） confirms the assumption that ET diurnal pattern is mainly influenced by stomatal regulation.

Effects of ecological projects on vegetation in the Three Gorges Area of Chongqing, China

The construction of the Three Gorges Reservoir and the resettlement project have caused increasing contradictions between human and land,and led to the deterioration of the ecological environment. In order to ameliorate ecological environment of the Three Gorges Area, the government carried out several ecological restoration projects to improve the vegetation coverage from 1990 s. This paper aims to quantitatively analyze the impact of ecological projects on the vegetation in the Three Gorges Area of Chongqing, China. Landsat and MODIS data from 1992 to 2015 were used to estimate vegetation coverage. In addition, the land cover data of the European Space Agency(ESA) was used to explore the impact of ecological projects on land cover change. The cropland accounted for about 62% and the forestland accounted for about 34% of the total area. There was more than 90% of the study area covered with high or very high vegetation coverage.From 1992 to 2015, a total of 272.7 km;croplands were converted into forestland in the Ecological Migration Project(EMP), 795.6 km;in the Grain for Green Project(GGP), and 13.77 km;in the Ecological Restoration Zone Project(ERZP). Among the three projects, the GGP was the most powerful measure,with a contribution rate of 1.6%. The implementation of the ecological projects improved vegetation coverage, which indicated that the ecological projects measures were effective in ecological restoration.

Evaluating the Impact of Different Vegetation Types on NEE: A Case Study of Banni Grasslands, India

Estimation of NEE of Grasslands ecosystems becomes mandatory as these grasslands with their wide spread (almost 40% of land of the earth) and high plant diversity play a major role in global carbon balances and NEE at both local and global scale. The present study has been focused on understanding the role of different plant species responsible for variation in NEE of the Banni Grasslands of India. These grasslands form a belt of arid grassland having low growing forbs, graminoids and scattered tree cover. Due to its wide spread and inaccessibility of Banni, this study utilized spatial approach for evaluating carbon emissions and NEE. Landsat data was utilized for vegetation type classification and SMAP data for extraction of NEE values proved their potential for categorising vegetation type and generating NEE values precisely. Three major plant types were identified from the study area viz., Grasslands, Land with Acacia and Land with Prosopis. Grasses were dominant covering 77% and the rest of the area was occupied by the other two classes, i.e. Acacia and Prosopis. The NEE values were higher for the grasses when compared to the other two plant species proving to be the active sinks when compared to other plants. The differential contribution of NEE by species has been depicted in the present work.

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.