Fractional Green Vegetation Cover Research Articles

Fractional vegetation cover estimation by using multi-angle vegetation index

The vegetation index-based (VI-based) mixture model is widely used to derive green fractional vegetation cover (FVC) from remotely sensed data. Two critical parameters of the model are the vegetation index values of fully-vegetated and bare soil pixels (denoted Vx and Vn hereafter). These are commonly empirically set according to spatial and/or temporal statistics. The uncertainty and difficulty of accurately determining Vx and Vn in many ecosystems limits the accuracy of resultant FVC estimates and hence reduces the utility of VI-based mixture model for FVC estimation. Here, an improved method called MultiVI is developed to quantitatively estimate Vx and Vn from angular VI acquired at two viewing angles. The directional VI is calculated from the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43A1) data. The results of simulated evaluation with 10% added noise show that the root mean square deviation (RMSD) of FVC is approximately 0.1 (the valid FVC range is [0, 1]). Direct evaluation against 34 globally-distributed FVC measurements from VAlidation of Land European Remote sensing Instruments (VALERI) sites during 2000 to 2014 demonstrated that the accuracy of MultiVI FVC (R2 = 0.866, RMSD = 0.092) exceeds than from SPOT/VEGETATION bioGEOphysical product version 1 (GEOV1) FVC (R2 = 0.795, RMSD = 0.159). MultiVI FVC also exhibits higher correlation to the VALERI reference FVC than does the MODIS fraction of photosynthetically active radiation product (MCD15A2H; R2 is 0.696). A key advantage of the MultiVI method is obvious in areas where fully-vegetated and/or bare soil pixels do not exist in moderate-coarse spatial resolution imagery when compared to the conventional VI-based mixture modelling. The MultiVI method can be flexibly implemented over regional or global scales to monitor FVC, with maps of Vx and Vn generated as two important byproducts.

A universal estimation model of fractional vegetation cover for different crops based on time series digital photographs

The green fractional vegetation cover (FVC) is an important parameter in monitoring crop growth and predicting aboveground biomass. In this study, we monitored crop growth with digital cameras installed at four automatic weather observation stations in different parts of China, from 2010 to 2016. With each station having a particular type of crop, nine color vegetation indices were calculated from the acquired time series digital photographs to arrive at an FVC estimation model applicable to sugarcane, maize, cotton and paddy rice. For individual crop types, our results show that the Excess Green (ExG) is the optimal color vegetation index for the estimation of sugarcane FVC, the Normalized Difference Index (NDI) is the optimal color vegetation index for the estimation of maize FVC, and the Vegetative (VEG) color vegetation index is optimal for the FVC estimation of cotton and paddy rice. However, owing to its higher coefficient of determination (R2), and lower root mean square error (RMSE) and mean absolute error (MAE) of 0.9504, 0.0721 and 0.0545, respectively, the Color Index of Vegetation Extraction (CIVE) is found more universally applicable for FVC estimation of the four crop types under investigation. The CIVE index has therefore been proposed in this study to be optimal for FVC estimation in sugarcane, maize, cotton and paddy rice mixed cropping agro-systems which are especially common in small and highly fragmented agricultural landscapes such as those in urban and peri-urban areas.

Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland

The decrease in vegetation cover is one of the main triggering factors for soil erosion of grasslands. Within the Revised Universal Soil Loss Equation (RUSLE), a model commonly used to describe soil erosion, the vegetation cover for grassland is expressed in the cover and management factor (C-factor). The site-specific C-factor is a combination of the relative erosion susceptibility of a particular plant development stage (here expressed as soil loss ratio SLR) and the corresponding rainfall pattern (here expressed as R-factor ratio). Thus, for grasslands the fraction of green vegetation cover (FGVC) determines the SLRs. Although Switzerland is a country dominated by grassland with high percentages of mountainous regions and evidence for high erosion rates of grassland exists, soil erosion risk modeling of grasslands and especially of mountainous grasslands in Switzerland is restricted to a few studies. Here, we present a spatio-temporal approach to assess the dynamics of the C-factor for Swiss grasslands and to identify erosion prone regions and seasons simultaneously. We combine different satellite data, aerial data, and derivative products like Climate Change Initiative (CCI) Land Cover, Swissimage false-color infrared (Swissimage FCIR), PROBA-V Fraction of green Vegetation Cover (FCover300m), and MODIS Vegetation Indices 16-Day L3 Global (MOD13Q1) for the FGVC mapping of grasslands. In the spatial mapping, the FGVC is extracted from Swissimage FCIR (spat. res. 2 m) by linear spectral unmixing (LSU). The spatially derived results are then fused with the 10-day deviations of temporal FGVC derived by FCover300m. Following the original RUSLE approach, the combined FGVC are transformed to SLRs and weighted with high spatio-temporal resolved ratios of R-factors to result in spatio-temporal C-factors for Swiss grasslands. The annual average C-factor of all Swiss grasslands is 0.012. Seasonal and regional patterns (low C in winter, high C in summer, dependency on elevation) are recognizable in the spatio-temporal mapping approach. They are mainly explicable by the R-factor distribution within a year. Knowledge about the spatio-temporal dynamic of erosion triggering factors is of high interest for agronomists who can introduce areal and time specific selective erosion control measures and thereby reduce the direct costs of mitigation as well as erosion measures.

Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes

The presence and distribution of green vegetation cover in the biosphere are of paramount importance in investigating cause-effect phenomena at the land/atmosphere interface, estimating primary production rates as part of global carbon and water cycle assessments and evaluating soil protection and land use change over time. The fraction of green vegetation cover (FCover) as estimated from satellite observations has already been demonstrated to be an extraordinarily useful product for understanding vegetation cover changes, for supporting ecosystem service assessments over areas with variable extents and for processes spanning a variable period of time (abrupt events or long-term processes). This study describes a methodology implemented to estimate global FCover (from 2001 to 2015) by applying a linear spectral mixture analysis with global endmembers to an entire temporal series of MODIS satellite observations and gap-filling missing FCover observations in temporal series using the DINEOF algorithm. The resulting global MODV1 FCover product was validated with two global validation datasets and showed an overall good thematic absolute accuracy (RMSE = 0.146) consistent with the validation performance of other FCover global products. Basic statistics performed on the product show changes in average and trend values and allow for the quantification of gross vegetation loss and gain over different temporal scales. To demonstrate the capacity of this global product to monitor specific dynamics, a multitemporal analysis was performed on selected sites and vegetation responses (i.e., cover changes), and specific dynamics resulting from cause-effect phenomena are briefly discussed. The product is intended to be used for monitoring vegetation dynamics, but it also has the potential to be integrated in other modeling frameworks (e.g., the carbon cycle, primary production, and soil erosion) in conjunction with other spatial datasets such as those on climate and soil type.

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

BackgroundAccurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments.ResultsIn this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy.ConclusionThe method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc.

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant- and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R2: 0.76–0.89) and spectral-based vegetation indices (R2: 0.77–0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands.

Monitoring fractional green vegetation cover dynamics over a seasonally inundated alpine wetland using dense time series HJ-1A/B constellation images and an adaptive endmember selection LSMM model

Time series fractional green vegetation cover (FVC) is crucial for monitoring vegetation cover status monitoring, simulating growth processes and modeling land surfaces. Through the integration of remotely sensed data and FVC estimation models, FVC can be routinely and periodically monitored using remote sensing images over large areas. However, due to frequent cloud contamination and trade-offs in satellite sensor design, the FVC estimates from remote sensing data are not continuous, either spatially or temporally, and cannot simultaneously depict details in spatio-temporal variation. Taking the seasonally inundated Zoige alpine wetland in China as a case area, the objective of this paper is to develop a practical and effective approach to quantifying the explicit vegetation FVC details with both high spatial and temporal resolution. In this approach, 30-m multi-spectral images from the Chinese HJ-1A/B (HuanJing (HJ), which means environment in Chinese) satellite constellation with a 2-day revisit time were first composited at 16-day intervals to improve spatio-temporal continuity. Then, a new adaptive endmember selection linear spectral mixture model (ASLSMM) was proposed to improve the accuracy of FVC estimation by considering the endmember dynamics for each pixel. FVC time series were finally estimated by applying the ASLSMM to the cloudless HJ composites. The performance of the model and the spatio-temporal representational capability of the FVC estimation results were comprehensively evaluated using Unmanned Aerial Vehicle (UAV) reference images and ground measurements from an integrated, multi-scale remote sensing experiment. A traditional LSMM with fixed endmembers and the Multiple Endmember Spectral Mixture Analysis (MESMA) model were also used for model performance comparison. The results showed that the R2 and RMSE values between the FVC estimated from the proposed model and the UAV reference were 0.7315 and 0.1016 (unitless) respectively, which was better than the results from the linear spectral mixture model with a fixed number of endmembers, with R2 of 0.5924 and RMSE of 0.3821. The R2 and RMSE values between the FVC estimated from MESMA and the UAV reference were 0.6327 and 0.1578, which was comparable with the ASLSMM. The accuracy evaluation using multi-temporal in situ measurements indicated the consistently high performance of the ASLSMM. This study highlights the feasibility of using HJ satellite constellation images to generate the temporally dense and fine spatial resolution FVC estimations for wetland and wetland-like heterogeneous landscape monitoring. The proposed approach can be viewed as a reference for generating FVC datasets from the on-going HJ constellation and similar constellation missions such as Sentinel-2A/B.

The contribution of plateau pika disturbance and erosion on patchy alpine grassland soil on the Qinghai-Tibetan Plateau: Implications for grassland restoration

Patchy alpine grassland with soil excavated by plateau pika (Ochotona curzoniae) and with bald patches is common on the Qinghai-Tibetan Plateau (QTP) where desertification has developed rapidly over the last few decades. This may have significant effects on alpine grassland restoration, by changing soil properties. In this study, the contribution of plateau pika disturbance and erosion on patchy alpine grassland soil were examined by classifying the surface of the patchy grassland into 4 types—vegetation patch, new pika pile, old pika pile and bald patch—and comparing the gravel content of the top layer of soil, soil moisture, soil hardness, soil organic carbon (SOC), and soil total nitrogen (TN) among them in the four study areas with different climatic conditions, altitudes, and grassland types on the QTP. We also analyzed the relationship between the amount of soil surface gravel and the Green Fractional Vegetation Cover (GFVC) using aerial photos. The results showed that (1) gravel content was significantly greater in bald patches and pika piles than in vegetation patches (p<0.05); (2) soil moisture, hardness, SOC and TN were the highest in vegetation patches, and significantly lower in pika piles than in vegetation patches (p<0.05); (3) GFVC was negatively and linearly correlated with the amount of soil surface gravel, with the amount of soil surface gravel in non-vegetation patches significantly greater than in vegetation patches (p<0.001). Our results suggested that pika burrowing activity may increase the gravel content of the top layer of soil and decrease soil moisture, hardness, SOC and TN, which can increase soil erosion and hinder vegetation restoration.

Insuring forage through satellites: testing alternative indices against grassland production estimates for France

ABSTRACTTo mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling . All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies – ISOP – system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error – SRMSE = 0.57 and coefficient of determination – R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.

Equatorial Forests Display Distinct Trends in Phenological Variation: A Time-Series Analysis of Vegetation Index Data from Three Continents

Recent studies have questioned the applicability of satellite-derived vegetation indices (VIs) for evaluating phenological variation in tropical forests, due to potential artifacts caused by the bidirectional reflectance distribution function (BRDF). For nadir-normalized data, BRDF will be driven principally by intraannual variation in solar elevation. Where areas lying on the same latitude are under similar solar elevation “regimes,” if the observed variation in VIs is indeed driven by BRDF, then different regions at the same latitude should display identical VI variations. That hypothesis was tested by comparing VI data for tropical evergreen forests in three zones north of the equator (the Guianas, central Africa, and northern Borneo). Enhanced vegetation index, the fraction of green vegetation cover, and leaf area index (LAI) from MODIS and SPOT VEGETATION ultimately showed that VI trends for the regions differ greatly. The trend for Borneo's forests is generally flat over the 12 years studied, while data for the Guianas and central Africa both exhibit strong but distinct seasonal patterns. Correlation analyses indicate that the VI trends between zones are neither strongly correlated to each other nor to variation in solar elevation (except in central Africa), suggesting that the observed variation in the VIs is not driven by BRDF. In contrast, regression analysis indicated that for the Guianas and central Africa, VI variation was most explained by variation in environmental factors, but not atmospheric effects, suggesting seasonally driven phenology.

Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands

The objective of this study was to develop an approach for estimating soil moisture and vegetation parameters in irrigated grasslands by coupling C-band polarimetric synthetic aperture radar (SAR) and optical data. A huge data set of satellite images acquired from RADARSAT-2 and LANDSAT-7/8, and in situ measurements were used to assess the relevance of several inversion configurations. A neural network (NN) inversion technique was used. The approach for this study was to use RADARSAT-2 and LANDSAT-7/8 images to investigate the potential for the combined use of new data from the new SAR sensor SENTINEL-1 and the new optical sensors LANDSAT-8 and SENTINEL-2. First, the impact of SAR polarization (mono-, dual-, and full-polarizations configurations) and the normalized difference vegetation index (NDVI) calculated from optical data for the estimation error of soil moisture and vegetation parameters was studied. Next, the effect of some polarimetric parameters [Shannon entropy (SE) and Pauli components] on the inversion technique was also analyzed. Finally, configurations using in situ measurements of the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of green vegetation cover (FCover) were also tested. The results showed that HH polarization is the SAR polarization most relevant to soil moisture estimates. A root-mean-square error (RMSE) for soil moisture estimates of approximately 6 vol.% was obtained even for dense grassland cover. The use of in situ FAPAR and FCover only improved the estimate of the leaf area index (LAI) with an RMSE of approximately $0.37\;\text {m}^2/\text {m}^2$ . The use of polarimetric parameters did not improve the estimate of soil moisture and vegetation parameters. Good results were obtained for the biomass (BIO) and vegetation water content (VWC) estimates for BIO and VWC values lower than 2 and $1.5\;\text{kg}/\text{m}^2$ , respectively (RMSE is of $0.38\;\text{kg}/\text{m}^2$ for BIO and $0.32\;\text{kg}/\text{m}^2$ for VWC). In addition, a high underestimate was observed for BIO and VWC higher than 2 and $1.5\;\text{kg}/\text{m}^2$ , respectively, (a bias of $- 0.65\;\text{kg}/\text{m}^2$ on BIO estimates and $- 0.49\;\text{kg}/\text{m}^2$ on VWC estimates). Finally, the estimation of vegetation height (VEH) was carried out with an RMSE of 13.45 cm.

Validation of a Forage Production Index (FPI) Derived from MODIS fCover Time-Series Using High-Resolution Satellite Imagery: Methodology, Results and Opportunities

An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km × 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R2 = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R2 ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images.

Extracting the Green Fractional Vegetation Cover from Digital Images Using a Shadow-Resistant Algorithm (SHAR-LABFVC)

Taking photographs with a commercially available digital camera is an efficient and objective method for determining the green fractional vegetation cover (FVC) for field validation of satellite products. However, classifying leaves under shadows in processing digital images remains challenging and results in classification errors. To address this problem, an automatic shadow-resistant algorithm in the Commission Internationale d’Eclairage L*a*b* color space (SHAR-LABFVC) based on a documented FVC estimation algorithm (LABFVC) is proposed in this paper. The hue saturation intensity (HSI) is introduced in SHAR-LABFVC to enhance the brightness of shaded parts of the image. The lognormal distribution is used to fit the frequency of vegetation greenness and to classify vegetation and the background. Real and synthesized images are used for evaluation, and the results are in good agreement with the visual interpretation, particularly when the FVC is high and the shadows are deep, indicating that SHAR-LABFVC is shadow resistant. Without specific improvements to reduce the shadow effect, the underestimation of FVC can be up to 0.2 in the flourishing period of vegetation at a scale of 10 m. Therefore, the proposed algorithm is expected to improve the validation accuracy of remote sensing products.

The impact of atmospheric conditions and instrument noise on atmospheric correction and spectral mixture analysis of multispectral imagery

The purpose of this study was to evaluate the effect of instrument noise, atmospheric conditions, and reflectance retrieval method on multispectral reflectance and fractional cover estimates derived from spectral mixture analysis. Top-of-atmosphere (TOA) radiance was simulated for pixels with various ground (fractional cover of green vegetation (GV), nonphotosynthetic vegetation (NPV), and soil) and atmospheric/geometric (solar zenith angle, visibility, and water vapor) conditions for four operational spaceborne multispectral images, Landsat 5 Thematic Mapper (TM), Landsat 7 Enhance Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Instrument noise was added to the TOA radiance. Three reflectance retrieval methods were used to estimate surface reflectance, and fractional cover estimates of GV, NPV, and soil were made using spectral mixture analysis (SMA) and multiple endmember spectral mixture analysis (MESMA). Among atmospheric characteristics, we found that visibility and solar zenith angle made the greatest contributions to spectral and fractional cover error. In all cases, GV fraction was estimated with lower error than NPV or soil fraction. The reflectance retrieval method that provided the best reflectance estimates did not produce the fractional cover estimates with the lowest error due to variable bias across bands. Fractional cover estimates for all ground components were strongly impacted by instrument noise, but MESMA significantly improved fractional cover estimates for NPV and soil, regardless of noise level. This study suggests that there are practical constraints on the level of accuracy that can be attained in multispectral estimates of NPV and soil. A set of recommendations for the use of spectral unmixing of multispectral reflectance data is developed.

Development of an index-based insurance product: validation of a forage production index derived from medium spatial resolution fCover time series

An index-based insurance is being developed to estimate and monitor forage production in France in near real-time based on a forage production index (FPI) derived from the fraction of green vegetation cover (fCover) integral, obtained from medium spatial resolution time series. This article presents the first step of the scientific validation implemented. The grassland parcels, the field protocol established to collect biomass production data, and the method used to get the fCover are described. Local ground measurements of biomass production are compared with FPI values obtained from high-resolution space-based images. Discrepancies between the two variables are quantified by the coefficient of determination, the mean square error and the normalised root mean square error. First, fCover derived from the four sensors are coherent demonstrating the ability of the algorithm used to provide a consistent way of calculating fCover. Second, for the whole data set, the scatter plot between FPI and biomass shows an acceptable correlation (R2 = 0.75) improved when only taking into account data recorded up until the production maximum (R2 = 0.81). Third, the analysis carried out on the scale of the parcels, grass species, period of mowing or climatic conditions reveals variability on the regression coefficients indicating that other explanatory variables should be integrated to better compute the FPI.

Assessment of sediment connectivity from vegetation cover and topography using remotely sensed data in a dryland catchment in the Spanish Pyrenees

Many Mediterranean drylands are characterized by strong erosion in headwater catchments, where connectivity processes play an important role in the redistribution of water and sediments. Sediment connectivity describes the ease with which sediment can move through a catchment. The spatial and temporal characterization of connectivity patterns in a catchment enables the estimation of sediment contribution and transfer paths. Apart from topography, vegetation cover is one of the main factors driving sediment connectivity. This is particularly true for the patchy vegetation cover typical of many dryland environments. Several connectivity measures have been developed in the last few years. At the same time, advances in remote sensing have enabled an improved catchment-wide estimation of ground cover at the subpixel level using hyperspectral imagery. The objective of this study was to assess the sediment connectivity for two adjacent subcatchments (~70 km2) of the Isabena River in the Spanish Pyrenees in contrasting seasons using a quantitative connectivity index based on fractional vegetation cover and topography data. The fractional cover of green vegetation, non-photosynthetic vegetation, bare soil and rock were derived by applying a multiple endmember spectral mixture analysis approach to the hyperspectral image data. Sediment connectivity was mapped using the index of connectivity, in which the effect of land cover on runoff and sediment fluxes is expressed by a spatially distributed weighting factor. In this study, the cover and management factor (C factor) of the Revised Universal Soil Loss Equation (RUSLE) was used as a weighting factor. Bi-temporal C factor maps were derived by linking the spatially explicit fractional ground cover and vegetation height obtained from the airborne data to the variables of the RUSLE subfactors. The resulting connectivity maps show that areas behave very differently with regard to connectivity, depending on the land cover and on the spatial distribution of vegetation abundances and topographic barriers. Most parts of the catchment show higher connectivity values in August as compared to April. The two subcatchments show a slightly different connectivity behaviour that reflects the different land cover proportions and their spatial configuration. The connectivity estimation can support a better understanding of processes controlling the redistribution of water and sediments from the hillslopes to the channel network at a scale appropriate for land management. It allows hot spot areas of erosion to be identified and the effects of erosion control measures, as well as different land management scenarios, to be studied.

Attribution of satellite-observed vegetation trends in a hyper-arid region of the Heihe River basin, Western China

Abstract. Terrestrial vegetation dynamics are closely influenced by both climate and by both climate and by land use and/or land cover change (LULCC) caused by human activities. Both can change over time in a monotonic way and it can be difficult to separate the effects of climate change from LULCC on vegetation. Here we attempt to attribute trends in the fractional green vegetation cover to climate variability and to human activity in Ejina Region, a hyper-arid landlocked region in northwest China. This region is dominated by extensive deserts with relatively small areas of irrigation located along the major water courses as is typical throughout much of Central Asia. Variations of fractional vegetation cover from 2000 to 2012 were determined using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index data with 250 m spatial resolution over 16-day intervals. We found that the fractional vegetation cover in this hyper-arid region is very low but that the mean growing season vegetation cover has increased from 3.4% in 2000 to 4.5% in 2012. The largest contribution to the overall greening was due to changes in green vegetation cover of the extensive desert areas with a smaller contribution due to changes in the area of irrigated land. Comprehensive analysis with different precipitation data sources found that the greening of the desert was associated with increases in regional precipitation. We further report that the area of land irrigated each year can be predicted using the runoff gauged 1 year earlier. Taken together, water availability both from precipitation in the desert and runoff inflow for the irrigation agricultural lands can explain at least 52% of the total variance in regional vegetation cover from 2000 to 2010. The results demonstrate that it is possible to separate the satellite-observed changes in green vegetation cover into components due to climate and human modifications. Such results inform management on the implications for water allocation between oases in the middle and lower reaches and for water management in the Ejina oasis.

An original interpretation of the wet edge of the surface temperature–albedo space to estimate crop evapotranspiration (SEB-1S), and its validation over an irrigated area in northwestern Mexico

Abstract. The space defined by the pair surface temperature (T) and surface albedo (α), and the space defined by the pair T and fractional green vegetation cover (fvg) have been extensively used to estimate evaporative fraction (EF) from solar/thermal remote sensing data. In both space-based approaches, evapotranspiration (ET) is estimated as remotely sensed EF times the available energy. For a given data point in the T-α space or in the T-fvg space, EF is derived as the ratio of the distance separating the point from the line identified as the dry edge to the distance separating the dry edge and the line identified as the wet edge. The dry and wet edges are classically defined as the upper and lower limit of the spaces, respectively. When investigating side by side the T-α and the T-fvg spaces, one observes that the range covered by T values on the (classically determined) wet edge is different for both spaces. In addition, when extending the wet and dry lines of the T-α space, both lines cross at α ≈ 0.4 although the wet and dry edges of the T-fvg space never cross for 0 &amp;amp;leq; fvg &lt; 1. In this paper, a new ET (EF) model (SEB-1S) is derived by revisiting the classical physical interpretation of the T-α space to make its wet edge consistent with that of the T-fvg space. SEB-1S is tested over a 16 km by 10 km irrigated area in northwestern Mexico during the 2007–2008 agricultural season. The classical T-α space-based model is implemented as benchmark to evaluate the performance of SEB-1S. Input data are composed of ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) thermal infrared, Formosat-2 shortwave, and station-based meteorological data. The fluxes simulated by SEB-1S and the classical T-α space-based model are compared on seven ASTER overpass dates with the in situ measurements collected at six locations within the study domain. The ET simulated by SEB-1S is significantly more accurate and robust than that predicted by the classical T-α space-based model. The correlation coefficient and slope of the linear regression between simulated and observed ET is improved from 0.82 to 0.93, and from 0.63 to 0.90, respectively. Moreover, constraining the wet edge using air temperature data improves the slope of the linear regression between simulated and observed ET.

Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA)

Encroachment of invasive shrubs into grassland areas on rangelands in the southwestern United States threatens the viability of livestock production and can severely alter hydrology and biodiversity. Novel remote sensing technologies may provide useful information for monitoring and remediating this threat. The objectives were to investigate multiple endmember spectral mixture analysis (MESMA) as an approach to map rangeland vegetation using hyperspectral remote sensing imagery and to test the sensitivity of MESMA to alternative image spatial resolutions and spectral waveband combinations. Data from two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) overflights at the Jornada Experimental Range in southwestern New Mexico were used in the analysis. Endmember spectra were selected from a library of ground-based spectral observations collected with a field spectroradiometer. A 4-endmember MESMA was conducted for both AVIRIS images at their native spatial resolutions using 113 10-nm wavebands from 422 to 2339nm. Additional MESMAs were conducted at 10 multiples of the images' native spatial resolution and for 6 alternative combinations of spectral waveband subregions. Maps of endmember fractional cover for green shrub vegetation, nonphotosynthetic grass vegetation, and bare soil were comparable to an earlier vegetation classification map of Jornada. MESMA fractional cover estimates for the green vegetation endmember were positively correlated with the normalized difference vegetation index (NDVI) with correlation coefficients (r) greater than 0.58. Correlation coefficients between the sum of the green and nonphotosynthetic vegetation endmembers and the cellulose absorption index (CAI) were greater than 0.59. Correlation coefficients between MESMA fractional green vegetation cover and NDVI from independent multispectral images were greater than 0.57. Despite losses in spatial detail at coarser image spatial resolutions, MESMA results for images with spatial resolution degraded by a factor of 10 (~150m) were similar to aggregated results for MESMA at the native spatial resolution (~15m). Additionally, MESMA results were shown to be substantially more sensitive to the spectral wavebands used in the analysis as compared to the spatial resolution of the images. Considered together, the MESMA results at Jornada indicated that fine spectral resolution with hyperspectral remote sensing was substantially more important than incremental changes in image spatial scale from 15m to 150m.

Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

Fractional green vegetation cover (FVC) is a useful parameter for many environmental and climate-related applications. A common approach for estimating FVC involves the linear unmixing of two spectral endmembers in a remote sensing image; bare soil and green vegetation. The spectral properties of these two endmembers are typically determined based on field measurements, estimated using additional data sources (e.g., soil databases or land cover maps), or extracted directly from the imagery. Most FVC estimation approaches do not consider that the spectral properties of endmembers may vary across space. However, due to local differences in climate, soil type, vegetation species, etc., the spectral characteristics of soil and green vegetation may exhibit positive spatial autocorrelation. When this is the case, it may be useful to take these local variations into account for estimating FVC. In this study, spatial interpolation (Inverse Distance Weighting and Ordinary Kriging) was used to predict variations in the spectral characteristics of bare soil and green vegetation across space. When the spatially-interpolated values were used in place of scene-invariant endmember values to estimate FVC in an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, the accuracy of FVC estimates increased, providing evidence that it may be useful to consider the effects of spatial autocorrelation for spectral mixture analysis.