Vegetation Fractional Cover (VFC) is an important indicator of the condition of the terrestrial surface of the Earth. The dynamics of bare soil (BS), non-photosynthetic vegetation (NPV) and photosynthetic vegetation (PV) fractions reveal patterns of growth, senescence, dormancy and regeneration. The dynamics also reveal trends in land cover and land use management across the globe. Analysis of satellite data has indicated increasing greenness of the land surface however the dynamics of the NPV and BS fractions are equally important indicators of many elements of ecosystem function and sustainability. In this study, we provide a comprehensive assessment of trends in VFC for the global terrestrial land surface based on a trend analysis over the period 2001-2018 using the Global Vegetation Fractional Cover Product. Trends were analysed by Mann-Kendell regressions over 18 years for each month of the year. Monthly maps of significant trend areas were aggregated to provide seasonal assessments. Areas and percentage areas of positive and negative trends were compiled for United Nations Subregions, Countries and Ecoregions. Significant negative trends in BS occurred across 20 M km2 of the terrestrial land surface. Although the areas of negative trends in BS were largest in China and India, the top 24 countries by area were widely distributed across the world. However, BS increased significantly across more than 11 M km2. Eleven of the top 24 ranked countries for area of increasing BS were in Africa. Substantial areas exhibited negative trends in NPV split between China and India where PV was increasing, and drylands in Asia and Africa where BS was increasing. Analysis of covariate factors at country scale inferred an association between significant increase in BS and long-term drought and increases in livestock populations. The positive trends in BS and negative trends in NPV are indicators of current and future risk for soil erosion, habitat change, and reduction in ecosystem health across heavily populated regions of the developing world.

This investigation used a modified parameterization of the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model to improve simulated growth and biomass yield of upland switchgrass (Panicum virgatum L.) ecotypes in northern U.S. locations. Leaf area development, biomass accumulation, and N utilization of upland ecotypes were parameterized by field evaluations from Montreal, QC, Canada, and sites throughout the northern U.S. Great Plains. Resulting ALMANAC simulations were validated against measured yields from 66 location-years of switchgrass production across 13 sites in Minnesota, North Dakota, and South Dakota. As contrasted to the model defaults, the modified parameterization reduced RMSE of annual simulated yields from 3.77 to 2.62 Mg ha(-1) and improved percentage bias from -16 to 13%. Model performance was most improved in environments with no N fertilization, where ALMANAC simulated annual yields with an RMSE of 1.73 Mg ha(-1) and percentage bias of -0.5%. Relative to the default ALMANAC parameterization, the modified parameterization also simulated a longer growing season and extended the median simulated maturity date from 1 to 28 August, greatly improving the estimation of switchgrass phenology within the study region. Sensitivity analyses revealed that simulated switchgrass yield was unaffected by modifications of runoff curve number and was most affected by modifications of radiation use efficiency. The other seven parameter modifications each had a median yield impact of 0.57-1.4 Mg ha(-1). This work provides an improved characterization of upland switchgrass ecotypes in northern U.S. locations for future ALMANAC users.

Abstract Greenup dates and their responses to elevation and temperature variations across the mountains of Acadia National Park are monitored using remote sensing data, including Landsat 8 surface reflectances (at a 30‐m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500‐m spatial resolution), during the 2013–2016 growing seasons. The 30‐m resolution provides a better scale for studying the phenology variation across elevational gradients than the 500‐m resolution, as greenup dates monitored at 30‐m scale have better agreement with leaf‐out dates recorded in the field alongside the north–south‐oriented hiking trails on three of the park’s tallest mountains (466 m, 418 m, and 380 m), and can provide landcover‐specific analysis. The spring phenology responses to temperature and elevation vary among different spatial scales. Greenup dates of Acadia National Park monitored at 30‐m scale show a weak advancing trend with higher spring temperature, while greenup dates monitored at 500 m show a weak delaying trend. The species mix within landcover at 30‐m scale could weaken the advancing trend detected at field observation level. The landcover mix and elevation variation within 500‐m scale could alter the spring phenology response to spring temperature variation. Greenup dates monitored at both 30‐m and 500‐m scales vary among different elevational zones, aspects, landcovers, and years. However, the relationship between greenup dates and elevation is rather weak.

Savannas and woodlands represent one of the most challenging targets for remote sensing [...]

The development of fractional vegetation cover products for Australia that resolve cover into photosynthetic vegetation (F-PV), non-photosynthetic vegetation (F-NPV) and bare soil/rock (F-BS) provides a new basis for examination of responses of vegetation cover to long term precipitation cycles, and to explore the interaction between these responses and land cover type, land use and other land surface properties. In this study, the relationship between accumulated antecedent precipitation (AAP) from 1 to 60 months and average monthly F-PV and F-NPV from the MODIS Fractional Cover Product is examined over a 17 year period from 2001 to 2018. The maximum R-2 value, regression coefficients for the maximum R-2, and number of accumulated months to the maximum R-2 were mapped for each month of the year for F-PV and F-NPV. Behaviour of responses in relation to land use, land cover, and soil water holding capacity was analysed based on pixel frequencies of classes at sub-region scale in the Interim Biogeographic Regionalisation of Australia. The analysis showed that F-PV is largely dependent upon AAP in the preceding 12 months, however responses to longer periods of AAP also occur in specific land use-vegetation type combinations. The study also showed that positive responses in F-NPV could be driven by AAP from as much as 60 months, but that F-NPV is reduced in many areas in response to increased AAP. The presence or absence of domesticated livestock grazing as defined by Australian land use mapping was a major influence on response to AAP of both F-PV and F-NPV with statistical analysis indicating interactions between major natural vegetation type and grazing. In highly responsive areas (R-2 > 0.6) monitoring of land condition could be enhanced by testing of regional cover levels for major deviations from the long terms responses that might suggest land management concerns.

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in FNPV and positive trends FBS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of FNPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation.

This paper reviews information about field observations of vegetation productivity in Australia’s rangeland systems and identifies the need to establish a national initiative to collect net primary productivity (NPP) and biomass data for rangeland pastures. Productivity data are needed for vegetation and carbon model parameterisation, calibration and validation. Several methods can be used to estimate pasture productivity at various spatial and temporal scales, ranging from in situ measurements to satellite-based approaches and biogeochemical modelling. However, there is a barrier to implementing national vegetation and carbon modelling schemes because of the lack of digitised and readily available data derived from field observations, not because of the lack of modelling expertise. Our main goal in this paper is to explore the potential for consolidation of existing NPP and biomass databases for Australian rangelands. A protocol structure was proposed to establish a productivity database for Australia. The TERN (Terrestrial Ecosystems Research Network) national field data network for rangeland pasture productivity monitoring and modelling team could potentially coordinate the database. Government agencies and national and international research institutions could use the outputs from productivity models to inform greenhouse gas emissions and in measuring mitigation activities relevant for reporting against the United Nations’ Sustainable Development Goals and other international obligations. Other applications include monitoring fire danger, tracking ecological restoration and protection, and estimating fodder availability. Australian researchers have the tools needed to succeed in creating such a national database and a robust community of practice to curate it, enhance it and benefit from its availability.

Circuit area scaling driven by Moore's law coupled with the push to thinner packages for mobile microprocessors has reduced the volume available for Air Core Inductors (ACI). This translates to a reduction in the conversion efficiency of Fully Integrated Voltage Regulator (FIVR). Magnetic Inductor Array (MIA) modules are used for the first time on the 10 th generation Intel Core™ microprocessor in mobile segments to improve FIVR efficiency. In addition to improving the peak efficiency, it is also possible to realize large light load efficiency gains and minimize voltage ripple through the use of magnetic inductors. This paper covers the magnetic inductor array (MIA) design considerations for improving performance as well as meeting high volume manufacturability and reliability requirements.

Mapping the spatial distribution of woody and herbaceous vegetation in high temporal resolution in savannas would be beneficial for modeling interrelationships between trees and grasses, and monitoring fuel loads and biomass for livestock. In this study, we developed a frequency decomposition method to separate woody and herbaceous vegetation components using Normalized Difference Vegetation Index (NDVI) time series. The results were validated using fractional cover data derived from high-resolution images. The validation revealed a close relationship between our decomposed NDVI and corresponding fractional cover (R-2 = 0.55 and 0.64 for woody and herbaceous components, respectively). We examined the spatial and temporal patterns of the decomposed NDVI, where woody and herbaceous NDVI showed different responses to precipitation. The methods proposed in this study can be used to separate the woody and herbaceous NDVI time series as an alternative approach for monitoring woody and herbaceous vegetation interrelationships related to climatic drivers.

Remnant midwestern oak savannas in the USA have been altered by fire suppression and the encroachment of woody evergreen trees and shrubs. The Gus Engeling Wildlife Management Area (GEWMA) near Palestine, Texas represents a relatively intact southern example of thickening and evergreen encroachment in oak savannas. In this study, 18 images from the CHRIS/PROBA (Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy) sensor were acquired between June 2009 and October 2010 and used to explore variation in canopy dynamics among deciduous and evergreen trees and shrubs, and savanna grassland in seasonal leaf-on and leaf-off conditions. Nadir CHRIS images from the 11 useable dates were processed to surface reflectance and a selection of vegetation indices (VIs) sensitive to pigments, photosynthetic efficiency, and canopy water content were calculated. An analysis of temporal VI phenology was undertaken using a fishnet polygon at 90 m resolution incorporating tree densities from a classified aerial photo and soil type polygons. The results showed that the major differences in spectral phenology were associated with deciduous tree density, the density of evergreen trees and shrubs—especially during deciduous leaf-off periods—broad vegetation types, and soil type interactions with elevation. The VIs were sensitive to high densities of evergreens during the leaf-off period and indicative of a photosynthetic advantage over deciduous trees. The largest differences in VI profiles were associated with high and low tree density, and soil types with the lowest and highest available soil water. The study showed how time series of hyperspectral data could be used to monitor the relative abundance and vigor of desirable and less desirable species in conservation lands.

Plants actively regulate excess absorbed energy to protect photosynthetic machinery through heat dissipation in a process known as non-photochemical quenching (NPQ), a process useful for quantifying plant health and productivity. NPQ can be indirectly measured in the visible wavelengths between 500 nm and 560 nm, most commonly through the Photochemical Reflectance Index (PRI). However, there remains a lack of consensus regarding the optimal functional form and band selection to calculate PRI for the purpose of measuring NPQ mechanisms. Here, we quantitatively evaluate the effectiveness of leaf-level parametric and non-parametric spectral formulations, band locations, and number of bands to track the xanthophyll pigment cycle in a tall mature Eucalypt forest. Subsequently, our recommended approach is the new 'tri-PRI' index robust to constitutive pigment pool sizes across the canopy profile. tri-PRI is a Triangular Vegetation Index (TVI) (tri-PRI = 0.5[(520 - 490)(R-545nm - R-490nm) - (545 - 490)(R-520nm - R-490nm)]) using three reflectance bands around 490 nm, 520 nm and 545 nm, and has a physiological photosynthetic basis. We found that tri-PRI significantly outperformed PRI and other two band combinations for quantifying the xanthophyll EPoxidation State 'EPS' (tri-PRI R-2 = 0.75 versus PRI R-2 = 0.23), as well as the Phi NPQ and Phi PSII active chlorophyll fluorescence quenching yields. The new band placement enhanced the dynamic EPS absorption peak, while the third band provided an additional normalisation to minimise the confounding effects of pigments with overlapping spectral features. tri-PRI also performed comparably to parametric and non-parametric hyperspectral techniques and formulations using continuous spectral regions, highlighting the utility of targeted multispectral indices over hyperspectral approaches. This leaf-level study represents a foundational step toward indirectly measuring dynamic photosynthetic activity across the canopy profile in a tall mature Eucalypt forest to inform upscaling efforts from above-canopy remote sensing platforms. The application of tri-PRI and other top-performing multi-band TVI formulations for predicting EPS presented here should be explored across different canopy types, temporal-, and spatial scales.

Abstract. Greenup dates of the mountainous Acadia National Park, were monitored using remote sensing data (including Landsat 8 surface reflectances (at a 30 m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500 m spatial resolution)) during the 2013–2016 growing seasons. Ground-level leaf-out monitoring in the areas alongside the north-south-oriented hiking trails on three of the park's tallest mountains (466 m, 418 m, and 380 m) was used to evaluate satellite derived greenup dates in this study. While the 30 m resolution would be expected to provide a better scale for phenology detection in this mountainous region than the 500 m resolution, the daily temporal resolution of the 500 m data would be expected to offer vastly superior monitoring of the rapid variations experienced during vegetation greenup along elevational gradients. Therefore, the greenup dates derived from the Landsat 8 Enhanced Vegetation Index (EVI) data, augmented with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) simulated EVI values, does provide more spatial details than VIIRS data alone and agree well with field monitored leaf out dates. Satellite derived greenup dates from the 30 m of Acadia National Park vary among different elevational zones, although the date of greenup is not always the most advanced at the lowest elevation. This indicates that the spring phenology is not only determined by microclimates associated with different elevations in this mountainous area, but is also possibly affected by the species mixture, localized temperatures, and other factors in Acadia.

The clumping index (CI) characterizes the grouping of foliage relative to a random spatial distribution of leaves and is an important structural parameter for plant canopies that can influence canopy radiation regimes. Consequently, the CI is very useful for ecological and meteorological models. One method used to retrieve the CIs of plant canopies is to construct a linear relationship between the CI and the normalized difference between hotspot and dark spot (NDHD) angular index. This method requires a particularly accurate reconstruction of hotspot signatures, which are difficult to measure. In this study, we propose a framework to retrieve CIs from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) parameters, which are generally based on linear CI-NDHD equations. The main algorithm is designed to retrieve CIs in the closed interval [0.33, 1.00]. This range is derived from the CI-NDHD equations and is thus called as the physical range here, although a modified lower boundary can be implemented in the future if necessary. If CIs are outside of this range, then a backup algorithm is designed to reprocess these so-called outlier CIs. The hotspot-adjusted version of the RossThick-LiSparseReciprocal (RTLSR) model (i.e., the RTCLSR model) is employed to reconstruct the hotspot signatures for the MODIS BRDF parameters. This method simplifies the hotspot reconstruction by using two hotspot parameters that are not distinctly scale-dependent particularly in the context of an inhomogeneous coarse spatial resolution. To evaluate this algorithm framework, we collect dozens of global field-measured CIs and calculate their determination coefficient (R-2), root mean square error (RMSE) and bias relative to MODIS CIs derived using both the main algorithm and the backup algorithm. Our results show that this framework can derive MODIS CIs with a high accuracy (i.e., R-2 = 0.80 (0.72), RMSE = 0.07 (0.12), bias = -0.03 (-0.10)) using the main (backup) algorithms and that it shows promise for various ecological applications, especially in combination with the leaf area index (LAI).

Fractional cover of photosynthetic vegetation (F-PV), non-photosynthetic vegetation (F-NPV) and bare soil (F-BS) is an important input for assessment of the productivity of global pastures and rangelands. Here we describe the updating of this product using the new Moderate Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-Adjusted Reflectance (NBAR) Collection 6 reflectance product (C6) and a major expansion of the field calibration database. Fractional cover based on the C6 input exhibited reduced bias and root mean square error compared with the Collection 5 (C5) product. The expanded calibration database with more sites in arid areas provided greater separation between reflectance values of end-members for F-NPV and F-BS. Specific site variations in F-NPV and F-BS in arid areas could be traced to small but consistent changes in blue and green band, and occasional changes in short wave infrared reflectance in C6 when compared to C5. The recalibration described here provides an Australian fractional cover product with reduced uncertainty and improves the basis for a prototype global product for use in modelling of rangeland and pasture productivity.

This chapter describes the bacterial metabolism of dietary fiber and defines dietary fiber. It considers the evidence that dietary fiber is metabolized during colonic transit and this evidence is from in vivo and in vitro studies. The chapter describes the enzymes involved in fiber metabolism and discusses the consequences to the host of fiber metabolism. Fermentation of dietary fiber in the colon is of major importance both to the gut bacterial flora and to the host. The major products of fiber metabolism are the short-chain fatty acids (SCFA's), gases and energy. Dietary fiber is equivalent to that fraction of the diet that used to be referred to as unavailable carbohydrate, and both terms have major shortcomings. The evidence that dietary fiber is degraded by the gut bacterial flora is very strong and copious, although there have been relatively few investigations of how the degradation takes place.

The so-called clumping factor (Omega) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Omega is essential to convert effective Plant or Leaf Area Index into actual LAI or PAL which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15 was the best performing clumping method, matching the reference values to within 0.05 Omega on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Omega (equivalent to a 30% PAI error) at 75 for all structural configurations. At larger zenith angles, PAS errors were found to be around 25-30% on average when derived from the 55-60 degrees zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible.

The Mediterranean-type oak/grass savanna of California is composed of widely spaced oak trees with understory grasses. These savanna regions are interspersed with large areas of more open grasslands. The ability of remotely sensed data (with various spatial resolutions) to monitor the phenology in these water-limited oak/grass savannas and open grasslands is explored over the 2012-2015 timeframe using data from Landsat (30 m), the MODerate resolution Imaging Spectroradiometer (MODIS - gridded 500 m), and the Visible Infrared Imaging Radiometer Suite (VIIRS gridded 500 m) data. Vegetation phenology detected from near-ground level, webcam based PhenoCam imagery from two sites in the Ameriflux Network (long-term flux measurement network of the Americas) (Tonzi Ranch and Vaira Ranch) is upscaled, using a National Agriculture Imagery Program (NAIP) aerial image (1 m), to evaluate the detection of vegetation phenology of these savannas and grasslands with the satellite data. Results show that the Normalized Difference Vegetation Index (NDVI) time series observed from the satellite sensors are all strongly correlated with the PhenoCam NDVI values from Tonzi Ranch (R-2 > 0.67) and Vaira Ranch (R-2 > 0.81). However, the different viewing geometries and spatial coverage of the PhenoCams and the various satellite sensors may cause differences in the absolute phenological transition dates. Analysis of frequency histograms of phenological dates illustrate that the phenological dates in the relatively homogeneous open grasslands are consistent across the different spatial resolutions, in contrast, the relatively heterogeneous oak/grass savannas display has somewhat later greenup, maturity, and dormancy dates at 30 m resolution than at 500 m scale due to the different phenological cycles exhibited by the overstory trees and the understory grasses. In addition, phenologies derived from the MODIS view angle corrected reflectance (Nadir BRDF-Adjusted Reflectance NBAR) and the newly developed VIIRS NBAR are shown to provide comparable phenological dates (majority absolute bias <= 2 days) in this area. (C) 2017 The Authors. Published by Elsevier B.V.

This study explores the use of the relationship between the normalized difference vegetation index NDVI and the shortwave infrared ratio SWIR32 vegetation indices VI to retrieve fractional cover over the structurally complex natural vegetation of the Cerrado of Brazil using a time series of imagery from the Moderate Resolution Imaging Spectroradiometer MODIS. Data from the EO-1 Hyperion sensor with 30 m pixel resolution is used to sample geographic and seasonal variation in NDVI, SWIR32, and the hyperspectral cellulose absorption index CAI, and to derive end-member values for photosynthetic vegetation PV, non-photosynthetic vegetation NPV, and bare soil BS from a suite of protected and/or natural vegetation sites across the Cerrado. The end-members derived from relatively pure 30 m pixels are then applied to a 500 m pixel resolution MODIS time series using linear spectral unmixing to retrieve PV, NPV, and BS fractional cover FPV, FNPV, and FBS. The two-way interaction response of MODIS-equivalent NDVI and SWIR32 was examined for regions of interest ROI collected within protected areas and nearby converted lands. The MODIS NDVI, SWIR32 and retrieved FPV, FNPV, and FBS are then compared to detailed cover and structural composition data from field sites, and the influence of the structural and compositional variation on the VIs and cover fractions is explored. The hyperion ROI analysis indicated that the two-way NDVI–SWIR32 response behaved as an effective surrogate for the two-way NDVI–CAI response for the campo limpo/grazed pasture to cerrado sensu stricto woody gradient. The SWIR32 sensitivity to the NPV and BS variation increased as the dry season progressed, but Cerrado savannah exhibited limited dynamic range in the NDVI–CAI and NDVI–SWIR32 two-way responses compared to the entire landscape, which also comprises fallow croplands and forests. Validation analysis of MODIS retrievals with Quickbird-2 images produced an RMSE value of 0.13 for FPV. However, the RMSE values of 0.16 and 0.18 for FBS and FNPV, respectively, were large relative to the seasonal and inter-annual variation. Analysis of site composition and structural data in relation to the MODIS-derived NDVI, SWIR32 and FPV, FNPV, and FBS, indicated that the VI signal and derived cover fractions were influenced by a complex mix of structure and cover but included a strong year-to-year seasonal effect. Therefore, although the MODIS NDVI–SWIR32 response could be used to retrieve cover fractions across all Cerrado land covers including bare cropland, pastures and forests, sensitivity may be limited within the natural Cerrado due to sub-pixel heterogeneity and limited BS and NPV sensitivity.