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Fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) has been retrieved for Australian tropical savannah based on linear unmixing of the two-dimensional response envelope of the normalized difference vegetation index (NDVI) and short wave infrared ratio (SWIR)32 vegetation indices (VI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The approach assumes that cover fractions are made up of a simple mixture of green leaves, senescent leaves, and bare soil. In this study, we examine retrieval of fractional cover using this approach for a study area in southern Africa with a more complex vegetation structure. Region-specific end-members were defined using Hyperion images from different locations and times of the season. These end-members were applied to a 10-year time series of MODIS-derived NDVI and SWIR32 (from 2002 to 2011) to unmixFPV,FNPV, andFBS. Results of validation with classified high-resolution imagery indicated major bias in estimation ofFNPVandFBS, with regression coefficients for predicted versus observed data substantially less than 1.0 and relatively large intercept values. Examination with Hyperion images of the inverse relationship between the MODIS-equivalent SWIR32 index and the Hyperion-derived cellulose absorption index (CAI) to which it nominally approximates revealed: (1) non-compliant positive regression coefficients for certain vegetation types; and (2) shifts in slope and intercept of compliant regression curves related to day of year and geographical location. The results suggest that the NDVI–SWIR32 response cannot be used to approximate the NDVI–CAI response in complex savannah systems like southern Africa that cannot be described as simple mixtures of green leaves, dry herbaceous material high in cellulose, and bare soil. Methods that use a complete set of multispectral channels at higher spatial resolution may be needed for accurate retrieval of fractional cover in Africa. [ABSTRACT FROM AUTHOR]

Retrieval from remote sensing of separate temporal dynamics for the understorey layer in tropical savannahs would be beneficial for monitoring fuel loads, biomass for livestock, interrelationships between trees and grasses, and modelling of savannah systems. In this study, we combined unmixing of fractional cover with normalized difference vegetation index (NDVI) and the short wave infrared ratio (SWIR32) with time series decomposition of the NDVI to attempt to fully resolve the dynamics of the herbaceous understorey in the Australian tropical savannah based on the fractions of photosynthetic herbaceous vegetation (FPVH) and non-photosynthetic vegetation (FNPV), from the woody overstorey, represented by the fraction of photosynthetic vegetation in the tree canopy (FPVW). Evaluation ofFPVHagainst field data gave moderate relationships between predicted and observed values (R2between 0.5 and 0.6); since semivariogram metrics of representativeness indicated that field sites were relatively unrepresentative of variation at the Moderate Resolution Imaging Spectroradiometer MODIS) pixel scale. BothFPVWandFPVHproduced strong linear relationships (root mean square error < 0.1 units) with high-resolution Orbview 3 cover fractions classified from tasselled cap transformations. However,FNPVH(non-photosynthetic herbaceous cover fraction) retrievals at southern arid locations produced an evaluation relationship with a greater deviation from the 1:1 line than for northern locations. This suggested that there may be limitations on the NDVI–SWIR32 unmixing approach in more sparsely vegetated savanna. Maps of average annual maximumFPVH,FNPVH, and total herbaceous cover fraction could be used as indicators of savannah productivity and landscape health. However, close examination of the limitations of the NDVI–SWIR32 response may be required for application of this method in other global savannahs. [ABSTRACT FROM AUTHOR]

Abstract: Grasslands and savannas form a heterogeneous and patchy mosaic of spectral properties that are challenging for characterization of vegetation states. This study examines the potential for use of suites of vegetation indices (VIs) from the proposed Sentinel 2 sensor to describe vegetation states in grasslands and savannas for a North American transect. Hyperion hyperspectral data from the EO-1 satellite were used to simulate Sentinel 2, MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) images for field sites in Alberta, North Dakota and Texas that represent the continuum from short grass prairie to oak savanna and are intermingled with agriculture. Indices representing photosynthetic pigments (Normalized Difference Vegetation Index, Carotenoid Reflectance Index, Anthocyanin Reflectance Index and Red-Green Ratio), vegetation and landscape water content (Normalized Difference Infrared Index), senescent vegetation and soil (Short Wave Infrared Ratio and Plant Senescence Reflectance Index) and herbaceous biomass (Soil Adjusted Total Vegetation Index) were used. There were distinct differences among sites in the relative sensitivity of different VIs depending upon moisture status, tree cover and type of grassland. Simple multi-variate models based on mean values of VIs showed limited ability to predict land cover classes and nominal vegetation states. However, analysis of sample areas using pixels as individual observations within a statistical distribution indicated that subtle variation and gradients within management or land units could be used to characterize fine differences in selected nominal states at each site. Despite some differences in band locations, all VIs except the anthocyanin reflectance index were scalable between Sentinel 2 and MODIS and VIIRS data. A framework for using suites of VIs as indicators of vegetation states that could be applied to the state and transition model approach applied by the US Natural Resource Conservation Service is described. Land types can be effectively characterized by pixel value distribution histograms, and statistical metrics may be used as indicators of status and change. However, time series are needed to fully capture states and state changes, since grasslands and savannas have such high levels of spectral and phenological variation. [Copyright &y& Elsevier]

In this study, we explored the capacity of vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products to characterize global savannas in Australia, Africa and South America. The savannas were spatially defined and subdivided using the World Wildlife Fund (WWF) global ecoregions and MODIS land cover classes. Average annual profiles of Normalized Difference Vegetation Index, shortwave infrared ratio (SWIR32), White Sky Albedo (WSA) and the Structural Scattering Index (SSI) were created. Metrics derived from average annual profiles of vegetation indices were used to classify savanna ecoregions. The response spaces between vegetation indices were used to examine the potential to derive structural and fractional cover measures. The ecoregions showed distinct temporal profiles and formed groups with similar structural properties, including higher levels of woody vegetation, similar forest–savanna mixtures and similar grassland predominance. The potential benefits from the use of combinations of indices to characterize savannas are discussed. [ABSTRACT FROM PUBLISHER]

Abstract: The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator. [Copyright &y& Elsevier]

Quantitative estimation of fractional cover of photosynthetic vegetation (f PV), non-photosynthetic vegetation (f NPV) and bare soil (f BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f PV, f NPV and f BS across the ~2 million km2 Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f PV, f NPV and f BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000–2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f PV, f NPV and f BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world. [Copyright &y& Elsevier]

Savannas and woodlands represent one of the most challenging targets for remote sensing. The grasslands, savannas and woodlands of sub-Saharan Africa played a key role in development of the remote sensing of vegetation cover and dynamics [[1], [3], [5], [7]]. The group of four papers on South America span the ecotone from the heavily altered Mato Grosso forests in Amazonia through the savanna of the Cerrado to the dry forests of the Caatinga. 10.3390/rs4113462 19 Hill M.J., Guerschman J.P. The MODIS Global Vegetation Fractional Cover Product 2001-2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands. [Extracted from the article]

The diameter distribution of savanna tree populations is a valuable indicator of savanna health because changes in the number and size of trees can signal a shift from savanna to grassland or forest. Savanna diameter distributions have traditionally been monitored with forestry techniques, where stem diameter at breast height (DBH) is measured in the field within defined sub-hectare plots. However, because the spatial scale of these plots is often misaligned with the scale of variability in tree populations, there is a need for techniques that can scale-up diameter distribution surveys. Dense point clouds collected from uncrewed aerial vehicle laser scanners (UAV-LS), also known as drone-based LiDAR (Light Detection and Ranging), can be segmented into individual tree crowns then related to stem diameter with the application of allometric scaling equations. Here, we sought to test the potential of UAV-LS tree segmentation and allometric scaling to model the diameter distributions of savanna trees. We collected both UAV-LS and field-survey data from five one-hectare savanna woodland plots in northern Australia, which were divided into two calibration and three validation plots. Within the two calibration plots, allometric scaling equations were developed by linking field-surveyed DBH to the tree metrics of manually delineated tree crowns, where the best performing model had a bias of 1.8% and the relatively high RMSE of 39.2%. A segmentation algorithm was then applied to segment individual tree crowns from UAV-LS derived point clouds, and individual tree level segmentation accuracy was assessed against the manually delineated crowns. 47% of crowns were accurately segmented within the calibration plots and 68% within the validation plots. Using the site-specific allometry, DBH was modelled from crown metrics within all five plots, and these modelled results were compared to field-surveyed diameter distributions. In all plots, there were significant differences between field-surveyed and UAV-LS modelled diameter distributions, which became similar at two of the plots when smaller trees (<10 cm DBH) were excluded. Although the modelled diameter distributions followed the overall trend of field surveys, the non-significant result demonstrates a need for the adoption of remotely detectable proxies of tree size which could replace DBH, as well as more accurate tree detection and segmentation methods for savanna ecosystems. [ABSTRACT FROM AUTHOR]

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Model-data fusion offers considerable promise in remote sensing for improved state and parameter estimation particularly when applied to multi-sensor image products. This paper demonstrates the application of a ‘multiple constraints’ model-data fusion (MCMDF) scheme to integrating AMSR-E soil moisture content (SMC) and MODIS land surface temperature (LST) data products with a coupled biophysical model of surface moisture and energy budgets for savannas of northern Australia. The focus in this paper is on the methods, difficulties and error sources encountered in developing an MCMDF scheme and enhancements for future schemes. An important aspect of the MCMDF approach emphasized here is the identification of inconsistencies between model and data, and among data sets. The MCMDF scheme was able to identify that an inconsistency existed between AMSR-E SMC and LST data when combined with the coupled SEB-MRT model. For the example presented, an optimal fit to both remote sensing data sets together resulted in an 84% increase in predicted SMC and 0.06% increase for LST relative to the fit to each data set separately. That is the model predicted on average cooler LST''s (∼1.7 K) and wetter SMC values (∼0.04 g cm−3) than the satellite image products. In this instance we found that the AMSR-E SMC data on their own were poor constraints on the model. Incorporating LST data via the MCMDF scheme ameliorated deficiencies in the SMC data and resulted in enhanced characterization of the land surface soil moisture and energy balance based on comparison with the MODIS evapotranspiration (ET) product of Mu et al. [Mu, Q., Heinsch, F.A, Zhao, M. and Running, S.W. (in press), Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment.]. Canopy conductance, g C, and latent heat flux, λE, from the MODIS ET product were in good agreement with RMSEs for g C =0.5 mm s−1 and for λE =18 W m−2, respectively. Differences were attributable to a greater canopy-to-air vapor pressure gradient in the MCMDF approach obtained from a more realistic partitioning of soil surface and canopy temperatures. [Copyright &y& Elsevier]