Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem

AbstractWe investigated the usefulness of a ground-based digital photography to evaluate seasonal changes in the aboveground green biomass and foliage phenology in a short-grass grassland in Japan. For ground-truthing purposes, the ecological variables of aboveground green biomass and spectral reflectance of aboveground plant parts were also measured monthly. Seasonal change in a camera-based index (rG: ratio of green channel) reflected the characteristic events of the foliage phenology such as the leaf-flush and leaf senescence. In addition, the seasonal pattern of the rG was similar to that of the aboveground green biomass throughout the year. Moreover, there was a positive linear relationship between rG and aboveground green biomass (R2=0.81, p<0.05), as was the case with spectra-based vegetation indices. On the basis of these results, we conclude that continuous observation using digital cameras is a useful tool that is less labor intensive than conventional methods for estimating aboveground green biomass and monitoring foliage phenology in short-grass grasslands in Japan

Using phenocams to monitor our changing earth: Toward a global phenocam network

Rapid changes to the biosphere are altering ecological processes worldwide. Developing informed policies for mitigating the impacts of environmental change requires an exponential increase in the quantity, diversity, and resolution of field-collected data, which, in turn, necessitates greater reliance on innovative technologies to monitor ecological processes across local to global scales. Automated digital time-lapse cameras – “phenocams” – can monitor vegetation status and environmental changes over long periods of time. Phenocams are ideal for documenting changes in phenology, snow cover, fire frequency, and other disturbance events. However, effective monitoring of global environmental change with phenocams requires adoption of data standards. New continental-scale ecological research networks, such as the US National Ecological Observatory Network (NEON) and the European Union's Integrated Carbon Observation System (ICOS), can serve as templates for developing rigorous data standards and extending the utility of phenocam data through standardized ground-truthing. Open-source tools for analysis, visualization, and collaboration will make phenocam data more widely usable

Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams

Grasslands of the Australian Southern Tablelands represent a patchwork of native and exotic systems, occupying a continuum of C3-dominated to C4-dominated grasslands where composition depends on disturbance factors (e.g., grazing) and climate. Managing these complex landscapes is both challenging and critical for maintaining the security of Australia's pasture industries, and for protecting the biodiversity of native remnants. Differentiating C3 from C4 vegetation has been a prominent theme in remote sensing research due to distinct C3/C4 seasonal productivity patterns (phenology) and high uncertainty about how C3/C4 vegetation will respond to a changing climate. Phenology is used in northern hemisphere ecosystems for a range of purposes but has not been widely adopted in Australia, where dynamic climate often results in non-repetitive seasonal vegetation patterns. We employed time-lapse cameras (phenocams) to study the phenology of twelve grassland areas dominated by cool season (C3) and warm season (C4), native or exotic grasses near Canberra, Australia. Our aims were to assess phenological characteristics of the functional types and to determine the drivers of phenological variability. We compared the fine-scale phenocam seasonal profiles with field sampling and MODIS/Landsat satellite products to assess paddock-to-landscape functioning. We found C3/C4 species dominance to be the primary driver of phenological differences among grassland types, with C3 grasslands demonstrating peak greenness in spring, and senescing rapidly in response to high summer temperatures. In contrast, C4 grasslands showed peak activity in Austral summer and autumn (January-March). Some sites displayed primary and secondary peaks dependent on rainfall and species composition. We found that the proportion of dead vegetation is an important biophysical driver of grassland phenology, as were grazing pressures and species-dependent responses to rainfall and temperature. The satellite and field datasets were in general agreement with the phenocam results. However, the higher temporal fidelity of the cameras captured changes in vegetation not observed in the coarser satellite or field results. Our phenocam data shows consistent periods of increasing and decreasing greenness over as little as 5 days. Applications for management of grasslands in temperate Australia include the identification of remnant native grasslands, tracking biosecurity issues, and assessing productivity responses to climate variability

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Remote sensing environmental change in southern African savannahs : a case study of Namibia

Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic species and most of its livestock and rangeland, while sustaining the livelihoods of an important proportion of its human population. They provide essential ecosystem services and functions, ranging from forest, grazing and water resources, to global climate regulation and carbon sequestration. However, savannahs are highly sensitive to human activities and climate change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing anthropogenic disturbances in the form of agricultural intensification and urbanization, have resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened ecosystems are some of the least studied or protected, and hence should be given high conservation priority. Importantly, the scale of land degradation has not been fully explored, thereby comprising an important knowledge gap in our understanding of ecosystem services and processes, and effectively impeding conservation and management of these biodiversity hotspots. The primary drivers of land degradation include deforestation, triggered by the increasing need for urban and arable land, and concurrently, shrub encroachment, a process in which the herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These processes have significant repercussions on ecosystem service provision, both locally and globally, although the extents, drivers and impacts of either remain poorly quantified and understood. Additionally, regional aridification anticipated under climate change, will lead to important shifts in vegetation composition, amplified warming and reduced carbon sequestration. Together with a growing human population, these processes are expected to compound the risk of land degradation, thus further impacting key ecosystem services. Namibia is undergoing significant environmental and socio-economic changes. The most pervasive change processes affecting its savannahs are deforestation, degradation and shrub encroachment. Yet, the extent and drivers of such change processes are not comprehensively quantified, nor are the implications for rural livelihoods, sustainable land management, the carbon cycle, climate and conservation fully explored. This is partly due to the complexities of mapping vegetation changes with satellite data in savannahs. They are naturally spatially and temporally variable owing to erratic rainfall, divergent plant functional type phenologies and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to (i) quantify distinct vegetation change processes across Namibia, and (ii) develop methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite data spanning a range of spatial, temporal and spectral resolutions are integrated with field datasets to achieve these aims, which are addressed in four journal articles. Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in land cover classes over five decades throughout the Namibian Kalahari woodlands. The approach addresses issues implicit in change detection of savannahs by capturing the distinct phenological phases of woody vegetation and integrating multi-sensor, multi-source data. Vegetation extent was found to have decreased due to urbanization and small-scale arable farming. An assessment of the limitations leads to Chapter 4, which elaborates on the previous chapter by quantifying aboveground biomass changes associated with deforestation and shrub encroachment. The approach centres on fusing multiple satellite datasets, each acting as a proxy for distinct vegetation properties, with calibration/validation data consisting of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating the contribution of land management to ecosystem carbon changes. To identify whether biomass is declining across the country, Chapter 5 focuses on regional, moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS data are used to model observed fractional woody vegetation cover, a proxy for biomass. Trends in modelled fractional woody cover are then evaluated in relation to the predominant land-uses and precipitation. Negative trends slightly outweighed positive trends, with decreases arising largely in protected, urban and communal areas. Since precipitation is a fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as a function of precipitation, with residuals assumed to describe the fraction of NDVI not explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend, although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for vegetation density, indicates a widespread shift to a denser condition. In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and temporal limitations characteristic of the previous approaches. Results, together with those of the previous chapters, are synthesized and a synopsis of the main findings is presented. Vegetation loss is predominantly caused by demand for urban and arable land. Greening trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry and rangeland management, with precipitation presenting little influence. Despite prevalent greening, degradation processes associated with shrub encroachment, including soil erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment occurs regionally. This thesis successfully integrates multi-source data to map, measure and monitor distinct change processes across scales

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Author Posting.© The Author(s), 2015. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 174 (2016): 44-55, doi:10.1016/j.rse.2015.12.003.Pastoralist households across East Africa face major livestock losses during drought periods that can cause persistent poverty. For Kenya and southern Ethiopia, an existing index insurance scheme aims to reduce the adverse effects of such losses. The scheme insures individual households through an area-aggregated seasonal forage scarcity index derived from remotely-sensed normalized difference vegetation index (NDVI) time series. Until recently, insurance contracts covered animal losses and indemnity payouts were consequently made late in the season, based on a forage scarcity index incorporating both wet and dry season NDVI data. Season timing and duration were fixed for the whole area (March-September for long rains, October-February for short rains). Due to demand for asset protection insurance (pre-loss intervention) our aim was to identify earlier payout options by shortening the temporal integration period of the index. We used 250m-resolution 10-day NDVI composites for 2001-2014 from the Moderate Resolution Imaging Spectroradiometer (MODIS). To better describe the period during which forage develops, we first retrieved per-pixel average season start- and end-dates using a phenological model. These dates were averaged per insurance unit to obtain unit-specific growing period definitions. With these definitions a new forage scarcity index was calculated. We then examined if shortening the temporal period further could effectively predict most (>90%) of the interannual variability of the new index, and assessed the effects of shortening the period on indemnity payouts. Our analysis shows that insurance payouts could be made one to three months earlier as compared to the current index definition, depending on the insurance unit. This would allow pastoralists to use indemnity payments to protect their livestock through purchase of forage, water, or medicines.AV was funded under a contract from the International Livestock Research Institute. CCU was supported by the U.S. National Science Foundation under grant OCE-1203892.2016-12-1

Remotely sensed and modelled pasture biomass, land condition and the potential to improve grazing-management decision tools across the Australian rangelands

This report assesses the potential for expanding on current capacity to monitor land condition using remotely sensed fractional cover products to improve biomass estimation, animal productivity, pasture growth models and grazing decision tools (e.g. safe carrying capacity) across the Australian rangelands. We focus on northern Australia and include relevant research and implementation from southern Australia where appropriate