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

Issues of multi-scale spatial variability in remotely sensed data

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Model Objects – Managing an environmental model from within the GIS

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Three regionalised analyses of a time-series of annual pasture production for southwest Western Australia

A thirteen year time-series (1994 to 2006) of gross annual pasture production (GAPP; representing both pasture and crop) was created for the Mediterranean-climate area in the southwest of Western Australia (SWWA) using a light-use efficiency model, incorporating NOAA-AVHRR and NASA- MODIS images in combination with climate data. Trends across the GAPP time-series were quantified by aggregating pixels to spatial regions (called a partition, unit, or spatial support) so that the effects of local spatial noise were minimized. We compared the GAPP analysis using the three spatial partitioning schemes (precipitation zones, Interim Biogeographic Regionalisation for Australia (IBRA) eco-regions, and Statistical Local Areas), and showed that the aggregation unit's size & shape impacted on the analysis. Our results demonstrate trends in GAPP that may be indicative of broader trends in climate change for the SWWA

Spacial-temporal analysis using a multiscale hierarchical ecoregionalization

We address the need for spatio-temporally explicit analysis techniques linking the scales of ecosystem, observation, and analysis, using a hierarchical ecoregionalization to examine remotely sensed data at spatial scales of ecological and management significance. Long- and short-term changes in vegetation functioning are a key indicator of ecological processes. We predict net primary production (NPP) at monthly temporal resolution for 16 years (1981–1996) at an 8-km spatial resolution for the approximately 106 km2 area of Ontario, Canada. We calculate landscape-level light use efficiency values that are tuned to monthly and long-term ecoclimates, and the Normalized Difference Vegetation Index from the NOAA-AVHRR sensor. Applying our spatio-temporal analysis tools, we show evidence for increasing NPP across most of the province. This increase varies seasonally and annually across Ontario, and its magnitude and distribution varies with the spatial scales of analysis. Bridging the gap between local and global studies, this research supports spatio-temporal monitoring and analysis of ecosystem functions

Evaluating satellite-based pasture measurement for Australian dairy farmers

Australian dairy farms rely on grazing pastures as their primary and cheapest source of feed. Accurate and timely measurement of pasture biomass is integral for effective grazing management practice, however few Australian dairy farmers record pasture mass or growth rate objectively. A system using satellite images has been developed to measure pasture biomass at a paddock-scale in Australia. The concept was evaluated through an 18 farm pilot study over the spring growth period, July to November 2008. The study was evaluated in terms of technology fit with grazing management practice of participant farmers. Qualitative research methods, including semi-structured interviews and a group workshop, were used to ascertain participant views on issues such as timeliness, accuracy, and value proposition within the context of farming systems. In this paper we discuss preliminary findings from the study, focussing on the farmer attitudes to the use of satellite-based measurement and delivery of pasture biomass information. The findings suggest that a technology such as satellite pasture measurement has potential application in Australian dairy farm systems. However the provision of data alone does not guarantee successful technology uptake. Support structures must also be provided to help farmers interpret the information within the specific context of their farm system. These support structures may include use of private agronomists, producer groups, agriculture extension personnel, or associated software applications

Satellite remote sensing to monitor weeds of national significance in key catchments of Northern WA – Phase 1

No abstract availabl

Ecoregionalization assessment: Spatio-temporal analysis of net primary production across Ontario

An ecoregionalization can be defined as a partitioning scheme that captures landscape patterns by dividing an area into hierarchically nested ecounits based on similar physiographic and ecological characteristics. In this paper, we introduce new spatio-temporally explicit methods to characterize spatio-temporal variability of static, a priori defined, ecounits by their dynamic “spatio-temporal signatures” (STSs) and assess the strength of the ecoregionalization boundaries using this information. To analyze the spatial and temporal patterns of net primary productivity (NPP) at the ecozone, ecoregion, and ecodistrict levels of the National Ecological Framework of Canada (NEF) ecoregionalization, we compute a 15-year monthly series of NPP for Ontario at 8-km by 8-km resolution based on satellite images (NOAA-AVHRR) and a light-use efficiency model. At each level of the NEF hierarchy, within-unit homogeneity of the monthly, annual, and 15-year average NPP of ecounits is characterized by the Getis statistic, and between-unit heterogeneity of these variables is characterized by the boundary contrast (squared difference across the boundary). Similarities across the levels of the hierarchy are assessed by the sum-of-squared differences of monthly, annual, and 15-year average NPP of nested ecounits. Temporal trends of NPP per ecounit are measured using Kendall’s correlation coefficient. The seasonal and annual variations in the growing season, as captured by a time series of NPP aggregated to the ecodistrict, ecoregion, and ecozone level, are shown to vary across Ontario. These results indicate the potential of our spatio-temporal approach for ecoregionalization assessment based on dynamic and spatially distributed data

Monte Carlo sensitivity analysis of spatial partitioning schemes : Regional predictions of nitrogen-loss

No abstract availabl