Detecting changes in riparian habitat conditions based on patterns of greenness change: A case study from the Upper San Pedro River Basin, USA

Jones, K. Bruce; Edmonds, Curtis E.; Slonecker, E. Terrance; Wickham, James D.; Neale, Anne C.; Wade, Timothy G.; Riitters, Kurt H.; Kepner, William G.

doi:10.1016/j.ecolind.2007.01.001

Cited by 30 publications

(30 citation statements)

References 28 publications

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“…NDVI is a measure of vegetation greenness determined using the red visible light and near infrared spectrums, covered here by Landsat 5 TM bands 3 (0.63-0.69 μm) and 4 (0.76-0.9 μm) respectively (USGS, 2015b). NDVI has been used extensively as a corollary for photosynthetically active vegetation in semi-arid riparian corridors (Jones et al, 2008;Letts et al, 2008;Dennison et al, 2009;Nguyen et al, Fu and Bergher, 2015). NDVI is calculated from reflectance (ρ) in the red visible light and near infrared spectrums (Deering and Haas, 1980):…”

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

Seasonality of Groundwater Recharge in the Basin and Range Province, Western North America

Neff

2017

Geological Society of America Abstracts With Programs

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Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

Seasonality of Groundwater Recharge in the Basin and Range Province, Western North America

Neff

2017

Geological Society of America Abstracts With Programs

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“…Medium to high spatial resolution sensors that allow detection of spatial vegetation patterns, such as MSS (Landsat MultiSpectral Scanner), TM (Landsat Thematic Mapper), and ETM+ (Landsat Enhanced Thematic Mapper Plus), are limited in their temporal resolution (Rivero et al, 2007;Jones et al, 2008). On the other hand, high temporal resolution sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer), which allow detailed monitoring of the temporal change in vegetation, often lack sufficient spatial resolution (Weissteiner et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed

Ouyang

Hao

Skidmore

et al. 2012

Ecological Indicators

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“…They play an important role in maintenance of biodiversity, serving as bird migratory corridors, and providing refuge to plant and animal species in arid to semi-arid landscapes or during drought (Jones et al, 2008). They also contribute to carbon, nitrogen and water cycles and surface energy balance (Rogan et al, 2002;O'Grady et al, 2006a).…”

Section: Groundwater-dependent Vegetationmentioning

confidence: 99%

“…Busch et al, 1992;Doody et al, 2009;Fu and Burgher et al, 2015;Horton et al, 2001;Jones et al, 2008;Scott et al, 1999;Shafroth et al,. 2002) due to their importance to riverine health and their vulnerability to climatic variability and anthropogenic activity (e.g.…”

Section: Spatial and Temporal Resolutionmentioning

confidence: 99%

“…Communities of groundwater-dependent vegetation (GDV) play an important role in the maintenance of biodiversity (Jones et al, 2008) and actively regulate the biosphere through interlinked processes of the carbon and nitrogen cycle, the hydrological cycle and the surface energy balance (O'Grady et al, 2006b;Rogan et al, 2002). Information at a landscape to regional scale about GDV location, water-use dynamics and response to changes in water availability are all necessary to sustainably manage these ecosystems (Eamus et al, 2015;Murray et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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A land surface temperature model-data differencing approach to quantifying subsurface water use by vegetation: Application in the Condamine region, south-eastern Queensland

Gow¹

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Water plays a crucial role in plant community and landscape function; plants use stomata to regulate water loss via transpiration as the 'cost' of carbon assimilation, which in turn regulates surface temperature of leaves. Presently there are a number of aspects of vegetation water-use dynamics that are poorly understood, including the degree of dependence on water sources at different depths in the soil profile; timing, frequency, duration and magnitude of water use; and ecosystem strategies to acquiring water from different sources as water availability varies.The research presented in this thesis aimed to: (1) quantify timing and frequency of the use of subsurface water by woody vegetation, (2) quantify the confidence of these predictions, and (3) identify subsurface water-use strategies and physiological responses employed by woody vegetation with variation in climatic and water-deficit conditions. These aims were achieved through development, application and assessment of a novel land surface temperature (LST) model-data differencing approach applied to a subtropical woodland environment in eastern Australia. The approach detects subsurface water use by vegetation in space and time within 95% confidence intervals, through differences in modelled LST (Ts.mod) and satellite observations of LST (Ts.obs) after accounting for random and systematic error in the model and data.Modelled LST was derived using a two-layer surface energy balance (SEB) model and captures water use from surface water and soil water in the top 30 cm of the soil profile (i.e. shallow soil water). Satellite observations of LST were obtained from Terra-MODIS thermal infrared imagery and captures water use from all available sources. When compared, temperature differences between independently-derived Ts.mod and Ts.obs reveal subsurface water use (i.e. below 30 cm depth) plus systematic and random error. Systematic error or bias, was estimated from temperature differences of grassland vegetation based on an assumption that these vegetation communities do not access subsurface water under certain climatic conditions. Therefore Ts.mod should equal Ts.obs.Random error in Ts.obs was estimated from the literature while sensitivity and uncertainty analyses were used to quantify Ts.mod random error arising from five SEB model resistance terms and their associated input parameters. A Student's t-test was then used to account for systematic and random error in the detection of subsurface water use by vegetation within 95% confidence intervals.The LST model-data differencing approach was applied in a 3200 km 2 region of the subtropical Condamine River Catchment, south-eastern Queensland Australia. The study area contains a mixture of native remnant vegetation, agriculturally-intensive areas, and coal seam gas development. Extensive eucalypt woodlands dominate low-lying hill slopes, while rainfed pastoral grasses and irrigated crops are common throughout the alluvial floodplain and topographic lows adjacent to hill slopes. Timing and frequency ...

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Detecting changes in riparian habitat conditions based on patterns of greenness change: A case study from the Upper San Pedro River Basin, USA

Cited by 30 publications

References 28 publications

Seasonality of Groundwater Recharge in the Basin and Range Province, Western North America

Seasonality of Groundwater Recharge in the Basin and Range Province, Western North America

Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed

A land surface temperature model-data differencing approach to quantifying subsurface water use by vegetation: Application in the Condamine region, south-eastern Queensland

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