Sub-pixel habitat mapping of a costal dune ecosystem

Lucas, Neil; Sanjeevi, S.; Barnsley, Michael J.

doi:10.1016/s0143-6228(02)00007-3

Cited by 30 publications

(20 citation statements)

References 28 publications

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“…The process of resolving the proportion of reflectance from the sand and vegetation (including live and dead biomass) is referred to as spectral unmixing. Pixels are deconvolved into the spectral components that make up the total reflectance (e.g., Smith et al, 1990;Okin et al, 2001;Lucas et al, 2002). In two related papers Asner and colleagues demonstrated that the spectral reflectance of green vegetation, dry biomass (e.g., non-photosynthetic vegetation), and bare soil can be separated within the shortwave infrared (2100-2400 nm), and that hyperspectral sensors can indeed unmix these spectral signatures at the pixel level in arid and semiarid ecosystems (Asner and Lobell, 2000;Asner and Heidebrecht, 2002).…”

Section: Challengesmentioning

confidence: 99%

Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook

Hugenholtz

Levin

Barchyn

et al. 2012

Earth-Science Reviews

174

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Section: Challengesmentioning

confidence: 99%

Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook

Hugenholtz

Levin

Barchyn

et al. 2012

Earth-Science Reviews

174

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“…As a main advantage, the proposed methodology easily enabled mapping the status of small water bodies, many of them at sub-pixel size, with medium spatial resolution imagery (i.e., Landsat) and accurate knowledge on the spatial location of ponds. It constitutes an alternative to spectral unmixing techniques (i.e., linear mixture modelling, fuzzy-c means clustering), which map the fractional coverage of each land cover class in a pixel (i.e., pond cover, tree cover…) based on knowledge of their pure reflectance spectra [50][51][52] or similar techniques which also require spectral records of the cover types of interest [i.e., 22]. It should be noted that we could have not applied unmixing techniques since we lacked "pure" land cover classes.…”

Section: Application Of Remote Sensing For the Monitoring Of Temporarmentioning

confidence: 99%

Evidence of Hydroperiod Shortening in a Preserved System of Temporary Ponds

2010

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Based on field data simultaneous with Landsat overpasses from six different dates, we developed a robust linear model to predict subpixel fractions of water cover. The model was applied to a time series of 174 Landsat TM and ETM+ images to reconstruct the flooding regime of a system of small temporary ponds and to study their spatio-temporal changes in a 23-year period. We tried to differentiate natural fluctuations from trends in hydrologic variables (i.e., hydroperiod shortening) that may threaten the preservation of the system. Although medium-resolution remote sensing data have rarely been applied to the monitoring of small-sized wetlands, this study evidences its utility to understand the hydrology of temporary ponds at a local scale. We show that the temporary ponds in Doñana National Park constitute a large and heterogeneous system with high intra and inter-annual variability. We also evidence that the conservation value of this ecosystem is threatened by the observed tendency to shorter annual hydroperiods in recent years, probably due to aquifer exploitation. This system of temporary ponds deserves special attention for the high density and heterogeneity of natural ponds, not common in Europe. For this reason, management decisions to avoid its destruction or degradation are critical.

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“…Within-pixel variation is also ignored [9]. Pixels are often composed of a mixture of habitat classes [10] that may not all be relevant to a species. In addition to exhibiting high within-habitat heterogeneity, semi-arid ecosystems sometimes have gradual transitions between some habitat types, which may lead to low classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Habitat components with very high spectral contrast (e.g. green vegetation and soil) can be particularly well assessed [10]. Quantifying the proportion of green vegetation in a given area using spectral unmixing is thus promising for characterizing bird habitat, especially in ecosystems where vegetation indices may be less reliable owing to strong soil background, such as semi-arid ecosystems [29,30].…”

Section: Introductionmentioning

confidence: 99%

Modelling avian biodiversity using raw, unclassified satellite imagery

St-Louis

Pidgeon

Kuemmerle

et al. 2014

Phil. Trans. R. Soc. B

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One contribution of 9 to a Theme Issue 'Satellite remote sensing for biodiversity research and conservation applications'. Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management.

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Sub-pixel habitat mapping of a costal dune ecosystem

Cited by 30 publications

References 28 publications

Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook

Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook

Evidence of Hydroperiod Shortening in a Preserved System of Temporary Ponds

Modelling avian biodiversity using raw, unclassified satellite imagery

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