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<pubDate>Sat, 19 Jul 2008 04:44:59 BST</pubDate>


	<title>CiteULike: neteler's evi</title>
	<description>CiteULike: neteler's evi</description>


	<link>http://www.citeulike.org/user/neteler/tag/evi</link>
	<dc:publisher>CiteULike.org</dc:publisher>
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        <rdf:li rdf:resource="http://www.citeulike.org/user/neteler/article/2239923"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/neteler/article/1457805"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/neteler/article/780510"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/neteler/article/777545"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/neteler/article/274524"/>
        <rdf:li rdf:resource="http://www.citeulike.org/user/neteler/article/172947"/>

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<item rdf:about="http://www.citeulike.org/user/neteler/article/2239923">
    <title>Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data.</title>
    <link>http://www.citeulike.org/user/neteler/article/2239923</link>
    <description>&lt;i&gt;PLoS ONE, Vol. 3, No. 1. (2008)&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;BACKGROUND: Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. METHODOLOGY/PRINCIPAL FINDINGS: We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. CONCLUSIONS/SIGNIFICANCE: Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling.</description>
    <dc:title>Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data.</dc:title>

    <dc:creator>JP Scharlemann</dc:creator>
    <dc:creator>D Benz</dc:creator>
    <dc:creator>SI Hay</dc:creator>
    <dc:creator>BV Purse</dc:creator>
    <dc:creator>AJ Tatem</dc:creator>
    <dc:creator>GR Wint</dc:creator>
    <dc:creator>DJ Rogers</dc:creator>
    <dc:identifier>doi:10.1371/journal.pone.0001408</dc:identifier>
    <dc:source>PLoS ONE, Vol. 3, No. 1. (2008)</dc:source>
    <dc:date>2008-01-16T16:23:08-00:00</dc:date>
    <prism:publicationYear>2008</prism:publicationYear>
    <prism:publicationName>PLoS ONE</prism:publicationName>
    <prism:issn>1932-6203</prism:issn>
    <prism:volume>3</prism:volume>
    <prism:number>1</prism:number>
    <prism:category>ecology</prism:category>
    <prism:category>epidemiology</prism:category>
    <prism:category>evi</prism:category>
    <prism:category>fourier</prism:category>
    <prism:category>lst</prism:category>
    <prism:category>modis</prism:category>
    <prism:category>ndvi</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/neteler/article/1457805">
    <title>An overview of MODIS Land data processing and product status</title>
    <link>http://www.citeulike.org/user/neteler/article/1457805</link>
    <description>&lt;i&gt;Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 3-15.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Data from the first Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the NASA Terra Platform are being used to provide a new generation of land data products in support of the National Aeronautics and Space Administration (NASA)'s Earth Science Enterprise, global change research and natural resource management. The MODIS products include global data sets heretofore unavailable, derived from new moderate resolution spectral bands with spatial resolutions of 250 m to 1 km. A partnership between Science Team members and the MODIS Science Data Support Team is producing data sets of unprecedented volume and number for the land research and applications. This overview paper provides a summary of the instrument performance and status, the data production system, the products, their status and availability for land studies.</description>
    <dc:title>An overview of MODIS Land data processing and product status</dc:title>

    <dc:creator>CO Justice</dc:creator>
    <dc:creator>JRG Townshend</dc:creator>
    <dc:creator>EF Vermote</dc:creator>
    <dc:creator>E Masuoka</dc:creator>
    <dc:creator>RE Wolfe</dc:creator>
    <dc:creator>N Saleous</dc:creator>
    <dc:creator>DP Roy</dc:creator>
    <dc:creator>JT Morisette</dc:creator>
    <dc:source>Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 3-15.</dc:source>
    <dc:date>2007-07-15T16:47:49-00:00</dc:date>
    <prism:publicationYear>2002</prism:publicationYear>
    <prism:publicationName>Remote Sensing of Environment</prism:publicationName>
    <prism:volume>83</prism:volume>
    <prism:number>1-2</prism:number>
    <prism:startingPage>3</prism:startingPage>
    <prism:endingPage>15</prism:endingPage>
    <prism:category>clouds</prism:category>
    <prism:category>evi</prism:category>
    <prism:category>fpar</prism:category>
    <prism:category>modis</prism:category>
    <prism:category>ndvi</prism:category>
    <prism:category>npp</prism:category>
    <prism:category>remote-sensing</prism:category>
    <prism:category>snow</prism:category>
    <prism:category>time-series</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/neteler/article/780510">
    <title>Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI Composite Data Using Agricultural Measurements: An Example at Corn Fields in Western Mexico.</title>
    <link>http://www.citeulike.org/user/neteler/article/780510</link>
    <description>&lt;i&gt;Environ Monit Assess (17 December 2005), pp. 1-14.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;Although several types of satellite data provide temporal information of the land use at no cost, digital satellite data applications for agricultural studies are limited compared to applications for forest management. This study assessed the suitability of vegetation indices derived from the TERRA-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and SPOT-VEGETATION (VGT) sensor for identifying corn growth in western Mexico. Overall, the Normalized Difference Vegetation Index (NDVI) composites from the VGT sensor based on bi-directional compositing method produced vegetation information most closely resembling actual crop conditions. The NDVI composites from the MODIS sensor exhibited saturated signals starting 30 days after planting, but corresponded to green leaf senescence in April. The temporal NDVI composites from the VGT sensor based on the maximum value method had a maximum plateau for 80 days, which masked the important crop transformation from vegetative stage to reproductive stage. The Enhanced Vegetation Index (EVI) composites from the MODIS sensor reached a maximum plateau 40 days earlier than the occurrence of maximum leaf area index (LAI) and maximum intercepted fraction of photosynthetic active radiation (fPAR) derived from in-situ measurements. The results of this study showed that the 250-m resolution MODIS data did not provide more accurate vegetation information for corn growth description than the 500-m and 1000-m resolution MODIS data.</description>
    <dc:title>Assessment of MODIS-EVI, MODIS-NDVI and VEGETATION-NDVI Composite Data Using Agricultural Measurements: An Example at Corn Fields in Western Mexico.</dc:title>

    <dc:creator>Pei-Yu Chen</dc:creator>
    <dc:creator>Gunar Fedosejevs</dc:creator>
    <dc:creator>Mario Tiscareño-López</dc:creator>
    <dc:creator>Jeffrey Arnold</dc:creator>
    <dc:identifier>doi:10.1007/s10661-005-9006-7</dc:identifier>
    <dc:source>Environ Monit Assess (17 December 2005), pp. 1-14.</dc:source>
    <dc:date>2006-07-30T19:44:12-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:publicationName>Environ Monit Assess</prism:publicationName>
    <prism:issn>0167-6369</prism:issn>
    <prism:startingPage>1</prism:startingPage>
    <prism:endingPage>14</prism:endingPage>
    <prism:category>evi</prism:category>
    <prism:category>lai</prism:category>
    <prism:category>modis</prism:category>
    <prism:category>ndvi</prism:category>
    <prism:category>spot-vgt</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/neteler/article/777545">
    <title>MODIS vegetation index (MOD 13). Algorithm theoretical basis document ATBD13</title>
    <link>http://www.citeulike.org/user/neteler/article/777545</link>
    <description>&lt;i&gt;(1999)&lt;/i&gt;</description>
    <dc:title>MODIS vegetation index (MOD 13). Algorithm theoretical basis document ATBD13</dc:title>

    <dc:creator>A Huete</dc:creator>
    <dc:creator>C Justice</dc:creator>
    <dc:creator>W Leeuwen</dc:creator>
    <dc:source>(1999)</dc:source>
    <dc:date>2006-07-28T09:33:12-00:00</dc:date>
    <prism:publicationYear>1999</prism:publicationYear>
    <prism:category>atbd</prism:category>
    <prism:category>evi</prism:category>
    <prism:category>modis</prism:category>
    <prism:category>ndvi</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/neteler/article/274524">
    <title>Overview of the radiometric and biophysical performance of the MODIS vegetation indices</title>
    <link>http://www.citeulike.org/user/neteler/article/274524</link>
    <description>&lt;i&gt;Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 195-213.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;We evaluated the initial 12 months of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System-Terra platform. Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), are produced at 1-km and 500-m resolutions and 16-day compositing periods. This paper presents an initial analysis of the MODIS NDVI and EVI performance from both radiometric and biophysical perspectives. We utilize a combination of site-intensive and regionally extensive approaches to demonstrate the performance and validity of the two indices. Our results showed a good correspondence between airborne-measured, top-of-canopy reflectances and VI values with those from the MODIS sensor at four intensively measured test sites representing semi-arid grass/shrub, savanna, and tropical forest biomes. Simultaneously derived field biophysical measures also demonstrated the scientific utility of the MODIS VI. Multitemporal profiles of the MODIS VIs over numerous biome types in North and South America well represented their seasonal phenologies. Comparisons of the MODIS-NDVI with the NOAA-14, 1-km AVHRR-NDVI temporal profiles showed that the MODIS-based index performed with higher fidelity. The dynamic range of the MODIS VIs are presented and their sensitivities in discriminating vegetation differences are evaluated in sparse and dense vegetation areas. We found the NDVI to asymptotically saturate in high biomass regions such as in the Amazon while the EVI remained sensitive to canopy variations.</description>
    <dc:title>Overview of the radiometric and biophysical performance of the MODIS vegetation indices</dc:title>

    <dc:creator>A Huete</dc:creator>
    <dc:creator>K Didan</dc:creator>
    <dc:creator>T Miura</dc:creator>
    <dc:creator>EP Rodriguez</dc:creator>
    <dc:creator>X Gao</dc:creator>
    <dc:creator>LG Ferreira</dc:creator>
    <dc:identifier>doi:10.1016/S0034-4257(02)00096-2</dc:identifier>
    <dc:source>Remote Sensing of Environment, Vol. 83, No. 1-2. (November 2002), pp. 195-213.</dc:source>
    <dc:date>2005-08-05T10:25:00-00:00</dc:date>
    <prism:publicationYear>2002</prism:publicationYear>
    <prism:publicationName>Remote Sensing of Environment</prism:publicationName>
    <prism:volume>83</prism:volume>
    <prism:number>1-2</prism:number>
    <prism:startingPage>195</prism:startingPage>
    <prism:endingPage>213</prism:endingPage>
    <prism:category>evi</prism:category>
    <prism:category>modis</prism:category>
    <prism:category>ndvi</prism:category>
    <prism:category>remote-sensing</prism:category>
</item>



<item rdf:about="http://www.citeulike.org/user/neteler/article/172947">
    <title>Time series processing of MODIS satellite data for landscape epidemiological applications</title>
    <link>http://www.citeulike.org/user/neteler/article/172947</link>
    <description>&lt;i&gt;International Journal of Geoinformatics. Special Issue on FOSS/GRASS 2004 &#38; GIS-IDEAS 2004, Vol. 1, No. 1. (March 2005), pp. 133-138.&lt;/i&gt;&lt;br /&gt;&lt;br /&gt;This paper reports on the processing of time series of MODIS NDVI/EVI and LST satellite data in a Geographical Information System (GIS). The required data preparations for the integration of MODIS data in GIS is described with focus on the reprojection from MODIS/Sinusoidal projection to national coordinate systems. To remove low quality pixels, the MODIS quality maps are utilised. We explain subsequent filtering of Land Surface Temperature maps with an outlier detector to eliminate originally undetected cloud pixels. Further analysis of time series is briefly discussed as well as related landscape epidemiological applications in the field of tick-borne diseases.</description>
    <dc:title>Time series processing of MODIS satellite data for landscape epidemiological applications</dc:title>

    <dc:creator>M Neteler</dc:creator>
    <dc:source>International Journal of Geoinformatics. Special Issue on FOSS/GRASS 2004 &#38; GIS-IDEAS 2004, Vol. 1, No. 1. (March 2005), pp. 133-138.</dc:source>
    <dc:date>2005-04-27T20:04:32-00:00</dc:date>
    <prism:publicationYear>2005</prism:publicationYear>
    <prism:publicationName>International Journal of Geoinformatics. Special Issue on FOSS/GRASS 2004 &#38; GIS-IDEAS 2004</prism:publicationName>
    <prism:volume>1</prism:volume>
    <prism:number>1</prism:number>
    <prism:startingPage>133</prism:startingPage>
    <prism:endingPage>138</prism:endingPage>
    <prism:category>evi</prism:category>
    <prism:category>gis</prism:category>
    <prism:category>grass</prism:category>
    <prism:category>lst</prism:category>
    <prism:category>modis</prism:category>
    <prism:category>remote-sensing</prism:category>
    <prism:category>temperature</prism:category>
    <prism:category>vegetation</prism:category>
</item>



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