Water table level in relation to EO-1 ALI and ETM+ data over a mountainous meadow in California

Gong, Peng; Miao, Xin; Tate, Ken; Battaglia, Charles F.; Biging, Gregory S.

doi:10.5589/m04-042

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…In arid and semi‐arid regions, such studies have been linked with analyses of the relationships between groundwater depth and vegetation distribution and structure. Thus, Gong et al () found a linear correlation between the normalized difference vegetation index (NDVI) and DWT in a Californian mountain meadow. With the use of MODIS NDVI and DWT data, correlations between the spatial distribution of the vegetation index and the DWT in the Ejina and Yinchuan Basins in Northwest China have also been analyzed (Jin et al , ; Sun et al , ; Jin et al , ).…”

Section: Introductionmentioning

confidence: 99%

Groundwater‐dependent distribution of vegetation in Hailiutu River catchment, a semi‐arid region in China

Wang

Zhou

et al. 2012

Ecohydrology

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In arid and semi‐arid regions, groundwater availability is one of the controls on vegetation distribution. This groundwater‐dependent distribution of vegetation has been particularly observed in the Hailiutu River catchment, a semi‐arid region in North China. We used remote sensing images of vegetation index (normalized difference vegetation index, NDVI) and field data of depth to water table (DWT) to assess the response of vegetation distribution on increase of DWT at the regional scale. The frequency distribution curves of NDVI with respect to different DWT were obtained. The statistical distributions of NDVI values at different DWT intervals indicate that higher vegetation coverage and more plant diversity exist at places of shallow groundwater. Both the mean and the standard deviation of NDVI values decrease with the increase of groundwater depth when DWT is less than 10 m. Beyond that depth, a low level of vegetation coverage and diversity is maintained. Comparisons of different sub‐areas within the region with different dominant species showed that the NDVI of shrubs is sensitive to DWT. In contrast, NDVI of herbs is not significantly influenced by DWT. The relationship between NDVI and groundwater depth in farmlands could not be reliably determined because of disturbance by human activities. We conclude that application of this methodology may significantly improve our ability on sustainable management of land and groundwater resources. Copyright © 2012 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

Groundwater‐dependent distribution of vegetation in Hailiutu River catchment, a semi‐arid region in China

Wang

Zhou

et al. 2012

Ecohydrology

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“…This could change as technology develops. For example, the water table depth and abundance of water may be inferred from vegetation growth and gravity changes, respectively through indirect estimation [9,10]. In the type of environmental changes, qualitative changes in species, fire burnt area, water bodies, impervious surface, snow and ice cover are related to Land-Use and Land-Cover Change (LUCC).…”

Section: Contents and Methods In Remote Sensing Of Environmental Changesmentioning

confidence: 99%

Remote sensing of environmental change over China: A review

Gong

2012

Chin. Sci. Bull.

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“…Tasselled cap transformed derivatives (TCTD) based on Landsat 4 (Crist & Cicone 1984;Crist 1985), Landsat 5 (Crist et al 1986) and Landsat 7 (Huang et al 2002) at-satellite reflectance are useful in estimating surface brightness, greenness, wetness, Remotely sensed data has been widely used for forest classification and their correlations with parameters like slope, soil moisture and water table (Gong et al 2004;Bhagat 2009). Forest studies extensively utilize ETM þ (Landsat 7…”

Section: Introductionmentioning

confidence: 99%

“…Enhanced Thematic Mapper Plus) datasets for forest identification and classification (Gong et al 2004;Ramsey et al 2004) with a good accuracy level. The Normalised Difference Vegetation Index (NDVI) based on the ETM þ dataset is potentially useful to delineate whether the lands have green cover or not (Kiage et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Use of Landsat ETM+ data for delineation of water bodies in hilly zones

Bhagat

Sonawane

2010

Journal of Hydroinformatics

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The remotely sensed Landsat Enhanced Thematic Mapper Plus (ETM þ ) dataset is used for the detection and delineation of water bodies in hilly zones. The water bodies were detected using Surface Wetness Index (SWI), Normalised Difference Vegetation Index (NDVI) and a slope map.The assessment of areas under dense vegetation in water bodies is omitted in the combined map prepared using classified raster images showing (1) the distribution of 'water' and 'non-water' based on SWI and (2) the distribution of 'vegetation' and 'non-vegetation' based on NDVI. The shadows' effect in estimated areas under water bodies is detected and delineated using the combination of (1) a combined raster image (classified SWI and NDVI) and (2) a slope map. About 3.8% (1370 ha) of the total area reviewed is estimated under water bodies with 91.74% overall accuracy. The water bodies include (1) major and minor dams, (2) watered streams, (3) springs distributed in foothill zones and (4) small dams on minor streams. The relatively smaller water body objects, i.e. streams and springs, have estimated less producer's (92-96%) and user's (85-92%) accuracy than the major water bodies, i.e. 96.77% producer's and 100% for user's accuracy.

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Water table level in relation to EO-1 ALI and ETM+ data over a mountainous meadow in California

Cited by 9 publications

References 9 publications

Groundwater‐dependent distribution of vegetation in Hailiutu River catchment, a semi‐arid region in China

Groundwater‐dependent distribution of vegetation in Hailiutu River catchment, a semi‐arid region in China

Remote sensing of environmental change over China: A review

Use of Landsat ETM+ data for delineation of water bodies in hilly zones

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