On the correlation of indices of vegetation and surface temperature over south-eastern Australia

Smith, Richard; Choudhury, Bhaskar J.

doi:10.1080/01431169008955164

Cited by 54 publications

(25 citation statements)

References 18 publications

(15 reference statements)

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“…For a desert in Nevada the observed values of GVI were around 0´1 throughout that year. Similar GVI ranges were reported by Smith and Choudhury (1990 ) using calibrated NDVI derived from NOAA-9, local area coverage ( LAC) data acquired on 28 January 1987. They found NDVI values ranging from 0´3 to 0´65 with most values around 0´52 for Australian Barmah forest ( 30± 40 m canopy, dominated by Eucalyptus camaldulensis with grass understory).…”

Section: Ndv I For Speci® C Vegetation Typessupporting

confidence: 74%

“…Dense forest NDVI observed in this work were similar to NDVI values of broad leaf evergreen forests in Latin America and Asia ( NDVI of 0´50 to 0´56; DeFries and Townshend 1994 ), 0´52 NDVI for Australian Eucalyptus sp. forest (Smith and Choudhury 1990), and the 0´53 NDVI of Central African evergreen forests ( Lambin and Ehrlich 1995). It is interesting to note the similarities between these values since they ranged across spatial resolutions from LAC (ca.…”

Section: Ndv I For Speci® C Vegetation Typesmentioning

confidence: 99%

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The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

Batista¹,

Shimabukuro²,

Lawrence³

1997

International Journal of Remote Sensing

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We have analysed monthly composites of normalized diVerence vegetation index (NDVI ) calculated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) for the Amazonian region of northern Brazil across a decade (August 1981 to June 1991 to ascertain if the dominant vegetation types could be diVerentiated, and to seek inter-annual climatic variation due to changing environmental conditions. The vegetation types observed included dense forest (submontana and terras baixas), open forest (submontana and terras baixas), transitional forest, seasonal forest (caatinga), and two types of savanna (cerrado). We found that monthly NDVI composites revealed seasonality in cerrado and especially in caatinga cover types, which can be used in their identi® cation, whilst the phenology of other forest cover types varies little throughout the year. Additionally, yearly composite NDVI values showed a clear and signi® cant reduction ( p>0´95) in dry years, such as those with El Nin Ä o Southern Oscillation events. These results indicate the potential use of multi-temporal NDVI data for the environmental characterization and identi® cation of forest ecosystems. Our research found NDVI images from NOAA AVHRR oVer a long-term data set that is unequalled for monitoring terrestrial land cover. However, these data have to be used with a degree of caution, especially in regards to atmospheric interference, such as cloud contamination and volcanic eruptions, and post-launch changes in calibration.

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Section: Ndv I For Speci® C Vegetation Typessupporting

confidence: 74%

Section: Ndv I For Speci® C Vegetation Typesmentioning

confidence: 99%

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

Batista¹,

Shimabukuro²,

Lawrence³

1997

International Journal of Remote Sensing

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“…Vegetation considerably enhances water vapor fluxes to the atmosphere through the transpiration process. Decreased vegetation is often associated with decreased latent heat flux and therefore an increase in sensible heat flux and surface temperature (e.g., Smith and Choudhury 1990). In addition, the loss of the vegetation canopy caused by fire decreases surface roughness and the efficiency of energy dissipation and thus increases surface temperature (e.g., Smith and Choudhury 1990).…”

Section: Discussionmentioning

confidence: 99%

“…Decreased vegetation is often associated with decreased latent heat flux and therefore an increase in sensible heat flux and surface temperature (e.g., Smith and Choudhury 1990). In addition, the loss of the vegetation canopy caused by fire decreases surface roughness and the efficiency of energy dissipation and thus increases surface temperature (e.g., Smith and Choudhury 1990). Furthermore, during daylight hours, plant leaves are cooler than exposed bare soil because the heat capacity of plant leaves is much lower than the heat capacity of soil (Gates 1980).…”

Section: Discussionmentioning

confidence: 99%

Satellite-Observed Surface Temperature Changes after the 2004 Taylor Complex Fire in Alaska

2011

Earth Interactions

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Land-use and land-cover change has been recognized as a key component in global climate change. In the boreal forest ecosystem, fires often cause significant changes in vegetation structure and surface biophysical characteristics, which in turn dramatically change energy and water balances of land surface. Several studies have characterized fire-induced changes in surface energy balance in boreal ecosystem based on site observations. This study provides satellite-observed impacts of a large fire on surface climate in Alaska's boreal forest. A land surface temperature (LST) product from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the primary data. Five years after fire, surface temperature over the burned area increased by an average of 2.08C in the May-August period. The increase reached a maximum of 3.28C in the year immediately following the fire. The warm anomaly decreased slightly after the second year but remained until the fifth year of the study. These changes in surface temperature may directly affect surface net radiation and thus partition of surface available energy. By documenting continuous and synoptic surface temperature changes over multiple years, this paper demonstrates the value of Earth Observing System (EOS) observations for landclimate interaction research. The observed characteristics of surface temperature change as well as changes in key surface biophysical parameters such as albedo and leaf area index (LAI) can be used in the next generation of climate models to improve the representation of large-scale ecosystem disturbances.

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“…Other studies have documented the significant relationship among the temporal variability of NDVI, temperature and precipitation (e.g. Smith & Choudhury 1990, Wang et al 2003.…”

Section: Remote Sensing Indicesmentioning

confidence: 99%

Climatological modeling of monthly air temperature and precipitation in Egypt through GIS techniques

Kenawy¹,

López‐Moreno²,

Vicente‐Serrano³

et al. 2010

Clim. Res.

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This paper shows the results of modeling and mapping monthly maximum and minimum temperature, and total precipitation in Egypt with the purpose of obtaining accurate climate maps. A multivariate linear regression model enhanced by spline interpolation was undertaken. Climate variables were obtained from 40 quality controlled and homogenized series for the period 1957 to 2006. The predictors, including geographical variables (e.g. latitude, longitude, altitude and distance to water bodies) and the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing indices, were integrated as raster layers in a Geographical Information System (GIS) environment. Inclusion of meaningful remote sensing indices (e.g. the Normalized Difference Vegetation Index and the Normalized Difference Temperature Index) generally improved accuracy of the predictions. The model integrating geographical and remote sensing indices explained an average of 76.5 and 51.7% of the spatial variability of maximum and minimum temperatures, respectively. For precipitation, the model explained an average of 60.2% of the spatial variability during the whole year and 70.7% during the wet season (September to April). The accuracy of the models was assessed through cross-validation between predicted and observed values using a set of statistics including the coefficient of determination (R 2 ), Mean Absolute Error (MAE) and Willmott's D. The cross-validation results were satisfactory for maximum temperature (average MAE = 1.03°C) and total precipitation (average MAE = 2.73 mm). A poorer fit of the model was obtained for minimum temperature (average MAE = 1.72°C). For each climatic variable, digital maps were finally obtained at a spatial resolution of 1 km. Considering the favourable results obtained using only a small number of observatories, such digital maps have significant potential for the study of climate change and climate impact assessment. KEY WORDS: Multivariate regression · Temperature · Precipitation · Digital elevation model · GIS · Remote sensing · Egypt Resale or republication not permitted without written consent of the publisherClim Res 42: [161][162][163][164][165][166][167][168][169][170][171][172][173][174][175][176] 2010 Thiessen polygons and Inverse Distance Weighting (IDW), to more complicated techniques, such as geostatistical procedures (e.g. kriging) (Phillips et al. 1992, Lennon & Turner 1995, Vicente-Serrano et al. 2003. Also, these approaches differ in their applicability according to terrain complexity and spatial density of stations (Allen & DeGaetano 2001). A list of some studies involving spatial interpolation of climate variables is included in Table 1.In general, local and geostatistical interpolation methods give more account to the spatial structure of points of interest rather than the physical influences of the surrounding environment on climate distribution (e.g. water bodies and vegetation) (Phillips et al. 1992, Ninyerola et al. 2000. Additionally, spatial interpolation has certain limi...

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On the correlation of indices of vegetation and surface temperature over south-eastern Australia

Cited by 54 publications

References 18 publications

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

The long-term monitoring of vegetation cover in the Amazonian region of northern Brazil using NOAA-AVHRR data

Satellite-Observed Surface Temperature Changes after the 2004 Taylor Complex Fire in Alaska

Climatological modeling of monthly air temperature and precipitation in Egypt through GIS techniques

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