The application of quicklook NOAA AVHRR satellite imagery in Northern Scandinavia for synoptic weather analyses

The suitability of imagery from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of satellites for the synoptic classification of circulation trends in the European Arctic is assessed with reference to data from three climate stations. Simplified synoptic cyclonic classifications are derived from the satellite imagery and tested against climate data. Five classes of frontal system are derived from the tracking of systems over the UK‐Scandinavia‐Baltic region using 1460 satellite images over two years. An index based on the qualitative interpretation of satellite imagery was related to the reference data. The tracking of the systems in the imagery also facilitates a comparison of travel times across the region and the frequency of occurrence. Frontal systems that remain largely stationary over the Baltic were found to correlate best with precipitation at the reference sites. The paper thus investigates the use of AVHRR imagery for the categorisation of weather patterns towards deriving quantitative relationships between circulation classes and weather elements (such as temperature and precipitation) where, for example, climate data are sparse or where skills required for the interpretation of Height Potential Fields are lacking.     

Detection of archaeological crop marks by using satellite QuickBird multispectral imagery

The capability of satellite QuickBird imagery for the identification of archaeological crop marks is herein presented and discussed for two test sites located in the South of Italy. The selected sites, dating back to Middle Ages, were buried under surfaces covered by herbaceous plants characterized by a different phenological status (dry/green) when the satellite data were acquired.     

NDVI variation and its relation to climate in Canadian ecozones

Parks Canada began the Northern Satellite Monitoring Program in 1997, with the objective of tracking large‐scale vegetation variation in Canadian ecosystems and helping land managers to develop appropriate management practices in response to climate change. Under this program, a sequence of 10‐day composite Advanced Very High Resolution Radiometer (AVHRR)‐derived Normalized Difference Vegetation Index (NDVI) data from 1985 to 2007 was examined to study seasonal and inter‐annual relationships between vegetation and climate data over Canadian ecosystems using statistical and wavelet analysis. Statistical analysis showed that temperature was the principal driver for seasonal variability in greenness, explaining more than 70 percent of seasonal variation in vegetation for most Canadian ecozones. In comparison with temperature, the relationships between NDVI and precipitation were weaker but still significant. Maximum annual NDVI showed increasing trends in Canadian ecozones during the study period, although increasing rates were spatially heterogeneous. Wavelet analysis confirmed that inter‐annual variation in NDVI was different at two ecozones in Canada. NDVI variation in the Northern Arctic was significant at scales of 3–4 years from 1997 to 2001, which was associated with temperature and precipitation variation. Comparatively, NDVI variation in the Boreal Shield was significant at scales of 5–8 years from 1991 to 1999, but did not correspond with climate variation.     

MODIS‐derived NDVI Characterisation of Drought‐Induced Evergreen Dieoff in Western North America

Vegetation stress or mortality can be the result of many factors including drought‐induced water deficit, insect infestations and failures of, or fluctuations in, precipitation sources typical to an area. Reduction of cover and reduced health are identifiable in remotely‐sensed multispectral satellite images. A suite of images from NASA's MODIS sensor was used to calculate the Normalised Difference Vegetation Index (NDVI) during the 2000–2006 North American growing seasons. Fluctuations in NDVI over this period show a significant decline in vegetative health in the region with specific areas showing changes linked to moisture sources, prevailing wind patterns, slope aspect and solar radiation receipt. Ground‐truthing of these areas has confirmed the extent and magnitude of the dieoff signal. Historically, dieoff has been reversed through regeneration as climate conditions return to a normal regime. However, quantification of recent vegetative change in western North America suggests that the degree of change may be too severe for regrowth to occur and may have far‐reaching impacts on a scale unseen in modern times. The loss of vegetative habitat and native species in semi‐arid regions and lack of regeneration in these marginal ecosystems due to prolonged drought are growing global problems. Similar drought stress impacts on marginal ecotypes have also been observed in semi‐arid regions of Australia, South America, Asia and Africa. Observations of the spatial pattern of temperate forest vegetation globally can be used to develop a precise picture of vegetative health in these regions and how they are reacting to global climate change.     

Assessing Recovery from the 2004 Indian Ocean Tsunami: An Application of Night‐time Light Data and Vegetation Index

It has been 10 years since the Indian Ocean Tsunami caused serious damage to the coastal areas in South and Southeast Asia. The effects on vegetation and human settlements in the affected areas were enormous. This study presents the results of an analysis estimating the long‐term recovery using two longitudinal remotely sensed dataset: 1. Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI), a dataset accounting for change in the landscape and vegetation; and 2. Defense Meteorological Satellite Program‐Optical Line Scanner (DMSP‐OLS) night‐time light data in order to estimate the effects on human and economic activities. It is evident from the results of this study that the night‐time light and vegetation index datasets can both be beneficial in identifying changes caused by natural disasters and can be used to track recovery. The results using night‐time light indicates a large loss of lighted area but also a rapid recovery of night‐time light after the tsunami. Already in year 2005–2006, the levels of lighted area and sum of the lighting (SOL) intensity reached the same levels as pre‐tsunami. For MODIS vegetation index, a drop can be observed in 2005/2006 on locations close to the coastline using 1 year temporal resolution; however, when utilizing the 16 day temporal resolution, the impact of the tsunami is illustrated as a dramatic drop, mostly in pixels located within 3km from the coast. Following the drop in vegetation index due to the tsunami, it was observed that most pixels exhibited at least some level of recovery in 2 years after the event.     

Assessment of Relationships Between Precipitation and Satellite Derived Vegetation Condition Within South Australia

The normalised difference vegetation index (NDVI) has evolved as a primary tool for monitoring continental‐scale vegetation changes and interpreting the impact of short to long‐term climatic events on the biosphere. The objective of this research was to assess the nature of relationships between precipitation and vegetation condition, as measured by the satellite‐derived NDVI within South Australia. The correlation, timing and magnitude of the NDVI response to precipitation were examined for different vegetation formations within the State (forest, scrubland, shrubland, woodland and grassland). Results from this study indicate that there are strong relationships between precipitation and NDVI both spatially and temporally within South Australia. Differences in the timing of the NDVI response to precipitation were evident among the five vegetation formations. The most significant relationship between rainfall and NDVI was within the forest formation. Negative correlations between NDVI and precipitation events indicated that vegetation green‐up is a result of seasonal patterns in precipitation. Spatial patterns in the average NDVI over the study period closely resembled the boundaries of the five classified vegetation formations within South Australia. Spatial variability within the NDVI data set over the study period differed greatly between and within the vegetation formations examined depending on the location within the state. ACRONYMS AVHRR Advanced Very High Resolution Radiometer ENVSAEnvironments of South Australia EOS Terra‐Earth Observing System EVIEnhanced Vegetation Index MODIS Moderate Resolution Imaging Spectro‐radiometer MVC Maximum Value Composite NDVINormalised Difference Vegetation Index NIRNear Infra‐Red NOAANational Oceanic and Atmospheric Administration SPOT Systeme Pour l’Observation de la Terre