GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data.  Part 2: Biophysical products for Northern ecosystems

Cihlar, J; Chen, J M; Li, Z; Latifovic, R; Fedosejevs, G; Adair, M; Park, W
M; Fraser, R; Trishchenko, A; Guindon, B; Stanley, D; Morse, D

doi:10.4095/219755

Cited by 6 publications

(9 citation statements)

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“…Alternative class structures (e.g., Table 4) derived from the International Geosphere Biosphere Project (IGBP, see Belward, 1996) were used primarily with regional-scale studies using coarser spatial resolution AVHRR imagery and, more recently, with Landsat image mosaics. In some cases, these were associated with larger national-scale mapping projects (e.g., Cihlar, Chen et al, 2002;Cihlar, Guindon et al, in press).…”

Section: Improved Land-cover Classificationmentioning

confidence: 97%

Remote sensing in BOREAS: Lessons learned

Gamón

Huemmrich

Peddle

et al. 2004

Remote Sensing of Environment

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Section: Improved Land-cover Classificationmentioning

confidence: 97%

Remote sensing in BOREAS: Lessons learned

Gamón

Huemmrich

Peddle

et al. 2004

Remote Sensing of Environment

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“…The NDVI data were extracted from Canada-wide 10-day Advanced Very High Resolution Radiometer (AVHRR) 1 km spatial resolution composites. The composites were processed via the New Geocoding and Compositing System (GEOCOMP-n) [31,32] by the Manitoba Remote Sensing Centre, Canada. Compared to the old compositing system, the GEOCOMP-n system produces higher level products with improvements on geocoding, inter-sensor calibration, atmospheric correction, bi-directional reflectance distribution function (BRDF) correction, and identification and removal of cloud contamination [32].…”

Section: Datamentioning

confidence: 99%

“…The composites were processed via the New Geocoding and Compositing System (GEOCOMP-n) [31,32] by the Manitoba Remote Sensing Centre, Canada. Compared to the old compositing system, the GEOCOMP-n system produces higher level products with improvements on geocoding, inter-sensor calibration, atmospheric correction, bi-directional reflectance distribution function (BRDF) correction, and identification and removal of cloud contamination [32]. The NDVI composites used were from April 1st to October 31st during the period of 1985 to 2007 and were produced from the imagery of AVHRR onboard the National Oceanic and Atmospheric Administration (NOAA) 9, 11, 14, 16, 17 and 18 satellites.…”

Section: Datamentioning

confidence: 99%

Detecting Climate Effects on Vegetation in Northern Mixed Prairie Using NOAA AVHRR 1-km Time-Series NDVI Data

Guo

2012

Remote Sensing

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Grasslands hold varied grazing capacity, provide multiple habitats for diverse wildlife, and are a key component of carbon stock. Research has indicated that grasslands are experiencing effects related to recent climate trends. Understanding how grasslands respond to climate variation thus is essential. However, it is difficult to separate the effects of climate variation from grazing. This study aims to document vegetation condition under climate variation in Grasslands National Park (GNP) of Canada, a grassland ecosystem without grazing for over 20 years, using Normalized Difference Vegetation Index (NDVI) data to establish vegetation baselines. The main findings are (1) precipitation has more effects than temperature on vegetation; (2) the growing season of vegetation had an expanding trend indicated by earlier green-up and later senescence; (3) phenologically-tuned annual NDVI had an increasing trend from 1985 to 2007; and (4) the baselines of annual NDVI range from 0.13 to 0.32, and only the NDVI in 1999 is beyond the upper bound of the baseline. Our results indicate that vegetation phenology and condition have slightly changed in GNP since 1985, although vegetation condition in most years was still within the baselines.

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“…The CCRS developed and commercialized a satellite image processing system named GEOCOMP-n which produces data products useful for terrestrial ecosystem monitoring and carbon cycle modeling (Cihlar, 1996;Cihlar et al, 1997aCihlar et al, , b, 2002. Scientists of CCRS and U. of T., in collaboration with the Canadian Forest Service and Meteorological Service of Canada, developed remote sensing-based carbon cycle models, namely the Boreal Ecosystems Productivity Simulator (BEPS) for short-term (several years) carbon cycle modeling in daily time steps (Liu et al, 1997Chen et al, 1999) and the Integrated Terrestrial Ecosystem Carbon Model (InTEC) for longterm (a few centuries) carbon modeling in annual time steps (Chen et al, 2000;Chen et al, 2003).…”

Section: Remote Sensing and Carbon Modelingmentioning

confidence: 99%