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- Seaquist, Jonathan, et al.
(författare)
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A remote sensing-based primary production model for grassland biomes
- 2003
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Ingår i: Ecological Modelling. - 0304-3800. ; 169:1, s. 131-155
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Forskningsöversikt (refereegranskat)abstract
- That data from polar orbiting satellites have detected a widespread increase in photosynthetic activity over the last 20 years in the grasslands of the Sahel is justifies investigating its role in the tropical carbon cycle. But this task is undermined because ground data that are generally used to support the use of primary production models elsewhere are lacking. In this paper, we profile a Light Use Efficiency (LUE) model of primary production parameterised with satellite information, and test it for the West African Sahel; solar radiation is absorbed by plants to provide energy for photosynthesis, while moisture shortfalls control the efficiency of light usage. In particular, we show how an economical use of existing, yet meagre data sets can be used to circumvent nominal, yet untenable approaches for achieving this for the region. Specifically, we use a cloudiness layer provided with the NOAA/NASA 8 km Pathfinder Land data archive (PAL) data set to derive solar radiation (and other energy balance terms) required to implement the model (monthly time-step). Of particular note, we index growth efficiency via transpiration by subsuming rangeland-yield formulations into our model. This is important for partially vegetated landscapes where the fate of rainfall is controlled by relative vegetation cover. We accomplish this by using PAL-derived Normalised Difference Vegetation Index (NDVI) to partition the landscape into fractional vegetation cover. A bare soil evaporation model that feeds into bucket model is then applied, thereafter deriving actual transpiration (quasi-daily time-step). We forgo a formal validation of the model due to problems of spatial scale and data limitations. Instead, we generate maps showing model robustness via Monte Carlo simulation. The precision of our Gross Primary Production (GPP) estimates is acceptable, but falls off rapidly for the northern fringes of the Sahel. We also map the locations where errors in the driving variables are mostly responsible for the bulk of uncertainty in predicted GPP, in this case the water stress factor and the NDVI. Comparisons with an independent model of primary production, CENTURY, are relatively poor, yet favourable comparisons are made with previous primary production estimates found for the region in the literature. A spatially exhaustive evaluation of our GPP map is carried out by regressing randomly sampled observations against integrated NDVI, a method traditionally used to quantify absolute amounts of primary production. Our model can be used to quantify stocks and flows of carbon in grasslands over the recent historical period. (C) 2003 Elsevier B.V. All rights reserved.
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- Seaquist, Jonathan, et al.
(författare)
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Exploring and improving NOAA AVHRR NDVI image quality for African Drylands
- 2002
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Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS). ; 4, s. 2006-2008
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Konferensbidrag (refereegranskat)abstract
- The accuracy of NOAA AVHRR NDVI maximum value composites can be poor due to interference from several sources, including cloud cover. The objectives of this paper are; 1. to accurately quantify noise in this imagery over Africa using geostatistics, and 2. to test four compositing techniques that may be able to reduce this noise. The nugget of the variogram model is used to compute standardized noise for five sites across Africa over 4 seasons. After removing trend and anisotropy in the NDVI sub-scenes, standardized noise estimates range from 18.5% in West Zambia to 68.2% in northern Congo. Four automated compositing methods are also tested over the West African Sahel for 13-day periods in order to improve image quality: the MVC, Maximum Value Temperature (MVT), a two-criteria algorithm that compares the two highest NDVI values for a period thereafter retaining the value with the smallest scan angle (MVCMISC), and a temperature-based algorithm similar to MVCMISC (MVTMISC). Results show that the MVT performs best for minimising cloud contamination, while the MVC is better for removing extreme scan angles. For the dual criteria algorithms, the MVTMISC performs best. The MVCMISC is better able to reduce scan angle bias for all land cover classes during the dry season, with the MVTMISC giving superior performance over the vegetative season. This work has implications for interpreting NDVI data in the context of famine early warning and developing biophysical descriptors of the African land surface at broad scales.
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- Seaquist, Jonathan
(författare)
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Mapping Primary Production for the West African Sahel with Satellite Data
- 2001
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A light Use Efficiency (LUE) model is developed that allows the mapping of total growing season Gross Primary Production (GPP) for the West African Sahel, using the Normalized Difference Vegetation Index (NDVI) together with other data. Image quality may be poor in monthly NDVI maximum value composites as shown by an improved geostatistical noise estimation technique. Quality may be improved by other compositing methods that use NOAA AVHRR-derived surface temperature and scan angle information to reduce residual cloud amount and off-nadir bias. These data are then used in conjunction with ancillary information to map total growing season GPP using the LUE approach, which reduces the complexities of plant growth to a simple parametric statement. To overcome the lack of ground data, NOAA AVHRR-derived CLAVR (CLouds from AVHRR) fields are used to derive several key parameters of energy balance, including Photosynthetically Active Radiation (PAR). Fraction of absorbed Photosynthetically Active Radiation (FPAR) is calculated from the NDVI and multiplied with PAR to yield Absorbed Photosynthetically Active Radiation (APAR). A water stress scalar is estimated with a two-layer hydrological model that treats separately bare soil evaporation and transpiration. This scalar is used to reduce potential photosynthetic capacity in the LUE model, as defined by the product of APAR and the potential growth efficiency. The absolute precision of GPP estimates decreases for dense vegetation while the relative precision increases. LUE primary production estimates are systematically higher for dense vegetation when compared to point estimates from the CENTURY model. This bias is not apparent when compared to previous work reported in the literature. The LUE model may be used to address issues related to desertification, food security, and climate change.
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