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Comparison of MODIS-Algorithms for Estimating Gross Primary Production from Satellite Data in semi-arid Africa

Nilsson, Martin LU (2013) In Student thesis series INES NGEK01 20122
Dept of Physical Geography and Ecosystem Science
Abstract
The climatic patterns of the world are changing and with them the spatial distribution of global terrestrial carbon; the food and fiber of the world and in itself an important factor in the changing climate. Knowledge of how the terrestrial carbon stock is changing, its distribution and quantity, is important in understanding how the patterns of the world are changing and large scale models using remotely sensed data have emerged for this purpose. This study compares four vegetation related MODIS (Moderate Resolution Imaging Spectroradiometer) products, derived from MODIS satellite data using algorithms which calculates the two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and the... (More)
The climatic patterns of the world are changing and with them the spatial distribution of global terrestrial carbon; the food and fiber of the world and in itself an important factor in the changing climate. Knowledge of how the terrestrial carbon stock is changing, its distribution and quantity, is important in understanding how the patterns of the world are changing and large scale models using remotely sensed data have emerged for this purpose. This study compares four vegetation related MODIS (Moderate Resolution Imaging Spectroradiometer) products, derived from MODIS satellite data using algorithms which calculates the two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and the two biophysical factors, Leaf Area Index (LAI) and absorbed Fraction of Photosynthetically Active Radiation (FPAR). The comparison is in their ability to estimate intra-annual variations of Gross Primary Production (GPP); this is done using the time-series data of quality screened eddy covariance (EC) Flux Tower stations from the Carbo Africa network as truth data.
The results show a modest agreement between the different vegetation metrics and EC Flux Tower derived GPP, with an overall average coefficient of determination (R2) of 0.63 for LAI, R2 of 0.51 for NDVI, R2 of 0.52 FPAR and a R2 of 0.49 for EVI, using all stations and years of data. When each station received the same weight, i.e. using the correlation of all observation for each station and then calculating the average, the overall correlation improved, still showing LAI as the best predictor of Flux Tower GPP with a R2 of 0.62, but with an improved EVI with a R2 of 0.61, while NDVI and FPAR had an R2 of 0.57 and 0.59 respectively. This result and the observed large variation in between stations, e.g. NDVI between an R2 of 0.62 and 0.83 for the station Demokeya compared to an R2 of 0.32 and 0.49 of NDVI for the station Tchizalamou, may indicate a site specific proficiency of the vegetation metrics. When the observations within the growing period were tested separately a strong decrease in correlation was observed, with an average R2 between 0.41 – 0.56 for all station and years and an average R2 between 0.36 – 0.45 for all sites using all observations for each station regardless of year, lending strength to the assumption that the non-vegetation period observations affect the correlation greatly.
The study concludes that up scaling of an intra-annual standardized major axis regression model based solely on the relationship between any of these metrics and Flux Tower estimated GPP is inadvisable due to the modest overall intra-annual agreement between the metrics and GPP. It is also concluded that since the vegetation metrics display site specific proficiency, models of GPP would benefit from site specific ancillary data that describes vegetation-limiting factors, e.g. water availability. (Less)
Abstract (Swedish)
Klimatmönstren världen över förändras och med dessa den globala distributionen och mängden av landbundet kol, dvs vegetationen som bland annat nyttjas som mat och fiber. Också I sig självt en viktig faktor i klimatets utveckling genom dess roll i energi- och vattenkretsloppen. Vetskap om kvantitet och distribution av landbundet kol och hur detta förändras är en viktigt del av arbetet i att förstå hur de globala mönster förändras, och för denna avsikt har bredskaliga modeller som nyttjar satellit data framtagits. Denna studie jämför fyra vegetations relaterade MODIS (Moderate Resolution Imaging Spectroradiometer) produkter, som erhålls från MODIS satellit data genom algoritmer som kalkylerar de två vegetation indexen, Normalized Vegetation... (More)
Klimatmönstren världen över förändras och med dessa den globala distributionen och mängden av landbundet kol, dvs vegetationen som bland annat nyttjas som mat och fiber. Också I sig självt en viktig faktor i klimatets utveckling genom dess roll i energi- och vattenkretsloppen. Vetskap om kvantitet och distribution av landbundet kol och hur detta förändras är en viktigt del av arbetet i att förstå hur de globala mönster förändras, och för denna avsikt har bredskaliga modeller som nyttjar satellit data framtagits. Denna studie jämför fyra vegetations relaterade MODIS (Moderate Resolution Imaging Spectroradiometer) produkter, som erhålls från MODIS satellit data genom algoritmer som kalkylerar de två vegetation indexen, Normalized Vegetation Index (NDVI) och Enhanced Vegetation Index (EVI), och de två biofysiska faktorerna, Leaf Area Index (LAI) och absorbed Fraction of Photosynthetically Active Radiation (FPAR). Deras förmåga att uppskatta variationen av den totala primär produktionen (Gross Primary Production, GPP) över året jämförs, genom tidsserier av eddy kovarians (EC) data från flux torn ur Carbo Africa nätverket, vars tidsserie-utveckling används som sanningspunkter varmot variationen från de motsvarande tidsserierna av algoritmerna jämförs.
Resultatet visar en blygsam korrelation mellan de olika vegetations algoritmernas reslutat och EC flux torn uppskattat GPP, med ett medel av determinationskoefficienter (R2) på 0.49 för EVI, 0.51 för NDVI,0.52 för FPAR och ett R2 på 0.63 för LAI, då data från alla stationer och år användes. När var station erhöll lika stor vikt, dvs då korrelationen kalkylerades för samtliga observationer från var station, varpå medel togs fram, förbättrades korrelationen över lag. fLAI visades fortfarande som den bästa prediktorn av flux-torns uppskattad GPP med ett R2 på 0.62, ett starkt förbättrat R2 för EVI på 0.61erhölls, medans NDVI och FPAR visa ett R2 på 0.57 respektive 0.59. Detta resultat och en stundtals stor variation mellan stationer, t.ex. NDVI med ett R2 mellan 0.62 och 0.83 för stationen Demokeya jämfört med ett R2 mellan 0.32 och 0.49 för NDVI och stationen Tchizalamou, visar kanske på plats specifika förmågor hos vegetations algoritmerna. När observationer inom vegetationsperioden testades separat observerades en starkt minskad korrelation, med ett medel R2 mellan 0.41 – 0.56 för alla stationer och år, och ett R2 mellan 0.36 – 0.45 för alla platser vid användning av samtliga observationer för varje station oberoende av år, vilket indikerar att observationerna utanför växtperioden har stort inflytande på korrelationen.
En slutsats av studien är att uppskalning av en standrardiserad storaxels regressions modell för inom annuell variation baserad endast på relationen mellan en av dessa vegetations algoritmer och flux-torns uppskattad GPP ej är att rekommendera med tanke på den blygsamma överrensstämmelsen mellan vegetationsalgoritmerna och flux torn uppskattat GPP. En annan slutsats är att eftersom dessa vegetations algoritmer uppvisar plats specifika förmågor skulle modeller av GPP ha fördel av plats specifik stöd-data som beskriver faktorer som begränsar vegetation, t.ex. vattentillgänglighet. (Less)
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author
Nilsson, Martin LU
supervisor
organization
alternative title
Jämförelse av MODIS-Algoritmer för uppskattning av brutto primärproduktion från satellitdata i semi-arida Afrika
course
NGEK01 20122
year
type
M2 - Bachelor Degree
subject
keywords
Leaf Area Index, LAI, Enhanced Vegetation Index, EVI, Normalized Vegetation Index, NDVI, Gross Primary Production, GPP, remote sensing, MODIS, FPAR, Fraction of Photosynthetically Active Radiation, Eddy Covariance, Africa, physical geography
publication/series
Student thesis series INES
report number
281
language
English
id
3878373
date added to LUP
2013-06-24 12:52:56
date last changed
2013-06-24 12:52:56
@misc{3878373,
  abstract     = {The climatic patterns of the world are changing and with them the spatial distribution of global terrestrial carbon; the food and fiber of the world and in itself an important factor in the changing climate. Knowledge of how the terrestrial carbon stock is changing, its distribution and quantity, is important in understanding how the patterns of the world are changing and large scale models using remotely sensed data have emerged for this purpose. This study compares four vegetation related MODIS (Moderate Resolution Imaging Spectroradiometer) products, derived from MODIS satellite data using algorithms which calculates the two vegetation indices, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), and the two biophysical factors, Leaf Area Index (LAI) and absorbed Fraction of Photosynthetically Active Radiation (FPAR). The comparison is in their ability to estimate intra-annual variations of Gross Primary Production (GPP); this is done using the time-series data of quality screened eddy covariance (EC) Flux Tower stations from the Carbo Africa network as truth data.
 The results show a modest agreement between the different vegetation metrics and EC Flux Tower derived GPP, with an overall average coefficient of determination (R2) of 0.63 for LAI, R2 of 0.51 for NDVI, R2 of 0.52 FPAR and a R2 of 0.49 for EVI, using all stations and years of data. When each station received the same weight, i.e. using the correlation of all observation for each station and then calculating the average, the overall correlation improved, still showing LAI as the best predictor of Flux Tower GPP with a R2 of 0.62, but with an improved EVI with a R2 of 0.61, while NDVI and FPAR had an R2 of 0.57 and 0.59 respectively. This result and the observed large variation in between stations, e.g. NDVI between an R2 of 0.62 and 0.83 for the station Demokeya compared to an R2 of 0.32 and 0.49 of NDVI for the station Tchizalamou, may indicate a site specific proficiency of the vegetation metrics. When the observations within the growing period were tested separately a strong decrease in correlation was observed, with an average R2 between 0.41 – 0.56 for all station and years and an average R2 between 0.36 – 0.45 for all sites using all observations for each station regardless of year, lending strength to the assumption that the non-vegetation period observations affect the correlation greatly. 
 The study concludes that up scaling of an intra-annual standardized major axis regression model based solely on the relationship between any of these metrics and Flux Tower estimated GPP is inadvisable due to the modest overall intra-annual agreement between the metrics and GPP. It is also concluded that since the vegetation metrics display site specific proficiency, models of GPP would benefit from site specific ancillary data that describes vegetation-limiting factors, e.g. water availability.},
  author       = {Nilsson, Martin},
  keyword      = {Leaf Area Index,LAI,Enhanced Vegetation Index,EVI,Normalized Vegetation Index,NDVI,Gross Primary Production,GPP,remote sensing,MODIS,FPAR,Fraction of Photosynthetically Active Radiation,Eddy Covariance,Africa,physical geography},
  language     = {eng},
  note         = {Student Paper},
  series       = {Student thesis series INES},
  title        = {Comparison of MODIS-Algorithms for Estimating Gross Primary Production from Satellite Data in semi-arid Africa},
  year         = {2013},
}