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The usefulness of coarse resolution satellite sensor data for identification of biomes in Kenya

Cronquist, Lova and Elg, Sofia (2000) In Lunds universitets Naturgeografiska institution - Seminarieuppsatser
Dept of Physical Geography and Ecosystem Science
Abstract
Vegetation phenology (seasonal rhythm) is closely related to seasonal dynamics in the lower
atmosphere and is therefore an important element in global climatic models and vegetation
monitoring. Remote sensing is the primary means by which we can observe the dynamic
characters of the earth's biosphere. Description and mapping of the natural vegetation, and its
interpretation in terms of soil, climatic and other information, are important in land use
planning, particularly in areas where detailed information on soil and climate is insufficient,
as is often the case in East Africa. The aim of this study is to investigate the usefulness of
coarse resolution (1 km) data from the NOAA AVHRR sensor for the identification and
separation of... (More)
Vegetation phenology (seasonal rhythm) is closely related to seasonal dynamics in the lower
atmosphere and is therefore an important element in global climatic models and vegetation
monitoring. Remote sensing is the primary means by which we can observe the dynamic
characters of the earth's biosphere. Description and mapping of the natural vegetation, and its
interpretation in terms of soil, climatic and other information, are important in land use
planning, particularly in areas where detailed information on soil and climate is insufficient,
as is often the case in East Africa. The aim of this study is to investigate the usefulness of
coarse resolution (1 km) data from the NOAA AVHRR sensor for the identification and
separation of principal vegetation communities (biomes) and to define seasonal rhythms for
these communities.
The Advanced Very High Resolution Radiometer (AVHRR) sensor is carried by a series of
meteorological satellites operated by the National Oceanic and Atmospheric Administration
(NOAA). The primary advantage of the AVHRR is the frequent temporal (daily) coverage
over large areas, which allows a better opportunity to obtain cloud free coverage during
important phenological stages. Normalised Difference Vegetation Index (NDVI) data derived
from AVHRR offer a means of evaluating phenological characteristics, such as flowering and
senescence. A total of 53 AVHRR NDVI composites covering Kenya between 1 April, 1992
and 21 September, 1993, were obtained from National Aeronautics and Space Administration
(NASA).
The results show that some essential biomes such as grassland and dry forest were possible to
identify and separate. Phenological characteristics, such as beginning and end of growing
season were only possible to extract for grassland and dry forest. Even though cloud detection
and interpolation methods were applied on the composites, the influence of clouds degraded
the image quality and it was therefore difficult to interpret the NDVI profiles.
This study indicates the potential usefulness of 1-km NOAA data for monitoring vegetation
phenological cycles and has demonstrated that atmospheric effects (e.g. clouds) must be
understood quantitatively for specific inventory purposes. (Less)
Abstract (Swedish)
Populärvetenskaplig sammanfattning: Beskrivning och kartläggning av vegetationen samt tolkning av jord, klimat och annan information är mycket viktigt för förståelse och planering för användande av naturresurser. Fenologi dvs växters årliga variationer, är nära korrelerad med dynamiken i atmosfären. Information om vegetationens fenologi på regional och global nivå är därför en viktigt komponent för globala klimatmodeller samt för vegetationsövervakning. Fjärranalys är idag det primära sättet för övervakning av jordytan. Inom fjärranalysområdet har skapats några av de mest heltäckande globala terrestra databaserna. Dessa data är extraherade från vädersatelliter och har fördelen att vara billiga samt att ha en hög temporal upplösning. Syftet... (More)
Populärvetenskaplig sammanfattning: Beskrivning och kartläggning av vegetationen samt tolkning av jord, klimat och annan information är mycket viktigt för förståelse och planering för användande av naturresurser. Fenologi dvs växters årliga variationer, är nära korrelerad med dynamiken i atmosfären. Information om vegetationens fenologi på regional och global nivå är därför en viktigt komponent för globala klimatmodeller samt för vegetationsövervakning. Fjärranalys är idag det primära sättet för övervakning av jordytan. Inom fjärranalysområdet har skapats några av de mest heltäckande globala terrestra databaserna. Dessa data är extraherade från vädersatelliter och har fördelen att vara billiga samt att ha en hög temporal upplösning. Syftet med denna studie är att testa metoder för klassificering av vegetation, baserad på analys av grovupplösande satellitdata.

I denna studie har scener från NOAA-satellitens AVHRR-sensor under tidsperioden april 1992 till och med september 1993 använts för vegetationsklassificering av områden kring Mount Kenya i Kenya. Vegetationsindex Normalised Difference Vegetation Index (NDVI) har tagits fram från AVHRR och ger möjlighet till utvärdering av fenologiska mått såsom lövsprickning, blomning och lövfällning. AVHRR sensorns främsta fördel är dess höga temporala täckning över stora områden vilket underlättar möjligheten att erhålla molnfria data under viktiga fenologiska händelser. Trots det störs NDVI data av moln och för molnmaskning används metoden APOLLO utvecklad av Saunders & Kriebel (1988).

Området kring Mount Kenya är ett av de molnigaste på den afrikanska kontinenten, vilket har påverkat studiens resultat. Resultatet visar att det är möjligt att skilja mellan stora huvudsakliga vegetationstyper som gräsmark, torr skog samt fuktig skog. Fenologiska faktorer (exempelvis början och slutet på en växtsäsong) för vegetationsklassificering, har endast extraherats för gräsmark och torr skog. Övriga vegetationsområden bedömdes vara så påverkade av moln att satellitdata inte ansågs ge en representativ bild av verkligheten.

Studien poängterar vikten av att atmosfäriska effekter, såsom moln, måste förstås och tas i beaktande inför varje tillämpning av NOAA AVHRR data för vegetationsklassificering. (Less)
Please use this url to cite or link to this publication:
author
Cronquist, Lova and Elg, Sofia
supervisor
organization
alternative title
Användbarheten av grovupplösande satellitdata för identifikation av biom i Kenya
year
type
H1 - Master's Degree (One Year)
subject
keywords
geomorphology, physical geography, mapping, remote sensing, climate models, vegetation monitoring, seasonal dynamics, pedology, cartography, climatology, naturgeografi, geomorfologi, marklära, kartografi, klimatologi
publication/series
Lunds universitets Naturgeografiska institution - Seminarieuppsatser
report number
72
funder
SIDA, Minor Field Study programme (MFS)
language
English
additional info
Dr. Francis Gichuki at the Department of Agricultural Engineering at
University of Nairobi, Kenya.
id
1332842
date added to LUP
2005-10-27 00:00:00
date last changed
2011-11-30 12:42:57
@misc{1332842,
  abstract     = {{Vegetation phenology (seasonal rhythm) is closely related to seasonal dynamics in the lower
atmosphere and is therefore an important element in global climatic models and vegetation
monitoring. Remote sensing is the primary means by which we can observe the dynamic
characters of the earth's biosphere. Description and mapping of the natural vegetation, and its
interpretation in terms of soil, climatic and other information, are important in land use
planning, particularly in areas where detailed information on soil and climate is insufficient,
as is often the case in East Africa. The aim of this study is to investigate the usefulness of
coarse resolution (1 km) data from the NOAA AVHRR sensor for the identification and
separation of principal vegetation communities (biomes) and to define seasonal rhythms for
these communities.
The Advanced Very High Resolution Radiometer (AVHRR) sensor is carried by a series of
meteorological satellites operated by the National Oceanic and Atmospheric Administration
(NOAA). The primary advantage of the AVHRR is the frequent temporal (daily) coverage
over large areas, which allows a better opportunity to obtain cloud free coverage during
important phenological stages. Normalised Difference Vegetation Index (NDVI) data derived
from AVHRR offer a means of evaluating phenological characteristics, such as flowering and
senescence. A total of 53 AVHRR NDVI composites covering Kenya between 1 April, 1992
and 21 September, 1993, were obtained from National Aeronautics and Space Administration
(NASA).
The results show that some essential biomes such as grassland and dry forest were possible to
identify and separate. Phenological characteristics, such as beginning and end of growing
season were only possible to extract for grassland and dry forest. Even though cloud detection
and interpolation methods were applied on the composites, the influence of clouds degraded
the image quality and it was therefore difficult to interpret the NDVI profiles.
This study indicates the potential usefulness of 1-km NOAA data for monitoring vegetation
phenological cycles and has demonstrated that atmospheric effects (e.g. clouds) must be
understood quantitatively for specific inventory purposes.}},
  author       = {{Cronquist, Lova and Elg, Sofia}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Lunds universitets Naturgeografiska institution - Seminarieuppsatser}},
  title        = {{The usefulness of coarse resolution satellite sensor data for identification of biomes in Kenya}},
  year         = {{2000}},
}