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Modeling Land-Cover using Bio-Climate Variables

Asiamah-Yeboah, Daniel Kwaku and Kofi Nyarko, Edward (2013) MASM01 20131
Mathematical Statistics
Abstract (Swedish)
MODELING LAND-COVER USING BIO-CLIMATE VARIABLES
Vegetation (land-cover) is an inherent part of the climate system (M.-J. Gaillard et al.
2010). Natural, primarily climate-driven, vegetation and ecosystem processes interact
with human land-use to determine vegetation patterns, stand structure and their
development through time (e.g. Vitousek et al., 1997). The resulting land surface
properties feedback on climate by modulating exchanges of energy, water vapour and
greenhouse gases with the atmosphere.
Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) was
introduced by M.-J. et al., (2010) as a new method to discuss issues related to pollenbased
reconstruction of the past land-cover. REVEALS requires raw pollen... (More)
MODELING LAND-COVER USING BIO-CLIMATE VARIABLES
Vegetation (land-cover) is an inherent part of the climate system (M.-J. Gaillard et al.
2010). Natural, primarily climate-driven, vegetation and ecosystem processes interact
with human land-use to determine vegetation patterns, stand structure and their
development through time (e.g. Vitousek et al., 1997). The resulting land surface
properties feedback on climate by modulating exchanges of energy, water vapour and
greenhouse gases with the atmosphere.
Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) was
introduced by M.-J. et al., (2010) as a new method to discuss issues related to pollenbased
reconstruction of the past land-cover. REVEALS requires raw pollen counts, site
radius, pollen productivity estimates (PPEs), and fall speed of pollen (FS) to estimate
vegetation cover in percentages, (M.-J. et al., (2010)). The REVEALS model-based landcover
reconstruction has been demonstrated to provide better estimates of regional
vegetation/land-cover changes than the traditional use of pollen percentages.
The LPJ (Lund Potsdam Jena) – GUESS (General Ecosystem Simulator) model (LPJGUESS,
Smith et al., 2001) is a dynamic, process-based vegetation model optimized for
application across a regional grid that simulates vegetation dynamics based on climate
data input. From both REVEALS and LPJ-GUESS datasets, we have Plant Functional
Types (PFTs) and Bio-climate variables.
The aim is to use multiple linear regressions to find the relationship between these PFTs
and the Bio-climate variables using the REVEALS dataset. Further, we will predict PFT
values using the regression models and the REVEALS dataset and compare them to those
in the LPJ-GUESS dataset. The PFTs will then be grouped into three different land-cover
types: Ever-green canopy, Summer-green canopy and Open-land. Then these land-covers
will be modelled using the bio-climate variables to provide a new way of modeling landcover
or vegetation of the past.
Conclusions
It has been seen that bio-climate variables are important to the growth of plants thereby
helping plants to produce pollens. Naturally, one should expect plants to grow well when
they have their favourable climatic conditions. These include, soil water content,
temperature, precipitation among others. Given a reliable and well-measured data of bioclimate
variables and plant functional types, it is possible to use regression analysis to
obtain a linear relationship between these plant functional types and the bio-climate
variables. Consequently, it is feasible to model land-cover when we have bio-climate
variables and plant functional types using multiple linear regressions. (Less)
Please use this url to cite or link to this publication:
author
Asiamah-Yeboah, Daniel Kwaku and Kofi Nyarko, Edward
supervisor
organization
course
MASM01 20131
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
3458228
date added to LUP
2013-02-05 16:09:10
date last changed
2019-11-26 06:45:39
@misc{3458228,
  abstract     = {MODELING LAND-COVER USING BIO-CLIMATE VARIABLES
Vegetation (land-cover) is an inherent part of the climate system (M.-J. Gaillard et al.
2010). Natural, primarily climate-driven, vegetation and ecosystem processes interact
with human land-use to determine vegetation patterns, stand structure and their
development through time (e.g. Vitousek et al., 1997). The resulting land surface
properties feedback on climate by modulating exchanges of energy, water vapour and
greenhouse gases with the atmosphere.
Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) was
introduced by M.-J. et al., (2010) as a new method to discuss issues related to pollenbased
reconstruction of the past land-cover. REVEALS requires raw pollen counts, site
radius, pollen productivity estimates (PPEs), and fall speed of pollen (FS) to estimate
vegetation cover in percentages, (M.-J. et al., (2010)). The REVEALS model-based landcover
reconstruction has been demonstrated to provide better estimates of regional
vegetation/land-cover changes than the traditional use of pollen percentages.
The LPJ (Lund Potsdam Jena) – GUESS (General Ecosystem Simulator) model (LPJGUESS,
Smith et al., 2001) is a dynamic, process-based vegetation model optimized for
application across a regional grid that simulates vegetation dynamics based on climate
data input. From both REVEALS and LPJ-GUESS datasets, we have Plant Functional
Types (PFTs) and Bio-climate variables.
The aim is to use multiple linear regressions to find the relationship between these PFTs
and the Bio-climate variables using the REVEALS dataset. Further, we will predict PFT
values using the regression models and the REVEALS dataset and compare them to those
in the LPJ-GUESS dataset. The PFTs will then be grouped into three different land-cover
types: Ever-green canopy, Summer-green canopy and Open-land. Then these land-covers
will be modelled using the bio-climate variables to provide a new way of modeling landcover
or vegetation of the past.
Conclusions
It has been seen that bio-climate variables are important to the growth of plants thereby
helping plants to produce pollens. Naturally, one should expect plants to grow well when
they have their favourable climatic conditions. These include, soil water content,
temperature, precipitation among others. Given a reliable and well-measured data of bioclimate
variables and plant functional types, it is possible to use regression analysis to
obtain a linear relationship between these plant functional types and the bio-climate
variables. Consequently, it is feasible to model land-cover when we have bio-climate
variables and plant functional types using multiple linear regressions.},
  author       = {Asiamah-Yeboah, Daniel Kwaku and Kofi Nyarko, Edward},
  language     = {eng},
  note         = {Student Paper},
  title        = {Modeling Land-Cover using Bio-Climate Variables},
  year         = {2013},
}