Abstract
The main purpose of this research is to model the spatial distribution
of carbon sequestration, CO2 absorption, and oxygen
production by trees within Isfahan city, Iran, in 2020. To quantify
carbon sequestration, we accessed a sample group of trees with measured
biophysical attributes. First, we calculated the biomass and carbon
sequestration of a tree using the allometric and photosynthesis
equations. Then, to model the spatial distribution of carbon
sequestration, we used Geographic Weighted Regression (GWR) method.
In this model, the
amount of calculated carbon
sequestration was the dependent variable, whereas the difference between
vegetation index of ΔExGR(Excess Green Plant Index minus Excess Red
Plant Index)from the Worldview image was
the independent variable.
Subsequently, the spatial distribution map of CO2absorption and oxygen production was generated. The total value of
annual carbon sequestration, CO2 absorption, and
O2 production was about 7704.22, 28274.502, and 20570.16
tons, respectively. The results showed that there was a strong
correlation between the ΔExGR index of the canopy with calculated
carbon. Integrating the ΔExGR index from a high-resolution image with
calculated carbon can contribute to developing a fast, accurate, and
low-cost method in estimating carbon sequestration and modeling its
spatial distribution in urban areas. In conclusion, the results of this
research can be implemented by land use planners in order to integrate
urban ecosystem service concept (i.e., carbon sequestration) in planning
process towards sustainability of the cities.
Key Word: Urban Ecosystem Service; Urban Ecology;
Climate Regulation Service; Green Infrastructure; Geographic Weighted
Regression (GWR);
Introduction
Increasing fossil fuel consumption in urban areas due to population
growth brings about a large amount of greenhouse gases emission into the
atmosphere (Houghton, 2001). Among greenhouse gases, carbon dioxide
plays a significant role in global warming (Lal, 2004; Peters, 2001;
Petit et al., 1999; Scott et al., 2002). In climate change studies,
urban areas attributes to emit a high proportion of carbon dioxide into
the atmosphere (Churkina, 2008, Grimm et al., 2008).
Urban green areas (lawns and trees) provide multitude of ecosystem
services like climate regulation and air purification. Carbon
sequestration service by vegetation cover is a well-recognized urban
ecosystem service mitigating the atmosphere’s carbon dioxide (Baró et
al., 2014, Gómez-Baggethun et al., 2013, Haase et al., 2014, Kiss et
al., 2015, Larondelle and Haase, 2013).
Carbon sequestration refers to a process in which the atmospheric carbon
dioxide is converted into organic compounds by photosynthesis process in
trees, plants, phytoplankton, and algae (Adams et al., 1990, Nanda et
al., 2016, NAYAK et al., 2020, Tornquist et al., 2009). Carbon
sequestration amount is related to the growth rate, species type, and
age of the tree (McPherson 1998). During the photosynthesis process,
CO2 stores in the form of cellulose. Also, the other
portion of the carbon transfers to the soil in organic form (Dwivedi et
al., 2009, Komiyama et al., 2005, MacFarlane, 2009, Miller et al., 2015,
Nowak and Crane, 2002, Nowak and Dwyer, 2007, Rowntree and Nowak, 1991,
Tang and Li, 2013, Ward Thompson et al., 2016, Zirkle et al., 2012). The
resources of carbon storage in an ecosystem include above-ground and
below-ground biomass, litter and plant residues, and soil organic matter
(Nowak and Crane, 2002, Sinoga et al., 2012).
Additionally, green spaces are considered as oxygen production resources
in urban areas. Oxygen production of plants directly related to the
carbon storage process. Estimating produced oxygen and carbon
sequestration by vegetation in an urban area is essential in dealing
with air pollution (Nowak et al., 2007).
Considering previous literature related to tree biomass estimation and
carbon sequestration, they can be divided into two general categories:
the first body of research is characterized by ground sampling or
measuring biological variables in a laboratory environment. Numerous
studies have been done in this area, which included: estimating carbon
storage in biomass in a forested area in Chile (Espinosa et al., 2005),
biomass estimation and leaf area index in mangrove forests of Japan (
Khan et al., 2005), estimating the biomass of ten tree species in
temperate forests of China (Wang, 2006), calculating soil biomass of
mangrove species in Brazil (Medeiros and Sampaio, 2008), and estimating
above-ground biomass and carbon sequestration in rainforests in Thailand
(Terakunpisut et al., 2007). Other studies in this field included the
research by Aguaron and McPherson, 2012, Bernal et al., 2018, Nowak et
al., 2013, Tor-ngern and Leksungnoen, 2020, Townsend ‐ Small and
Czimczik, 2010, Velasco et al., 2016. The second class of research is
organized based on satellite image and remote sensing techniques to
measure the biomass of the plants. For instance, estimating the amount
of biomass of Acacia species, silver cypress, berry tree using linear
regression model and applying Quick bird data and NDVI and DVI indices
in Isfahan, Iran (Hosseini et al., 2015). Mirrajabi and colleagues in
2016 estimated biomass of broadleaf and coniferous species using GeoEye
images in Chitgar park, Tehran, Iran (Mirrajabi et al., 2016). Amini and
Sadeghi benefitted from ALOS data and multiple regression equations to
estimate the amount of forest biomass (Aliabadi and Entezari, 2014). The
other research in this category can be found in related papers done by
Deng et al., 2011, Günlü et al., 2014, Hall et al., 2006, Raciti et al.,
2014, Strunk et al., 2014.
Previous studies show that allometric equations were used to determine
the above-ground and below-ground biomass of trees as well as to
estimate the amount of carbon storage.
These equations consider several parameters like diameter at breast
height (DBH), tree height, and wood density to estimate biomass in a
single tree unit (Aguaron and McPherson, 2012).
In this research, we used allometric equations to calculate
the biomass of a tree. Then to model the spatial distribution of carbon
sequestration, we integrated the results of the allometric equation with
the spectral data of the satellite image.
Another influential factor in the accuracy of estimating carbon storage
is statistical modeling methods. A wide range of methods, including
parametric, semi-parametric, and non-parametric methods, have been used
to quantify carbon storage using remote sensing (Wu et al., 2016)
In a cumulative body of research, different statistic methods were used
to integrate ground data (from the allometric equation) into
remotely-sensed data to quantify various characteristic of trees like a
canopy, biomass, and carbon storage (Carreiras et al., 2006, Cartus et
al., 2012, Cutler et al., 2012, Ghanbari Motlagh et al., 2020, Hamdan et
al., 2015, Lucas et al., 2010, Moradi et al., 2018, Mutanga et al.,
2012, Raciti et al., 2014, Shataee et al., 2012).
To the best of our knowledge, to estimate carbon sequestration, the
previous studies measured several trees as samples in a plot to examine
the relationship between ground data and satellite data through the
linear regression (Deng et al., 2011, Mirrajabi et al., 2016, Raciti et
al., 2014). However, in this study, for the first time, we used
geographic weighted regression (GWR) to create a relationship between
spectral data of each tree (canopy reflection (drip line)) with the
calculated ground biomass. It was assumed that integrating the remotely
sensed data with the measured biomass in the GWR model can provide
reliable results, contributing to addressing the spatial distribution of
carbon sequestration as well as estimating the amount of carbon
sequestration.
Considering the background previously discussed, therefore, this article
aims to develop a model to analyze the spatial distribution of carbon
sequestration, CO2 absorption, and O2production within Isfahan city. To achieve this goal, we pursued the
following sub-objectives:
- Calculating the above-ground and below-ground biomass of sampled trees
using the allometric equations.
- Estimating carbon sequestration of sampled trees using photosynthesis
equation.
- Processing the satellite image with different spectral variables to
recognize the most appropriate index.
- Creating a GWR model to integrate the spectral data of each tree with
carbon sequestration of the tree.
- Providing the spatial distribution map of carbon sequestration,
CO2 absorption, and oxygen production.