eraction with the environment, has led to increased concerns about the impact of such disruption on major areas of sustainable development. This has resulted in various innovations in technology, policy and forged alliances at regional and international scales in an effort to reduce humans’ impact on climate. Forests provide a suitable option for reducing the net amount of carbon dioxide in the atmosphere by acting as carbon sinks, thereby forming one part of a more complete solution for combating climate change. At the same time, forests are also sensitive to changes in climate, making sustainable forest management a critical component of present and future climate change strategies. This paper examines the contribution of geospatial technologies in supporting sustainable forest management, emphasizing its use in the classification of forests, estimation of their structure, detecting change and modeling of carbon stocks.

1. 1
Climate Change and Forest Management: Adaptation
of Geospatial Technologies
Ron Mahabir
Department of Geography and Geoinformation Science
George Mason University
Fairfax, VA, 22030
rmahabir@gmu.edu
Ranjay M. Shrestha
Department of Geography and Geoinformation Science
George Mason University
Fairfax, VA, 22030
rshresth@gmu.edu
Abstract—Global climate disruption, mainly due to human
interaction with the environment, has led to increased concerns
about the impact of such disruption on major areas of sustainable
development. This has resulted in various innovations in technology,
policy and forged alliances at regional and international scales in
an effort to reduce humans’ impact on climate. Forests provide a
suitable option for reducing the net amount of carbon dioxide in the
atmosphere by acting as carbon sinks, thereby forming one part of
a more complete solution for combating climate change. At the same
time, forests are also sensitive to changes in climate, making
sustainable forest management a critical component of present and
future climate change strategies. This paper examines the
contribution of geospatial technologies in supporting sustainable
forest management, emphasizing its use in the classification of
forests, estimation of their structure, detecting change and modeling
of carbon stocks.
Keywords— Sustainable Forest Management; Climate Change;
Geospatial Technology
I. INTRODUCTION: CLIMATE CHANGE & IMPACTS
Over the last century there has been increased concern
about changes in climate and its impact on human society and
the environment. These changes are largely due to
anthropogenic forces, accelerated by both economic activity
and population growth[1]. The fifth IPCC report predicts an
increase in global mean temperature by 1.5 to 2o
C by 2100,
leading to significant changes in precipitation patterns, large
scale flooding and water shortages worldwide, with
populations being disproportionately affected [2]. These
impacts highlight the critical need to respond to the problem
of climate change, which includes the management of
activities that influence climate. A major subset of these
activities looks at those which increase the emission of
greenhouse gases such as carbon dioxide. Major contributors
in this area are the burning of fossil fuels and the unmanaged
clearing of forest for human development. These problems
are largely spatial in nature, occurring at a particular location
and with some areas being affected more than others. Such
problems lend themselves favorable for the use of geospatial
technologies such as geographical information systems (GIS)
and remote sensing. In this paper we highlight these uses with
the overall goal of showing how such tools continue to be
beneficial in aiding better forest management practices in
order to help reduce the impact of climate change. Section 2
highlights the relationship between forest and climate, along
with suggesting the need for more sustainable forest
management practices as a means of reducing net carbon
emissions. Section 3 introduces the use of geospatial
technologies as an enabling means for achieving sustainable
forest management, while section 4 concludes this paper.
II. THE INTERPLAY BETWEEN CLIMATE CHANGE AND FOREST
Forests and climate are closely linked. On the one hand,
forests help moderate the amount of atmospheric carbon
dioxide by converting it to the element carbon during
photosynthesis, and storing it as wood and vegetation. On the
other hand, changes in climate continue to stress forests
through higher mean annual temperatures, altered
precipitation patterns and more frequent and extreme weather
events [3]. According to Solomon (2007)[4], these events are
expected to increase in frequency and magnitude in
forthcoming years, further placing added pressure on the
environment. Already, changes in climate during the past
100,000 years have affected the geographic distribution of
forest worldwide. The next 100-200 years are expected to see
changes of a comparable magnitude, but at a faster rate as a
consequence of the greenhouse effect [5].
Although variations exist in the amount of carbon stored
in each tree species, it’s generally accepted that trees are made
up of about 20% of carbon by weight. This, together with
dead wood and organic matter in forests soils account for the
largest sources of carbon deposits worldwide[6]. It is
estimated that the world's forests and forest soils currently
store more than one trillion tons of carbon, twice the amount
found in the atmosphere [3]. In the tropics, forests store
almost 375 billion tons of carbon, with Boreal forest
sequestering the largest amount, close to 703 billion tons [7]
and account for about 30-35% of the world’s carbon stocks
[8]. Over the past few decades Boreal forests have drawn
considerable attention by climate change scientist, not only
because of their large carbon sink capacity, but also because
future climate change scenarios suggest that the largest
temperature increases will occur in the Northern Hemisphere
upper latitudes where these forests are located. In Canada and
Russia, there has already been a noticeable increase in major
4th International Conference on Agro-Geoinformatics, 20-24 July 2015, Istanbul, Turkey

2. 2
winter and spring warming over the past few decades of 2-
3°C affecting the health of forests [9]. Simulations by Koven
(2013)[10] further suggest that as temperatures rise, forest
will increasingly shift more northwards and shrink in size.
The net effect of this movement is a loss in the retention
capacity of Boreal forests to store carbon, which could further
accelerate climate impacts.
Sustainable forest management (discussed further in
Section 3) provides a suitable option for reducing net carbon
emissions. As Newell (2000)[11] suggests, this provides a
low cost alternative compared to other strategies such as
storing carbon underground. However, if forests continue to
be destroyed or over-harvested and burned, they become
sources of carbon dioxide and therefore, become a problem
rather than a solution. Already, the destruction of forest is
adding almost six billion tons of carbon dioxide into the
atmosphere each year [3]. Almost 15-20% of this carbon
comes from human-induced deforestation [12]. Experts
estimate that global carbon retention resulting from reduced
deforestation, increased forest re-growth and more agro-
forestry and plantations could store as much as 15% of carbon
emissions generated from fossil fuels over the next 50 years
[3]. Additionally, in the tropics where vegetation grows at a
much faster rate, the planting of trees can sequester larger
amounts of carbon from the air. In light of the various
situations that are taking place due to climate change and
which could be intensified further to an unknown potential,
there is no doubt that sustainable forest management practices
will assist in reducing net carbon emissions.
III. GEOSPATIAL TECHNOLOGIES FOR SUSTAINABLE FOREST
MANAGEMENT
Sustainable forest management (SFM) can be defined as
activities designed to maintain and enhance the long term
health of forest ecosystems, while providing ecological,
economic, social and cultural opportunities for the benefit of
present and future generations [13]. Since forests present a
favorable option to the successful reduction of atmospheric
carbon, activities encouraging SFM are critical in reducing
the impacts of present and future climate change. One option
aiding the sustainable management of forests is the use of
geospatial technologies such as GIS and remote sensing.
Broadly defined, GIS refers to systems used for the capture,
storage, dissemination and analysis of geographic data and
the converting of this data into knowledge. Remote sensing,
other the hand, refers to the capturing of spatial information
from sensors positioned remotely above features of interest.
The distance between features and sensors can range from a
few meters (e.g. laser scanners collecting 3D information on
internal structure of forest canopy) to several thousand
kilometers (e.g. geostationary satellites orbiting the Earth’s
surface). Both technologies work hand in hand as remote
sensing can be a source of GIS data, while GIS data can be
used for both calibrating and validating remotely sensed data
for improving accuracy It is worth noting that although many
forms of data can be considered remotely sensed in nature
(e.g. sonar, sound and air quality measurements), more often
than not, images used for forest management. Geospatial
technologies have been used extensively in the literature to
study forests. Their contributions can be grouped into to four
broad areas as it pertains to SFM practices: classification of
forest cover type, estimation of forest structure, forest change
detection and forest modeling [13]. Each of these areas is
discussed in brief in the proceeding subsections, with the
overall goal of identifying how these technologies have been
used.
3.1. Classification of forest cover type
Information on the amount of carbon stored in different
forest species and their geographic distribution plays a key
role in informing more SFM strategies. The ability to
discriminate between different forest types may not be very
tasking for small areas using traditional survey-based
approaches. However, as the geographic extent of the forest
area becomes larger (national, regional or global),
discrimination becomes an increasingly challenging task
[14]. Remote sensing provides a unique opportunity for
distinguishing between different forest types utilizing their
spectral response patterns. This information is also captured
for large areas and at an affordable cost when compared to
field surveys covering a similar geographic extent. Using this
data, scientists try to identify a unique spectral signature (a
fingerprint) for each forest species. Such information can
then be fed into a GIS for mapping the extent and spatial
distribution of the various forest types across captured image
scenes. Using information on the estimated carbon storage for
each tree species, the total carbon storage for an area can then
be quantified. Figure 1 shows the spectral reflectance of ten
different tree species collected from the WorldView-2 sensor.
In 1(a), an extended view of the spectrum shows greater
separation between tree species at larger wavelengths, about
0.75 to 0.90 micrometers. Whereas, in 1(b), only the visible
portion of the electromagnetic spectrum is shown, with most
tree species showing poor separability within this range.
Typically, high spatial and spectral resolution data is better
for discriminating between tree species, however, this comes
at a higher cost.
Presently, there exist a wide variety of sensors collecting
remote sensing information at a range of spatial and spectral
resolutions, allowing various customizable products for forest
managers. Hyperspectral sensors such as Hyperion, for
example, collect information for several hundred small
discrete spectral bands at a spatial resolution of 30m, and are
free for use. Others sensors such as Quickbird collect
information for eight multispectral and one panchromatic
band at spatial resolutions of 2.4m and lower. This data can be
purchased at a cost of $17/km2
for archive imagery and
$23/km2
for newer image scenes [15]. Forest managers
therefore are tasked with finding the right sensor data meeting
the cost and coverage demands necessary for their specific
needs.

3. 3
Fig. 1. Mean spectral signatures of the 10 tree species derived from the 8
WorldView-2 Bands [14]
3.2. Estimation of forest structure
Knowledge of the different forest species, as discussed in
section 3.1, are important for maintaining an up-to-date
inventory of forests, as well as a mandate of the United
Nations Framework Convention for Climate Change for
devising cost-effective ways for reducing greenhouse gas
emissions [16]. However, in order to be able to better
discriminate between different types of forest and to quantify
the amount of carbon stored in them, more information is
needed. The most accurate way to measure the carbon content
of a forest would be to cut down all the trees, dry them out,
and weigh them. From the derived biomass collected, the
carbon content can then be quantified. However, this method
entails the destruction of all forests, which clearly goes
against the principle of SFM. Another option would be to
measure several forest structural attributes such as the size
and height of trees and then use statistical formulas to
estimate the amount of carbon in a region. This process is
known as allometry. Allometric relationships relate plant
biomass to one or more variables that reflect size of
vegetation [17]. Allometric approaches provide a more
accurate estimation of carbon stored in vegetation, as
different parts of tree species vary in the amount of carbon
content they store [17]. However, for large geographic areas,
this approach may be infeasible because of the large amount
of resources required for mapping each tree in detail [14].
Among the various sensor technologies used to collect
information on forest structure is the use of LiDAR (Light
Detection and Ranging) remote sensing. LiDAR is an active
sensor used to collect 3D point cloud data about objects on
the earth’s surface. LiDAR works by bombarding an object
with pulses of light and retrieving feedback either in the form
of pulses and/or signal waves [18]. Waveforms are especially
useful in forestry applications because they can highlight the
various transitions taking place between forest and
understory. Generally speaking, the denser the point cloud
representing an object, the better the discernment of the
object because of the increased detail captured, resulting in
the greater use of resources (e.g. computing power) required
to process this information. A balance is usually struck
between the density of the point cloud used and application
requirements. Forest structural attributes such as canopy
height, stand volume and basal area can be directly retrieved
from LiDAR data [18]. This information can then be used to
model above ground biomass and further, to estimate carbon
stock capacity. Structural attributes of forests provide new
opportunities for enhanced forest monitoring, management
and planning [19]. In Sherrill et al. (2008)[20], researchers
used fine scale LiDAR to predict various components of
forest structure and carbon stocks in various landscapes of
subalpine coniferous forest. This work resulted in various
LiDAR-derived biomass and related estimates, which were
then used to parameterize decision support tools for the
analysis of carbon cycle impacts as part of the North
American Carbon Program. Other similar studies using
LiDAR data to collect structural attributes of forests include
work by Jusoff and Ibrahim (2009) [21] and Goodenough et
al. (2002)[22]. Figure 2 shows LiDAR coverage for parts of
the Puruvian Amazon, highlighting sources of variation in
forest structure discernable using this technology. A review
of other LiDAR studies for varying forest landscapes can be
found in Drake et al. (2002)[23]. These studies and others
confirm the paramount value of using LiDAR and forest
structural characteristics to improve global estimates of
carbon stocks.
Fig. 2. Sources of variation in forest canopy height detected with high-
resolution Carnegie Airborne Observatory LiDAR in the Peruvian Amazon:
(A) artisanal gold mining; (B) selective logging; (C) deforestation for cattle
ranching; (D) infrastructural development in towns, cities, and supporting
land uses; and (E) alluvial and geologic substrate. White bars indicate a
distance of 0.5 km in each example image [24].

4. 4
3.3. Forest change detection
With continuing degradation of forests, it becomes
increasingly important to monitor them to ensure their
successful growth and survival. This involves the multidate
mapping of forest and examining change for both individual
forest species and for forests in general. As discussed in
Section 3.1, forest can be mapped from remotely sensed
imagery at a range of different spatial resolutions. High spatial
resolution provides greater granularity and therefore can
represent an object more accurately in images. As the
resolution becomes increasingly coarser, objects smaller than
the resolution being used, become averaged together with
other surrounding objects, leading to a loss in thematic
mapping accuracy of features [16]. In the case of forests, the
definition of what a forest is should be used to guide the choice
of spatial resolution data for mapping [25]. Another important
consideration when mapping forests is the temporal period
between successive mapping events. For forested areas that
are being threatened to a larger extent, for example, due to
issues such as fires and clearing for human development, a
smaller time scale is needed. Timely information is especially
important for informing policy and making sound decisions
[26]. The large constellation of satellites orbiting the earth
provides a variety of options for mapping forests at a range of
different temporal resolutions. For example, Worldview-2
collects information over the same geographic area every one
to three days, while Landsat 8 collects information every 16
days. Other sensors such MODIS (Moderate-resolution
Imaging Spectroradiometer), onboard the Terra and Aqua
space platforms, collect 4 images every day. Usually, airborne
sensors onboard light aircrafts or mounted on unmanned aerial
vehicles can be flown on demand to collect information at
much higher temporal resolutions. These systems can also be
flown below cloud cover to capture a more complete view of
the landscape. This is especially important in the tropics and
for high latitude areas where the presence of cloud cover is
very frequent in satellite imagery [26], [27]. However, these
are a much more expensive options and should be used in
select cases.
Although data from remotely sensed sources have existed
since the early 1800s, it was not until the launch of the first
Landsat mission in 1972 did large scale and timelier mapping
of earth resources take place. Landsat still continues to be the
longest spaceborne mission today, with an archive containing
over 40 years of data [16]. This is beneficial for observing
changes in forest and for forest modeling, as will be discussed
the next section. Figure 3 shows forested areas for two
periods, 1975 and 2001, for Rondônia in the Brazilian
Amazon collected from Landsat imagery. As can be seen in
this figure, there has been substantial loss of forest in this
region, owing mainly to the conversion of forests to cropland,
pasture and built up areas. Traditionally, the mapping of forest
from such data would be done using photointerpretation, with
skilled persons laboring over light tables and tracing
noticeable changes in the extent of forest between the two
image dates. This was not only expensive but both time
consuming and prone to human error as well. Newer methods
utilize computer-based algorithms and GIS software for
classifying images and detecting changes between image
dates. Two major classes of classification algorithms are
typically used: pixel-based and object based methods. In
pixel-based methods, the spectral values of pixels are used to
identify features in imagery based on the grouping of pixels
according to different statistical rules. Object based methods,
on the other hand, utilize other components similar to those
used in computer vision such as tone, texture, orientation and
size to identify objects in imagery. A review of various studies
using these techniques for detecting forest cover and by
extension land use land cover changes can be found in Desclee
et al. (2006)[28] and Hese & Schmullius (2005) [29]. The
availability of automated and semi-automated algorithms and
the many sources of remotely sensed data that exist today have
substantially reduced the time needed to detect changes in the
spatial extent and health of forest. This has allowed forest
managers to react to change in a much more efficient and
effective manner and to prevent the substantial loss of forest.
Fig. 3. An image of Rondônia in the Brazilian Amazon shows little human
impact on the forest in 1975. By 2001, large sections of the forest had been
cleared, creating bright patches on the Landsat image [12].
3.4. Forest Modeling
Models allow generalizations from sites to regions and can
be used to predict, investigate, or simulate effects over a wide
range of conditions and scales[13]. Such approaches are
critical for forest management planning, which involves
making forecasts about the future of forests relative to
different management scenarios, including economic, social
or environmental impacts as a result of proposed activity. This
is even more so important given that forests can take decades
to hundreds of years to become established [30]. For example,
models can be used to predict the spread of forest fires based
on different factors such as the type and density of vegetation,
the wind speed and direction and the spotting phenomenon
(transfer of fire to new areas) as was done by Alexandridis et
al. (2008) [31] for the Island of Spetses in Greece. Such
factors are usually analyzed using GIS, allowing the
possibility for an infinite number of questions to be answered,
along with gaining greater understanding of the dynamics of
forests and their interaction with human and natural
ecosystems. Using GIS, information on both the geographic
and attribute nature of forest stands can be stored and linked
to planning models. This allows forest managers to
effectively add important temporal and spatial dimensions to
the management planning process. Within the limits of the
inventory and model, the manager can then map what the
forest will look like in 5,10, 25, or even 100 years in the
future, providing a quantitative estimate of carbon retention
for an area. Such information is important for making

5. 5
decisions and developing more effective policies [32]. Figure
4 show forest cover maps for the Luangprabang province in
Laos. Both the 1993 and 2000 Landsat images were used to
predict the 2007 forest cover map for this region based on the
business-as-usual climate change scenario.
Fig. 4. Forest cover maps for Luangprabang province [33].
Over the past decade there has been increase interest and
use of models in forest applications. Ludeke et al. (1990)[34],
for example, used a logistic regression model to determine
the natural and cultural landscape variables most closely
associated with deforestation. GIS was used to verify the
spatial and statistical relationship between selected variables
known to cause deforestation to help predict the most
susceptible areas in the Cordillera Nombre de Dios region of
Honduras. In another study, Rorstad et al. (2010)[35]
combined GIS with an existing forest model to improve
estimates of the supply of harvest residues in a forested area
of Southern Norway, more than 40,000 hectares in size. In
this study, different environmental and economic constraints
were taken into consideration. The results of this research
showed that the energy utilization of harvest residues, for the
study area used, was not profitable below an energy price of
about €3.2/GJ (NOK 0.10 /kWh) when the distance from
roadside to industry is 20 km. Forest modeling applications
have also been used for investigating carbon source/sink
relationships. Yarie (1996)[36], for instance, incorporated
GIS into a forest dynamics model for the calculation of
carbon source/sink relationships for large land areas with
different tree composition. In this model, differences in
carbon dynamics due to potentially important factors like
topography, soils, differences in climate, variation in
vegetation community types, and the community age
structure within a region were considered. Results for both
carbon release and capture were presented at both the stand
and plot level. These studies and those of other researchers
highlight the importance of forest modeling applications in
creating sustainably managed forests.
3. CONCLUSION
Climate change is perhaps the greatest challenge of the
21st century. As more information is collected and examined
about changes in climate, scientists realize that yet so much
more is still unknown about this phenomenon and its
interrelationships within different operational environments,
especially at the micro-habitat level. Moreover, a virtual
pandora’s box of major global threats, such as hunger,
poverty, population growth, armed conflict, displacement, air
pollution, soil degradation, desertification and deforestation
are intricately intertwined and all contribute to climate
change, necessitating a more comprehensive approach to a
complete solution [37]. Forests provide one suitable option
for reducing the net amount of carbon dioxide in the
atmosphere by acting as carbon sinks. At the same time,
forests are also sensitive to changes in climate, making
sustainable forest management a critical component of
present and future climate change strategies. These
disturbances can shape forest systems by influencing their
composition, structure, and functional processes and can
come from both human and natural sources [30].
This paper has briefly reviewed the use of geospatial
technologies GIS and remote sensing as enabling tools for
supporting SFM. As discussed, geospatial technologies can
support SFM in four major areas; classification of forest
covers type, estimation of forest structure, forest change
detection and forest modeling. Classification of forest types
allows forest managers to effectively implement different
strategies or to give different priorities to forests with known
high carbon stock capacity. This can be done using the spectral
signatures for different forest types at varying scales. In areas
where large heterogeneous mixtures of forests and other land
use land covers occur, which can lead to similar spectral
signatures, this can sometimes prove problematic. However,
newer systems such as hyperspectral provide the opportunity
for a much finer scale analysis in order to distinguish between
different forest covers and for separating forests from other
features in the environment. Knowing where forests are
located it is then necessary to use various structural attributes
such as height and size of trees to calculate the net carbon
stock for the forested areas. LiDAR remote sensing presents a
unique opportunity for quantitatively collecting such
information. In this regard, it is also critical to continually
monitor changes in land use land cover for forested areas since
these directly impact the amount of carbon that can be stored
in those areas. Changes in forest cover can give way to many
other land use land cover types such as croplands, buildup area
and invasive vegetation, which may further accelerate the
degradation of forest ecosystems. It is therefore important for
forested areas to be monitored in a timely manner, fitting the
periodicity of disturbances occurring at these locations. Using
remote sensing, data can be collected at a range of spectral and
spatial resolutions providing forest managers with various
customizable products. These can then be inputted in GIS for
updating forest inventories. Lastly, combining GIS with
different forest models, forest managers can then
predict/simulate forest and carbon storage trajectories in the
near to distant future. This allows the testing of various
hypotheses, choosing only those that meet with the goals of

6. 6
SFM. Using geospatial technologies, it is expected that forest
managers will have tools at their disposal, allowing them to be
more effective and efficient in their daily workflows. This will
have the net effect of creating more sustainable forest
management strategies, leading to an overall decrease in the
greenhouse gas carbon dioxide.
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