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Determination of the Optimum Number of Sample Points to Classify Land Cover Types and Estimate the Contribution of Trees on Ecosystem Services Using I‐Tree Canopy Tool


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The process of producing information about dynamic land use/land cover and ecosystem health in a short time with high accuracy and low cost is important. This information is one of the basic data used for sustainable land management. For this purpose, remote sensing technologies are generally used, and sampling points are mostly assigned. Determination of the optimum number of sampling points using the I‐Tree Canopy Tool was the main focus of this study. The I‐Tree Canopy Tool classifies land cover, revealing the effects of tree cover on ecosystem services, such as carbon sequestration and storage, temperature regulation, air pollutant filtering, and air quality improvement, with numerical data. It is used since it is practical, open source, and user‐friendly. This software works based on sampling point assignment, but it is unclear how many sampling points should be assigned. Therefore, determining the optimum number of sample points by statistical methods will increase the effectiveness of this tool and will guide users. For this purpose, reference data were created for comparison. Then, 31 I‐Tree Canopy reports were created with 100‐point increments up to 3100. The data obtained from the reports were compared with the reference data, and statistical analysis based on Gaussian and a second‐order polynomial fit was performed. At the end of the analysis, the following results were obtained; the results of this study showed that the optimum number of sample points for a 1‐ha area is 760±32 from the comparison of the real area and I‐Tree Canopy results. Similar results from the Gaussian fit of annually sequestered and stored carbon and CO2 amounts in trees and the reduction of air pollution in grams were obtained as 714±16. Therefore, we may conclude that the sample points taken more than 800 will not create a statistically significant difference. This article is protected by copyright. All rights reserved. Integr Environ Assess Manag 2022;00:0–0.
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Integrated Environmental Assessment and Management Volume 00, Number 00pp. 19
Received: 31 May 2022
Revised: 6 September 2022
Accepted: 19 October 2022 1
Ecosystem Services
Determination of the optimum number of sample points
to classify land cover types and estimate the contribution
of trees on ecosystem services using the ITree Canopy tool
Serdar Selim,*Burçin Dönmez, and Ali Kilçik
Department of Space Sciences and Technologies, Faculty of Science, Akdeniz University, Antalya, Turkey
The process of producing information about dynamic land use and land cover and ecosystem health quickly with high
accuracy and low cost is important. This information is one of the basic data used for sustainable land management. For this
purpose, remote sensing technologies are generally used, and sampling points are mostly assigned. Determination of the
optimum number of sampling points using the ITree Canopy tool was the main focus of this study. The ITree Canopy tool
classies land cover, revealing the effects of tree cover on ecosystem services, such as carbon (C) sequestration and storage,
temperature regulation, air pollutant ltering, and air quality improvement, with numerical data. It is used because it is
practical, open source, and userfriendly. This software works based on sampling point assignment, but it is unclear how
many sampling points should be assigned. Therefore, determining the optimum number of sample points by statistical
methods will increase the effectiveness of this tool and guide users. For this purpose, reference data were created for
comparison. Then, 31 ITree Canopy reports were created with 100point increments up to 3100. The data obtained from the
reports were compared with the reference data, and statistical analysis based on Gaussian and a secondorder polynomial t
was performed. At the end of the analysis, the following results were obtained; the results of this study demonstrated that the
optimum number of sample points for a 1ha area is 760 ± 32 from the comparison of the real area and ITree Canopy results.
Similar results from the Gaussian t of annually sequestered and stored C and carbon dioxide (CO
) amounts in trees and the
reduction in air pollution in grams were obtained as 714 ±16. Therefore, we may conclude that taking more than 800 sample
points will not be statistically signicant. Integr Environ Assess Manag 2022;00:19. © 2022 SETAC
KEYWORDS: Gaussian t; land cover classication; random sample point; remote sensing; tree canopy
Land use and land cover information, such as forest, set-
tlement, agriculture, water surfaces, impervious surfaces,
opengreen areas, is important and must be obtained with
high accuracy for environmental management applications at
various scales (Hundera et al., 2020; Zhang et al., 2019). Using
this information, researchers determine the causes of land use
dynamics, predict their results, and reveal their effects on
ecosystem services (Veldkamp & Verburg, 2004). They are
also important sources of information used to monitor con-
tinuous changes to the earth's surface and to understand
ecological interactions (Patino & Duque, 2013). In parallel with
the rapid developments in sensor technology, high spatial
resolution images using remote sensing offer important op-
portunities for extracting information from land cover in detail
(Zhao et al., 2016). In addition, classifying land cover from
satellite images is still a problem due to the spectral and
spatial complexity of the images as well as the difculty and
variety of the methods used (Lu & Weng, 2006). Various
methods have been developed to solve these problems using
geographical information systems. Techniques such as pixel
and objectbased simple classication (Deng et al., 2019;
Toure et al., 2018), fuzzy classication (Barakat et al., 2019),
articial neural network and/or articial immune systems
(Zhang et al., 2019; Zhong et al., 2006), decision trees (Chen
et al., 2018; Talukdar et al., 2020), support vector machines
(Mustafa et al., 2018; Wu et al., 2018), machine learning al-
gorithms (Abdi, 2020; Camargo et al., 2019), deep learning
(Wu et al., 2018; Zhang et al., 2018), and histogram estimation
classiers (Liu et al., 2019) are widely used. These techniques
provide information about land cover classes. This information
is used socioeconomically (Cheng et al., 2018; Gounaridis
et al., 2019), socioculturally (Rimal et al., 2018; Tu et al.,2018),
and ecologically (Clerici et al., 2019; Zou et al., 2021) in the
evaluation and management of lands (Arowolo et al., 2018;
Wellmann et al., 2018). From an ecological perspective, de-
termining and estimating vegetation in land cover classes
(Vargas et al., 2019) and classifying and calculating its effects
on ecosystem services (Sun et al., 2019) are important issues
on the world agenda within the scope of global climate
Integr Environ Assess Manag 2022:19 © 2022 SETAC.DOI: 10.1002/ieam.4704
*Address correspondence to
Published 25 October 2022 on
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change, rapid urbanization, and population growth (Bhan
et al., 2021; Wei et al., 2021; Yee et al., 2021). Therefore,
determining the vegetation in land cover is the rst step in
ecological studies, environmental planning, and ecological
sustainability (Honeck et al., 2018; Svoray et al., 2013).
Vegetation cover plays a critical role in stabilizing the cli-
mate and carbon (C) storage and regulating the hydrological
cycle (Köhl et al., 2015; Sankey et al., 2021). In particular,
forest cover provides a habitat for various plant and animal
species. It also provides many ecosystem services for all
populations (Miura et al., 2015; Sujetovienė&Daba-
šinskas, 2022). Several important ecosystem services are
provided by forest cover (Van Wensem et al., 2017). These
services are absorbed C and CO
within the scope of the
climate, produced oxygen (O
), holding nitrogen dioxide
), ozone (O
), sulfur dioxide (SO
); and in the scope of the
hydrological cycle, avoided runoff, evaporation, interception,
and transpiration (Bösch et al., 2018; Jenkins& Schaap, 2018).
The sustainability and management of these services depend
primarily on the determination of vegetation, its ecological
benets, and the development of environmental manage-
ment strategies (Leal Filho et al., 2021).
In recent years, the ITree Tools software suite has been
used by many researchers around the world, both in land
cover classication and in calculating the environmental
benets and ecosystem services of vegetation (Hirabayashi,
2014; Jacobs et al., 2014; Omodior et al., 2021; Parmehr
et al., 2016; and references therein). In addition, this software
produces accurate results based on the operator's ability to
accurately interpret aerial or satellite imagery and detect the
presence or absence of tree cover at each sample point
(Parmehr et al., 2016). This software, developed by the US
Department of Agriculture (USDA) Forest Service in 2006, is
designed to evaluate forest resources in urban and rural areas,
demonstrate the environmental benets of forests, calculate
the effect of vegetation on ecosystem services, and improve
environmental quality and human health (Nowak, 2021). This
freely available software is used by more than 510000 re-
searchers in 159 countries as of 2020 and is cited in more than
1000 scientic papers (Nowak, 2021). Scientic studies using
this software generally focus on understanding and evaluating
local ecosystem services provided by vegetation and classi-
fying land cover (Omodior et al., 2021; Raum et al., 2019; Ross
et al., 2020; Walters & Sinnett, 2021), determining environ-
mental management strategies for sustainable use of forests
and green lands (Hirabayashi et al., 2011; Hwang &
Wiseman, 2020; Song et al., 2020), calculating air pollution
reduction trends of green areas (Hirabayashi, 2014; Ma et al.,
2020; Pace et al., 2021; Wu et al., 2019), determining land use
change (Ersoy Tonyaloğlu & Atak, 2021), and calculating the
material value of the ecological benets provided by vege-
tation (Hilde & Paterson, 2014; Qian et al., 2019).
ITree Canopy, an ITree tool, is used frequently in the in-
ternational arena due to its ease of use and data require-
ments. According to Nowak (2021), this tool allows users to
easily photointerpret Google aerial images to produce stat-
istically reliable estimates of tree and other cover types along
with calculations of the uncertainty of their estimates. This tool
appears to provide a quick and inexpensive means to esti-
mate tree cover and other cover types accurately. The ITree
Canopy tool works in three stages: selecting the eld, de-
ning the ecological values of the vegetation per unit area,
and determining random sampling points in the relevant re-
gions. Area selection can be made by the user via Google
Earth, which is the base map of the software. Predetermined
values can be used by the USDA Forest Service to calculate
the ecological value of vegetation per unit area, and these
values can also be determined by users for any selected area
using ITree Eco. Randomly sampled points are manually
classied as with treesand without treesto estimate the
vegetation cover of a predened area. Online and stock sat-
ellite imagery is used via Google Maps to determine the land
cover of random points. Estimated vegetation cover is given
as a percentage or area (m
), and its contribution to eco-
system services is calculated. The accuracy of the estimated
values depends on the operator's ability to correctly interpret
the satellite images and detect the presence or absence of
trees at each sample point (Parmehr et al., 2016). However,
the identication and classication of random sample points
may increase the data collection time and cost. Although the
number of random sampling points suggested by ITree
Canopy software is between 500 and 1000 (https://canopy., the high accuracy and minimum error the user
seeks is directly proportional to the increase in the number of
points. To achieve high accuracy, many random sampling
points are needed, and there is no upper limit. This means
that much time and money will be needed for the user to
achieve high accuracy and precision. There must be a balance
between accuracy and time spent for thepractical application.
The lack of comprehensive and statistical research in the lit-
erature on determining the optimum number of sampling
points for land cover classication, including the contribution
of trees to ecosystem health using ITree Canopy, has moti-
vated this study.
This study focused on the ITree Canopy software. ITree
Canopy is a free and practical software that can calculate the
contribution of tree cover to ecosystem services depending
on land cover types while making land use classications.
Additionally, the European Green Deal has reported that
ecosystem services should be mapped and assessed for
ecosystems and their services (Maes et al., 2013), and ITree
Canopy can provide all this information.
To determine the land cover classication and the con-
tribution of the tree cover in an ecosystem, the optimum
number of sample points is determined. For this purpose, on
screen digitation was produced with the help of remote
sensing (RS) and geographical information system (GIS)
technology specic to the study area, and these data were
used as a starting point. Although this software recommends
taking 5001000 sampling points for a reliable analysis, in the
literature, different sampling numbers are used by different
researchers. In this study, each report produced from 100
points gave data, including its statistical error. After 31 re-
ports, the level of statistical error difference between each
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report remained nonsignicant. Then, 31 ITree Canopy re-
ports were used with 100point increments up to 3100 in the
study. Each report was compared with the reference data. As
a result, the optimum number of sample points was de-
termined with high accuracy. It is expected that this study will
be a guide for the data collection stage for the scientic
research community using this software.
Study area
The study area is located in the şemealtıdistrict in
Antalya, which is regulated for urban development, where
the land use cover is analyzed. The geographical coor-
dinates of the study area are 36°5843.31N and 30°35
49.43E, and it covers an area of 1 ha (Figure 1). It is a
moderately urbanized area located in southern Turkey. The
presence of urban forests in the region is also noteworthy.
The study area was divided into six classes, considering the
existing land cover classes and the ITree Canopy cover
classes. These classes are listed as impervious buildings, grass
and/or herbaceous, impervious surface, soil and/or bare
ground, tree and/or shrub, and water. The presence of land
cover belonging to each class was effective in the selection of
the study area (Table 1).
Data preparation
Data generation was done in two stages as reference and
random point sampling datasets (Figure 2). The reference
data were produced by processing the Google Earth satellite
image of September 2022 using ArcGIS 10.4.1 software
( Georeference processes
were done by transferring the satellite image whose corner
coordinates were dened to the software. The image is div-
ided into land use classes with onscreen digitation. High
precision manual classication is veried by a land survey. As
a result of the classication, the percentage and area of each
class were obtained. Random point sampling data were ob-
tained using the ITree Canopy tool of the same image. Then,
explanations for each land use class and the presence or
absence of trees are dened after determining the study area
boundaries. Therefore, appropriate tree utility values were
assigned to each land cover class. For this purpose, ITree Eco
software was used, and analyses conducted in the region
were referenced in the scientic literature (Ersoy Tonyaloğlu &
Atak, 2021; Selim & Atabey, 2020). Altogether, 3100 points
were assigned to the study area in the ITree Canopy tool,
Integr Environ Assess Manag 2022:19 © 2022 SETAC.DOI: 10.1002/ieam.4704
FIGURE 1 Location of the study area
TABLE 1 Land cover classes of the study area
Number Cover class Description
1 Impervious buildings Areas with impervious cover occupied by buildings
2 Grass and/or herbaceous Grass and other herbaceous ground cover
3 Impervious surface Other impervious cover (e.g., pavements, roads, concrete areas)
4Soiland/or bare ground Soil surface and bare areas without vegetation
5 Tree and/or shrub Areas covered with trees and tall bush vegetation
6 Water surface Articial and natural water surface without vegetation
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provided that a report was generated for every 100 points
and the land cover class was dened for each point.
The method was based on the classication and com-
parative analysis of the current satellite image of the same
area using RS techniques and random sample points taken
from ITree Canopy.
To obtain the optimum number of sample points for the 1
ha (see Figure 1) area, the variation in the investigated pa-
rameters (land cover classes, sequestered and stored
amount in trees, and the air pollution in grams in
the study area) were analyzed. All the abovementioned pa-
rameters for 100point resolution (from 100 to 3100) were
produced. Then, all these variations were plotted with respect
to the sample points with errors that were taken directly from
Integr Environ Assess Manag 2022:19 © 2022
FIGURE 2 Reference data (left) and random point sampling data (right) of the study area
FIGURE 3 Total area of each class with error in the study area
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the ITree Canopy tool reports. To describe the optimum
number of sampling points, Gaussian t (Equation 1) was
applied to the peak points of sequestered and stored C and
amounts in trees and the air pollution in grams in the
study area.
μσ σπ
Gx x
,, 1
2exp 1
where μis the mean value and σis the standard deviation of
the applied t.
Comparison of ITree Canopy with remote sensing
In the rst step of our analysis, the areas of different classes
with the number of sample points are compared (Figure 3).
The distribution of the total area of each class reveals that
each exhibits rather different behavior from one another. It
was difcult to obtain a certain number of sampling points.
However, the distribution of all plots does not indicate serious
saturation after approximately 700 sample points, as shown in
Figure 3. To obtain a more reliable determination of the
necessary sample point average, the percentage difference of
all land classes for each set is calculated using the real area
obtained from RS classications (Table 2). Then, the combined
average error of each class is plotted for each set, as shown in
Figure 4. It can be seen from this gure that the error has a
minimum of approximately 800 and 3000 points. Therefore, a
secondorder polynomial t is applied for points around the
rst minimum. The necessary number of points obtained from
the t for minimum error in the area estimation is 760 ± 32
with 1 standard deviation. Taking more datapoints will not
reduce the error level, and therefore, taking more datapoints
will not be necessary (Table 3).
Determination of optimum number of sampling points
To investigate the variation in annually sequestered and
stored C and CO
amounts in trees, each parameter is plotted
against the number of sample points, as shown in Figure 5.
Integr Environ Assess Manag 2022:19 © 2022 SETAC.DOI: 10.1002/ieam.4704
TABLE 2 Real area of each cover class calculated using onscreen
digitation method
Cover class Area (m
Impervious buildings 528.06
Grass and/or herbaceous 1596.03
Impervious surface 2029.84
Soil and/or bare ground 1187.34
Tree and/or shrub 4603.17
Water surface 55.56
FIGURE 4 Average error in all land classes calculated with ITree Canopy and the real area of each class given in Table 2
TABLE 3 Number of necessary sampling points calculated from the
Gaussian t for tree benets estimates (carbon and CO
Description Number of points
Sequestered annually in trees (carbon) 722 ± 16
Sequestered annually in trees (CO
715 ± 16
Stored in tree (carbon) 715 ± 16
Stored in tree (CO
equivalent) 714 ± 16
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It can be seen that both annually sequestered and stored
C and CO
amounts in trees exhibit similar behavior in
that all datasets have a local maximum for approximately
700 datapoints. Therefore, a Gaussian t is made around
these points. The obtained t results are listed in Table 4.
A similar analysis is made for the reduction in air pollution
in grams in the study area. As shown in Figure 6, all selected
parameters (CO, NO
, PM2.5, and PM10) exhibit
similar behavior without exception. Therefore, again, a
Gaussian t was done, and the results are also shown in
Table 4.
It is important to have reliable, accurate, and uptodate
information for the sustainability of the environment (Martins
et al., 2021). This contributes to the implementation of
decisionmaking strategies related to a better understanding
of ecological dynamics, management, and sustainability of
the environment (Fassnacht et al., 2016). In general, RS im-
ages and GIS software are used for this purpose, which are
expensive to purchase and require expertise for analysis
(Miraki et al., 2021). In addition, RS data for monitoring and
assessing land use often cannot keep up with the rapid
change in the urban environment (Musakwa & Van Nie-
kerk, 2013). Cities have undergone rapid change due to both
reconstruction and the expansion of concretion over natural
areas as a result of socioeconomic transformation (Mansour
et al., 2020). Therefore, acquiring uptodate, accurate, and
practical ecological information provides important in-
formation to managers in the planning phase. The free ITree
Integr Environ Assess Manag 2022:19 © 2022
FIGURE 5 Annually sequestered and stored carbon and carbon dioxide (CO
) amount in trees
TABLE 4 Number of necessary sampling points calculated from the Gaussian t for tree benets estimates (air pollution)
Abbreviation Description Number of points
CO Carbon monoxide removed annually 714 ± 16
Nitrogen dioxide removed annually 714 ± 16
Ozone removed annually 714 ± 16
Sulfur dioxide removed annually 714 ± 16
PM2.5 Particulate matter <2.5 μm removed annually 714 ± 16
PM10 Particulate matter >2.5 μm and <10 μm removed annually 714 ± 16
6Integr Environ Assess Manag 00, 2022SELIM ET AL.
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Canopy tool provides a lowcost, fast, and repeatable
method to evaluate tree cover and explain its impact on
ecosystem services with numerical data. It is also stated that
this software is promising in predicting and evaluating the
benets generated by ecosystem services, providing high
accuracy; data produced largely overlap with results obtained
by image classication techniques (Olivatto, 2019; Raza-
ghirad, 2021; Richardson & Moskal, 2014). However, there
are some restrictions on the use of this method. The most
obvious and timeconsuming limitation is the inability to de-
termine the appropriate number of sampling points. Several
examples can be given as follows: Omodior et al. (2021) used
an average of 280 random points for vegetation cover clas-
sication; Jacobs et al. (2014) used 1000 random points to
classify landscape features across Australia; 500 points were
used to examine the tree cover differences in the study area
between 2008 and 2011 by Hwang and Wiseman (2020);
Richardson and Moskal (2014) used 1000 points to evaluate
the urban forest cover; Ersoy Tonyaloğlu and Atak (2021)
used 10 608 points in a study investigating the effects of land
use and land cover change on potential regulating ecosystem
services. It has been seen that a different number of sample
points are used regardless of the size of the land (Benjamin
et al., 2015; Walters & Sinnett, 2021; Zho, 2017). Even in the
user's guide of the ITree Canopy software, it is emphasized
that the number of random sample points increases the ac-
curacy, but no information for the upper limit is given (https:// The motivation of this study was to
determine the optimum number of random sample points for
this software that appeals to a large number of users and
produces important information about regulatory ecosystem
services. Therefore, it can be used quickly, practically, and
with high accuracy.
The results of this study demonstrated that the optimum
number of sample points for a 1ha area is 760 ± 32 from the
comparison of the real area and ITree Canopy results.
Similar results from the Gaussian t of annually sequestered
and stored C and CO
amounts in trees and the reduction in
air pollution in grams were obtained as 714 ± 16. It is seen
that points taking more than 800 samples did not create a
statistically signicant difference. Therefore, the timecost
balance can be achieved with the optimum number of
points determined within the scope of this study.
There is no specic recommendation in the literature re-
garding the number of points that should be assigned in
different sizes and in different land covers. It is envisioned
that the optimum number of points may vary statistically to
reach maximum accuracy with area size. This requires a
study to determine the optimum number of sampling points
in different area sizes and different land textures. The de-
termination of the optimum number of points to be as-
signed in a unit area (e.g., for m
or km
) by analyzing areas
of different sizes is the subject of future study.
There are no funders to report for this submission.
Serdar Selim: Conceptualization; Data curation; Formal
analysis; Investigation; Methodology; Resources; Software;
Supervision; Visualization; Writingoriginal draft; Writing
review & editing. Burçin Dönmez: Data curation;
Integr Environ Assess Manag 2022:19 © 2022 SETAC.DOI: 10.1002/ieam.4704
FIGURE 6 Reduction in air pollution in grams in the study area
15513793, 0, Downloaded from by Akdeniz University, Wiley Online Library on [18/11/2022]. See the Terms and Conditions ( on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Investigation; Methodology; Software; Validation; Visual-
ization; Writingoriginal draft; Writingreview & editing. Ali
Kilçik: Data curation; Formal analysis; Methodology; Vali-
dation; Writingoriginal draft; Writingreview & editing.
Data, associated metadata, and calculation tools are
available from corresponding author Serdar Selim
Serdar Selim
Burçin Dönmez
Ali Kilçik
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... For this reason, the observation of these changes is quite essential. Spectral indices (SI) are the best quantitative representation of the urbanization-related land use/land cover (LULC) biophysical properties and continuous transformation (Zhou and Wang, 2011;Selim et al., 2022). SI are mostly in the spatial resolution of optical data and are variables obtained by deriving the reflections of two or more bands (Alexander, 2020). ...
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As the main grain production area in China, Jilin Province has a significant ecological function in Northeast China. Scientifically understanding the spatial and temporal characteristics of ecological vulnerability can aid in effectively managing environmental changes, guiding rational use of land resources and developing strategies for regional environmental protection. Applying the Sensitivity Resilience Pressure (SRP) conceptual model of ecological vulnerability, integrated with meteorological, remote sensing, and statistical data, a comprehensive evaluation index system for ecological vulnerability was established in Jilin Province. An entropy weight model constructed with analytic hierarchy process (AHP) and principal component analysis (PCA) was employed to calculate weights of each indicator. Using a geographical information system, the geospatial and temporal features of ecological vulnerability were analyzed from 2000 to 2018 in the study area. Spatial autocorrelation analysis was utilized to probe the spatial relationship. The results indicate that the ecological vulnerability was categorized a potential and light within the study area and that variations among different regions increased gradually from east to west. High levels of ecological vulnerability were mainly distributed in the western area, displaying a significant global spatial autocorrelation with high-high (HH) aggregation. Over time, the proportion of high vulnerability areas decreased from 14% in 2000 to 9% in 2018, while low vulnerability areas increased from 26% in 2000 to 29% in 2018. The results reflect the condition of the regional ecological environment, which could have implications for ecological protection and sustainable development in the Jilin Province.
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The availability of urban tree cover (UTC) is one of the most important components of healthy urban environments that support the provision of many ecosystem services. The development in Turkey keeps going within/around urban environments, and the degradation and loss of urban vegetation and associated ecosystem services continues. In this study, we aimed to assess the impacts of land cover change on UTC and ecosystem services provided by them in the case of Efeler District, in Aydın Province of Turkey between 2004 and 2021. The ecosystem services and benefits estimated with the i-Tree Canopy tool includes carbon sequestration and storage as well as the removal of nitrogen dioxide, ozone, sulphur dioxide, PM2.5 and PM10 as well as their economic valuation. Our results show that there was a decline trend in UTC between the years of 2004 and 2021. The loss of UTC was caused by the combined effects of new development pressure in and around the natural/semi-natural and agricultural areas, as well as the new reconstruction sites around old settlements. During the last 17 years, the loss of 61.38 ha UTC created a decrease in the carbon and air pollution benefits and economic valuation estimates. It would be premature to make direct use of our results in planning, without further analysis of the composition and age structure of tree/shrub species. However, the approach used in this study does demonstrate the principle and potential nature of such a methodology where comprehensive analysis are limited by the lack of sufficient data, time and labour.
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Trees and urban forests remove particulate matter (PM) from the air through the deposition of particles on the leaf surface, thus helping to improve air quality and reduce respiratory problems in urban areas. Leaf deposited PM, in turn, is either resuspended back into the atmosphere, washed off during rain events or transported to the ground with litterfall. The net amount of PM removed depends on crown and leaf characteristics, air pollution concentration, and weather conditions, such as wind speed and precipitation. Many existing deposition models, such as i-Tree Eco, calculate PM 2.5 removal using a uniform deposition velocity function and resuspension rate for all tree species, which vary based on leaf area and wind speed. However, model results are seldom validated with experimental data. In this study, we compared i-Tree Eco calculations of PM 2.5 deposition with fluxes determined by eddy covariance assessments (canopy scale) and particulate matter accumulated on leaves derived from measurements of vacuum/filtration technique as well as scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (leaf scale). These investigations were carried out at the Capodimonte Royal Forest in Naples. Modeled and measured fluxes showed good overall agreement, demonstrating that net deposition mostly happened in the first part of the day when atmospheric PM concentration is higher, followed by high resuspension rates in the second part of the day, corresponding with increased wind speeds. The sensitivity analysis of the model parameters showed that a better representation of PM deposition fluxes could be achieved with adjusted deposition velocities. It is also likely that the standard assumption of a complete removal of particulate matter, after precipitation events that exceed the water storage capacity of the canopy (Ps), should be reconsidered to better account for specific leaf traits. These results represent the first validation of i-Tree Eco PM removal with experimental data and are a starting point for improving the model parametrization and the estimate of particulate matter removed by urban trees.
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Biomass production generates land use impacts in the form of emissions from Forestry and Other Land Use (FOLU), i.e. due to changes in ecosystem carbon stocks. Recently, consumption-based accounting (CBA) approaches have emerged as alternatives to conventional production-based accounts, quantifying FOLU emissions associated with biomass consumption, for example, of particular territories. However, the quantification and allocation of FOLU emissions to individual biomass products, a fundamental part of CBA approaches, is a complex endeavour. Existing studies make diverging methodological choices, which are rarely critically discussed. In this study, we provide a structured overview of existing CBA approaches to estimating FOLU emissions. We cluster the literature in a two-by-two grid, distinguishing the primary element under investigation (impacts of changing consumption patterns in a region vs. impacts of consumption on production landscapes) and the analytical lens (prospective vs retrospective). Further, we identify three distinct dimensions which characterise the way in which different studies allocate FOLU emissions to biomass products: the choice of reference system and the spatial and temporal scales. Finally, we identify three frontiers that require future attention: (1) overcoming structural biases which underestimate FOLU emissions from territories that experienced deforestation in the distant past, (2) explicitly tackling the interdependence of proximate causes and ultimate drivers of land use change, and (3) assessing uncertainties and understanding the effects of land management. In this way, we enable a critical assessment of appropriate methods, support a nuanced interpretation of results from particular approaches as well as enhance the informative value of CBA approaches related to FOLU emissions. Our analysis contributes to discussions on sustainable land use practices with respect to biomass consumption and has implications for informing international climate policy in scenarios where consumption-based approaches are adopted in practice.
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After decades of rapid development, there exists insufficient and contradictory land use in the world, and social, economic and ecological sustainable development is facing severe challenges. Balanced land use functions (LUFs) can promote sustainable land use and reduces land pressures from limited land resources. In this study, we propose a new conceptual index system using the entropy weight method, regional center of gravity theory, coupling coordination degree model and obstacle factor identification model for LUFs assessment and spatial-temporal analysis. This framework was applied to 17 cities in central China’s Hubei Province using 39 indicators in terms of production–living–ecology analysis during 1996–2016. The result shows that (1) LUFs showed an overall upward trend during the study period, while the way of promotion varied with different dimensions. Production function (PF) experienced a continuous enhancement during the study period. Living function (LF) was similar in this aspect, but showed a faster rising tendency. EF continued to increase during 1996–2011, but declined during 2011–2016. LUFs were higher in the east than in the west, and slightly higher in the south than in the north. The spatial coordination was enhanced during the study period. (2) The overall level of coupling coordination degree continued to increase during 1996–2016, while regional difference declined obviously, indicating a good developing trend. However, the absolute level was still not satisfactory. (3) The obstacle degree of PF was always dominant, and LF showed a downward trend, while EF showed an increasing trend. Benefit index (A2), Comfort index (B2) and Green index (C1) constituted the primary obstacle factor for each dimension. Added-value of high and new technology industry (A2-3) and land use intensity (A3-2) were key factors restricting PF. Number of medical practitioner (B1-4) and internet penetration rate (B2-3) were key factors restricting LF. Air quality rate (C3-1) and wetland coverage rate (C1-4) were key factors restricting EF. This study can help to give a more detailed understanding of sustainable land use for the particularity of China from a land function perspective and provide lessons and suggestions for other developing countries in the world.
The growing interest in ecosystem services is mainly related to land use changes. The aim of the study is to analyse spatial-temporal changes in the capacity to supply of ES in Lithuania based on land use changes in 1990–2018. The results show some balance between loss and gains of ecosystem services capacity due to land use changes. Decrease in heterogenous agricultural areas had negative impact on provision of ecosystem integrity and services. Considerable increase in scrubland and herbaceous vegetation areas significantly increased the ecosystem service potential. The conversion of former agricultural land to less intensively managed ecosystems enhance the potential of valuable habitats for biodiversity and ecosystem services associated with natural grasslands, moors and heathland, transitional woodland shrubs. The urbanization process along with increase in urban fabric areas had little effect on ESs potential since artificial vegetated areas had compensated the loss of ESs due to increased areas of urbanized ecosystems. Despite the area of open spaces slightly increased, this led to decrease of provision of ESs. Temporal changes in overall ESs capacity indicated an increase in Lithuania over the last two decades. Given the observed dynamic context of land cover, the structure of ecosystem services may face potential threats from land use change due to urban development and agricultural activities.
This paper presents a novel approach combining the Simple Linear Iterative Clustering (SLIC) superpixel algorithm with a Convolutional Neural Network (CNN) over high-resolution imagery to detect trees in a typical urban environment of the Brazilian Cerrado biome. Our analysis approach for better results uses the deep learning classifier ResNet-50, with a variation in the batch size and five traditional shallow learning methods. The results were processed and avaliated using mainly accuracy as a metric, but we show that the accuracy poorly represent the overlap between the manual annotation and the resulting map, so we bring the IoU metric results to better show the Network learning classification maps results. The combined SLIC algorithm and the best CNN resulted in an accuracy of 93.20%, IoU of 0.700 and a variation of 1% for difference in the area of tree canopies if compared to our labels, while the best shallow presented an accuracy of 91.70%, IoU of 0.200 and a variation in area of 12.52%. Demonstrating that the proposed CNN method is suitable for segmenting trees from high-resolution images acquired over urban environments. The segmentation with SLIC and CNN can provide very useful results for urban management using low cost RGB images. Such outcomes are of great interest for local managers since reliable maps showing the spatial distribution of trees in urban areas are often required for many applications.
Many cities are increasing urban canopy cover to mitigate the impacts of urbanisation and climate change. Ambitious canopy cover targets are being implemented, with little consideration on how best to achieve them. The City of Bristol has recently published a One City Plan, which includes a target to double tree canopy cover by 2045. This study aims to investigate its feasibility, and explore different planting scenarios to achieve the target and maximise ecosystem service delivery. Using an existing i-Tree Eco assessment of the urban forest, i-Tree Forecast was used to project future urban forest growth under a number of different user-defined planting scenarios. Sixteen scenarios were forecasted over 27 years to test a variety of approaches. Tree stock size and number, timing of planting and annual mortality rates were varied to test the performance of each scenario. Planting 18,000 large tree stock, equivalent to ‘heavy standards’ every year for 27 years provided the most feasible scenario in balancing canopy cover, leaf area index and pollutant removal whilst providing a stable population. This increased to 44,000 trees per year assuming an annual mortality rate of 3% more typical of urban areas, demonstrating the importance of good stewardship. The size of planted tree and timing of planting had a strong impact on the development of canopy cover. It was concluded that the One City Plan target is feasible if the planting rate is increased to at least 18,000 large tree stock from the current 10,000 trees per year.
Climate change is one of the major challenges societies round the world face at present. Apart from efforts to achieve a reduction of emissions of greenhouse gases so as to mitigate the problem, there is a perceived need for adaptation initiatives urgently. Ecosystems are known to play an important role in climate change adaptation processes, since some of the services they provide, may reduce the impacts of extreme events and disturbance, such as wildfires, floods, and droughts. This role is especially important in regions vulnerable to climate change such as the African continent, whose adaptation capacity is limited by many geographic and socio-economic constraints. In Africa, interventions aimed at enhancing ecosystem services may play a key role in supporting climate change adaptation efforts. In order to shed some light on this aspect, this paper reviews the role of ecosystems services and investigates how they are being influenced by climate change in Africa. It contains a set of case studies from a sample of African countries, which serve the purpose to demonstrate the damages incurred, and how such damages disrupt ecosystem services. Based on the data gathered, some measures which may assist in fostering the cause of ecosystems services are listed, so as to cater for a better protection of some of the endangered Africa ecosystems, and the services they provide.
While extant literature has generally indicated significant associations between vegetation cover and tick activity, no study has demonstrated the relative association between peri-domestic area vegetation cover subtypes and tick presence. In this study, we seek to determine whether neighborhood wood index and residential tick control practices confound or modify the effect of peri-domestic vegetation cover subtypes on tick presence. We conducted an ecological inventory of vegetation cover distribution using i-Tree Canopy on 210 private residential/peri-domestic properties in Indiana, USA. Results were paired with field obtained tick presence/absence data for each property together with online survey data provided by primary occupant of the property. Amblyomma americanum was the predominant tick species in peri-domestic areas. Higher proportion of vegetation cover in the peri-domestic area was significantly associated with tick presence. Of the four vegetation cover subtypes, (grass, shrubs, understory, and canopy), canopy was the most prevalent vegetation in peri-domestic areas of Indiana, USA. It was also the most significant predictor of tick presence. Among residential tick control processes, frequent leaf litter removal was significantly associated with reduced likelihood of peri-domestic tick presence. Neighborhood Wood Index (NWI) confounded the relationship between canopy and peri-domestic tick presence, while leaf-litter removal confounded the effect of understory vegetation subtype on peri-domestic tick presence. Compared to peri-domestic areas in neighborhoods with sparse NWI, those in neighborhoods with heavy/dense NWI had a 3.5x odd of peri-domestic tick presence (AOR = 3.46; 95% CI: 1.23 – 9.65). Compared to peri-domestic areas in the central region, those in the southern region of Indiana were 8.7x more likely to have peri-domestic tick presence. Canopy as a vegetation cover subtype and frequent leaf litter removal represent particularly key peri-domestic variables that have significant implications for peri-domestic tick presence. Beyond parcel-scale landscape features, neighborhood wood index also plays an important role in peri-domestic tick presence. Additionally, i-Tree Canopy represents a promising methodological tool for identifying landscape features that predict tick presence.