1. No category

UNIVERSITY OF GOTHENBURG
Department of Earth Sciences
Geovetarcentrum/Earth Science Centre
Mapping grass areas
in urban environments:
developing a general
grass detection model
Emanuel Vogel
ISSN 1400-3821
Mailing address
Geovetarcentrum
S 405 30 Göteborg
Address
Geovetarcentrum
Guldhedsgatan 5A
B813
Bachelor of Science thesis
Göteborg 2014
Telephone
031-786 19 56
Telefax
031-786 19 86
Geovetarcentrum
Göteborg University
S-405 30 Göteborg
SWEDEN
Abstract
Urban vegetation is important from many aspects such as urban climate, recreation,
biodiversity etc. Grass areas are typically a major component of the urban green areas, and
knowing the total area of those is important when assessing it´s effects. This paper
investigates remote sensing based techniques of identifying and mapping grass surfaces,
primarily in urban areas ,based on LiDAR (Light detection and ranging) data and NDVI
(normalized difference vegetation index) calculated from orthophotos. The goal was to
develop a general model that can be used to map grass surfaces in any city where data of the
same kind exists. The results from the analysis of LiDAR and NDVI indicates that LiDAR
data is more suitable for mapping grass, and this lead to the designing of a general ArcMAP
model based on LiDAR data processing. Comparing the model output to orthophotos and data
of known grass areas indicates that the model is highly accurate in detecting grass surfaces
that are visible from above, but that the large fraction of grass that is obstructed from view
from above, most often overgrown by trees, is not detected and is something that should be
addressed in future work to more accurately capture the total grass area.
Sammanfattning
Urban vegetation är viktigt ur många aspekter såsom stadsklimat, rekreation, biologisk
mångfald etc. Gräsområden utgör ofta en betydande del av urbana grönområden, och att veta
dess totala arealen är viktig vid bedömningen av dess effekter . Denna uppsats ämnar utveckla
en fjärranalysbaserad metod för att identifiera och kartlägga gräs, framförallt i
storstadsområden, baserade på LiDAR data (Light detection and ranging) och NDVI
(normalized difference vegetation index) beräknat från ortofoton. Målet har varit att utveckla
en allmän modell som kan användas för att kartera gräs varhelst data av samma slag finns
tillgänglig. Resultaten från den första analysen av LiDAR och NDVI indikerar att LiDAR är
mer lämplig för kartläggning av gräs, vilket ledde till utformningen av en allmän ArcMapmodell baserad på endast LiDAR-data. Jämförelse av modellens resultat och högupplöst
ortofoto, samt uppgifter om kända gräsområden, visar att modellen är mycket exakt i att
upptäcka gräs som är synlig från ovan. Att gräsytor som inte är synliga från ovan, främst
p.g.a. överväxning av träd, inte registreras av modellen leder till att en betydande del av den
totala gräsmängden lämnas utanför. Detta är det viktigaste problemet att lösa vid framtida
utveckling av metoden
Table of Contents
1. Introduction ............................................................................................................................ 1
1.1
LiDAR – Light detection and ranging ......................................................................... 2
1.2 Normalized difference vegetation index .......................................................................... 3
2. Method ................................................................................................................................... 4
2.1 LiDAR data processing .................................................................................................... 6
2.2 Orthophoto NDVI calculation .......................................................................................... 8
3. Results .................................................................................................................................... 9
3.1 Method development - LiDAR ........................................................................................ 9
3.2 Method development - NDVI ........................................................................................ 12
3.3 ArcGIS grass identification model ................................................................................. 14
3.4 Comparing study area model grass to municipal grass areas data ................................. 16
3.5 Applying the model to the larger study area .................................................................. 17
3.6 Applying the model to Säve low intensity test area ....................................................... 19
4. Discussion ............................................................................................................................ 20
4.1 Accuracy of the results ................................................................................................... 20
4.2 Improving the results and increasing the accuracy of the model ................................... 23
4.3 Säve low intensity test area ............................................................................................ 25
5. Conclusions .......................................................................................................................... 25
Acknowledgements .................................................................................................................. 25
1. Introduction
In cities all over the world and in the western world in particular, lawns are a very common
feature in the urban environment. Paul G. Johnson Johnson (2013) argues in his article
Priorities for Turfgrass Management and Education to Enhance Urban Sustainability
Worldwide, that there are many reasons why the lawn (or turfgrass) has become such an
important component of cities all over the world. The first of which he discusses is the
historical and ancestral one: to some extent turfgrass of different kinds has been present in
human communities since the migration from the savannahs of Africa. The typical lawn of
today however, probably originates from the Victorian era Europe, and through British
colonialization spread throughout the world and became a part of the common idea of the
“beautiful” city (Ignateva, 2012). The other reasons Johnson (2013) present, and believe are
more important, are the functional, environmental and health related aspects of urban grass
areas. Bolund and Hunhammar (1999) present six major groups of important urban ecosystem
services: air filtering, micro-climate regulation, noise reduction, rainwater drainage, sewage
treatment and recreational/cultural values. Out of these six, the one where lawns are most
important is the rainwater drainage. In vegetation-free cities, up to 60% of the rain water ends
up as surface runoff. In areas with a permeable surface, such as a lawn, only 5-15% of the rain
water becomes surface runoff, whereas the rest evaporates or infiltrates into the ground
providing important soil-moisture for trees and other vegetation that further contributes to
many of the abovementioned ecosystem services. Lawns are also important areas for physical
activities in the cities, promoting physical health. Negative aspects of lawns include high
maintenance intensity and high fertilization, lawn chemical and water needs (Milesi et al.,
2005). Escobedo et al. (2010) lists urban ecosystem services and disservices, as well as stress
the importance of including context, scale and heterogeneity when discussing urban
ecosystem services: what constitutes a service or disservice is relative to a community´s
context, scale and the heterogeneity of the urban forest. This point to the importance of
knowing the total area of ecosystems, in this case grass areas, when assessing its effects on
the urban environment.
Previous similar work has mainly been focused on urban vegetation in general and not grass
in specific. Milesi et al. (2005) estimated the total turf grass area in United States using an
indirect method based on the concept that the turf grass area is inversely related to the
impervious surface area. The study aimed at estimating the total turf grass area of continental
USA, and the biogeochemical cycling associated to turf grass. Milesi et al. (2005) estimates
that cultivated turf grass cover a total of 163 800 km2, or 1,9 % of the total area of continental
USA. The method used by Milesi et al. (2005) is designed to make continental scale
estimations to be able to make broad, large scale, calculations of the biogeochemical cycle.
Huang et al.(2013) use LiDAR (light detection and ranging) and IR (infrared) orthophotos to
develop an automated method for calculating total urban green volume. LiDAR data is used to
construct a digital surface model (DSM) and orthophotos to calculate NDVI (normalized
difference vegetation index) for vegetation identification. All green areas not classified as
trees are considered to be grassland. The authors conclude that an integrated use of LiDAR
and orthophoto data provides an effective way of analyzing urban vegetation. Tooke et al.
1
(2009) analyzed urban vegetation in Vancouver, Canada based on spectral mixture analysis
and decision tree classification from satellite data, using LiDAR data to calculate shadow
patterns. Their results showed success in extraction of vegetation types such as trees and
grassland, but also in distinguishing between evergreen and needleleaf trees and manicured
and non-manicured grasses. Similar multispectral analysis-based work to investigate the
validity of using satellite imagery in vegetation classification was made by Small (2001) over
New York.
This project will investigate methods for detecting and estimating areas of grass surfaces in
cities, based on LiDAR and NDVI remote sensing techniques.

Is it possible to use Lidar and NDVI to develop a method to detect and map grass?

How does the results compare to available data from the municipality office?

What are the main challenges to developing an accurate method?
The aim is to find a general method useable wherever data of similar quality is available, that
is accurate on a small scale and applicable over large areas. This project is based on data
covering the Swedish city of Gothenburg.
1.1 LiDAR – Light detection and ranging
LiDAR is a remote sensing technique based on emission and return-registration of short
duration IR laser pulses, and in so it is a so called active remote sensing technique (Lefsky,
Cohen, Parker, & Harding, 2002). Measurements are typically conducted from an aircraft
flying along a transect, sampling points along a wide band on the ground which can later be
gridded into an image (figure 1). A discrete LiDAR return captures multiple elevations on a
small footprint, e.g. a tree will get a first return at the top of the canopy, a last return at the
ground and a number of returns, depending on the sensor, in between (Bergen et al., 2009).
The return pulse will get different intensity values depending on a number of factors: the
power of the emitted pulse, the fraction of the pulse that is intercepted by a surface, the
fraction that is reflected, and the fraction that travels back in the direction of the sensor. As
vegetation is highly reflective to IR-light and grass surfaces are typically flat, a large fraction
of the pulse will be reflected giving a high intensity value. Other types of vegetation such as
trees and bushes will scatter the pulse and give a lower intensity value. The intensity value
makes it possible to classify the LiDAR-data in classes such as ground, building, low
vegetation, high vegetation etc. In combination with accurate GPS positioning of every
measuring point, this provides an advanced remote sensing method that is cost- and timeefficient as well as highly spatially accurate.
2
Figure 1 Descriptive illustration of LiDAR data collection
1.2 Normalized difference vegetation index
The normalized difference vegetation index (NDVI) is defined as
(
)
(
)
(eq. 1)
where
and
represents surface reflectance in visible and near-infrared wavelengths
(Carlson & Ripley, 1997). In difference to LiDAR, NDVI is based on classic orthophotos,
using the red and near infrared band, and therefore it is a passive remote sensing technique. It
is correlated with a number of vegetation properties: leaf area index, fractional vegetation
cover, vegetation condition and biomass. Chlorophyll absorbs visible light for use in the
photosynthesis, while the cell structure of the leaves is highly reflective to near-infrared light.
This means that the stronger the photosynthesis and the more leaves a plant has, the more
visible light it absorbs and the more near-infrared light it reflects. The NDVI stretches from -1
to 1 where values above 0 represent vegetation (Weier & Herring, 2000).
3
2. Method
The study was carried out based on data covering the majority of municipality of Gothenburg,
Sweden (figure 2.).
Figure 2 Map of the study area. Background map © Lantmäteriet, i2012/901
All analysis of the data was carried out in ArcGIS. The central and southern parts of the area
are mainly of urban and suburban character, while the northern parts are mainly rural. A small
part of the south-central area was chosen as a study area to develop the methods. Both the
orthophoto and the LiDAR datasets were provided by the Building and Planning authority of
the city of Gothenburg (Stadsbyggnadskontoret). The LiDAR data was collected at a height of
550 m with a swath angel of 20o. It covers all of the study area (figure 1) and it is tiled into
1000 x 1000 m segments with 13.65 points per m2, each point with a 0,13 m diameter
footprint (the area of the pulse when it hits the ground). It is classified into 10 classes, where
class 1 (unassigned) and class 2 (ground) are of interest to this project. The orthophotos have
a resolution of 0.25m. The photos contain both IR and visible bands, and are tiled into 2500 x
2500 m segments. A shapefile with grass areas maintained by the municipality was also
4
provided by the Park and nature administration authority of Gothenburg (park- och
naturförvaltningen). This shapefile contains polygons of all grass areas that are maintained by
the municipality in some way, and will hereafter be refered to as municipal grass areas.
Figure 3 Orto photo of the study area. The purple areas represent the municipal maintenance grass areas
The study area is located in the southern parts of central Gothenburg. The area is dominated
by residential houses, a hospital and a university area with a lot of green areas and parks in
between (figure 3).
5
2.1 LiDAR data processing
To be able to analyze the LiDAR data, the raw point data had to be converted to raster format.
After importing the LiDAR data into ArcGIS, it was converted to a 1 m resolution raster
based on intensity using the LAS dataset to raster tool. To remove data unnecessary to the
further analysis, it was necessary to single out measurements classed as ground. This was
done by making another raster from the LiDAR data, based on the predominant class in each
1x1 m pixel using the LAS point statistics tool. Comparing the LiDAR data to orthophotos in
ArcMap, see figure 4, revealed that points classed as unassigned was almost exclusively
located on the ground, hence ground and unassigned was both extracted from the predominant
class raster, and used to extract the ground pixels from the intensity raster.
Figure 4 LiDAR classification illustration. The red rectangle in (B) marks the extent of (A), allowing
comparison of the rad lidar data to the orthophoto. The comparison shows the high accuracy of the classification
of building and trees, and that a large fraction of the points located on the ground are classed as unassigned.
6
To be able to extract the lawns from the intensity raster, an intensity threshold value was
needed. Based on manual comparison of the intensity raster and lawns visible on the
orthophotos, and distribution of the intensity values (figure 5) of pixels in the municipal
maintenance grass areas
Figure 5 Histogram of intensity raster pixels in confirmed grass areas in the municipality maintenance plan. A
rise can be seen at around intensity value 150.
the threshold was set to 150. Since not only grass show intensity values >150, but also things
like white paint on roads and other highly reflective surfaces, it was necessary to find a way to
minimize the number of pixels indicating grass where there is no grass. To do this, the raster
was first run through a Majority filter tool; if a pixel has another value than at least 3 of its 4
neighbors, the pixel gets the value of these 3 neighbors. This way outliers, like single nongrass pixels inside a grass area or grass pixels in the middle of a road, was removed. It seems
reasonable to assume that grass areas are seldom as small as 1 m2, and that the significance of
such a small area might be negligible compared to the effect of the effect on the total area
from large amounts of non-grass single or small clusters of pixels. After this the region group
tool was used, which groups any connecting clusters of pixels of the same value and gives the
group a unique ID. To further filter out non-grass areas registered as grass, a grass area
threshold value was set at 7m² and all groups with an area smaller than this was removed. The
threshold was set to 7m² after visually comparing the results of different threshold between 10
m² and 5 m² in the study area with the intention of keeping the threshold as low as possible
while still removing the majority of non-grass surfaces registered as grass.
7
2.2 Orthophoto NDVI calculation
To calculate NDVI, the orthophotos were imported to ArcGIS. The Raster calculator tool was
used to run the photos through eq(1), see figure 6, to produce a NDVI raster of the study area.
Figure 6 Flowchart showing the NDVI calculation with ArcGIS raster calculator
8
3. Results
The following section will start by showing the results from the different steps in the method
development leading up to the final model. The model will then be presented, the results will
be compared to the grass areas previously known from the municipality maintenance plan and
finally the model will be applied to a larger study area
3.1 Method development - LiDAR
The first step in the method development was to convert the LiDAR data to an intensity raster
using only the LiDAR points classed as ground or unassigned for the study area. The
resulting raster is shown in figure 7.
Figure 7 Intensity raster map of the study area produced from LiDAR data. High values (>150) indicate grass.
Buildings, trees and other non-ground features have been set to 0.
9
The process of minimizing the amount of non-grass pixels is shown in figure 8 A-C. In figure
8 A a significant amount of pixels indicating grass can be seen in an area (the road) where
there is no grass, and a number of pixels can been seen where it is hard to verify if there is
grass or not (the right red circle). Figure 8 B show the result after the majority filter. The
number of isolated pixels in both the red circles has been reduced, and holes have been filled
in the large grass area in the bottom left corner. In figure 8 C the final result after applying
region group and removing all clusters smaller than 7 m2 can be seen in detail. There are no
longer any grass pixels on the road in the left red circle, and no isolated pixels in the right
circle.
Figure 8 Step-by-step illustration of the filtering process. The red circles indicate problem areas. (A) shows the
raw intensity raster reclassified to <150 = 0 and >=150 = 1. A relatively large amount of non-grass pixels
registered as grass can be seen in the red circles. (B) shows the raster after applying the Majority filter. There are
still some pixels showing grass where there is no grass. (C) shows the resulting raster after removing all clusters
smaller than 7m2.. No grass pixels can be seen on the asphalt in the left red circle.
10
Figure 9 show the resulting filtered raster for the study area. As shown in tabel 1. the raster
contains substantially more grass than the municipal grass areas.
Figure 9 Final study area grass area map. The map shows the resulting raster after the filtering process in figure
7. White represents no data pixels. The total area of the study area is 430.3 ha (excluding no data pixels). The
total detected grass area is 56.9 ha or 13.2%.
Tabel 1. Study area grass areas.
Study area raster
Total Area
Unfiltered grass
Filtered grass
Verified grass
ha % of total area
430.3
100%
62.8
14.6%
56.9
13.2%
24.9
5.8%
11
3.2 Method development - NDVI
As seen in figure 10 and described in the introduction, IR photos capture vegetation well. IR
is represented by the red band in figure 10, meaning that anything highly reflective in the IR
band gets a red color. It can be seen that vegetation such as trees and grass is red, while other
surfaces are not.
Figure 10 Study area IR orthophoto. Vegetation is easily identified by the red color.
12
The resulting raster from the NDVI calculation based on the orthophotos in figure 3 and
figure 10 is shown in figure 11. When comparing to the orthophoto (figure 3) it can be noted
that the NDVI identifies grass and gives it a positive value, e.g. in the football field in the
lower center of the map. Forested areas, e.g. in the lower left corner also show positive values.
Figure 11 Study area NDVI map. Values >0 represent vegetation and are shown as green. The higher the value,
the more dense is the vegetation. It is obvious that grass is detected, eg the football field in the lower central part
of the map.
The results of a detailed comparison of the filtered LiDAR-based grass raster and the NDVI
raster are shown in figure 12. Notable when comparing figure 12 B and C is the NDVI´s
inability to capture vegetation in shaded areas, and also that it is hard to distinguish grass
from trees and other vegetation in the NDVI raster shown in figure 12 C.
13
Figure 12 Detailed comparison of filtered lidar-based grass raster and NDVI raster. (A) show the basic orto
photo. (B) show the orto photo overlaid by the filtered LiDAR based raster. (C) show the NDVI raster. The red
circles indicate areas of interest. It is apparent from looking at all 3 circles that NDVI, in difference to LiDAR,
does not capture vegetation in shaded areas. When comparing (A) and (C) it can also be noted that it is hard to
distinguish trees from grass in (C).
Because of these obvious large deficiencies in using the NDVI it was excluded from the final
method.
3.3 ArcGIS grass identification model
The steps visualized in figure 7-9 lead to the ArcpMAP model builder model shown in figure
13. The model automates the process from input raw LiDAR data to output filtered grass
raster. The data input and output are model parameters, meaning that the model can be
programmed to batch run large datasets automatically.
14
15
Figure 13 ArcMAP model builder model automating the method. The light blue ellipse represent input LiDAR data, the yellow rectangles represent ArcGIS tools. The
green ellipses represent intermediate data, the blue hexagons logical operator input values and the red ellipse the final output raster. P indicates model parameter.
3.4 Comparing study area model grass to municipal grass areas data
It is apparent from looking at table 1 and 2 that the model detects more total grass in the area
than what is registered in the municipality maintenance plan. For the study area the model
identifies 2.3 times more total grass than what, for the larger area the factor is 3.3. Figure 14
illustrates how only parts of the municipal maintenance areas identified as grass by the model
because a lot of the area is covered by trees that obstruct the view from above, and also how it
identifies areas not included in the municipality maintenance plan such as private lawns etc.
Figure 14 Illustration of municipal maintenance grass area vs grass identified by the model. It is apparent that
the model does not capture all of the municipal grass. It can also be seen that the model identifies a lot of grass
that is not registered in the municipality maintenance plan.
Table 2 shows that the model detects grass in 42.6% of the total verified grass area.
Table 2 Study area model grass and municipal maintenance grass area
A
B
C
D
E
Total raster area (ha)
Raster grass (ha)
Polygon file (verified grass) (ha)
Polygons covered by raster (B covered by C) (ha)
Detected/Municipal maintenance grass (D/C) %
16
430.3
56.9
24.9
10.6
42.6
3.5 Applying the model to the larger study area
Applying the model in figure 13. to the full LiDAR data set revealed large scale intensity
variations in the data set (see the bandings in figure 15).
Figure 15 Total data coverage intensity raster. Large scale intensity variations can be seen over the whole area.
According to the data provider (The Building and Planning autority of Gothenburg) this
depends on inaccurate calibration of the LiDAR sensor by the operators between the
collection events. Since calibration is done by flying once or preferably twice in cross-lines
perpendicular to the parallell flight lines, this is not possible to post-adjust. The intensity
variations means that it is not possible to use the same grass threshold for the entire area.
Since the data was separated into tiles after collection, without considering the intensity
differences between the collection events, it is also not possible to manually set different
intensity threshold for different tiles.
17
To be able to apply the method to an area as
large as possible, a larger study area was fitted
to a large area in the same intensity regime as
the primary study area (figure 2). The larger
study area is a good representation of the total
urban areas to suburban and rural areas. The
resulting raster along with the municipal
maintenance grass area is displayed in figure 16.
The largest amount of grass areas identified by
the model can be seen in the northern half of
area. On the opposite, the amount of municipal
maintenance grass areas is notably lower in the
north since there are few public lawns in the
rural area.
Table 3 shows the amount of grass in the larger
study area before and after filtering, along with
the amount of municipal maintenance grass
areas When comparing with the numbers from
the study area (table 1) it can be seen that the
relative amount of grass identified by the
LiDAR method is larger in the larger study area.
Table 3 Larger study area grass areas.
Larger study raster
ha % of total area
Total Area
6656.2
100%
Unfiltered grass
1283.9
19.3%
Filtered grass
956.0
14.4%
Municipal maintenance grass 290.6
4.4%
The relative amount removed by the filtering
process (from 19,3% to 14,4 % grass) however,
is also larger.
Figure 16 Detected and municipal grass areas in the
larger study area
18
3.6 Applying the model to Säve low intensity test area
To investigate the possibility of using the model in one of the low intensity zones, a small
area surrounding Säve airport (figure 2) was selected as a test area. From looking at the
background orthophoto in figure 18 B it can be seen that the area is dominated by grasslands
surrounding the airport buildings and landing strips, meaning that an accurate model should
class a large part of the area as grass. Comparing the intensity distributions for the Säve area
and the test area (figure 17) reveals that the rise at around 150 (figure 4, figure 17) defining
the grass threshold in the study area occurs at around 70 in Säve. The rasters resulting from
running the model in the Säve area using both 70 and 150 as grass threshold values are
presented in figure 17. From table 4 it can be seen that with an intensity threshold of 150, only
0.2% of the area is recorded as grass, while with a threshold of 70, 62.1% of the area is
identified as grass.
Figure 17 Intensity value histogram for the study area and Säve test area
Figure 18 Säve test area grass raster. In (A) the intensity threshold was set to 70. In (B) the threshold was set to
150.
19
Table 4 Säve test area grass areas.
Säve test area
Total area
>= 150
>=70
ha % of total area
96.0
100%
0.2
0.2%
59.6
62.1%
4. Discussion
As shown in table 2, the model detects substantially more grass in the study area than what is
registered in the municipality maintenance plan. This is natural, since only a fraction of the
grass surfaces in a city is maintained by the city. The fact that the model only detects 42.6%
(table 2) of the municipal maintenance grass areas in the study area illustrates that the model,
as remote sensing based ground classification techniques in general, cannot detect grass that is
overgrown by trees or are otherwise obstructed from view from above, and also that a large
fraction of the total grass area is located below trees. This can be compared to a study
conducted in Shanghai by Huang et al. (2013), where 77.73% of the green areas was
identified as grass. The large difference is most likely due to differences in the green area
characteristics between the two cities, where Gothenburg have a higher fraction of trees
covering vegetated areas.
From looking at the grass maps in figure 9 and 16, it is clear that the amount of grass detected
varies throughout the areas. This is probably both due to the fact that there are more grass in
some areas than in others, e.g the most central parts of the city have fewer lawns than the
suburbs, but also that the fraction of detectable grass is likely to vary with the characteristics
of the area. For example is it likely that in a forested park, most of the grass would be
obstructed from view from above, while in an open area like a golf course, a large fraction of
the grass can be detected.
4.1 Accuracy of the results
Comparing the detected grass to grass visible in the example orthophoto in figure 19 indicates
that the developed method is highly accurate in detecting grass visible from above, and in
distinguishing grass from hardened surfaces such as asphalt. The general scientific view on
using LiDAR data for ground classification seems to agree with this. Song et. Al. (2002)
investigated the possibility of using LiDAR intensity data for making land cover
classifications, where one of the classes they used was grass. The authors conclude that
LiDAR intensity data is highly useful for land cover classifications. Brennan & Webster
(2006) developed a LiDAR intensity and height data based land cover classification method
capable of separating 10 classes, agreeing with Song et al. (2002) about the suitability of
using LiDAR data for land cover classifications.
20
Figure 19 Detected grass example area. Comparing the detected grass to the orthophoto indicates that the model
is highly accurate in detecting grass that is visible from above.
It can be seen that the LiDAR classification filtering is successful in removing non-ground
features such as buildings and trees from the results; large light-colored, highly reflective
rooftops as seen in the orthophoto would otherwise have been likely to have intensity values
classified as grass.
As exemplified in figure 20 and 21, there are still areas where the results could be improved.
Figure 20 show the problem of actual grass not being registered because it does not reach
above the intensity threshold. A large hole can be seen in the grass raster overlying the large
lawn visible in the orthophoto. The cause for this is likely damage to the lawn because of high
wear from people, but could also be coupled to low growth because of bad soil, drought,
extensive shadowing etc. A similar example can be seen in the football field in figure 7. Since
there is no absolute true intensity threshold value for grass, and setting it will mean a
compromise between including as much grass as possible, while excluding as much lower
intensity surfaces as possible, this problem is hard to get around. A possible solution could be
to have two thresholds: the existing one, grass, and a lower one for potential grass, and then
include potential grass surrounded or partly surrounded by grass to the final raster.
21
Figure 20 Non-detected grass. Example of an area where grass is not detected because it´s intensity value does
not exceed the threshold.
Figure 21 show that the issue addressed by the filtering process shown in figure 8, is still
present in some areas. Typically, light surfaces are highly reflective, and in the event that
there are clusters of pixels larger than 7m² with intensity values above 150, they have not
been removed by the filtering process. This is typically ground covered with concrete or
asphalt with white paint such as road crossings. Looking at the whole study area, the number
of areas of the kind still left after the filtering process is low and should have a very limited
impact on the total grass area. Most of these areas are small, just above the minimum area of
7m² meaning that increasing the minimum area could reduce the amount of non-grass areas.
Just as setting the intensity threshold, setting the minimum area means compromising:
increasing the minimum area would also lead to removing areas that are actually small grass
areas which probably has a larger impact on the results than the few non-grass areas. A
solution to this could be to include GIS data of hardened surfaces (roads, parking lots etc.)
where this is available, and simply remove any grass pixels located in registered hardened
surfaces.
22
Figure 21 Non-grass registered as grass. Example of a non-grass area registered as grass that has not been
removed by the filtering process.
4.2 Improving the results and increasing the accuracy of the model
To better capture the true grass area, finding a way to include the grass below trees is
essential. A number of different methods could be used: If the relation between
detected/obstructed grass presented above is considered to be true for all grass in the entire
study area and not just in the municipal maintenance grass areas, an estimation of the total
grass can be done by using the fraction of the municipal maintenance areas that the model
detects as a factor with which to divide the amount of detected grass:
133.5 ha. 133.5
ha corresponds to 31% of the total primary study area, compared to the 13.2% detected by the
model. This is a general estimation, but likely closer to the real area than the 13.2% primarily
detected grass. It is important to note that the factor used above only represents the urban
characteristics of the primary study area, and would probably give a less accurate estimation
in the larger study area. An estimation like this also does not specify where the non-detected
grass is located.
Since no study of the exact same kind as this has been conducted, where the aim is to find the
total area of grass surfaces in an area, and there is a general lack of total grass area data, there
is little to compare the results to. There are however some comparisons to be made. Akbari et
al. (2003) conducted an orthophoto based land cover classification, including grass as one of
the classes, in Sacramento, California. The results vary between different areas of the city,
from 6% grass in an industrial area, 7.5% in downtown to 25-30% in typical residential areas.
23
The average amount for the total area is 20%. In Stockholm, Sweden, 26% of the total land
area is made up by parks and green areas (Bolund & Hunhammar, 1999), out of which a
significant amount is likely either grass or grass overgrown by trees. This indicates that the
total grass coverage is likely to be somewhere between 15-25% in Stockholm. Both
Stockholm and Sacramento are considered by the authors of the respective reports to be
relatively green cities. The estimated 31% in the primary study area is higher than the
numbers for both Stockholm and Sacramento, but the primary study area is an area with a lot
of green areas, and including other parts of the city would likely lower the number for
Gothenburg. Still, it indicates that the results are within range of data from other cities.
To further increase the accuracy of the results, a method for identifying grass below trees,
more accurate than the estimation above, should be included in the model. Figure 22 show a
typical urban green area with lawns covered by trees. It is reasonable to assume that the
majority of the ground below the trees is covered by grass.
Figure 22 Typical grass area partly covered by tress. The red circle indicate trees completely surrounded by
grass. Road outlines. (Building and planning authority of Gothenburg (2012)
To identify this grass, a first step could be to produce a tree canopy map from the LiDAR
data. This canopy map could be used to identify trees surrounded by grass, as the ones in the
middle of figure 22, and simply add the area of those trees to the grass map, possibly
excluding an estimated area occupied by the trunk. For trees partly surrounded by grass, one
could calculate the fraction of the tree´s circumference that is in contact with grass, and add a
corresponding fraction of the tree´s area to the grass map under the tree. Multiple trees with
connecting canopies, as the row of connecting trees between the grass and the road in figure
22 or larger forests, complicates things and will give less accurate results. One possible
approach is to include other kinds of geo data, such as the road outlines in figure 22, and
assume that the area obstructed from view from above and outside of the road outlines is
covered by grass.
24
Another interesting factor that could increase maybe not the accuracy but the value of the
results would be to investigate the possibility to distinguish between different types of grass
areas such as high maintenance lawns and low maintenance meadows, since different types of
grass areas have significantly different effects on its surroundings. This was partly
accomplished by Tooke et al. (2009) in a urban vegetation study conducted in Vancouver
based mainly on satellite imagery NDVI, where they by using differences in albedo could
separate grass into two categories, manicured and mixed.
4.3 Säve low intensity test area
The results from the Säve test area shown in figure 18 indicate that the model is adaptable to
variations in intensity, but that separate intensity thresholds must be set manually by looking
at the intensity distribution for each area. Since the LiDAR-dataset is tiled into 1 km² tiles
with no consideration to the intensity variations, it would not be possible to set intensity
values tile by tile.
If attempting to apply the method to an area with large scale intensity variations such as this, a
possibility would be to manually divide the dataset into sub-areas based on intensity
distribution and set an appropriate threshold for each sub-area separately. This problem is not
however, a general challenge to this method, as it purely depends on deficiencies in the
dataset used in this particular study.
5. Conclusions
This project indicates that there is great potential in the use of LiDAR data as an objective
method to detect and map grass on a micro scale over large areas. NDVI was found less
suitable because of its inability to capture shaded vegetation. The developed model is highly
accurate in detecting grass that is not obstructed from view from above, and it detects
substantially more total grass than what is registered by the building and planning office in
Gothenburg. This demonstrates the large amount of private and informal grass areas in the
city.
Since a large portion of the grass in cities is typically overgrown by trees, the fact that this
model does not detect this grass is an obvious deficiency, and the main challenge to the
method, which is clearly illustrated by the fact that the model only detects 42.6% of the
municipal maintenance grass areas grass. This study is a first step in developing a general
grass area identification model, and including a method for detecting or accurately estimating
grass overgrown by trees was beyond the scope of this study, but should be the main focus if
trying to increase the accuracy of the model in future works.
Acknowledgements
First of all I want to thank my supervisor Fredrik Lindberg, University of Gothenburg, for
introducing me to this project and providing a lot of indispensable help along the way. I also
want to thank my co-supervisor Marcus Hedblom, SLU Uppsala, for inspiration and
assistance with finding literature on the subject, as well as inviting me to present my work for
25
the LAWNS research group at SLU Uppsala. Further, I would like to thank my examiner
Sofia Thorsson and Alexander Walther.
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26
Weier, J., & Herring, D. (2000, August 30th). Measuring Vegetation (NDVI &EVI).
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Data:
Grass maintenance areas: Park- och naturförvaltningen, Göteborg (2013)
LiDAR-data: Stadsbyggnadskontoret, Göteborg (2010), geodataavdelningen.
Orthophotos: Stadsbyggnadskontoret, Göteborg (2012), geodataavdelningen.
Road
outlines: Göteborgs
geodataavdelningen.
stads
primärkarta
27
(2012).
Stadsbyggnadskontoret,