ArticlePDF Available

Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing Techniques: A Case Study of Makueni County, Kenya

Authors:

Abstract and Figures

The surface of the earth is undergoing rapid land-use/land-cover (LULC) changes due to various socioeconomic activities and natural phenomena. The main aim of this study was to gain a quantitative understanding of land use and land cover changes in Makueni County over the period 2000- 2016. Supervised classification-maximum likelihood algorithm in ERDAS imagine was applied in this study to detect land use /land cover changes observed in Makueni County using multispectral satellite data obtained from Landsat 7 for the years 2000, 2005 and 2016 respectively. The County was classified into seven major LU/LC classes viz. Built up areas, croplands, water bodies, evergreen forests, bush-lands, grassland and bare-land. Change detection analysis was performed to compare the quantities of land cover class conversions between time intervals. The results revealed both increase and decrease of the different LULC classes from 2000 through to 2016. Significant shifts from some classes to others was also observed. Drivers of the observed changes ranged from Climatic factors such as rainfall and drought to socio-economic factors. Consistent LULC mapping should be carried out in order to quantify and characterize LULC changes. This will help establish trends and enable resource managers to project realistic change scenarios helpful for natural resource management.
Content may be subject to copyright.
Research Article Open Access
Cheruto et al., J Remote Sensing & GIS 2016, 5:4
DOI: 10.4175/2469-4134.1000175
Volume 5 • Issue 4 • 1000175
Journal of Remote Sensing & GIS
J
o
u
r
n
a
l
o
f
R
e
m
o
t
e
S
e
n
s
i
n
g
&
G
I
S
ISSN: 2469-4134
J Remote Sensing & GIS
ISSN: 2469-4134 JRSG, an open access journal
Keywords: Land use; Land cover change; Change detection;
Supervised classication; Makueni county
Introduction
LULC change is a major issue of concern with regards to change in
the global environment [1]. e rapid growth and expansion of urban
centres, rapid population growth, scarcity of land, the need for more
production, changing technologies are among the many drivers of
LULCC in the world today [2]. According to Ref. [3], LULCCs respond
to socioeconomic, political, cultural, demographic and environmental
conditions and forces which are largely characterized by high human
populations. LULCC has become one of the major concerns of
researchers and decision makers around the world today.
Many researchers argue that LULCC emerged as a major aspect
in the wider debate of global change; and that change originates from
human-induced impacts on the environment and their implications for
climate change [4-6]. e indicators of these changes can be clearly seen
in the current major global concerns such as increasing concentrations
of carbon dioxide (CO2) in the atmosphere, loss of biological diversity,
conversion and fragmentation of natural vegetation areas and
accelerated emission of greenhouses gases [7].
LULC dynamics are widespread, accelerating, and signicant
processes majorly impelled by human actions and at the same time
resulting to changes that impact human livelihood [8]. e LULC
dynamics modify the availability of dierent important resources
including vegetation, soil, water, and others [9,10].
Due to rising population over the years, lots of pressure has
been imposed on the land resources in Kenya where approximately
75% of the populace engages in agriculture but only 20% of its land
is arable. As a result, the shortage of arable land has led to expansion
of cultivation into the wetter margins of rangelands, deforestation
and decline of grassland as a result of overgrazing, charcoal burning
and other unsustainable land uses. ese actions have far reaching
implications on the integrity of natural resources and ecosystems in
the country [11,12].
*Corresponding author: Mercy C Cheruto, Department of Environmental
Science and Technology, School of Environment and Natural Resources
Management, South Eastern Kenya University, Kitui, Kenya, Tel:
+254723928878; E-mail: mercycheruto@gmail.com
Received October 22, 2016; Accepted November 02, 2016; Published November
03, 2016
Citation: Cheruto MC, Kauti MK, Kisangau PD, Kariuki P (2016) Assessment of
Land Use and Land Cover Change Using GIS and Remote Sensing Techniques:
A Case Study of Makueni County, Kenya. J Remote Sensing & GIS 5: 175. doi:
10.4175/2469-4134.1000175
Copyright: © 2016 Cheruto MC, et al. This is an open-access article distributed under
the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited.
Abstract
The surface of the earth is undergoing rapid land-use/land-cover (LULC) changes due to various socioeconomic
activities and natural phenomena. The main aim of this study was to gain a quantitative understanding of land use
and land cover changes in Makueni County over the period 2000- 2016. Supervised classication-maximum likelihood
algorithm in ERDAS imagine was applied in this study to detect land use /land cover changes observed in Makueni
County using multispectral satellite data obtained from Landsat 7 for the years 2000, 2005 and 2016 respectively.
The County was classied into seven major LU/LC classes viz. Built up areas, croplands, water bodies, evergreen
forests, bush-lands, grassland and bare-land. Change detection analysis was performed to compare the quantities of
land cover class conversions between time intervals. The results revealed both increase and decrease of the different
LULC classes from 2000 through to 2016. Signicant shifts from some classes to others was also observed. Drivers of
the observed changes ranged from Climatic factors such as rainfall and drought to socio-economic factors. Consistent
LULC mapping should be carried out in order to quantify and characterize LULC changes. This will help establish trends
and enable resource managers to project realistic change scenarios helpful for natural resource management.
Assessment of Land Use and Land Cover Change Using GIS and Remote
Sensing Techniques: A Case Study of Makueni County, Kenya
Mercy C Cheruto1*, Matheaus K Kauti1, Patrick D Kisangau2 and Patrick Kariuki3
1Department of Environmental Science and Technology, School of Environment and Natural Resources Management, South Eastern Kenya University, Kitui, Kenya
2Department of Biology, School of Pure and Applied Sciences, South Eastern Kenya University, Kitui, Kenya
3Department of Geology, Institute of Mineral Processing and Mining, South Eastern Kenya University, Kitui, Kenya
LULCCs has also taken place in Makueni County over the years.
Land has been subjected to a lot of pressure due to over-reliance on its
resources. ere has also been rapid population growth in the county
in the recent past and this has translated to over-utilization of land and
its resources. Most communities are farmers and they therefore depend
on land for their livelihood well-being and sustenance. However, the
county is located in ASALs and thus the environmental and climatic
conditions are not favorable for crop production. is has resulted
to the locals engaging in other sustenance activities such as charcoal
burning, logging and even sand harvesting, all of which result to
environmental degradation.
Materials and Methods
Study area
Makueni County covers an area of 8,034.7 Km2. e county lies
between Latitude 1°35´ and 3°00 South and Longitude 37°10´ and
38°30´East [13]. e map boundary for this area stretches in a north
west to south east direction (Figure 1). e County boarders Kajiado,
to the West, Taita Taveta to the South, Kitui to the East and Machakos
County to the North. e county lies in the arid and semi-arid zone
in Eastern Kenya. It consists of hills and small plateaus rising between
600-1900 metres above sea level (masl) [13,14].
Citation: Cheruto MC, Kauti MK, Kisangau PD, Kariuki P (2016) Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing
Techniques: A Case Study of Makueni County, Kenya. J Remote Sensing & GIS 5: 175. doi: 10.4175/2469-4134.1000175
Page 2 of 6
Volume 5 • Issue 4 • 1000175
J Remote Sensing & GIS
ISSN: 2469-4134 JRSG, an open access journal
Data collection
Two types of data were used in this research. Satellite data that
comprised of three years multi- temporal satellite imageries (LANDSAT
7 imageries of 2000, 2005 and 2016) for the month of February acquired
from the USGS GLOVIS website (Table 1). Ancillary data included the
ground truth data for the LU/LC classes. e ground truth data was in
the form of reference points collected using Geographical Positioning
System (GPS) for the 2016 image analysis, used for image classication
and overall accuracy assessment of the classication results.
Image pre-processing and classication
Pre-processing of satellite images before detection of changes is a
very vital procedure and has a unique aim of building a more direct
association between the biophysical phenomena on the ground and
the acquired data [15]. Data were preprocessed in ERDAS imagine for
geo-referencing, mosaicking and sub-setting of the image on the basis
of Area of Interest (AOI). e main objective of image classication
is to place all pixels in an image into LU/LC classes in order to draw
out useful thematic information [16]. Image classication was done
in order to assign dierent spectral signatures from the LANDSAT
datasets to dierent LULC. is was done on the basis of reectance
characteristics of the dierent LULC types. Dierent color composites
were used to improve visualization of dierent objects on the imagery.
Infrared color composite NIR (4), SWIR (5) and Red (3) was applied in
the identication of varied levels of vegetation growth and in separating
dierent shades of vegetation. Other color composites such as Short
Wave Infra-red (7), Near Infra-red (4) and Red (2) combination
which are sensitive to variations in moisture content were applied in
identifying the built-up areas and bare soils. is was supplemented
by a number of eld visits that made it possible to establish the main
land use land cover types. For each of the predetermined LU/LC
type, training samples were selected by delineating polygons around
representative sites. Spectral signatures for the respective LU/LC types
derived from the satellite imagery were recorded by using the pixels
enclosed by these polygons. A satisfactory spectral signature is the one
ensuring that there is ‘minimal confusion’ among the land covers to
be mapped [17]. Maximum Likelihood classier (MAXLIKE) scheme
with decision rule was used for supervised classication by taking 89
training sites for seven major LU/LC classes. e number of training
sites varied from one LU/LC class to another depending on ease of
identication and the level of variability. e Maximum Likelihood
Classication is the most widely used per-pixel method by taking into
account spectral information of land cover classes [1]. e delineated
LU/LC classes were; built up areas, water bodies, croplands, evergreen
forests, bush-lands, grasslands and bare-lands as described in Table 2.
Post classication
Post-classication renement is done to improve classication
accuracy and reduction of misclassications [18]. Aer classication,
ground verication was done in order to check the precision of the
classied LU/LC map. Based on the ground verication necessary
correction and adjustments were made. e map from t1 (e.g., 2000)
(Figure 2) was compared with the map produced at time t2 (2005)
(Figure 3) and a complete matrix of categorical change obtained
Accuracy assessment
If the classication data are to be useful in detection of change
analysis, it is essential to perform accuracy assessment for individual
classication [19]. Accuracy assessment is an essential and crucial part
of studying image classication and thus LULCC detection in order to
understand and estimate the changes accurately. It reveals the extent of
correspondence between what is on the ground and the classication
results. It is important to be able to derive accuracy for individual
classication if the resulting data are to be useful in change detection
analysis [19]. In this study, accuracy assessment was done for the
Landsat 7 ETM+ 2016 satellite image, for which the ground truth data
likely equates. An overall accuracy was calculated by dividing the sum
of the correctly classied sample units by the total number of sample
units.
Results and Discussion
Classication
e overall classication accuracy was 88.0%. e study area was
dened to have seven land use and land cover categories, which were:
Water Bodies, Grassland, Forest, Cropland, Bush-land, Built up Area
and Bare-land. e land use land cover classication for 2000 is shown
in Figure 4.
Gross percentage change in LU/LC classes between 2000-2016
Generally, over sixteen years (2000- 2016), the gross changes in
area coverage varied from one LULC class to another with bush-land
experiencing the most increase and evergreen trees undergoing the
most decrease in area coverage as shown in Figure 5.
LULC change detection for the years 2000, 2005 and 2016
Change detection between 2000 and 2005: 95%, 78%, 36% and
35% of land under Evergreen forests, water bodies, bare-lands and
croplands, respectively in 2000 remained under the same LULC
categories in 2005. is was also the case with land under built up
areas (33%), bush-lands (20.4%) and grasslands (10.7%). However,
there were also signicant conversions from one land cover category
to another within the same period. ere were signicant conversions
from evergreen forests to bush-land (58.2%) and to croplands (51%).
9% and 8.7% of what was croplands in 2000 was converted to bare-
lands and grasslands respectively. 42.4% of bush-lands, 22.6% of
Figure 1: Makueni County.
Citation: Cheruto MC, Kauti MK, Kisangau PD, Kariuki P (2016) Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing
Techniques: A Case Study of Makueni County, Kenya. J Remote Sensing & GIS 5: 175. doi: 10.4175/2469-4134.1000175
Page 3 of 6
Volume 5 • Issue 4 • 1000175
J Remote Sensing & GIS
ISSN: 2469-4134 JRSG, an open access journal
Figure 2: LU/LC Classication map of the study area for the year 2000.
Figure 3: LU/LC Classication map of the study area for the year 2005.
Figure 4: LU/LC Classication map of the study area for the year 2016.
476.2
-232.1
4.8
-1774.8
1830.2
-172 -132.2
-2000
-1500
-1000
-500
0
500
1000
1500
2000
2500
% CHANGE
LULCC CLASSES IN Km2
Built-up Area/ mining Cropland Water Body
Evergreen forest Bushland Grassland
Bareland
Figure 5: Gross percentage change in LULC categories from 2000-2016.
evergreen forests, 15.4% of bare-lands and 8.7% of croplands were
converted to grasslands while 36% of bare-lands, 30% of bush-lands
and 15% of evergreen trees were converted to bare-lands by the year
2005. Bush-lands were majorly converted to grasslands (42.4%) and
bare-lands (30%) (Table 3).
Change detection between 2005 and 2016: e second
comparison made during 2005 to 2016, 66%, 46%, 27.9%, 25%, 8% and
Citation: Cheruto MC, Kauti MK, Kisangau PD, Kariuki P (2016) Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing
Techniques: A Case Study of Makueni County, Kenya. J Remote Sensing & GIS 5: 175. doi: 10.4175/2469-4134.1000175
Page 4 of 6
Volume 5 • Issue 4 • 1000175
J Remote Sensing & GIS
ISSN: 2469-4134 JRSG, an open access journal
2005
LULC Type Built-up Area Cropland Water Body Evergreen Trees Bush-land Grassland Bare-land
Area (km2) % Area Area (km2) % Area Area
(km2)% Area Area
(km2)% Area Area (km2) % Area Area
(km2)% Area Area
(km2)% Area
2000
Built-up Area 82.5 33% 1.4 1% 0.0004 16% 0.5 0% 32.2 1.1% 4.4 0.3% 63.1 3%
Cropland 19.2 8% 49.4 35% 0.0000 0% 10.2 2% 248.7 8.2% 141.3 8.7% 236.5 9%
Water Body 0.2 0% 0.0 0% 0.0021 78% 0.1 0% 0.6 0.0% 0.0 0.0% 0.0 0%
Evergreen
Forests 55.4 22% 73.1 51% 0.0002 6% 464.7 95% 1756.9 58.2% 368.4 22.6% 373.0 15%
Bush-land 54.4 21% 12.4 9% 0.0000 0% 6.9 1% 615.5 20.4% 693.2 42.4% 741.0 30%
Grassland 24.9 10% 4.8 3% 0.0000 0% 1.4 1% 157.7 5.2% 174.2 10.7% 194.5 8%
Bare-land 16.8 7% 1.4 1% 0.0000 0% 3.3 1% 207.1 6.9% 251.8 15.4% 894.6 36%
TOTAL 253.4 100 142.5 100 0.003 100 487.1 100 3018.8 100 1633.3 100 2502.7 100
Table 3: Change detection matrix of 2000 to 2005.
Year Day and Month Scene/Tile Entity Id
2000
1/ March 167/061 LE71670612000061SGS02
1/ March 167/062 LE71670622000061SGS02
21/ February 168/061 LE71680612000052EDC00
21/ February 168/062 LE71680622000052EDC00
2005
12/February 167/061 LE71760612005042PFS00
12/February 167/062 LE71670622005042PFS00
19/February 168/061 LE71680612005049ASN00
19/February 168/062 LE71680622005049ASN00
2016
26/ February 167/061 LE71670612016057SG100
26/ February 167/062 LE71670622016057SG100
17/ February 168/061 LE71680612016048SG100
17/ February 168/062 LE71680622016048SG100
Table 1: Dates and scene ID numbers of Landsat Images used.
Land Cover Description
1. Forest This describes the areas with evergreen trees mainly growing naturally in the reserved land, along the rivers and on the hills.
2. Bush land Describes areas with sparse trees and shrubs.
3. Crop land The land which is mainly used for growing food crops such as maize, green grams, beans, cassava, mangos. Crops in this land are either grown by
irrigation or rain-fed.
4. Water bodies This class of land cover describes the areas covered with water either along the river bed or man-made earth dams, lled sand dams and ponds.
5. Bare-land This describes the land left without vegetation cover. This result from abandoned crop land, eroded land due to land degradation and weathered road
surface.
6. Grassland This class of land cover denes grass as the main vegetation cover.
7. Built-up area This class describes the land covered with buildings in the rural and urban. It includes commercial, residential, industrial and transportation infrastructures.
Table 2: Land class and denitions for supervised classication.
7% of land under bare-lands, bush-lands, grasslands, evergreen forests,
built up areas and croplands respectively in 2005 remained under the
same LULC categories in 2016. Some area under evergreen forests
were converted to water bodies (14%), croplands (4%) and built-
up areas (4%). Despite the conversion of evergreen forests to other
LULC classes, there was also the conversion of 50% of bush-lands,
8% of grasslands and 4% of croplands to evergreen forests. Signicant
conversion to croplands emanated from bare-lands (45%) and bush-
lands (25%). e table also shows that 25% and 22.8% of grasslands in
2005 was converted to bush-lands and bare-lands respectively in 2016.
ere was a strong conversional relationship from bare-lands to other
classes such as grasslands (54%), croplands (45%) and built up areas
(36%). 50% of bush-lands were converted to evergreen trees while 36%
was converted to both water bodies and built up areas (Table 4).
Land use and land cover analysis: e result of this study showed
that built up areas, water bodies and bush-lands increased from
160.7 km2, 1.1 km2 and 2159.77 km2 in 2000 to 644.57 km2, 5.77 km2
and 3893.27 km2 in 2016 respectively. Croplands, evergreen forests,
grasslands and bare-lands decreased during this period with evergreen
forests decreasing the most from 39% coverage in 2000 to 17% coverage
in 2016 (Table 5). ese changes took place at the expense of other LU/
LC classes as seen in the change detection matrices (Tables 3 and 4).
LU/LC changes are complex and at the same time interrelated such that
the expansion of one LU/LC type occurs at the expense of other LU/
LC classes [20,21]. e results of this study agrees with the results of
other studies. In their study in Dembecha area, northwestern Ethiopia,
Ref. [22] found out that the expansion of cultivated land took place
at the expense of forest land between 1957 and 1982. Similarly, recent
researches have revealed that the expansion of agricultural land has
been at the expense of lands with natural vegetation cover [6,23-26].
Conclusion
In this work, it was proven that the supervised classication of
multi-temporal satellite images is an eective tool to quantify current
land use as well as to detect changes in a changing environment.
Landsat 7 satellite images of 2000, 20005 and 2016 were used for the
Citation: Cheruto MC, Kauti MK, Kisangau PD, Kariuki P (2016) Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing
Techniques: A Case Study of Makueni County, Kenya. J Remote Sensing & GIS 5: 175. doi: 10.4175/2469-4134.1000175
Page 5 of 6
Volume 5 • Issue 4 • 1000175
J Remote Sensing & GIS
ISSN: 2469-4134 JRSG, an open access journal
2016
LULC Type Built-up Area Cropland Water Body Evergreen
Forests Bush-land Grassland Bare-land
Area (km2) % Area Area
(km2)% Area Area
(km2)% Area Area
(km2)
%
Area Area (km2) % Area Area
(km2)% Area Area
(km2)% Area
2005
Built-up Area 50.1 8% 23.9 5% 1.0 17% 74.7 5% 84.3 2% 1.1 0.3% 16.6 1.3%
Cropland 10.7 2% 32.8 7% 0.1 1% 48.8 4% 41.2 1% 3.4 0.9% 6.0 0.5%
Water Body 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0% 0.0 0.0% 0.0 0.0%
Evergreen Forests 25.6 4% 17.3 4% 0.8 14% 345.9 25% 87.5 2% 2.0 0.5% 4.2 0.3%
Bush-land 233.3 36% 122.1 25% 2.1 36% 684.3 50% 1799.6 46% 64.5 16.4% 112.9 9.1%
Grassland 95.8 15% 70.1 15% 1.1 20% 104.1 8% 969.2 25% 109.8 27.9% 284.0 22.8%
Bare-land 229.1 36% 214.7 45% 0.7 13% 115.2 8% 911.4 23% 212.6 54.0% 823.5 66.0%
Total 644.5 100 480.9 100 5.7 100 1373.0 100 3893.2 100 393.4 100 1247.1 100
Table 4: Change detection matrix of 2005 to 2016.
LULC Type 2000 2005 2016
Area (km2) % Area Area (km2) % Area Area (km2) % Area
Built-up Area 160.7 2 253.4 3 644.5 8
Cropland 723.1 9 142.5 2 480.9 6
Water Body 1.1 0.01 0.0 0.0 5.7 0.1
Evergreen forest 3105.8 39 487.1 6 1373 17
Bush-land 2159.7 27 3018.8 38 3893.2 48
Grassland 562.9 7 1633.3 20 393.4 5
Bare-land 1324.5 16 2502.7 31 1247.1 16
Table 5: Area transition for Land Cover classes between 2000, 2005 and 2016.
GIS and RS image analysis. e observed changes varied from one
LU/LC category to another with some maintaining a constant change
(increase or decrease) over the two analysis periods (2000-2005 and
2005-2016). Some classes underwent decrease in the rst period and
an increase in the second period and vice versa was true for other
LULC categories. is study advocates that multi-temporal satellite
data is very useful to detect the changes in land use and land cover
comprehensively. Land use and land cover changes have wide range of
consequences at all spatial and temporal scales. e study reveals that
the LULC pattern and its spatial distribution are the major rudiments
for the foundation of a successful land-use strategy required for the
appropriate development of any area.
References
1. Qian J, Zhou Q, Hou Q (2007) Comparison of pixel-based and object-oriented
classication methods for extracting built-up areas in arid zone. In: ISPRS
Workshop on Updating Geo-Spatial Databases with Imagery & the 5th ISPRS
Workshop on DMGISs, pp: 163-171.
2. Barros JX (2004) Urban growth in Latin American cities: exploring urban
dynamics through agent-based simulation. Doctoral Thesis, University of
London, London.
3. Masek JG, Lindsay FE, Goward SN (2000) Dynamics of urban growth in the
Washington DC metropolitan area, 1973-1996, from Landsat observations.
International Journal of Remote Sensing 21: 3473-3486.
4. Ginblett R (2006) Modelling human-landscape interactions in spatially complex
settings: Where are we and where are we going? MODISM05, pp: 11-20.
5. Lambin EF, Geist HJ (2001) Global land-use and land-cover change: what
have we learned so far. Global Change Newsletter 46: 27-30.
6. Woldeamlak B (2002) Land cover dynamics since the 1950s in Chemoga
Watershed, Blue Nile Basin, Ethiopia. Mountain Research and Development
22: 263-269.
7. Steffen W, Tyson P (2001) Global Change and the Earth System: A planet
under pressure. Environmental Policy Collection, UNT Digital Library, USA, p: 33.
8. Agarwal C, Green GM, Grove JM, Evans TP, Schweik CM (2002) A Review
and Assessment of Land-Use Change Models: Dynamics of Space, Time
and Human Choice. Center for the Study of Institutions, Population, and
Environmental Change, USDA Forest Service Northeastern Forest Research
Station, USA, pp: 1-62.
9. Bruijnzeel LA (2004) Hydrological functions of tropical forests: not seeing the
soil for the trees? Agriculture, Ecosystems and Environment 104: 185-228.
10. Chomitz KM, Kamari K (1998) The domestic benets of tropical forests. The
World Bank Observer 13: 13-35.
11. Campbell DJ, Lusch DP, Smucker T, Wangui EE (2003) Root causes of land
use change in the Loitoktok area, Kajiado District, Kenya. LUCID Working
Paper Series No. 19, Michigan State University, USA, pp: 1-32.
12. Mwagore D (2002) Land use in Kenya-the case of a national land use policy.
Land Reform, Kenya.
13. Republic of Kenya (2013) Makueni First County Integrated Development Plan
2013-2017. Government of Kenya, Kenya, pp: 2-36.
14. Muhammad L, Mwabu D, Mulwa R, Mwangi WM, Langyintuo AS, et al. (2010)
Characterisation of Maize Producing Households in Machakos and Makueni
Districts in Kenya. Nairobi.
15. Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Digital change
detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:
1565-1596.
16. Boakye E, Odai N, Adjei A, Annor O (2008) Landsat images for assessment of
the impact of land use and land cover changes on the Barekese Catchment in
Ghana. European Journal of Scientic Research 22: 269-278.
17. Gao J, Liu Y (2010) Determination of land degradation causes in Tongyu
County, Northeast China via land cover change detection. International Journal
of Applied Earth Observation and Geoinformation 12: 9-16.
18. Harris PM, Ventura SJ (1995) The integration of geographic data with remotely
sensed imagery to improve classication in an urban area. Photogrammetric
engineering and remote sensing 61: 993-998.
19. Owojori A, Xie H (2005) Landsat Image-Based LULC Changes of San Antonio,
Texas Using Advanced Atmospheric Correction and Object-Oriented Image
Analysis Approaches. 5th International Symposium on Remote Sensing of
Urban Areas, Tempe, USA.
20. Abate S (2011) Evaluating the land use and land cover dynamics in
Borena Woreda of South Wollo highlands, Ethiopia. Journal of Sustainable
Development in Africa 13: 87-105.
Citation: Cheruto MC, Kauti MK, Kisangau PD, Kariuki P (2016) Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing
Techniques: A Case Study of Makueni County, Kenya. J Remote Sensing & GIS 5: 175. doi: 10.4175/2469-4134.1000175
Page 6 of 6
Volume 5 • Issue 4 • 1000175
J Remote Sensing & GIS
ISSN: 2469-4134 JRSG, an open access journal
21. Belay T (2002) Land-cover/land-use changes in the Derekolli catchment of the South
Welo Zone of Amhara Region, Ethiopia. Michigan State University Press 18: 1-20.
22. Gete Z, Hurni H (2001) Implications of land use and land cover dynamics
for mountain resource degradation in the Northwestern Ethiopian Highlands.
Mountain Research and Development 21: 184-191.
23. Amsalu A, Leo S, Jan de G (2006) Long-term dynamics in land resource use
and the driving forces in Beressa watershed, highlands of Ethiopia. Journal of
Environmental management 83: 13-32.
24. Gessesse D, Kleman J (2007) Pattern and Magnitude of Deforestation in
the South Central Rift Valley Region of Ethiopia. Mountain Research and
Development 27: 162-168.
25. Prakasam C (2010) Land use and land cover change detection through remote
sensing approach: A case study of Kodaikanal Taluk, Tamil Nadu. International
journal of Geomatics and Geosciences 1: 150-158.
26. Schneider LC, Pontius RG (2001) Modeling land-use change in the Ipswich
watershed, Massachusetts, USA. Agriculture, Ecosystems and Environment
85: 83-94.
... The most significant environmental impact is changes in the LST, which directly affecting the convective and latent heat transfer processes, building energy demand, air quality, and human comfort conditions (Voogt and Oke 2003;Lu and Weng 2006;SARRAT et al. 2006;). LST has been widely used by various researchers worldwide in land use and land cover (LULC) change analysis to determine the state of surroundings for a particular area (Mundia and James 2014;Avdan and Jovanovska 2016;Cheruto et al. 2016;Meshesha et al. 2016;Meer and Mishra 2020). The urban heat variation has a great impact on the vulnerability of the urban environment due to green space dynamics, low infiltration rate, water shortage, soil moisture loss, and erosion Karimi et al. 2020;Gohain et al. 2021). ...
... In this study, the land uses were divided into five categories, urban, forest, farmland, lake/river, and bare land, respectively. Post-classification techniques are commonly used for determining the accuracy of LULC classification results (Cheruto et al. 2016). Researchers often find it challenging to work on different years of satellite images with mixed pixels of Landsat data (TM and OLI) in medium spatial resolution. ...
Article
Full-text available
Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km², 15.47 km², and 9.86 km² for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km² of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development.
... The Food and Agriculture Organization of the United Nations (FAO) reports a decrease in areas covered by forests (6.58%) and grasslands (2.07%) in Kenya, while areas under settlement, cropland, and wetlands increased by 150.88%, 11%, and 1. 09%, respectively, between 199009%, respectively, between and 201509%, respectively, between ( FAO, 2015. Several studies have been done to quantify and map historical LULC changes in different regions of Kenya ( Campbell et al., 2005 ;Cheruto et al., 2016 ;Kogo et al., 2021 ;Muhati et al., 2018 ;Petersen et al., 2021 ) and estimate the economic values of ecosystems ( Langat et al., 2021 ; Ministry of Environment and Forestry, 2019 ; Nature Kenya, 2019 ;Okumu and Muchapondwa, 2017 ), but studies linking the two are scarce. ...
Article
Full-text available
Land Use/Land Cover (LULC) changes alter the ecosystem structure and function, resulting in variations of the Ecosystem Service Values (ESVs). This study investigated the impacts of LULC changes on ESVs over 37 years in the Cherangany Hills Water Tower (CHWT) of Kenya. Landsat images from 1985 and 2022 were used to examine historical LULC changes in the CHWT. Supervised classification was carried out using the Random Forest (RF) classifier in R-Studio while ArcGIS desktop software was used for mapping to evaluate the LULC changes. Accuracy assessments were also conducted for each reference year. The estimation of ESVs was done using the Benefit Transfer Approach (BTA), employing modified local value coefficients. Six LULC types (Forest, Cropland, Grassland, Water bodies, Bareland, and Built-up area) were successfully classified, with overall accuracies of more than 92.5% and Kappa coefficients greater than 0.91. Our study findings showed an expansion in built-up areas (201.63%), cropland (36.78%), and water bodies (40.05%) whereas grassland, forest, and bareland experienced a reduction in their land areas by 28.26%, 13.38%, and 24.15% respectively between 1985 and 2022 in the CHWT. Consequently, there was an increase in the ESV of cropland while forest and grassland registered a decrease in their ESVs. Overall, the total ESV of the CHWT declined by 7.16% from 121.22 million United States Dollars (USD) in the year 1985 to 112.54 million USD in 2022. As for the individual ESVs, 15 out of the 17 individual Ecosystem Services (ES) registered negative changes in their ESVs. Food production and biological control were the two individual ES with positive ESV changes over the study period. There is a need to curb the current drivers of LULC changes within the water tower, especially the expansion of croplands, to stop further ecosystem degradation for optimum delivery of ES.
... After image pre-processing and sub-setting the area of interest (AOI), satellite images are classified based on a supervised image classification technique where a maximum likelihood algorithm is used for delineating the classification (Cohen et al. 2012;Shahkooeei et al. 2014). Visual interpretation and field-based studies are used for signature generation because supervised classification is a technique which is used the notified signature values of the selected classes of the lands (Cheruto et al. 2016). Each signature indicates the different classes and pixel values are divided into the pixel's DN values with the help of the signature. ...
Article
Full-text available
Extreme weather and global sea-level rise are the concerning factor for environmental disturbances in the coastal regions, where South 24 Parganas Islands are mostly affected by several cyclonic activities shoreline shifting and defenestration. Land alteration, cropland dynamics, soil water intrusion, and ecological disturbances are located due to these natural and anthropogenic activities. Sea-level rise is a triggering factor for coastal erosion and mangrove degradation. Earth observational medium-resolution Landsat datasets were used for vulnerability assessment of Mausuni Island, where north, north-west, and south-western parts are mostly eroded and lost mangrove forest over the decades. Land alteration study recorded 6.60 sq. km of agricultural land was lost due to climatic conditions and 0.32 sq. km of built-up land is increased. Around 2.65 sq. km of mangrove forest increased in the northern parts of the study which is mostly converted into aqua-cultural land to mangrove. The overall study periods (1991–2021) located 2.94 sq. km of eroded land and 0.76 sq. km of accretion land in Mousuni Island. Most shoreline shifting is located high in 1.63 sq. km (2001–2011) and 2.94 sq. km (1991–2021). The average temperature is increased by 0.07 °C throughout the study years. The geospatial indices like NDVI, NDSI, and NDWI are also identified noticeable changes due to climate change and anthropogenic activities. Sundarban Biosphere Reserve (SBR) is mostly affected by several extreme weather conditions and global sea-level rise; those study results help to understand the ecological and anthropogenic effects in Mausuni Island, which is more important for inhabitant and tourism industries.
... After land use/land cover classification, maps were subjected to an accuracy assessment. The kappa coefficient identification ( Table 2) was used for monitoring the error matrix of LU/LC classification [52]. The ERDAS Imagine version 14 software was used for the accuracy assessment and the kappa coefficient. ...
Article
Full-text available
Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility; around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area.
... Land use and land cover (LULC) changes also affect the regional climate through changes in surface energy and water balance (Pielke et al. 2002). In developing countries, in particular, the transformation of the original land cover (Sankhala and Singh 2008;Hegazy and Kaloop 2015;Gebru et al. 2019) has drastically reduced and degraded natural resources, including water, soil and vegetation (Bruijnzeel 2004;Cheruto et al. 2016). Changes in land cover triggered by deforestation followed by crop cultivation and heavy livestock grazing are proximate causes of severe dryland degradation and desertification in many parts of Sub-Saharan Africa (Zeleke and Hurni 2001;Lemenih 2004;Teketay et al. 2010). ...
... LULC classification accuracy assessment is the most significant factor for generating the classification achievement Cheruto et al., 2016 ). Using different years satellite images, medium-spatial resolution of Landsat data (TM and OLI) mixed pixels are a common problem ( Lu and Weng, 2006 ). ...
Article
Full-text available
Worldwide fertility rate is becoming a most significant context of anthropological condition. Rapid population pressure is one of the increasing factors for global land crisis and gradually effect in the environment and boosting the climatic vulnerability. Rapid urbanization, industrialization and technologically build-up area created more land scarcity in worldwide. Many administrative authorities extended the urban area due to extreme population or people migrant another parts of some area. Bilaspur planning is situated in Chhattisgarh state, India. Population pressure is increased urban expsansion over the Bilaspur city. This study is to focus out that land use and land cover change (LULCC), thermal variation in the Bilaspur planning area using remote sensing and geographic information system (GIS). Multi-temporal Landsat imagers were used to identifying the urban expansion in the last 40 years (1981–2020). Population pressure, change in the urban household, climate change and extreme events area change the agricultural land and forest area into built-up land. An urban agglomeration is increased transportation accessibility and also enlarged the traffic volume. The findings shows that the urban area has been expended around 47.9 sq. km between 1981 and 2020. Basically central, south, north and north-western parts has been experienced a huge amount of urban development and decreased vegetation and forest land due to anthropogenic activities. The annual UHI increased 0.11 while UTFVI high values were located 0.0206 (1981) and 0.0326 (2020), which is indicated the ecological condition in Bilaspur. This study may helpful for the urban planners, administrators and policy makers for sustainable development and urban planning of Bilaspur area.
... The Food and Agriculture Organization of the United Nations (FAO) reports a decrease in areas covered by forests (6.58%) and grasslands (2.07%) in Kenya, while areas under settlement, cropland, and wetlands increased by 150.88%, 11%, and 1. 09%, respectively, between 199009%, respectively, between and 201509%, respectively, between ( FAO, 2015. Several studies have been done to quantify and map historical LULC changes in different regions of Kenya ( Campbell et al., 2005 ;Cheruto et al., 2016 ;Kogo et al., 2021 ;Muhati et al., 2018 ;Petersen et al., 2021 ) and estimate the economic values of ecosystems ( Langat et al., 2021 ; Ministry of Environment and Forestry, 2019 ; Nature Kenya, 2019 ;Okumu and Muchapondwa, 2017 ), but studies linking the two are scarce. ...
Preprint
Changes in Land Use/ Land Cover (LULC) due to anthropogenic and natural drivers alter the ecosystem structure and function, resulting in variations of the Ecosystem Service Values (ESVs). This study investigated the impacts of LULC Changes on ESVs over 37 years in the Cherangany Hills Water Tower (CHWT) of Kenya. Landsat images from 1985 and 2022 were used to examine historical land cover changes in the CHWT. Supervised classification was carried out using Random Forest (RF) classifier in R-Studio while ArcGIS desktop software (version 10.8) was used for mapping to evaluate the LULC changes. Accuracy assessments were also conducted for each reference year. The estimation of ESVs was done using the benefit transfer approach, employing modified local value coefficients. Six LULC types (Forest, Cropland, Grassland, Water features, Bareland, Built-up area) were successfully classified, with overall accuracies of more than 92.5% and Kappa coefficients greater than 0.91. Results showed an expansion in cropland areas by about 36.78% (35,082 hectares [ha]) whereas grassland and forest experienced a reduction of their land areas by 28.26% (-22,181ha) and 13.38% (-10,353ha) respectively between 1985 and 2022. Consequently, there was an increase in the ESV of cropland by 7.91 million United States Dollars (USD) while forest and grassland registered a decrease in their ESVs by 10.22 million USD and 6.50 million USD respectively. Overall, the total ESV of the CHWT declined by 7.16% (-8.68 million USD) from 121.22 million USD in the year 1985 to 112.54 million USD in 2022. As for the individual ESVs, 15 out of the 17 individual ESs registered negative changes in their ESVs. Food production and biological control were the two individual ESs with positive ESV changes over the study period. There is a need to curb the current drivers of LULC changes within the water tower, especially the expansion of croplands, to stop further ecosystem degradation for optimum delivery of ecosystem services.
Chapter
Surface water and ground water are used for agricultural, industrial, and domestic purposes. Rainfall and the corresponding runoff generated are important hydrological processes which depend on the local physiographic, climatic, and biotic factors. Remotely sensed data provide valuable and real-time spatial information on natural resources and physical parameter. Due to climate changes and human interference to the river systems, flood risks have also increased. Flood losses can be reduced by proper floodplain management. Watershed means a naturally occurring hydrologic unit that contributes storm runoff to a single waterway classified on the basis of its geographical area. The aim of the study is to throw some light on the importance of watershed management using geospatial techniques. In this analysis, studies of the slope, contour, and terrain profile of study area and behavior of stream segments, drainage direction, flow accumulation, Land Use Land Cover (LULC), drainage map, etc. were carried out using QGIS-ArcMap 10.1. There are two river basins in upper Tapi region—one is Tapi River and the other is Purna River. Results show the depletion of both ground and surface water in the watershed. Green cover is considerably reduced in the region, and hence, the watershed is less humid and dry. Study also reveals that due to change in land use and land cover, there are more wastelands in the watershed. The study also provides an indication to restore the vegetation cover and will be able to help policy and decision-makers to understand the importance of watershed and need for its characteristics analysis.
Chapter
Climate change has put tremendous impact on the environment in the current scenario. The consequences are extensive consequences on the atmosphere, agronomy, water resources, biome, natural reserves, budget, biodiversity, and social security. Odisha, lying in the Eastern Coast of India connecting the Bay of Bengal, has a stretch of 480 km of coastline and is always vulnerable to climate change in terms of heavy storm like cyclones, beach erosion, coastal flooding, storm swell, and denudation. This state has scores of agro-climatic sectors which require improvement in the shape of diverse reworking approaches keeping pace with the ongoing scenario of climate change. Adaptation strategies such as agriculture, fisheries and animal husbandry, water, health, and coastal and disaster risk management have been formulated looking at the vulnerability, food security, and other parameters. Major steps have been initiated to mitigate the impact of climate change; still a lot of further strategies need to be dealt with to keep the region safe and disaster free. These include energy, urban development, transport, industries, and waste disposal. Proper attention must be adhered to embracing judicious policies on energy efficiency based on enactment, modifying state building codes and development codes to improve LULC, transportation, and energy productivities, establishment of new renewable energy policies, assortment and related criterions and reinforce multi-segment parameters to cater to the upcoming challenges related to reduction in poverty and increasing the adaptive capacity. Several initiatives have been undertaken in the government, private, and NGO sectors, but still lack of proper vision, appropriate mission, and slow pace of implementation has jeopardized the entire development.
Thesis
Full-text available
The high rates of urban growth in Latin America during the 1960s and 1970s produced rapid urbanisation and housing problems. Planning policies as well as the research community have approached urban growth as a static problem rather than as a spatial form that emerges from the urban development process and that is part of a constant dynamic process. This thesis focuses on a specific kind of urban growth that happens in Latin American cities, called 'peripherisation'. This is characterised by the formation of low-income residential areas in the peripheral ring of the city and a perpetuation of a dynamic core-periphery spatial pattern. The dynamics of growth and change in Latin American cities are explored using agentbased simulation. The objective is to increase the understanding of urban spatial phenomena in Latin American cities, which is essential to providing a basis for future planning actions and policies. The thesis consists of two parts. The first part presents an overview of urban growth and dynamics in Latin American cities, drawing on previous work on urbanisation in Latin American cities, spontaneous settlements and inner city dynamic processes. The second part focuses on the development of a simulation model based on the theoretical framework established in the first part. A brief review of the literature of automata models is presented with particular reference to agent-based simulation for land-use dynamics. The Peripherisation Model is introduced, its computer implementation described, and sensitivity analysis tests reported.Simulation exercises were used to revisit assumptions about urbanisation issues in Latin American cities and investigate important aspects of growth and change in these cities.These exercises allowed the problem of urban growth in Latin American cities to be unfolded through their dynamics, relating these dynamics to urban morphology,and thus presenting a new and important perspective on the phenomenon.
Article
Full-text available
This paper describes assessment of the land use and land cover changes in the Barekese catchment of Ghana. The Barekese catchment forms part of the Offin River catchment which flows through the catchment before eventually feeding into the Barekese reservoir. Because of the economic importance of the reservoir, the Barekese catchment has been earmarked and reserved for the protection and development of the water resource. However increasing siltation in the reservoir has been attributed to land use and land cover changes in the catchment due to encroachment. LandSat TM images of 1973, 1986 and 2000 were analyzed using Erdas Imagine software and ArcGIS. A total of five broad land use and land cover classes were identified and mapped for 1973, 1986 and 2000. These were forest (close), open forest, grasslands, water bodies and open areas/towns. The results of the analysis showed that between 1973 and 2000, forest decreased by about 43%, open forest decreased by about 32%, while grassland and open areas/towns increased by about 700% and 1000%, respectively. The study identified population growth, timber logging and lack of proper education as causes of the changes in land use and land cover in the catchment area.
Article
Full-text available
Techniques based on multi-temporal, multi-spectral, satellite-sensor- acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches,digital change,detection from,three angles. First, the different perspectives from which the variability in ecosystems and the change,events have been dealt with are summarized.,Change,detection between pairs of images,(bi-temporal) as well as between,time profiles of imagery,derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change,detection,methods,themselves,are categorized,in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological,aspects are being,fine-tuned as this review,is being written, and we summarize the new developments that can be expected in the near future. The review,highlights the high complementarity,between,different change,detection methods.
Article
Land use and land cover changes that occurred from 1957 to 1995 in the Dembecha area, Gojam, in the Northwestern highlands of Ethiopia, were monitored using a geographic information system (GIS) and a remote sensing approach with field verification. The study area covers 27,100 ha and is representative of Gojam, which is known for its cereal production and export of surplus to major cities of the country. However, given the age-old tradition of clearing increasingly steeper land for cultivation and the lack of appropriate land use policies, productivity is currently heavily threatened by soil degradation. The results show that the natural forest cover declined from 27‰ in 1957 to 2‰ in 1982 and 0.3‰ in 1995. The total natural forest cleared between 1957 and 1995 amounts to 7259 ha. This is 99% of the forest cover that existed in 1957. On the other hand, cultivated land increased from 39‰ in 1957 to 70‰ in 1982 and 77‰ in 1995. The greatest expansion occurred between 1957 and 1982 (about 78‰) and slowed down between 1982 and 1995 (only 10‰) because almost no land was left for further expansion. Throughout the period covered by the study, cultivation encroached upon the very last marginal areas and steep slopes with gradients >30‰. Such a dramatic change in 4 decades and the increasing proportion of completely degraded lands, from virtually nil in 1957 to about 3‰ in 1995, clearly indicates the prevailing danger of land degradation in the area.
Article
Like other human-induced landcover changes, urbanization represents a response to specific economic, demographic, or environmental conditions. We use the Washington D.C. area as a case study to relate satellite-derived estimates of urban growth to these economic and demographic drivers. Using the Landsat data archive we have created a three epoch timeseries for urban growth for the period 1973-1996. This map is based on a NDVI-differencing approach for establishing urban change filtered with a landcover classification to minimize confusion with agriculture. Results show that the built-up area surrounding Washington DC has expanded at a rate of ∼22km per year during this period, with notably higher growth during the late-1980s. Comparisons with census data indicate that the physical growth of the urban plan, observable from space, can be reasonably correlated with regional and national economic patterns.
Article
A land-cover analysis carried out in the catchment of Derekolli stream, using image analysis and GIS technologies, in conjunction with data collected through field surveys, revealed two types of changes, i.e., land-cover modification and conversion. The shrubland, which apparently formed the climax vegetation of the study site, and accounted for 16.4 % of the watershed in 1957, disappeared at the rate of 1.6 and 0.31 per cent per year from 1957 to 1986, and from 1986 to 2000, respectively. This change involved a gradual thinning of the shrub and its modification to shrub grassland, and then grassland, due to the selective cutting of the woody biomass for fuelwood and charcoal production. A significant conversion from natural vegetation cover to cropland was observed only between 1957 and 1986, where the cultivated land expanded by 7 per cent. There was very little change in the cropland area since 1986, as most of the land suitable for cultivation was already in use and the limit for expansion had almost been reached. The other type of conversion, i.e., the change from cultivated land to urban area, was insignificant since the land taken up by the emerging town, together with the roads accounted for less than 1.5 per cent of the total area of the catchment.
Article
This paper investigates the incorporation of ancillary spatial data to improve the accuracy and specificity of a land-use classification from Landsat Thematic Mapper (TM) imagery for nonpoint source pollution modeling in a small urban area - the city of Beaver Dam, Wisconsin. A post-classification model was developed to identify and correct areas of confusion in the Landsat TM classification. Zoning and housing density data were used to modify the initial classification. Land-use classification accuracy improved and the number of identifiable classes increased. Additionally, confusion between classes that were commonly misclassified (for example, commercial and industrial areas) was reduced. -from Authors