Document Type : Original Article
Abstract
Highlights
AUCES |
Assessment, Prediction and Future Simulation of Land Cover Dynamics Using remote sensing and GIS techniques
Soha A. Mohamed1 and Mohamed E. El-Raey2
Institute of Graduate Studies and Research; University of Alexandria, Egypt
1igsr.soha.ahmed@alexu.edu.eg, 2mohamed.elraey@alexu.edu.eg
Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of environmental hazards and risk, including global warming, earthquakes, landslide, erosion, flooding, etc. These rapid changes adversely affect the environment and have potential economic and social impacts. Thus, detailed accurate information about changes is urgently needed for updating LULC maps, to provide information for policymakers to support sustainable development, and the management of natural resources. The purpose of this paper was to extract reliable land cover information from two Landsat imageries with moderate resolution (Landsat 5 TM and Landsat 8 OLI) over a 15 years period (1999 to 2014) using post-classification change detection analysis. Traditional post-classification change detection approach based on pixel-based classification. However, in this paper, both of pixel based and segment-based classification approaches are deployed and the appropriateness of the classifications to derive accurate land cover maps. Then, Markov model is used to predict and simulate trends of LULC changes during the period of 1999 to 2014 and a future land cover map of the year 2050 are produced. The results showed that image segmentations led to better classification accuracy (86.67% in 1999 and 94.09% in 2014). Vice versa, traditional classification led to poorer accuracy (83.33% in 1999 and 93.33% in 2014).
Keywords: Image Classification; Segmentation; Change Detection; Prediction; Markov Chain.
Monitoring and evaluation of environmental changes play major roles in the study of global change. Human/natural modifications have largely resulted in deforestation, biodiversity loss, global warming and increase of natural disaster-flooding (Dwivedi et al., 2005; Zhao and Warner, 2004). Moreover, the growing population and increasing socio-economic necessities created a pressure on LULC. These environmental hazards are often related to unplanned and uncontrolled changes in LULC (Seto et al., 2002). Therefore, information on LULC changes could provide critical input to decision-making of environmental management and planning for the future (Fan et al., 2007). Consequently, a large and growing literature has focused specifically on the problem of accurately monitoring land-cover and land-use change in a wide variety of environments change detection methods (Atasoy et al., 2006; Shalaby and Tateishi, 2007).
Change detection can be defined as the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Singh (1989), Coppin and Bauer (1996); Macleod and Congalton (1998), Robb and Congalton (1998); Lu et al., (2004); Alqurashi and Kumar (2013) reviewed and summarized a variety of change detection techniques. Other authors developed new change detection techniques. Adams et al. (1995) deployed spectral mixture analysis, Macomber and Woodcock, (1994) developed Li–Strahler Canopy Model, Ridd and Liu (1998) applied chi-square transformation, Metternicht (1999 and 2001) used fuzzy sets. Abuelgasim et al. (1999) used artificial neural networks (ANN) and Petit and Lambin (2001) integrated multi-data source to detect changes. El-Raey et al., (2000) examined using GIS to study changes in Rosetta, Egypt. Almutairi and Warner (2010) compared a change detection approaches based on the image classification. Blaschke (2009) dealt with problems associated with multi-temporal object recognition using a post-classification comparison method and proposed a framework for image object-based change detection. IM et al., (2008) introduced object-based change detection using correlation image analysis and image segmentation. Abdu et al. (2014) proposed the use of combination of pixel based and segment-based classification for better change detection results as poor classification approach leads to wrong results hence leading to poor change detection results.
Pixel based classification is a traditional method of image classification (Dean and Smith, 2003). Pixel-based classification used multi-spectral classification techniques that assign a pixel to a class by considering the spectral similarities with the class or with other classes (Lu and Weng, 2007). In pixel-based classification, two kinds of traditional classification methods: unsupervised classification and supervised classification were commonly used methods (Dehvari and Heck, 2009). Image segmentation classification is based on image objects which mean a set of similar pixels (Shakelford and Davis, 2003). Image segmentation is the process of partitioning a digital image into multiple segments (Bora and Gupta, 2014). Segmentation employs a watershed delineation approach to partition input imagery based on their variance (Morgan, 2012). A derived variance image is treated as a surface image allocating pixels to particular objects based on variance similarity. Segmentation is a relatively new technique for extracting information from remotely sensed imagery (Blaschke, 2010).
Many studies have been done for LULC change modeling. For this study Markov Chain analysis is used for modeling land use dynamics and projecting future land use. Andrei Andreyevich Markov invented the Markov chain mathematical model in 1906 (Seneta, 1996). It is a stochastic process based on probabilities and the next state depends only on current state (Al-sharif and Pradhan, 2013). The basic assumption in the model was: the state at some point in the future (t+1) could be determined as a function of the current state (t), in other words the future change would be only depend on the existing change, so the transition between two times could be modeled mathematically (Otunga et al., 2014 and Mubea et al., 2010). Markov chain analysis assumed that land cover in a later date could be predicted by the state of land cover in the earlier date, given a matrix of transition probabilities from each land cover class to every other land cover class. The dynamics of land cover transitions are described in context of a Markovian analysis by three items (Luijten, 2003):
According to Subedi et al. (2013), Markov model could be represented mathematically as:
L(t+1) = Pij * L(t) and
|
(1)
|
Where, L(t+1) and L(t) are the land-use status at time “t+1” and “t” respectively. (0≼Pij≼1 and , (i, j=1, 2…, m)) is the transition probability matrix.
Markov chain is a module called Markov/CA_Markov in the raster GIS IDRISI (Eastman, 2012) and performed in order to estimate the transition matrix between the two past and documented dates (date 1 and date 2) and to estimate probabilities of change for the third date (date 3) to be predicted. This present study examined the land cover changes and the nature of urban sprawl in the city of Alexandria using remotely sensed data for the years 1999 and 2014. It aimed to classify land cover types in each year; detect changes that occurred in each class and finally simulated the situation in the future using Markov Chain.
2.1. STUDY AREA
Alexandria city is the chief port of Egypt and is located approximately between 30°50' to 31°40' north and 29°40' to 32°35' east. The city has a waterfront that extended for 60 km, from Abu-Qir Bay in the east to Sidi Krier in the west. It extended about (32 km) along the coast of the Mediterranean Sea in north-central Egypt as shown in Figure (1).
|
Figure (1). Study area |
2.2. REMOTELY SENSED DATA
Two separate Landsat OLI and TM data from 1999 and 2014 covering the study area are acquired freely from the U.S. Geological Survey’s (USGS) Earth explorer website (http://earthexplorer.usgs.gov/). Details about the data are given in Table (1).
Table (1). Characteristics of the Landsat datasets used in the study |
|||||
Acquisition Date |
Sensor |
Path/Row |
Spatial Resolution |
Number of Bands |
Format |
31-1-1999 23-10-2014 |
Landsat-5 TM Landsat-8 OLI/TIRS |
178/38 – 177/38 178-38 |
30 30 |
7 11 |
GeoTIFF GeoTIFF |
2.3. CLASSIFICATION AND ACCURACY ASSESSMENT
The Anderson classification level I scheme is used to identify four land cover categories (water bodies, vegetation, built-up area, and bare soil) as given in Table (2). A pixel-based classification based on hybrid classification (unsupervised classification (ISODATA) and supervised maximum likelihood classification (MLC)) method (using signatures from a total of 90 training sites) is used to classify the Landsat images of the two years (1999 and 2014). Hybrid classification is used to achieve better classification accuracy. Then segment-based classification of the same two images is performed.
Table (2). Land cover classification scheme used in the study |
|
Land Cover Type |
Description |
Water Bodies |
Water areas (sea, lakes, canals, etc.) |
Bare Soil |
Areas with no vegetation cover, uncultivated agricultural lands, open space and sand |
Vegetation |
Trees, natural vegetation, gardens, parks and playgrounds, grassland, vegetated |
Urban Areas |
All types of manmade structures: residential, industrial, agricultural commercial and services; transportation and utilities; mixed urban or built-up. |
The classification accuracy is assessed using field trips-ground truth data where 200 locations points are collected and distributed using stratified sampling strategy.
2.4. POST CLASSIFICATION CHANGE DETECTION
The post-classification change detection method is applied by simply comparing two classified images. It resulted in a complete from-to change matrix showing the changes between each class. Post-classification comparison proved to be the most effective technique as the data from two dates are separately classified thereby, minimizing the problem of normalizing for atmospheric and sensor differences between different dates.
2.5. MARKOV CHAIN
After the changes were detected, a simulation for the future has been performed. The produced land cover maps from the previous steps are used to model land cover dynamics quantitatively using Markov Chain analysis through the following steps:
1. The land cover changes for the two dates is provided as two images;
2. The interval of time between the two documented dates (date 1 and date 2) as well as the one between the second date and the date to be predicted (date 2 and date 3) are expressed as regular time steps (iterations);
3. A mask image is introduced in order to limit the development and change to another LULC category due to constraint rules. This modified the transition probability matrix values;
4. A transition probability matrix is produced. It expressed the possibility that a cell of a given land cover category would be changed into any other category;
5. A transition area matrix is derived. It contained the total area (in cells) expected to change in the next time period;
6. Finally, a group of conditional probability images are generated, one image for each category to express the probability that each cell will belong to the designated category in the next time.
3.1. IMAGE CLASSIFICATION
Hybrid pixel-based classified images that combined unsupervised and supervised classification techniques. Image segmentation was based on a window width of 3, a weight mean factor of 0.5, a weight variance factor of 0.5, and a similarity tolerance of 30. Other similarity variances were also tested. The results of the image classification can be seen in Figures (2).
The classified change images were compared to the entire reference change image, generated from the original scene models, to evaluate the accuracy of each change detection method. Accuracy assessment was carried out using 200 points from field data and existing land cover maps. The results of the accuracy analysis were summarized by an overall accuracy percentage as showed in Table )3).
Table (3). Accuracy assessment of Landsat 1999 and 2014 images |
|||
1999 |
2014 |
||
Overall pixel-based classification accuracy |
83.33% |
93.33% |
|
Overall segmentation-based classification accuracy |
86.67% |
94.09% |
|
|
|
|
Figure (2). Classification results |
3.2. LAND COVER CHANGE DETECTION
Based on the results of the land cover classification, change analysis for the study periods was performed. The change detection procedure involved classified images for both dates. Change detection results showed an urban expansion from 1999 to 2014. The built-up areas in the study area as shown in Table (4) increased from 306.86 km2 in the year 1999 to 393.49 km2 in the year 2014.
Table (4). Pixel-based classification statistic summary for during 1999 and 2014 |
||||
Land Cover Types |
Year |
|||
1999 |
2014 |
|||
Area (km2) |
Area (%) |
Area (km2) |
Area (%) |
|
Water Bodies |
2011.37 |
57% |
2044.58 |
58% |
Bare Soil |
523.02 |
15% |
579.58 |
17% |
Vegetation |
660.52 |
19% |
493.87 |
14% |
Urban Areas |
306.86 |
9% |
393.49 |
11% |
Change detection results using segment-based classification are presented in Table (5). It could be observed that there are big losses in agricultural areas resulted mainly from urban encroachment in the agricultural land.
Table (5). Segment-based classification statistic summary for during 1999 and 2014 |
||||||||
Land Cover Types |
Year |
|||||||
|
1999 |
|
2014 |
|||||
Area (km2) |
Area (%) |
Area (km2) |
Area (%) |
|||||
Water Bodies |
2028.10 |
58% |
2059.69 |
59% |
||||
Bare Soil |
525.66 |
15% |
616.04 |
18% |
||||
Vegetation |
662.11 |
19% |
477.31 |
14% |
||||
Urban Areas |
285.72 |
8% |
359.19 |
10% |
The main change of land cover was the change of agricultural land and urban or built-up land. A lot of agricultural lands are converted into urban or built-up land. The area coverage of bare soil land (unused land) is increased. The cause of increasing bare soil land is due to clearing of agricultural areas. The transition from both agriculture and bare soil to urban area is illustrated in Figure (3) and (4).
|
|
Figure (3). Transition from other categories to urban (1999-2014) |
The contribution to urban area from other classes can be observed from Figure (4).
|
|
Figure (4). Contribution from other categories to Urban (1999-2014)
|
3.3. FUTURE PREDICTION
In this study, land cover predictions were based on the state of land cover in 1999 and 2014 using Markov models. The results of the Markov model are; a transition probability matrix, a transition areas matrix, and a set of conditional probability images. A transition matrix contains the probability of each land use/cover category which could change to every other category as presented in Table (6).
Table (6). Markov conditional probability of changing among land cover type |
|||||||||||||||||||||||||
|
Analysisbased on the Markov transition probability matrix, it is clear that in the future years, the urban or built-up land will continue increasing, at the same time the agricultural land will continue decreasing. A transition areas matrix contains the number of pixels expected to change from each land cover type to each other land cover type over the specified time period. Table (7) showed the area coverage of different land use and land cover on year 2050 by square kilometers.
Table (7). Cells expected to be transformed to other classes (in km2) in year 2050 |
|||||||||||||||||||||||||
|
According to the result of simulation, it is expected that; the urban area would increase 263.38 km2 in total and about 330.76 km2 losses in agricultural lands and about 100.44 km2 would be transformed to the urban area if the existing trend continued. Conditional probability images reported the probability that each land cover type would be found at each pixel after the specified time period. As it could be observed from Figure (5) that the probability was in scale of “0-1” and the pink color represented the highest probability which was “1” and the black color represented the lowest probability which is “0”.
|
|
|
|
c. Probability of being vegetation |
d. Probability of being urban and built-up |
Figure (5). The map of future land use and cover of Alexandria in year 2050
|
Land use/ land cover change analysis are the major information required for planning and decision making. This paper demonstrated techniques and tools for assessing land cover changes in Alexandria city up until 2050. Two kinds of classification approaches are performed to generate reliable and accurate classified maps of land use and land cover. The results indicated that the use of segment-based classification approach enhanced the classification accuracy and the ability to categorize land cover classes. Moreover, Markov model methods are found to have a high accuracy, so that it is used for predicting land cover of the year 2050 over the study area. Regardless of the used classification type, the results showed that urbanized areas increased gradually, while the agricultural areas have been continued to decrease. The observed trends of increasing urban encroachment in agricultural land and built-up areas and decreasing agricultural land in the study area could be explained by:
التقييم والتنبؤ ومحاکاة التغييرات المستقبلية فى الغلاف الأرضي باستخدام تقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية
2،1معهد الدراسات العليا والبحوث – جامعة الاسکندرية
1igsr.soha.ahmed@alexu.edu.eg،2mohamed.elraey@alexu.edu.eg
الملخص العربي :
تُعد دراسة التغيرات فى الغطاء الأرضي أمرًا أساسيًا للحد من عدد کبير من المخاطر البيئية المحتملة، بما في ذلک التغيرات المناخية والزلازل والانهيارات الأرضية والتعرية والفيضانات وغيرها من المخاطر. إن التغيرات السريعة فى الغطاء الأرضي ومايتبعها من تغيرات فى الغطاء الأرضى لا تؤثر فقط سلبًا على البيئة بل تسبب ايضا ًفى خسائر للمجتمع والاقتصاد. وبالتالي، فهناک ضرورة إلى معلومات دقيقة مفصلة عن تلک التغييرات لتحديث الخرائط الطبوغرافية ولتقديم معلومات لصناع القرار لدعم التنمية المستدامة، وإدارة الموارد الطبيعية. الدراسة المقدمة تهدف الى استخراج معلومات حديثة وموثوقة بها عن الغطاء الأرضي والتغيرات به باستخدام صورتان للقمر الصناعى الامريکي لاندسات Landsat-5 (TM) وLandsat-8 OLI)) ذات الدقة المکانية 30 متر والتابع لوکالة ناسا للفضاء على مدى 15 عامًا من عام 1999 إلى عام 2014. تناولت الدراسة تطبيق طريقتان لتصنيف الغطاء الأرضي، الطريقة الاولى تعتمد على التصنيف التقليدي القائم على مستوي البيکسل والثانية تعتمد على تقسيم صورة القمر الصناعى الى اجزاء ومناطق متجانسة. تم استخدام نماذج سلاسل مارکوف للتنبؤ ومحاکاة إنتاج خريطة التغييرات المستقبلية فى الغطاء الأرضي حتى عام 2050. وأظهرت النتائج أن عمليات تصنيف الصورالفضائية بطريقة التقسيم الى مناطق متجانسة أدت إلى دقة تصنيف أفضل فقد کانت 86.67٪ في عام 1999 و 94.09٪ في عام 2014 فى حين ان التصنيف التقليدي اظهر ضعف فى الدقة حيث ظهرت الدقة 83.33٪ و 93.33٪ في عام 1999 و2014 على التوالى.
Keywords
AUCES |
Assessment, Prediction and Future Simulation of Land Cover Dynamics Using remote sensing and GIS techniques
Soha A. Mohamed1 and Mohamed E. El-Raey2
Institute of Graduate Studies and Research; University of Alexandria, Egypt
1igsr.soha.ahmed@alexu.edu.eg, 2mohamed.elraey@alexu.edu.eg
Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of environmental hazards and risk, including global warming, earthquakes, landslide, erosion, flooding, etc. These rapid changes adversely affect the environment and have potential economic and social impacts. Thus, detailed accurate information about changes is urgently needed for updating LULC maps, to provide information for policymakers to support sustainable development, and the management of natural resources. The purpose of this paper was to extract reliable land cover information from two Landsat imageries with moderate resolution (Landsat 5 TM and Landsat 8 OLI) over a 15 years period (1999 to 2014) using post-classification change detection analysis. Traditional post-classification change detection approach based on pixel-based classification. However, in this paper, both of pixel based and segment-based classification approaches are deployed and the appropriateness of the classifications to derive accurate land cover maps. Then, Markov model is used to predict and simulate trends of LULC changes during the period of 1999 to 2014 and a future land cover map of the year 2050 are produced. The results showed that image segmentations led to better classification accuracy (86.67% in 1999 and 94.09% in 2014). Vice versa, traditional classification led to poorer accuracy (83.33% in 1999 and 93.33% in 2014).
Keywords: Image Classification; Segmentation; Change Detection; Prediction; Markov Chain.
Monitoring and evaluation of environmental changes play major roles in the study of global change. Human/natural modifications have largely resulted in deforestation, biodiversity loss, global warming and increase of natural disaster-flooding (Dwivedi et al., 2005; Zhao and Warner, 2004). Moreover, the growing population and increasing socio-economic necessities created a pressure on LULC. These environmental hazards are often related to unplanned and uncontrolled changes in LULC (Seto et al., 2002). Therefore, information on LULC changes could provide critical input to decision-making of environmental management and planning for the future (Fan et al., 2007). Consequently, a large and growing literature has focused specifically on the problem of accurately monitoring land-cover and land-use change in a wide variety of environments change detection methods (Atasoy et al., 2006; Shalaby and Tateishi, 2007).
Change detection can be defined as the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Singh (1989), Coppin and Bauer (1996); Macleod and Congalton (1998), Robb and Congalton (1998); Lu et al., (2004); Alqurashi and Kumar (2013) reviewed and summarized a variety of change detection techniques. Other authors developed new change detection techniques. Adams et al. (1995) deployed spectral mixture analysis, Macomber and Woodcock, (1994) developed Li–Strahler Canopy Model, Ridd and Liu (1998) applied chi-square transformation, Metternicht (1999 and 2001) used fuzzy sets. Abuelgasim et al. (1999) used artificial neural networks (ANN) and Petit and Lambin (2001) integrated multi-data source to detect changes. El-Raey et al., (2000) examined using GIS to study changes in Rosetta, Egypt. Almutairi and Warner (2010) compared a change detection approaches based on the image classification. Blaschke (2009) dealt with problems associated with multi-temporal object recognition using a post-classification comparison method and proposed a framework for image object-based change detection. IM et al., (2008) introduced object-based change detection using correlation image analysis and image segmentation. Abdu et al. (2014) proposed the use of combination of pixel based and segment-based classification for better change detection results as poor classification approach leads to wrong results hence leading to poor change detection results.
Pixel based classification is a traditional method of image classification (Dean and Smith, 2003). Pixel-based classification used multi-spectral classification techniques that assign a pixel to a class by considering the spectral similarities with the class or with other classes (Lu and Weng, 2007). In pixel-based classification, two kinds of traditional classification methods: unsupervised classification and supervised classification were commonly used methods (Dehvari and Heck, 2009). Image segmentation classification is based on image objects which mean a set of similar pixels (Shakelford and Davis, 2003). Image segmentation is the process of partitioning a digital image into multiple segments (Bora and Gupta, 2014). Segmentation employs a watershed delineation approach to partition input imagery based on their variance (Morgan, 2012). A derived variance image is treated as a surface image allocating pixels to particular objects based on variance similarity. Segmentation is a relatively new technique for extracting information from remotely sensed imagery (Blaschke, 2010).
Many studies have been done for LULC change modeling. For this study Markov Chain analysis is used for modeling land use dynamics and projecting future land use. Andrei Andreyevich Markov invented the Markov chain mathematical model in 1906 (Seneta, 1996). It is a stochastic process based on probabilities and the next state depends only on current state (Al-sharif and Pradhan, 2013). The basic assumption in the model was: the state at some point in the future (t+1) could be determined as a function of the current state (t), in other words the future change would be only depend on the existing change, so the transition between two times could be modeled mathematically (Otunga et al., 2014 and Mubea et al., 2010). Markov chain analysis assumed that land cover in a later date could be predicted by the state of land cover in the earlier date, given a matrix of transition probabilities from each land cover class to every other land cover class. The dynamics of land cover transitions are described in context of a Markovian analysis by three items (Luijten, 2003):
According to Subedi et al. (2013), Markov model could be represented mathematically as:
L(t+1) = Pij * L(t) and
|
(1)
|
Where, L(t+1) and L(t) are the land-use status at time “t+1” and “t” respectively. (0≼Pij≼1 and , (i, j=1, 2…, m)) is the transition probability matrix.
Markov chain is a module called Markov/CA_Markov in the raster GIS IDRISI (Eastman, 2012) and performed in order to estimate the transition matrix between the two past and documented dates (date 1 and date 2) and to estimate probabilities of change for the third date (date 3) to be predicted. This present study examined the land cover changes and the nature of urban sprawl in the city of Alexandria using remotely sensed data for the years 1999 and 2014. It aimed to classify land cover types in each year; detect changes that occurred in each class and finally simulated the situation in the future using Markov Chain.
2.1. STUDY AREA
Alexandria city is the chief port of Egypt and is located approximately between 30°50' to 31°40' north and 29°40' to 32°35' east. The city has a waterfront that extended for 60 km, from Abu-Qir Bay in the east to Sidi Krier in the west. It extended about (32 km) along the coast of the Mediterranean Sea in north-central Egypt as shown in Figure (1).
|
Figure (1). Study area |
2.2. REMOTELY SENSED DATA
Two separate Landsat OLI and TM data from 1999 and 2014 covering the study area are acquired freely from the U.S. Geological Survey’s (USGS) Earth explorer website (http://earthexplorer.usgs.gov/). Details about the data are given in Table (1).
Table (1). Characteristics of the Landsat datasets used in the study |
|||||
Acquisition Date |
Sensor |
Path/Row |
Spatial Resolution |
Number of Bands |
Format |
31-1-1999 23-10-2014 |
Landsat-5 TM Landsat-8 OLI/TIRS |
178/38 – 177/38 178-38 |
30 30 |
7 11 |
GeoTIFF GeoTIFF |
2.3. CLASSIFICATION AND ACCURACY ASSESSMENT
The Anderson classification level I scheme is used to identify four land cover categories (water bodies, vegetation, built-up area, and bare soil) as given in Table (2). A pixel-based classification based on hybrid classification (unsupervised classification (ISODATA) and supervised maximum likelihood classification (MLC)) method (using signatures from a total of 90 training sites) is used to classify the Landsat images of the two years (1999 and 2014). Hybrid classification is used to achieve better classification accuracy. Then segment-based classification of the same two images is performed.
Table (2). Land cover classification scheme used in the study |
|
Land Cover Type |
Description |
Water Bodies |
Water areas (sea, lakes, canals, etc.) |
Bare Soil |
Areas with no vegetation cover, uncultivated agricultural lands, open space and sand |
Vegetation |
Trees, natural vegetation, gardens, parks and playgrounds, grassland, vegetated |
Urban Areas |
All types of manmade structures: residential, industrial, agricultural commercial and services; transportation and utilities; mixed urban or built-up. |
The classification accuracy is assessed using field trips-ground truth data where 200 locations points are collected and distributed using stratified sampling strategy.
2.4. POST CLASSIFICATION CHANGE DETECTION
The post-classification change detection method is applied by simply comparing two classified images. It resulted in a complete from-to change matrix showing the changes between each class. Post-classification comparison proved to be the most effective technique as the data from two dates are separately classified thereby, minimizing the problem of normalizing for atmospheric and sensor differences between different dates.
2.5. MARKOV CHAIN
After the changes were detected, a simulation for the future has been performed. The produced land cover maps from the previous steps are used to model land cover dynamics quantitatively using Markov Chain analysis through the following steps:
1. The land cover changes for the two dates is provided as two images;
2. The interval of time between the two documented dates (date 1 and date 2) as well as the one between the second date and the date to be predicted (date 2 and date 3) are expressed as regular time steps (iterations);
3. A mask image is introduced in order to limit the development and change to another LULC category due to constraint rules. This modified the transition probability matrix values;
4. A transition probability matrix is produced. It expressed the possibility that a cell of a given land cover category would be changed into any other category;
5. A transition area matrix is derived. It contained the total area (in cells) expected to change in the next time period;
6. Finally, a group of conditional probability images are generated, one image for each category to express the probability that each cell will belong to the designated category in the next time.
3.1. IMAGE CLASSIFICATION
Hybrid pixel-based classified images that combined unsupervised and supervised classification techniques. Image segmentation was based on a window width of 3, a weight mean factor of 0.5, a weight variance factor of 0.5, and a similarity tolerance of 30. Other similarity variances were also tested. The results of the image classification can be seen in Figures (2).
The classified change images were compared to the entire reference change image, generated from the original scene models, to evaluate the accuracy of each change detection method. Accuracy assessment was carried out using 200 points from field data and existing land cover maps. The results of the accuracy analysis were summarized by an overall accuracy percentage as showed in Table )3).
Table (3). Accuracy assessment of Landsat 1999 and 2014 images |
|||
1999 |
2014 |
||
Overall pixel-based classification accuracy |
83.33% |
93.33% |
|
Overall segmentation-based classification accuracy |
86.67% |
94.09% |
|
|
|
|
Figure (2). Classification results |
3.2. LAND COVER CHANGE DETECTION
Based on the results of the land cover classification, change analysis for the study periods was performed. The change detection procedure involved classified images for both dates. Change detection results showed an urban expansion from 1999 to 2014. The built-up areas in the study area as shown in Table (4) increased from 306.86 km2 in the year 1999 to 393.49 km2 in the year 2014.
Table (4). Pixel-based classification statistic summary for during 1999 and 2014 |
||||
Land Cover Types |
Year |
|||
1999 |
2014 |
|||
Area (km2) |
Area (%) |
Area (km2) |
Area (%) |
|
Water Bodies |
2011.37 |
57% |
2044.58 |
58% |
Bare Soil |
523.02 |
15% |
579.58 |
17% |
Vegetation |
660.52 |
19% |
493.87 |
14% |
Urban Areas |
306.86 |
9% |
393.49 |
11% |
Change detection results using segment-based classification are presented in Table (5). It could be observed that there are big losses in agricultural areas resulted mainly from urban encroachment in the agricultural land.
Table (5). Segment-based classification statistic summary for during 1999 and 2014 |
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Land Cover Types |
Year |
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|
1999 |
|
2014 |
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Area (km2) |
Area (%) |
Area (km2) |
Area (%) |
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Water Bodies |
2028.10 |
58% |
2059.69 |
59% |
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Bare Soil |
525.66 |
15% |
616.04 |
18% |
||||
Vegetation |
662.11 |
19% |
477.31 |
14% |
||||
Urban Areas |
285.72 |
8% |
359.19 |
10% |
The main change of land cover was the change of agricultural land and urban or built-up land. A lot of agricultural lands are converted into urban or built-up land. The area coverage of bare soil land (unused land) is increased. The cause of increasing bare soil land is due to clearing of agricultural areas. The transition from both agriculture and bare soil to urban area is illustrated in Figure (3) and (4).
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Figure (3). Transition from other categories to urban (1999-2014) |
The contribution to urban area from other classes can be observed from Figure (4).
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Figure (4). Contribution from other categories to Urban (1999-2014)
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3.3. FUTURE PREDICTION
In this study, land cover predictions were based on the state of land cover in 1999 and 2014 using Markov models. The results of the Markov model are; a transition probability matrix, a transition areas matrix, and a set of conditional probability images. A transition matrix contains the probability of each land use/cover category which could change to every other category as presented in Table (6).
Table (6). Markov conditional probability of changing among land cover type |
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Analysisbased on the Markov transition probability matrix, it is clear that in the future years, the urban or built-up land will continue increasing, at the same time the agricultural land will continue decreasing. A transition areas matrix contains the number of pixels expected to change from each land cover type to each other land cover type over the specified time period. Table (7) showed the area coverage of different land use and land cover on year 2050 by square kilometers.
Table (7). Cells expected to be transformed to other classes (in km2) in year 2050 |
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According to the result of simulation, it is expected that; the urban area would increase 263.38 km2 in total and about 330.76 km2 losses in agricultural lands and about 100.44 km2 would be transformed to the urban area if the existing trend continued. Conditional probability images reported the probability that each land cover type would be found at each pixel after the specified time period. As it could be observed from Figure (5) that the probability was in scale of “0-1” and the pink color represented the highest probability which was “1” and the black color represented the lowest probability which is “0”.
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c. Probability of being vegetation |
d. Probability of being urban and built-up |
Figure (5). The map of future land use and cover of Alexandria in year 2050
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Land use/ land cover change analysis are the major information required for planning and decision making. This paper demonstrated techniques and tools for assessing land cover changes in Alexandria city up until 2050. Two kinds of classification approaches are performed to generate reliable and accurate classified maps of land use and land cover. The results indicated that the use of segment-based classification approach enhanced the classification accuracy and the ability to categorize land cover classes. Moreover, Markov model methods are found to have a high accuracy, so that it is used for predicting land cover of the year 2050 over the study area. Regardless of the used classification type, the results showed that urbanized areas increased gradually, while the agricultural areas have been continued to decrease. The observed trends of increasing urban encroachment in agricultural land and built-up areas and decreasing agricultural land in the study area could be explained by:
التقييم والتنبؤ ومحاکاة التغييرات المستقبلية فى الغلاف الأرضي باستخدام تقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية
2،1معهد الدراسات العليا والبحوث – جامعة الاسکندرية
1igsr.soha.ahmed@alexu.edu.eg،2mohamed.elraey@alexu.edu.eg
الملخص العربي :
تُعد دراسة التغيرات فى الغطاء الأرضي أمرًا أساسيًا للحد من عدد کبير من المخاطر البيئية المحتملة، بما في ذلک التغيرات المناخية والزلازل والانهيارات الأرضية والتعرية والفيضانات وغيرها من المخاطر. إن التغيرات السريعة فى الغطاء الأرضي ومايتبعها من تغيرات فى الغطاء الأرضى لا تؤثر فقط سلبًا على البيئة بل تسبب ايضا ًفى خسائر للمجتمع والاقتصاد. وبالتالي، فهناک ضرورة إلى معلومات دقيقة مفصلة عن تلک التغييرات لتحديث الخرائط الطبوغرافية ولتقديم معلومات لصناع القرار لدعم التنمية المستدامة، وإدارة الموارد الطبيعية. الدراسة المقدمة تهدف الى استخراج معلومات حديثة وموثوقة بها عن الغطاء الأرضي والتغيرات به باستخدام صورتان للقمر الصناعى الامريکي لاندسات Landsat-5 (TM) وLandsat-8 OLI)) ذات الدقة المکانية 30 متر والتابع لوکالة ناسا للفضاء على مدى 15 عامًا من عام 1999 إلى عام 2014. تناولت الدراسة تطبيق طريقتان لتصنيف الغطاء الأرضي، الطريقة الاولى تعتمد على التصنيف التقليدي القائم على مستوي البيکسل والثانية تعتمد على تقسيم صورة القمر الصناعى الى اجزاء ومناطق متجانسة. تم استخدام نماذج سلاسل مارکوف للتنبؤ ومحاکاة إنتاج خريطة التغييرات المستقبلية فى الغطاء الأرضي حتى عام 2050. وأظهرت النتائج أن عمليات تصنيف الصورالفضائية بطريقة التقسيم الى مناطق متجانسة أدت إلى دقة تصنيف أفضل فقد کانت 86.67٪ في عام 1999 و 94.09٪ في عام 2014 فى حين ان التصنيف التقليدي اظهر ضعف فى الدقة حيث ظهرت الدقة 83.33٪ و 93.33٪ في عام 1999 و2014 على التوالى.