Deforestation causes extinction, changes to climatic conditions, desertification, and displacement of populations (Sahney, et al, 2010). It is a contributor to global warming (Fearnsidel and Laurance, 2004) and is often cited as one of the major causes of the enhanced greenhouse effect. According to the Intergovernmental Panel on Climate Change, deforestation mainly in tropical areas, could account for up to one-third of total anthropogenic carbon dioxide emissions (IPCC Fourth Assessment Report). But recent calculations suggest that carbon dioxide emissions from deforestation and forest degradation (excluding peatland emissions) contribute about 12% of total anthropogenic carbon dioxide emissions with a range from 6 to 17% (Van der Werf, et al, 2009). Scientists also state that, Tropical deforestation

Reducing emissions from the tropical deforestation and forest degradation (REDD) in developing countries has emerged as new potential to complement ongoing climate policies. The idea consists in providing financial compensations for the reduction of greenhouse gas (GHG) emissions from deforestation and forest degradation" (Wertz-Kanounnikoff and

Deforestation results in declines in biodiversity. Estimate shows that rainforest is losing 137 plant, animal and insect species every single day due to deforestation, which equates to 50,000 species a year (Rainforest Facts, 2010). Leakey and Roger, (1996) state that tropical rainforest deforestation contributed to the ongoing Holocene mass extinction. But scientific understanding of the process of extinction is insufficient to accurately make predictions about the impact of deforestation on biodiversity (Pimm, et al, 1995). Most predictions of forestry related biodiversity loss are based on species-area models, with an underlying assumption that as the forest declines species diversity will decline similarly. However, many such models have been proven to be wrong and loss of habitat does not necessarily lead to large scale loss of species. Species-area models are known to over-predict the number of species known to be threatened in areas where actual deforestation is ongoing, and greatly over-predict the number

Major international organizations, including the United Nations and the World Bank, have begun to develop programs aimed at curbing deforestation. The blanket term Reducing

releases 1.5 billion tones of carbon each year into the atmosphere (Defries, 2007).

of threatened species that are widespread (Pimm, et al, 1995).

2005; Butler and Laurance, 2008).

**3.4 Effects of deforestation**

Alvarado, 2007).

**3.5 Control**

#### **4.1 Data acquisition and image processing**

Remotely sensed data of different sources were used for this study (Figures 1, 2, 3, & 4). This is because of the constraint of the availability of field data in this part of the world. These are Landsat MSS, acquired on March 15, 1978 of 80m spatial resolution; SPOT XS, obtained on May 19, 1986 and SPOT XS acquired on November 28, 1994, of 20m spatial resolution respectively; NigeriaSat\_1 acquired on September 23, 2003, of 32m spatial resolution (Table 1).

Source: Author's field survey

Table 1. Attributes of the images used for the Study

Ideally, studies such as this would be better conceived if images were acquired twice a year to allow for seasonal variation in foliage coverage. In southwestern Nigeria for instance, the rainy season spans the period of eight months that is, between March and October while the dry season starts from November and lasts until February. The images used are both acquired in rainy season (Landsat MSS, 1978, SPOT XS, 1986 and NigeriaSat-1, 2003) and dry season (SPOT XS, 1994). These differences in the date of acquisition may cause disparity in

Geospatial Analysis of Deforestation

Fig. 4. SPOT Satellite XS 1986

were below 0.5 pixel.

and Land Use Dynamics in a Region of Southwestern Nigeria 155

With constraints such as spatial, spectral, temporal and radiometric resolution, relatively simple remote sensing devices cannot record adequately the complexity of the Earth's land and water surfaces. Consequently, error creeps into the data acquisition process and can degrade the quality of the remotely sensed data collected. Therefore, it is necessary to preprocess the remotely sensed data before the actual analysis. Radiometric and geometric errors are the most common types of errors encountered in remotely sensed imagery. The commercial data provider has removed the radiometric and systematic errors of the data used. However, while the unsystematic geometric distortion remains in the image. The

The process of georeferencing in this study started with the identification of features on the image data, which can be clearly recognized on the topographical map of the study area and whose geographical locations were clearly defined. Stream intersections and the intersection of the highways were used as ground control points (GCP). The latitude and longitude of the GCPs of clearly seen features obtained in the base map were used to register the coordinates of the image data used for the study. All the images were georeferenced to Universal Transverse Mercator projection of WGS84 coordinate system, zone 31N with Clarke 1880 Spheroid. Nearest-neighbor re-sampling method was used to correct the data geometrically. A correlation threshold was used to accept or discard points. The correlation range was within limits that is, 1 pixel size. The x and y corrections

geometric errors were corrected using ground control points (GCP).

the results. However, they were used because of the spectral information of the study area they contain.

The data were extracted as a sub-scene from the original dataset. For the purpose of temporal land use/cover change detection, a common window covering the same geographical coordinates of the study area was extracted from the scene of the images obtained. The sub-map operation of ILWIS 3.2 Academic allows the user to specify a rectangular part of a raster map to be used. To extract the study area from the whole scene of the images obtained, the numbers of rows and columns of the area were specified. While Landsat MSS 1978 contained 600 lines and 733 columns pixels, SPOT XS 1986 and 1994 consist of 1373 lines and 2005 columns pixels, respectively, when NigeriaSat\_1 2003 has 1083 lines and 1150 columns of pixels.

Fig. 3. Landsat Multispectral (MSS) 1978

The false colour composite was used for all the image data to relate colours and patterns in the image data to the real world features. For Landsat MSS 1978, channel 1 was assigned to red plane, channel 2 to green plane, and channel 3 to blue plane. This makes the band Red, Blue, Green (RBG-123) false colour composite. For SPOT XS 1986 and 1994, channel 3 was assigned red plane, channel 2 to green and channel 1 to blue plane. The band combination then consisted of Blue, Green and Red (BGR-321) to produce false colour composite. For NigeriaSat\_1 2003 false colour composite, channel 1 was assigned to red plane, channel 2 to green plane and channel 3 to blue plane. This puts the band combination as Red, Green and Blue (RGB-123).

the results. However, they were used because of the spectral information of the study area

The data were extracted as a sub-scene from the original dataset. For the purpose of temporal land use/cover change detection, a common window covering the same geographical coordinates of the study area was extracted from the scene of the images obtained. The sub-map operation of ILWIS 3.2 Academic allows the user to specify a rectangular part of a raster map to be used. To extract the study area from the whole scene of the images obtained, the numbers of rows and columns of the area were specified. While Landsat MSS 1978 contained 600 lines and 733 columns pixels, SPOT XS 1986 and 1994 consist of 1373 lines and 2005 columns pixels, respectively, when NigeriaSat\_1 2003 has 1083

The false colour composite was used for all the image data to relate colours and patterns in the image data to the real world features. For Landsat MSS 1978, channel 1 was assigned to red plane, channel 2 to green plane, and channel 3 to blue plane. This makes the band Red, Blue, Green (RBG-123) false colour composite. For SPOT XS 1986 and 1994, channel 3 was assigned red plane, channel 2 to green and channel 1 to blue plane. The band combination then consisted of Blue, Green and Red (BGR-321) to produce false colour composite. For NigeriaSat\_1 2003 false colour composite, channel 1 was assigned to red plane, channel 2 to green plane and channel 3 to blue plane. This puts the band combination as Red, Green and

they contain.

lines and 1150 columns of pixels.

Fig. 3. Landsat Multispectral (MSS) 1978

Blue (RGB-123).

Fig. 4. SPOT Satellite XS 1986

With constraints such as spatial, spectral, temporal and radiometric resolution, relatively simple remote sensing devices cannot record adequately the complexity of the Earth's land and water surfaces. Consequently, error creeps into the data acquisition process and can degrade the quality of the remotely sensed data collected. Therefore, it is necessary to preprocess the remotely sensed data before the actual analysis. Radiometric and geometric errors are the most common types of errors encountered in remotely sensed imagery. The commercial data provider has removed the radiometric and systematic errors of the data used. However, while the unsystematic geometric distortion remains in the image. The geometric errors were corrected using ground control points (GCP).

The process of georeferencing in this study started with the identification of features on the image data, which can be clearly recognized on the topographical map of the study area and whose geographical locations were clearly defined. Stream intersections and the intersection of the highways were used as ground control points (GCP). The latitude and longitude of the GCPs of clearly seen features obtained in the base map were used to register the coordinates of the image data used for the study. All the images were georeferenced to Universal Transverse Mercator projection of WGS84 coordinate system, zone 31N with Clarke 1880 Spheroid. Nearest-neighbor re-sampling method was used to correct the data geometrically. A correlation threshold was used to accept or discard points. The correlation range was within limits that is, 1 pixel size. The x and y corrections were below 0.5 pixel.

Geospatial Analysis of Deforestation

the remainder of the images.

**4.3 Accuracy assessment**

**5. Results and discussion**

**5.1 Accuracy assessment of satellite images**

accuracy was 72.05% (Tables 2, 3, 4 and 5).

**Arable farmlands**

**Bare soils** **Exposed rock**

Agroforestry 216 4 0 12 82 0 190 0 0.43

13 1801 302 74 1309 0 2782 10 0.29

**Forest Settlement Shrub/**

**Fallow**

**Water body**

**ACCURACY**

 **Agroforestry**

Arable farmlands

**4.2 Classification**

and Land Use Dynamics in a Region of Southwestern Nigeria 157

In this study, the satellite images were classified using supervised classification method. The combine process of visual image interpretation of tones/colours, patterns, shape, size, and texture of the imageries and digital image processing were used to identify homogeneous groups of pixels, which represent various land use classes of interest. This process is commonly referred to as training sites because the spectral characteristics of those known areas are used to train the classification algorithm for eventual land use/cover mapping of

To validate the tonal values recorded on the satellite images with the features obtained on the ground and also to know what type of land use/cover is actually present, the study engaged in ground truthing to five communities of the study area. These are Ilesa, Ijebu-Ijesa, Efon-Alaaye, Iloko-Ijesa and Erin-Oke (Figure 2). Before the ground truthing, map of the study area was printed and was used as guide to locate and identify features both on ground and on the image data. The geographical locations of the identified features on the ground were clearly defined. These were used as training samples for supervised classification of the remotely sensed images. Eight categories of land uses and land covers were clearly identified during ground truthing. These are forest/secondary re-growth, agro-forestry, arable farmlands,

Determination of meaningful change categories was conducted by evaluating the classification accuracy. Every classified pixel has accuracy for a particular land use/cover type. The most common and typical method to assess classification accuracy Error Matrix (sometimes called a confusion matrix or contingency table) was used to assess the accuracy assessment for this study. Error matrix compares, on a category-by-category basis, the relationship between known referenced data and the corresponding results of an automated classification. Such matrices are square, with the number of rows and columns equal to the

The accuracy assessment of four temporal data shows that most land use types were classified with acceptable level of accuracies. The low classification accuracies found in Arable farmlands, Agro-forestry and Fallow/Shrub classes was due to the similar spectral characteristics in them and the prevailing season, which posed constraint to the classification process. However, the overall accuracy of the land use categories makes the study reliable for planning. The average reliability of Landsat MSS 1978 was 57.24% while the overall accuracy was 76.20%; SPOT XS 1986 average reliability was 66.46% and the overall accuracy of 67.43%; SPOT XS 1994 average reliability was 65.04 % while the overall accuracy was 60.88%. NigeriaSat\_1 2003 average reliability was 63.25 % and the overall

fallow/shrub, bare soils, water body, bare rocks and built-up areas/settlements.

number of categories whose classification accuracy is being assessed (Jensen, 1996).

Fig. 5. SPOT Satellite (XS) 1994

Fig. 6. NigeriaSat-1 2003

#### **4.2 Classification**

156 Deforestation Around the World

Fig. 5. SPOT Satellite (XS) 1994

Fig. 6. NigeriaSat-1 2003

In this study, the satellite images were classified using supervised classification method. The combine process of visual image interpretation of tones/colours, patterns, shape, size, and texture of the imageries and digital image processing were used to identify homogeneous groups of pixels, which represent various land use classes of interest. This process is commonly referred to as training sites because the spectral characteristics of those known areas are used to train the classification algorithm for eventual land use/cover mapping of the remainder of the images.

To validate the tonal values recorded on the satellite images with the features obtained on the ground and also to know what type of land use/cover is actually present, the study engaged in ground truthing to five communities of the study area. These are Ilesa, Ijebu-Ijesa, Efon-Alaaye, Iloko-Ijesa and Erin-Oke (Figure 2). Before the ground truthing, map of the study area was printed and was used as guide to locate and identify features both on ground and on the image data. The geographical locations of the identified features on the ground were clearly defined. These were used as training samples for supervised classification of the remotely sensed images. Eight categories of land uses and land covers were clearly identified during ground truthing. These are forest/secondary re-growth, agro-forestry, arable farmlands, fallow/shrub, bare soils, water body, bare rocks and built-up areas/settlements.

#### **4.3 Accuracy assessment**

Determination of meaningful change categories was conducted by evaluating the classification accuracy. Every classified pixel has accuracy for a particular land use/cover type. The most common and typical method to assess classification accuracy Error Matrix (sometimes called a confusion matrix or contingency table) was used to assess the accuracy assessment for this study. Error matrix compares, on a category-by-category basis, the relationship between known referenced data and the corresponding results of an automated classification. Such matrices are square, with the number of rows and columns equal to the number of categories whose classification accuracy is being assessed (Jensen, 1996).

#### **5. Results and discussion**

#### **5.1 Accuracy assessment of satellite images**

The accuracy assessment of four temporal data shows that most land use types were classified with acceptable level of accuracies. The low classification accuracies found in Arable farmlands, Agro-forestry and Fallow/Shrub classes was due to the similar spectral characteristics in them and the prevailing season, which posed constraint to the classification process. However, the overall accuracy of the land use categories makes the study reliable for planning. The average reliability of Landsat MSS 1978 was 57.24% while the overall accuracy was 76.20%; SPOT XS 1986 average reliability was 66.46% and the overall accuracy of 67.43%; SPOT XS 1994 average reliability was 65.04 % while the overall accuracy was 60.88%. NigeriaSat\_1 2003 average reliability was 63.25 % and the overall accuracy was 72.05% (Tables 2, 3, 4 and 5).

Geospatial Analysis of Deforestation

**Agroforestry**

Arable

Average Accuracy = 70.97 % Average Reliability = 63.25 % Overall Accuracy = 72.05 %

Table 5. Matrix of land use/land cover for 2003

**5.2 Land use change between 1978 and 2003**

proportionally increased (Table 6).

0%

Source: Author's Data Analysis

Forest

Arable

Fig. 7. Land use/land cover change 1978-2003

Agro-forestry

Bare soils

Fallow

Bare rock

**Land use type**

Water body

Settlement

20%

40%

**Arae in Percentage**

60%

80%

100%

**Arable farmland** **Bare**

RELIABILITY 0.26 0.44 0.95 0.94 0.76 0.94 0.66 0.10

and Land Use Dynamics in a Region of Southwestern Nigeria 159

**soils Settlement Shrub/**

**fallow**

**Water**

**body ACCURACY**

**rock Forest Bare**

Agroforestry 1333 0 0 54 0 0 44 0 0.93

farmland 0 5485 363 0 464 0 1662 44 0.68 Bare rock 0 928 13507 0 34 30 19 1530 0.84 Forest 2244 0 10 11412 0 0 4558 26 0.63 Bare soils 1 5174 41 4 10517 720 6118 124 0.46 Settlement 0 174 22 0 1438 13267 7 632 0.85 Shrub/fallow 1475 504 64 657 1351 0 24127 123 0.85 Water body 0 86 207 0 4 58 0 263 0.43

The overall results of the study indicate that the area of forestland has been continuously declined, while the area of shrub/fallow, built-up area (settlements) and waterbody was

Average Accuracy = 56.92 % Average Reliability = 57.24 % Overall Accuracy = 76.20 %

Table 2. Matrix of land use/land cover for 1978

Average Accuracy = 65.68 % Average Reliability = 66.46 % Overall Accuracy = 67.43 %

Average Accuracy = 71.28 % Average Reliability = 65.04 % Overall Accuracy = 60.88 %

Table 4. Matrix of land use/land cover for 1994

Average Accuracy = 70.97 % Average Reliability = 63.25 % Overall Accuracy = 72.05 %

158 Deforestation Around the World

**Forest Settlement Shrub/**

**Forest Settlement Shrub/**

0 1254 241 10 0 0 38 544 0.57

**Forest Settlement Shrub/**

0 33 2 1 0 0 3 0 0.82

**fallow**

**Water body**

**Fallow**

**fallow**

**Water body**

**Water body**

**ACCURACY**

**ACCURACY**

**ACCURACY**

**Exposed rock**

Bare soils 3 1460 2417 44 271 74 359 6 0.52 Exposed rock 0 82 77 4580 443 0 1 73 0.87 Forest 70 549 50 2274 45232 0 3011 48 0.88 Settlement 0 0 52 0 0 2695 0 0 0.98 Shrub/Fallow 653 882 54 6 3809 0 5809 0 0.52 Water body 0 55 26 129 274 0 1 33 0.06 RELIABILITY 0.23 0.37 0.81 0.64 0.88 0.97 0.48 0.19

 **Agroforestry**

Average Accuracy = 56.92 % Average Reliability = 57.24 % Overall Accuracy = 76.20 %

Average Accuracy = 65.68 % Average Reliability = 66.46 % Overall Accuracy = 67.43 %

Average Accuracy = 71.28 % Average Reliability = 65.04 % Overall Accuracy = 60.88 %

**Agroforestry**

Arable farmland

Arable farmlands

**Arable farmlands**

Table 2. Matrix of land use/land cover for 1978

Table 3. Matrix of land use/land cover for 1986

Table 4. Matrix of land use/land cover for 1994

**Arable farmlands** **Bare soils**

**farmland**

**Bare soils**

RELIABILITY 0.79 0.17 0.64 0.88 0.98 1.00 0.67 0.19

**Exposed rock**

RELIABILITY 0.82 0.00 0.92 0.88 0.88 1.00 0.63 0.09

Agro-forestry 3763 0 0 0 124 0 109 0 0.78

Bare soils 5 7825 11370 1222<PHONE_NUMBER> 274 0.41 Exposed rock 0<PHONE_NUMBER>9 1890 0 940 19489 0.35 Forest 711 4 0 678 22169 4 1071 141 0.87 Settlement 0 0 54 0 0 25244 0 0 0.86 Shrub/fallow 137 1031 466 120 861 3 8163 7 0.70 Water body 0 5 34 64 8 0 33 1926 0.89

**Exposed rock**

Agroforestry 2027 0 1 0 1 0 29 0 0.82

Bare soils 113 5000 3227 238 78 68 1000 3002 0.21 Exposed rock 0<PHONE_NUMBER> 21 0 29 2303 0.50 Forest 214 15<PHONE_NUMBER>2 0 4431 4 0.72 Settlement 0 3 41 0 0 22488 0 0 0.85 Shrub/fallow 226 559 959 32 511 0 11444 21 0.80 Water body 0 195 85 107 0 0 3 1381 0.78

 **Agroforestry Arable**

**Bare soils**

Table 5. Matrix of land use/land cover for 2003

#### **5.2 Land use change between 1978 and 2003**

The overall results of the study indicate that the area of forestland has been continuously declined, while the area of shrub/fallow, built-up area (settlements) and waterbody was proportionally increased (Table 6).

Source: Author's Data Analysis

Fig. 7. Land use/land cover change 1978-2003

Geospatial Analysis of Deforestation

and Land Use Dynamics in a Region of Southwestern Nigeria 161

The disturbances are not only in terms of its ability to regenerate through the natural process,

In recent times, precisely June 1992, the Earth Summit in Rio de Janeiro, Brazil more correctly known as the United Nations Conference on Environment and Development (UNCED) was convened. There was an agreement on a set of "Principles for a global consensus on the management, conservation and sustainable development of all types of forests" and devoted a full chapter of its Agenda 21 to "Combating deforestation". Since this conference, there has been debate on the phenomenon and conscious efforts are being made to carefully use and protect natural resources mostly in the developed countries. But there seems to be limited enthusiasm from developing countries, including Nigeria, where ineptitude, particularly as regards the implementation of environmental laws, has been the greatest bane to a sustainable environmental practice (Lines et al.1997; Ibah. 2001). For instance, between 1995 and 1999 the military junta in Nigeria showed nonchalant attitude to the sustainability of the forest resources. This gave room to illegal lumbermen to penetrate the forest in the country, both reserved (protected) and the opened forest (unprotected), as the case of the study area, thus declined the area of forest nationwide. In poor and developing countries where often the administrative structures are either too weak and/or corrupt to enforce rules and regulations effectively and where tax and incentive systems do

not work, forest clearing and environmental degradation has proceeded unabated.

Arable farming is one of the prominent economic activities among the people of the study area as it is reflected in Table 6. The farmlands occupied 9,441.88 hectares, which represented 7.89% of the entire land use in 1978. In 1986, the land area of arable farmlands expanded from 9,441.88 to 11,830.08 hectares, making an increase of 2,388.20 hectares. But there seems to be a decrease in the cultivated area in 1994. This is due to the period at which the image was obtained that is, November, the beginning of dry season in southwestern Nigeria. At this time, most crops had been harvested except few such as, cassava, maize, among others which are left for proper maturation. Thus, majority of the farmlands are abandoned till the next planting season and they are sometimes overgrown with shrubs during this period. Between 1994 and 2003, arable farmlands increased from 6,830.37 to

A close assessment of arable farmland areas between 1978 and 2003 shows that, 6,482.32 hectares of the land area was gained. This is negligible if we are going to solve the problem of food insecurity. However, the expansion of farmlands and declining in the forest area affirms the claim of Ola-Adams, (1981) and Williams, (1990). In their studies it was discovered that forests were removed to pave way for food and tree crops. Besides, the United Nations Framework Convention on Climate Change (UNFCCC, 2007) asserted that the overwhelming direct cause of deforestation is agriculture. In their discovery, subsistence farming was responsible for 48% of deforestation; commercial agriculture 32%; logging 14% and fuel wood removals make up 5% of deforestation. This was the observation of the study during the ground truthing. Besides, arable farmlands were scattered all over the study area especially around the settlements and some newly deforested areas and there were no large scale mechanized farming in the area. Again, arable farmlands were common features around the mountainous terrain and crops such as, maize, yam, cassava, rice, among others

**5.4 Arable farmland status from 1978 to 2003**

15,923.20 hectares, representing an increase of 133.13%.

were mainly grown in the study area.

but some species of trees and fauna are being endangered (Okafor 1988).

Table 6. Land use change between 1978 and 2003

#### **5.3 Forest status between 1978 and 2003**

The area of forest in the study area was becoming smaller with time (Table 6). In 1978, forest area was 72,310.93 hectares, which constituted 60.40% of the entire land use. By 1986, the areal extent had decreased from 72,310.93 to 30,587.80 hectares, a decline of 57.7% within the period of 9 years. In 1994, there seems to be an increase in the areal extent of the resources. This is because in 1986, it was 30,587.80 hectares but in 1994 it increased to 36,635.75 hectares. This increase was not an expansion in the coverage but re-growth of the forest area. In 2003, the forest resources in the study area had almost disappeared when the areal extent was reduced to 18,841.70 hectares. This represented a decline of 48.57% within the period of 10 years. From the analysis, a total area of 53,469 hectares of the forest resources was removed.

The decline in the forest area as discovered in the study confirms the report of FAO (2001) that tropical forest including Nigeria was on the decline. According to the United Nations Food and Agriculture Organization (FAO), 93,900 km2 of forest were cleared per year during the 1990s, with annual rates of forest loss (positive for all continents with tropical forests): Africa 0.8%, Asia 0.1%, Oceania 0.2%, North and Central America 0.1%, and South America 0.4% (FAO, 2001). The study also upholds the recognition of Cassel-Gintz, and Petschel-Hels (2001) that discovered deforestation in their recent study and concluded that it is one of the core problems of global environmental change. UNDP, UNEP, World Bank and WRI, (2000) associated extensive deforestation with loss of biodiversity, climate change, watershed degradation and these pose threat to cultural survival of indigenous population, economic loss among others. According to the estimates by the Food and Agricultural Organization (FAO 1983), Nigeria through careless exploitation and husbandry, destroys about 600,000 ha of her forest every year, as against the reforestation efforts of about 25,000 ha a year, which replenishes only about 4 percent of the loss. At such rates, Nigeria's remaining forest area would disappear by 2020 (Jaiyeoba 2002).The general effect of timber exploitation mostly from the high forests which cover only about 12.41 million ha of the country's 91.1 million ha of land space, (i.e. about 13.5%), is that the forest areas are fast reducing to savanna vegetation. Consequently, the forest stability is disrupted and its ecosystem has been seriously disturbed.

The area of forest in the study area was becoming smaller with time (Table 6). In 1978, forest area was 72,310.93 hectares, which constituted 60.40% of the entire land use. By 1986, the areal extent had decreased from 72,310.93 to 30,587.80 hectares, a decline of 57.7% within the period of 9 years. In 1994, there seems to be an increase in the areal extent of the resources. This is because in 1986, it was 30,587.80 hectares but in 1994 it increased to 36,635.75 hectares. This increase was not an expansion in the coverage but re-growth of the forest area. In 2003, the forest resources in the study area had almost disappeared when the areal extent was reduced to 18,841.70 hectares. This represented a decline of 48.57% within the period of 10 years. From the analysis, a total area of 53,469 hectares of the forest resources

The decline in the forest area as discovered in the study confirms the report of FAO (2001) that tropical forest including Nigeria was on the decline. According to the United Nations Food and Agriculture Organization (FAO), 93,900 km2 of forest were cleared per year during the 1990s, with annual rates of forest loss (positive for all continents with tropical forests): Africa 0.8%, Asia 0.1%, Oceania 0.2%, North and Central America 0.1%, and South America 0.4% (FAO, 2001). The study also upholds the recognition of Cassel-Gintz, and Petschel-Hels (2001) that discovered deforestation in their recent study and concluded that it is one of the core problems of global environmental change. UNDP, UNEP, World Bank and WRI, (2000) associated extensive deforestation with loss of biodiversity, climate change, watershed degradation and these pose threat to cultural survival of indigenous population, economic loss among others. According to the estimates by the Food and Agricultural Organization (FAO 1983), Nigeria through careless exploitation and husbandry, destroys about 600,000 ha of her forest every year, as against the reforestation efforts of about 25,000 ha a year, which replenishes only about 4 percent of the loss. At such rates, Nigeria's remaining forest area would disappear by 2020 (Jaiyeoba 2002).The general effect of timber exploitation mostly from the high forests which cover only about 12.41 million ha of the country's 91.1 million ha of land space, (i.e. about 13.5%), is that the forest areas are fast reducing to savanna vegetation. Consequently, the forest stability is disrupted and its ecosystem has been seriously disturbed.

Table 6. Land use change between 1978 and 2003

**5.3 Forest status between 1978 and 2003**

was removed.

The disturbances are not only in terms of its ability to regenerate through the natural process, but some species of trees and fauna are being endangered (Okafor 1988).

In recent times, precisely June 1992, the Earth Summit in Rio de Janeiro, Brazil more correctly known as the United Nations Conference on Environment and Development (UNCED) was convened. There was an agreement on a set of "Principles for a global consensus on the management, conservation and sustainable development of all types of forests" and devoted a full chapter of its Agenda 21 to "Combating deforestation". Since this conference, there has been debate on the phenomenon and conscious efforts are being made to carefully use and protect natural resources mostly in the developed countries. But there seems to be limited enthusiasm from developing countries, including Nigeria, where ineptitude, particularly as regards the implementation of environmental laws, has been the greatest bane to a sustainable environmental practice (Lines et al.1997; Ibah. 2001). For instance, between 1995 and 1999 the military junta in Nigeria showed nonchalant attitude to the sustainability of the forest resources. This gave room to illegal lumbermen to penetrate the forest in the country, both reserved (protected) and the opened forest (unprotected), as the case of the study area, thus declined the area of forest nationwide. In poor and developing countries where often the administrative structures are either too weak and/or corrupt to enforce rules and regulations effectively and where tax and incentive systems do not work, forest clearing and environmental degradation has proceeded unabated.

#### **5.4 Arable farmland status from 1978 to 2003**

Arable farming is one of the prominent economic activities among the people of the study area as it is reflected in Table 6. The farmlands occupied 9,441.88 hectares, which represented 7.89% of the entire land use in 1978. In 1986, the land area of arable farmlands expanded from 9,441.88 to 11,830.08 hectares, making an increase of 2,388.20 hectares. But there seems to be a decrease in the cultivated area in 1994. This is due to the period at which the image was obtained that is, November, the beginning of dry season in southwestern Nigeria. At this time, most crops had been harvested except few such as, cassava, maize, among others which are left for proper maturation. Thus, majority of the farmlands are abandoned till the next planting season and they are sometimes overgrown with shrubs during this period. Between 1994 and 2003, arable farmlands increased from 6,830.37 to 15,923.20 hectares, representing an increase of 133.13%.

A close assessment of arable farmland areas between 1978 and 2003 shows that, 6,482.32 hectares of the land area was gained. This is negligible if we are going to solve the problem of food insecurity. However, the expansion of farmlands and declining in the forest area affirms the claim of Ola-Adams, (1981) and Williams, (1990). In their studies it was discovered that forests were removed to pave way for food and tree crops. Besides, the United Nations Framework Convention on Climate Change (UNFCCC, 2007) asserted that the overwhelming direct cause of deforestation is agriculture. In their discovery, subsistence farming was responsible for 48% of deforestation; commercial agriculture 32%; logging 14% and fuel wood removals make up 5% of deforestation. This was the observation of the study during the ground truthing. Besides, arable farmlands were scattered all over the study area especially around the settlements and some newly deforested areas and there were no large scale mechanized farming in the area. Again, arable farmlands were common features around the mountainous terrain and crops such as, maize, yam, cassava, rice, among others were mainly grown in the study area.

Geospatial Analysis of Deforestation

urban centres, thereby increasing urbanization process.

**5.8 Water body status from 1978 to 2003**

**5.9 Status of fallow/shrub from 1978 to 2003**

hectares (Table 6).

and Land Use Dynamics in a Region of Southwestern Nigeria 163

the entire land use area. This increased to 9,347.52 hectares in 1986, representing 322.30% increase over the period of 9 years (1978 - 1986). The bare soils decreased by 53.48% in 1994 and expanded by 33.73% in the year 2003. Between 1978 and 2003, the estimated area of bare soils expanded from 2213.3 hectares to 5814.9 hectares, which put an increase at 3601.6

Although the prevailing season partly influenced the trend, it was obvious some land areas were void of vegetation in the study area. Continuous exposure of soils could pose serious problem to agricultural sector and consequently continuous food insecurity. This is because clearing of forest lands contribute to severe soil erosion, soil infertility and flooding. The multiplier effect on the economy could be severe if quick action is not taken. In the first place, soil erosion would reduce agricultural land area, which in effect could induce food shortages and out-migration of the able-body from the rural community to the urban environment. This claim is already manifesting in most rural communities in Nigeria including the study area. Arokoyu (1999) reported that "environmental devastation has led to the loss of the means of livelihood of people, fall in agricultural outputs, out-migration of able-bodied youths, and engendered social rifts and intensified confusion. Presently, a community in the study area (Efon-Alaaye) has already been devastated by soil erosion and government had sunk millions of Naira to redeem the community from being swept away by erosion. This situation is capable of discouraging the able-bodied youths from the community and induces migration to

The drainage basin area in 1978 was 1.39 km2. This increased to 57.83 km2 in 1986, which was equivalent to 4060.43% increase. This increment was dictated by the improved spatial resolution of SPOT XS 1986, which is 20m as against 80m spatial resolution of Landsat MSS 1978. In SPOT XS 1986, streams, rivers, ponds and dams were more clearly visible, which enhanced the area calculation. In 1994, the drainage basin area calculated reduced by 20.15%, which consequently put the total area at 46.18 km2. The reason for this can be traced to the prevailing season. At the time the image data was obtained, the rain had just ended in southwestern Nigeria and the tree canopy was still fresh and dense. Most streams and rivers were covered with riparian forest thus, making it difficult for rivers to be assigned to class ID for eventual classification. In 2003, the area of water body was 46.09 km2, which was still a reduction in the coverage of water body. The reason for this is connected to the prevailing season that is, wet season and some noises in the image data, which in effect reduced its optimum visual systems. The pixel statistical result of water body in the study does not reflect the situation on ground and does not support the present claim of climate change.

High spatial resolution images is therefore, recommended for study on hydrology.

Bush fallow is a periodic relocation of farmland for the purpose of allowing soil to regain its fertility. Table 6 shows that the area of fallow has been increasing over the years, the highest being recorded between 1994 and 2003. For example, between 1978 and 1986, the fallow area increased from 23,397.9 hectares to 35,330.3 hectares, which puts the percentage change within the period of 8 years at 51%. This therefore implies that per year, the land areas that were left to fallow in the study area were 1,492 hectares. The spatial coverage, however, decreased from 35,330.3 hectares in 1986 to 26,804.2 hectares in 1994, which put decline at

#### **5.5 Status of agro-forestry between 1978 and 2003**

Agro-forestry, otherwise known as tree crop plantation in the study only constituted 0.47% that is, 558.76 hectares of the entire land use in 1978 (Table 6). The improved spatial resolution of the image data (SPOT 1986 and 1994) however, brought a significant difference to what was recorded in Landsat MSS 1978. Also, variation in the period at which the image data were taken influenced the differences in the pixel statistics of the two images. It should be recalled that while SPOT XS 1986 was obtained in May, the beginning of rainy season in southwestern Nigeria, SPOT XS 1994 was acquired in November, the beginning of dry season in the area. In May, tree canopies are just springing up due to the long periods of dryness. On the other hand, the tree canopies are still fresh in November despite the inception of the dry period. This difference in the time period is capable of influencing the canopy characteristics of the farmlands, consequently affecting the result. The estimated area of agro-forestry in 2003 was 4,774.30 hectares, which represented an increase of 342.03%. Although agro-forestry is not commonly found in the study area due to poor topography in some areas, the tree crop plantations are well grown in some areas around Ilesa, Erin-Oke, Aramoko-Ekiti (Figure 2). Because of the economic value of the crops, many deforested areas have been replaced with the cocoa plantations in particular, which confirms the findings of Ola-Adams, (1981) and Williams, (1990).

Although research in agro-forestry especially in Africa, has paid a significant quantum of emphasis on this concept for about three decades, it again is adaptation of earlier system of slash and burn and perennial forest gardens, although on a more scientific basis. The benefits of incorporating trees, especially fast growing legume species, lies in increasing soil fertility, obtaining complimentarity in resource use, reducing environmental stress and protecting crops.

#### **5.6 Bare rock status from 1978 to 2003**

The pixel statistics in Table 6 shows that the underlying rocks were being exposed in the study area. For instance, the area occupied by the exposed rock was 7,340.62 hectares in 1978, which was 6.13% of the total land use. The proportional decreased in 1986 was due to the prevailing season that is, wet season. During this period, the whole landscape was covered with vegetation making it difficult for rock to be seen as rock. This constraint in effect influenced the recorded areal extent of 2,246.29 hectares, which represented a decrease of 69.40%. In 1994, the area occupied by rock increased to 12,979.33 hectares, because of the prevailing season that is, dry season. At this time, the vegetation had dried up thus, making exposed rocks visible for classification. The year 2003 showed a decrease of 43.89% in the area of bare rocks. This could be because of the prevailing season at the time the image was acquired that is, September, the peak period of raining season in southwestern Nigeria. It is instructive to note that the rocks of the study area show great variation in grain size and in mineral composition, which enhanced the growth of dense vegetation whenever there is enough moisture. During the field survey, some quarry sites were sighted, which could in effect lead to the reduction in the size of the bare rocks in the study area.

#### **5.7 Status of bare soils from 1978 to 2003**

There are continuous exposure of soils to intense solar radiation and direct precipitation in the study area. This is shown by the expansion of bare soils discovered in the study. For example, in 1978, the area occupied by bare soils was 2,213.28 hectares representing 1.85% of

Agro-forestry, otherwise known as tree crop plantation in the study only constituted 0.47% that is, 558.76 hectares of the entire land use in 1978 (Table 6). The improved spatial resolution of the image data (SPOT 1986 and 1994) however, brought a significant difference to what was recorded in Landsat MSS 1978. Also, variation in the period at which the image data were taken influenced the differences in the pixel statistics of the two images. It should be recalled that while SPOT XS 1986 was obtained in May, the beginning of rainy season in southwestern Nigeria, SPOT XS 1994 was acquired in November, the beginning of dry season in the area. In May, tree canopies are just springing up due to the long periods of dryness. On the other hand, the tree canopies are still fresh in November despite the inception of the dry period. This difference in the time period is capable of influencing the canopy characteristics of the farmlands, consequently affecting the result. The estimated area of agro-forestry in 2003 was 4,774.30 hectares, which represented an increase of 342.03%. Although agro-forestry is not commonly found in the study area due to poor topography in some areas, the tree crop plantations are well grown in some areas around Ilesa, Erin-Oke, Aramoko-Ekiti (Figure 2). Because of the economic value of the crops, many deforested areas have been replaced with the cocoa plantations in particular, which

Although research in agro-forestry especially in Africa, has paid a significant quantum of emphasis on this concept for about three decades, it again is adaptation of earlier system of slash and burn and perennial forest gardens, although on a more scientific basis. The benefits of incorporating trees, especially fast growing legume species, lies in increasing soil fertility, obtaining complimentarity in resource use, reducing environmental stress and

The pixel statistics in Table 6 shows that the underlying rocks were being exposed in the study area. For instance, the area occupied by the exposed rock was 7,340.62 hectares in 1978, which was 6.13% of the total land use. The proportional decreased in 1986 was due to the prevailing season that is, wet season. During this period, the whole landscape was covered with vegetation making it difficult for rock to be seen as rock. This constraint in effect influenced the recorded areal extent of 2,246.29 hectares, which represented a decrease of 69.40%. In 1994, the area occupied by rock increased to 12,979.33 hectares, because of the prevailing season that is, dry season. At this time, the vegetation had dried up thus, making exposed rocks visible for classification. The year 2003 showed a decrease of 43.89% in the area of bare rocks. This could be because of the prevailing season at the time the image was acquired that is, September, the peak period of raining season in southwestern Nigeria. It is instructive to note that the rocks of the study area show great variation in grain size and in mineral composition, which enhanced the growth of dense vegetation whenever there is enough moisture. During the field survey, some quarry sites were sighted, which could in

There are continuous exposure of soils to intense solar radiation and direct precipitation in the study area. This is shown by the expansion of bare soils discovered in the study. For example, in 1978, the area occupied by bare soils was 2,213.28 hectares representing 1.85% of

**5.5 Status of agro-forestry between 1978 and 2003**

confirms the findings of Ola-Adams, (1981) and Williams, (1990).

effect lead to the reduction in the size of the bare rocks in the study area.

protecting crops.

**5.6 Bare rock status from 1978 to 2003**

**5.7 Status of bare soils from 1978 to 2003**

the entire land use area. This increased to 9,347.52 hectares in 1986, representing 322.30% increase over the period of 9 years (1978 - 1986). The bare soils decreased by 53.48% in 1994 and expanded by 33.73% in the year 2003. Between 1978 and 2003, the estimated area of bare soils expanded from 2213.3 hectares to 5814.9 hectares, which put an increase at 3601.6 hectares (Table 6).

Although the prevailing season partly influenced the trend, it was obvious some land areas were void of vegetation in the study area. Continuous exposure of soils could pose serious problem to agricultural sector and consequently continuous food insecurity. This is because clearing of forest lands contribute to severe soil erosion, soil infertility and flooding. The multiplier effect on the economy could be severe if quick action is not taken. In the first place, soil erosion would reduce agricultural land area, which in effect could induce food shortages and out-migration of the able-body from the rural community to the urban environment. This claim is already manifesting in most rural communities in Nigeria including the study area. Arokoyu (1999) reported that "environmental devastation has led to the loss of the means of livelihood of people, fall in agricultural outputs, out-migration of able-bodied youths, and engendered social rifts and intensified confusion. Presently, a community in the study area (Efon-Alaaye) has already been devastated by soil erosion and government had sunk millions of Naira to redeem the community from being swept away by erosion. This situation is capable of discouraging the able-bodied youths from the community and induces migration to urban centres, thereby increasing urbanization process.

#### **5.8 Water body status from 1978 to 2003**

The drainage basin area in 1978 was 1.39 km2. This increased to 57.83 km2 in 1986, which was equivalent to 4060.43% increase. This increment was dictated by the improved spatial resolution of SPOT XS 1986, which is 20m as against 80m spatial resolution of Landsat MSS 1978. In SPOT XS 1986, streams, rivers, ponds and dams were more clearly visible, which enhanced the area calculation. In 1994, the drainage basin area calculated reduced by 20.15%, which consequently put the total area at 46.18 km2. The reason for this can be traced to the prevailing season. At the time the image data was obtained, the rain had just ended in southwestern Nigeria and the tree canopy was still fresh and dense. Most streams and rivers were covered with riparian forest thus, making it difficult for rivers to be assigned to class ID for eventual classification. In 2003, the area of water body was 46.09 km2, which was still a reduction in the coverage of water body. The reason for this is connected to the prevailing season that is, wet season and some noises in the image data, which in effect reduced its optimum visual systems. The pixel statistical result of water body in the study does not reflect the situation on ground and does not support the present claim of climate change. High spatial resolution images is therefore, recommended for study on hydrology.

#### **5.9 Status of fallow/shrub from 1978 to 2003**

Bush fallow is a periodic relocation of farmland for the purpose of allowing soil to regain its fertility. Table 6 shows that the area of fallow has been increasing over the years, the highest being recorded between 1994 and 2003. For example, between 1978 and 1986, the fallow area increased from 23,397.9 hectares to 35,330.3 hectares, which puts the percentage change within the period of 8 years at 51%. This therefore implies that per year, the land areas that were left to fallow in the study area were 1,492 hectares. The spatial coverage, however, decreased from 35,330.3 hectares in 1986 to 26,804.2 hectares in 1994, which put decline at

Geospatial Analysis of Deforestation

**7. Acknowledgements**

**8. Definition of terms**

**Deforestation:**

**Land use:**

**Land cover:**

**Biodiversity:**

and Land Use Dynamics in a Region of Southwestern Nigeria 165

conservation. There is therefore need to protect our forest since unprotected forest will disappear faster than the protected one. Demarcating some forest zones as forest reserve areas could do this. Also, the indigenous participation and the involvement of the community people together with the forest guards in forest monitoring will go a long way to salvaging our forest from total depletion. The approach of GIS in this study was found useful as adequate

The impetus received from Prof. Bola Ayeni (Department of Geography, University of Ibadan, Nigeria) and Prof. F. A. Adesina (Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria) in contributing a chapter to this book was appreciated. The authors valued the cooperation of the staff of RECTAS, Obafemi Awolowo University, Ile-Ife; NASRDA, Abuja and GIS unit of the Department of Geography where the satellite images used for this study were collected. Finally, the privilege offered the authors by the CEO of INTECH open access publisher to contribute a chapter to this book was treasured.

Deforestation is the removal of a forest or stand of trees where the land is thereafter converted to a non-forest use. Examples of deforestation include conversion of forestland to farms, ranches, or urban use. Deforestation occurs for many reasons: trees or derived charcoal are used as, or sold, for fuel or as timber, while cleared land is used as pasture for

Land use is characterized by the arrangement, activities and inputs people undertake in a certain land cover type to produce change or maintain it (FAO, 2005). Land use is the specific activity a piece of land is put into. Various land use patterns emerge after the land has been subjected to use over time. In the rural area for instance, the type of land use

The definition of land cover is fundamental, because in many existing classifications and legends, it is confused with land use. Land cover is the observed (bio) physical cover on the

Biological diversity or biodiversity means the variety of plant and animal life at the ecosystem, community or species level and even at the generic level, Biodiversity is most commonly measured and reported at species level with characteristics such as species riches (number of species), species diversity (types of species) and endemism (uniqueness of species to a certain area) being the most useful elements for comparison (UNEP, 2002).

Environmental degradation can be defined as any modification of the environment that implies a reduction or loss of its physical and biological quantities caused by natural

tool for regular and up-to-date monitoring of forest and earth resources.

livestock, plantations of commodities, and settlements.

include farming, plantation, grazing, etc.

earth's surface (FAO, 2005).

**Environmental degradation:**

24.13%. This indicated that 8,526.1 hectares of fallow lands were used up within the period of 9 years. But between 1994 and 2003, the trend changed as fallow area increased from 26,804.2 hectares in 1994 to 59,436.8 hectares in 2003. This shifted the percentage change over the period of 9 years from 24.13 to 121.74.

The increase in the areal extent of shrub/fallow and a decrease in the forest area suggested that part of the deforested areas were abandoned to secondary re-growth. According to Aweto (1990), in Nigeria, the area previously characterized by continuous forest cover has been converted into secondary re-growth vegetation, mainly as a result of shifting cultivation and lumbering. In study area, some deforested areas were left uncultivated thereby converted to secondary re-growth while some farmlands were left to fallow thus, created a large expanse of fallow lands in the area.

#### **5.10 Built-up area status from 1978 to 2003**

The development of settlements in the study area has been rather gradual. This is because the study area comprises of many rural settlements and few urban centres and why major development takes place in the urban settlements, little or no development takes place in the rural settlements. This accounted for the proportion of 943.39 hectares in 1978. Although there was an increase in the areal extent in 1986, it only amounted to an increase of 66.17% over the period of 8 years. Settlements also expanded in their areal extent between 1986 and 1994, (i.e. 1567.6 to 1,947.9 hectares). This shows that within those periods, all the settlements expanded by 380.3 hectares, which constituted 24.26% increase. Between 1994 and 2003, settlements expanded by 142 hectares, which makes the total area covered by settlements to increase from 1,947.9 hectares, to 2,089.6 hectares. This shows that annually, between 1994 and 2003, 15.78 hectares of lands were gained for settlements, which represented 7.29% growth between 1994 and 2003.

The expansion of settlements, due to increased in human population and decrease in forest area shows that forests were been destroyed in the study area to pave way for human habitation. This confirms the findings of the Mather, (1990) and Harcourt, (1992) that reported an inverse relationship between population and forest cover. Geist and Lambin, (2002) also revealed that population increases due to high fertility rates were a primary driver of tropical deforestation in only 8% of cases.

#### **6. Conclusion**

The study, through the capability of GIS technology and remote sensing data revealed a steady decline in forest area and land use intensification with the expansion in farmlands, fallow ground and built up/residential areas. This indicates that forests were being converted to agricultural use and housing estate. However, the disappearance of forest resources could pose serious threat to biodiversity. For instance, there has been an underlying assumption that as the forest declines, species diversity will decline similarly. Many such models have been proven to be wrong and loss of habitat does not necessarily lead to large scale loss of species. Study that will give more insight on the process of biodiversity extinction resulted from deforestation will be needed since the scientific understanding of the process of extinction is insufficient to accurately make predictions about the impact of deforestation on biodiversity. Moreover, it should be borne in mind that forest resources preserve ecosystem, offers economic and social opportunity for people. Besides, forests foster medicinal conservation. There is therefore need to protect our forest since unprotected forest will disappear faster than the protected one. Demarcating some forest zones as forest reserve areas could do this. Also, the indigenous participation and the involvement of the community people together with the forest guards in forest monitoring will go a long way to salvaging our forest from total depletion. The approach of GIS in this study was found useful as adequate tool for regular and up-to-date monitoring of forest and earth resources.

#### **7. Acknowledgements**

164 Deforestation Around the World

24.13%. This indicated that 8,526.1 hectares of fallow lands were used up within the period of 9 years. But between 1994 and 2003, the trend changed as fallow area increased from 26,804.2 hectares in 1994 to 59,436.8 hectares in 2003. This shifted the percentage change over

The increase in the areal extent of shrub/fallow and a decrease in the forest area suggested that part of the deforested areas were abandoned to secondary re-growth. According to Aweto (1990), in Nigeria, the area previously characterized by continuous forest cover has been converted into secondary re-growth vegetation, mainly as a result of shifting cultivation and lumbering. In study area, some deforested areas were left uncultivated thereby converted to secondary re-growth while some farmlands were left to fallow thus,

The development of settlements in the study area has been rather gradual. This is because the study area comprises of many rural settlements and few urban centres and why major development takes place in the urban settlements, little or no development takes place in the rural settlements. This accounted for the proportion of 943.39 hectares in 1978. Although there was an increase in the areal extent in 1986, it only amounted to an increase of 66.17% over the period of 8 years. Settlements also expanded in their areal extent between 1986 and 1994, (i.e. 1567.6 to 1,947.9 hectares). This shows that within those periods, all the settlements expanded by 380.3 hectares, which constituted 24.26% increase. Between 1994 and 2003, settlements expanded by 142 hectares, which makes the total area covered by settlements to increase from 1,947.9 hectares, to 2,089.6 hectares. This shows that annually, between 1994 and 2003, 15.78 hectares of lands were gained for settlements, which

The expansion of settlements, due to increased in human population and decrease in forest area shows that forests were been destroyed in the study area to pave way for human habitation. This confirms the findings of the Mather, (1990) and Harcourt, (1992) that reported an inverse relationship between population and forest cover. Geist and Lambin, (2002) also revealed that population increases due to high fertility rates were a primary

The study, through the capability of GIS technology and remote sensing data revealed a steady decline in forest area and land use intensification with the expansion in farmlands, fallow ground and built up/residential areas. This indicates that forests were being converted to agricultural use and housing estate. However, the disappearance of forest resources could pose serious threat to biodiversity. For instance, there has been an underlying assumption that as the forest declines, species diversity will decline similarly. Many such models have been proven to be wrong and loss of habitat does not necessarily lead to large scale loss of species. Study that will give more insight on the process of biodiversity extinction resulted from deforestation will be needed since the scientific understanding of the process of extinction is insufficient to accurately make predictions about the impact of deforestation on biodiversity. Moreover, it should be borne in mind that forest resources preserve ecosystem, offers economic and social opportunity for people. Besides, forests foster medicinal

the period of 9 years from 24.13 to 121.74.

created a large expanse of fallow lands in the area.

represented 7.29% growth between 1994 and 2003.

driver of tropical deforestation in only 8% of cases.

**6. Conclusion**

**5.10 Built-up area status from 1978 to 2003**

The impetus received from Prof. Bola Ayeni (Department of Geography, University of Ibadan, Nigeria) and Prof. F. A. Adesina (Department of Geography, Obafemi Awolowo University, Ile-Ife, Nigeria) in contributing a chapter to this book was appreciated. The authors valued the cooperation of the staff of RECTAS, Obafemi Awolowo University, Ile-Ife; NASRDA, Abuja and GIS unit of the Department of Geography where the satellite images used for this study were collected. Finally, the privilege offered the authors by the CEO of INTECH open access publisher to contribute a chapter to this book was treasured.

### **8. Definition of terms**

#### **Deforestation:**

Deforestation is the removal of a forest or stand of trees where the land is thereafter converted to a non-forest use. Examples of deforestation include conversion of forestland to farms, ranches, or urban use. Deforestation occurs for many reasons: trees or derived charcoal are used as, or sold, for fuel or as timber, while cleared land is used as pasture for livestock, plantations of commodities, and settlements.

#### **Land use:**

Land use is characterized by the arrangement, activities and inputs people undertake in a certain land cover type to produce change or maintain it (FAO, 2005). Land use is the specific activity a piece of land is put into. Various land use patterns emerge after the land has been subjected to use over time. In the rural area for instance, the type of land use include farming, plantation, grazing, etc.

#### **Land cover:**

The definition of land cover is fundamental, because in many existing classifications and legends, it is confused with land use. Land cover is the observed (bio) physical cover on the earth's surface (FAO, 2005).

#### **Biodiversity:**

Biological diversity or biodiversity means the variety of plant and animal life at the ecosystem, community or species level and even at the generic level, Biodiversity is most commonly measured and reported at species level with characteristics such as species riches (number of species), species diversity (types of species) and endemism (uniqueness of species to a certain area) being the most useful elements for comparison (UNEP, 2002).

#### **Environmental degradation:**

Environmental degradation can be defined as any modification of the environment that implies a reduction or loss of its physical and biological quantities caused by natural

Geospatial Analysis of Deforestation

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#### **Spatial & spectral resolution:**

The spatial resolution refers to the size of the area on the ground that is summarized by one data value in the imagery. This is the Instantaneous Field of View (IFOV). Spectral resolution refers to the number and width of the spectral bands that the satellite sensor detects (Eastman, 2001).

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**9**

*1,3México 2España*

**Unsupervised Classification**

Antonia Macedo-Cruz1, I. Villegas-Romero3, M. Santos-Peñas2 and G. Pajares-Martinsanz2

 *Colegio de Postgraduados, Montecillo, Estado de México, 2Facultad de Informática, Universidad Complutense de Madrid,*

*1Hidrociencias, Campus Montecillo,*

*3Universidad Autónoma Chapingo,*

**of Aerial Images Based on the Otsu's Method**

Remote-sensing research focusing on image classification has long attracted the attention of the remote-sensing community because classification results are the basis for many environmental and socioeconomic applications. However, classifying remotely sensed data into a thematic map remains a challenge because many factors, such as the complexity of the landscape in a study area, selected remotely sensed data, and image-processing and

In forest management, a number of activities are oriented towards wood production or forest inventories with the aims of controlling parameters of interest such as diameter of trees, height, crown height, bark thickness, canopy, humidity, illumination, CO2 transformation among others, always with the goal of environmental sustainability with high social impact. The unsupervised classification of aerial image offer solutions for monitoring production in forest trees while the same time costs are minimized. Also with Unmanned Aerial Vehicles (UAV) equipped with an appropriate image classification system, have become powerful tools for early fire forest detection and posterior monitoring. This technology has also been applied for crop monitoring under wireless sensor network

Clustering is the task of categorizing objects having several attributes into different classes so that the objects belonging to the same class are similar, and those that are broken down into different classes are not. Clustering is the subject of active research in several fields such as statistics, pattern recognition, machine learning, data mining, information science, agriculture technology and spatial databases. A wide variety of clustering algorithms have

Classification and segmentation in agriculture and forest management is an interesting topic but not new. There are many classification approaches that are oriented toward the identification of textures in agricultural and forest images. Most of them can be grouped

classification approaches, may affect the success of a classification [1].

been proposed for different applications [1], [2].

**1. Introduction**

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### **Unsupervised Classification of Aerial Images Based on the Otsu's Method**

Antonia Macedo-Cruz1, I. Villegas-Romero3,

 M. Santos-Peñas2 and G. Pajares-Martinsanz2 *1Hidrociencias, Campus Montecillo, Colegio de Postgraduados, Montecillo, Estado de México, 2Facultad de Informática, Universidad Complutense de Madrid, 3Universidad Autónoma Chapingo, 1,3México 2España*

#### **1. Introduction**

170 Deforestation Around the World

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12-28.

Remote-sensing research focusing on image classification has long attracted the attention of the remote-sensing community because classification results are the basis for many environmental and socioeconomic applications. However, classifying remotely sensed data into a thematic map remains a challenge because many factors, such as the complexity of the landscape in a study area, selected remotely sensed data, and image-processing and classification approaches, may affect the success of a classification [1].

In forest management, a number of activities are oriented towards wood production or forest inventories with the aims of controlling parameters of interest such as diameter of trees, height, crown height, bark thickness, canopy, humidity, illumination, CO2 transformation among others, always with the goal of environmental sustainability with high social impact. The unsupervised classification of aerial image offer solutions for monitoring production in forest trees while the same time costs are minimized. Also with Unmanned Aerial Vehicles (UAV) equipped with an appropriate image classification system, have become powerful tools for early fire forest detection and posterior monitoring. This technology has also been applied for crop monitoring under wireless sensor network architecture.

Clustering is the task of categorizing objects having several attributes into different classes so that the objects belonging to the same class are similar, and those that are broken down into different classes are not. Clustering is the subject of active research in several fields such as statistics, pattern recognition, machine learning, data mining, information science, agriculture technology and spatial databases. A wide variety of clustering algorithms have been proposed for different applications [1], [2].

Classification and segmentation in agriculture and forest management is an interesting topic but not new. There are many classification approaches that are oriented toward the identification of textures in agricultural and forest images. Most of them can be grouped as follows.

Unsupervised Classification of Aerial Images Based on the Otsu's Method 173

In the present investigation 16 color aerial photographs in digital format were used, owned by the Institute of Geography of the Autonomous University of Mexico, taken in October 1997. The photographs correspond to the catchments of the river La Sabana, Guerrero, with spatial resolution of 1:19500, and three-band spectral resolution visible and radiometric resolution RGB from 0 to 255 levels. As an example, Fig. 1 displays a representative image of the set of images analyzed in this work. As we can see it contains several textures which

**2. Materials and methods**

must be classified as belonging to a cluster.

Fig. 1. Land cover images in RGB color model

**2.2.1 Brief description of the Otsu's method**

image of

**2.2 Classifier based on the theory of the Otsu's method**

unsupervised classification strategy justifying its choice.

Then, the probability distributions for each class is

of the color image by applying the algorithm modified [27, 28]:

Otsu's method [10], one of the most widely used thresholding techniques in image analysis, has showed great success in image enhancement and segmentation. As mentioned before, it is an automatic thresholding strategy; we exploit the automatic capability for designing the

This research sought the optimal threshold (single or multiple) for each of the spectral bands

The number of pixels with gray level *i* is denoted *<sup>i</sup> f* giving a probability of gray level *i* in an

*i i*

*<sup>f</sup> <sup>p</sup> N* (1)

**2.1 Study area**

Based on the presented considerations and in order to tackle the classification problem addressed in this paper, we have designed a new automatic strategy based on the thresholding Otsu´s method is proposed. The first step consists in the thresholding of each R, G and B channels into two parts based on within-class and between-class variances suggested by Otsu [10]. This allows to classify each pixel to one part of each channel, so that conveniently combined the a pixel should be classified as belonging to one of the eight possible classes. Although in this paper we only use eight classes, the method can be easily extended to more classes, as described in section 2.2.2, even we can achieve until twenty seven. This makes the main contribution of this paper.

Additionally, one major advantage of this algorithm is that it does not need to know how many classes are required to be clustered in advance, as it is required for most supervised clustering processes. The termination criterion is established based on the within-class variance, according to the Otsu's method.

The proposed method is compared against the well-known Fuzzy c-means clustering [11], [12]. The prediction of the correct number of clusters is a fundamental problem in unsupervised classification problems. Many clustering algorithms require the definition of the number of clusters beforehand. To overcome this problem, various cluster validity indices have been proposed to assess the quality of a clustering partition [13]-[16]. Five cluster validation indices have been used in our tests, they are: Dunn's [15]-[19], Davies-Bouldin [15], [19]-[21], Calinski-Harabasz [15], [21]-[24], Krzanowski and Lai [22], [25]; and Hartigan [21], [22], [26]. We have used five because there is not relevant conclusions in the literature about their performance, depending on the application their behavior could vary considerably. Based on the above indices we have verified the best performance of our approach against the MS method, particularly in the images where water bodies are present.

The remainder of the paper is organized as follows. In Section 2, materials and methods; two issues will be addressed, unsupervised classification of color images and five of cluster validation indexes. Section 3, result and discussions; Conclusions are presented in Section 4.

### **2. Materials and methods**

#### **2.1 Study area**

172 Deforestation Around the World

 Currently, many of the agriculture, livestock and forestry are planned using spatial analysis tools, seeking different specific objectives [3]. In this sense, the images acquired by remote sensors provide the necessary spatial resolution to obtain information about objects, areas, or phenomena on the earth's surface, at different scales. These sensors measure the intensity of the energy emitted or reflected by the objects by means of the

 There are many segmentation techniques reported in the literature [4, 5]. Most color image segmentation techniques are usually derived from methods designed for graylevel images. Processing each channel individually by directly applying graylevel where the channels are assumed independent and only their intra-spatial interactions are considered [6]. Another option is decomposing the image into luminance and chromatic channel: after transforming the image data into the desired (usually application dependent) color space, texture features are extracted from the luminance channel while chromatic features are extracted from the chromatic channels, each in a specific manner [7]. Reference [8] and [9] show combining spatial interaction within each channel and interaction between spectral channels and gray level texture analysis techniques are applied in each channel, while pixel interactions between different

Based on the presented considerations and in order to tackle the classification problem addressed in this paper, we have designed a new automatic strategy based on the thresholding Otsu´s method is proposed. The first step consists in the thresholding of each R, G and B channels into two parts based on within-class and between-class variances suggested by Otsu [10]. This allows to classify each pixel to one part of each channel, so that conveniently combined the a pixel should be classified as belonging to one of the eight possible classes. Although in this paper we only use eight classes, the method can be easily extended to more classes, as described in section 2.2.2, even we can achieve until twenty

Additionally, one major advantage of this algorithm is that it does not need to know how many classes are required to be clustered in advance, as it is required for most supervised clustering processes. The termination criterion is established based on the within-class

The proposed method is compared against the well-known Fuzzy c-means clustering [11], [12]. The prediction of the correct number of clusters is a fundamental problem in unsupervised classification problems. Many clustering algorithms require the definition of the number of clusters beforehand. To overcome this problem, various cluster validity indices have been proposed to assess the quality of a clustering partition [13]-[16]. Five cluster validation indices have been used in our tests, they are: Dunn's [15]-[19], Davies-Bouldin [15], [19]-[21], Calinski-Harabasz [15], [21]-[24], Krzanowski and Lai [22], [25]; and Hartigan [21], [22], [26]. We have used five because there is not relevant conclusions in the literature about their performance, depending on the application their behavior could vary considerably. Based on the above indices we have verified the best performance of our approach against the MS

The remainder of the paper is organized as follows. In Section 2, materials and methods; two issues will be addressed, unsupervised classification of color images and five of cluster validation indexes. Section 3, result and discussions; Conclusions are presented in Section 4.

electromagnetic spectrum [1].

channels are also taken into account.

seven. This makes the main contribution of this paper.

method, particularly in the images where water bodies are present.

variance, according to the Otsu's method.

In the present investigation 16 color aerial photographs in digital format were used, owned by the Institute of Geography of the Autonomous University of Mexico, taken in October 1997. The photographs correspond to the catchments of the river La Sabana, Guerrero, with spatial resolution of 1:19500, and three-band spectral resolution visible and radiometric resolution RGB from 0 to 255 levels. As an example, Fig. 1 displays a representative image of the set of images analyzed in this work. As we can see it contains several textures which must be classified as belonging to a cluster.

Fig. 1. Land cover images in RGB color model

#### **2.2 Classifier based on the theory of the Otsu's method 2.2.1 Brief description of the Otsu's method**

Otsu's method [10], one of the most widely used thresholding techniques in image analysis, has showed great success in image enhancement and segmentation. As mentioned before, it is an automatic thresholding strategy; we exploit the automatic capability for designing the unsupervised classification strategy justifying its choice.

This research sought the optimal threshold (single or multiple) for each of the spectral bands of the color image by applying the algorithm modified [27, 28]:

The number of pixels with gray level *i* is denoted *<sup>i</sup> f* giving a probability of gray level *i* in an image of

$$p\_{i=} \frac{f\_i}{N} \tag{1}$$

Then, the probability distributions for each class is

Unsupervised Classification of Aerial Images Based on the Otsu's Method 175

There are three steps in the proposed classification strategy. First, the assignment process, that consists in assigning one of the possible classes to each pixel. Second, the codification of each cluster, which is identified by a label. Finally, a regrouping process so that very similar

Given a pixel i located at *(x, y)* in the original RGB image. Its three spectral components in

As already mentioned, the thresholding methods split the histogram into two regions. As there are three spectral components, six sub-regions are obtained. If necessary, successive thresholding can be applied to each spectral channel. The second thresholding produces three partitions per channel. If a third thresholding is applied, four regions per component are obtained and so on. Therefore, assuming that eventually the number of thresholds per channel is M, there will be *tR1, tR2, … tRM* , thresholds for channel R, and in the same way, *tG1*, *tG2*, …, *tGM* for component *G*, and *tB1, tB2, …, tBM*, for component *B*. Based on this, each pixel *i*

according to its spectral components by Equation (10):

*s s ss*

*M if i t*

where s denotes the spectral component, i.e., *s* = *R*, *G* or *B*, and *tsi* are the consecutive

For example, it is known that in the *RGB* colour space values are in the range [0, 255]. So, considering the spectral component *R* with two thresholds, *tR1* = 120 and *tR2* = 199, a pixel will be coded as 0, 1, or 2, if its spectral value R is smaller than 120, between 120 and 199, or

Once the whole image has been coded, the next step is the labelling of the existing classes. If M thresholds haven been obtained, there are *n* = *M + 1* histogram partitions per channel, and therefore the number of possible combinations is nd, where d is the number of spectral components, i.e., *d* = 3 in the RGB colour space. This number of combinations represents the number of classes. Each cluster is identified by its label. Every pixel is assigned its corresponding label according to Equation (11). So, given the pixel *i ≡ (x, y)* with codes ��

*i if t i t*

0 1 2

� as follows:

2

Let *Ck* be the number of clusters obtained by the classification procedure, where *k* identifies a class between 1 and nd, each class containing *Nk* pixels of the original image. It could be

1 1 2 2 3

(10)

*i R GB <sup>p</sup> n i ni i* (11)

�*,* ı� �,

*s s s ss*

*if i t if t i t*

*s sM*

this space are obtained, namely *R(x, y)* = *ir*, *G(x, y)* = *ig* and *B(x, y)* = *ib*.

**2.2.2 Unsupervised classification strategy by within-class and between-classes**

**spectral variances**

can be coded as *si*

thresholds.

and ı�

greater than 199, respectively.

�, its label will be given by P�

**<IP_ADDRESS> Cluster labelling**

**<IP_ADDRESS> Merging process**

classes are merged into one. **<IP_ADDRESS> Assignment process**

$$w\_k = \sum\_{i \in \overline{\mathbb{C}}\_k} pi \tag{2}$$

The *wk* is regarded as the zeroth-order cumulative moment of the kth class Ck. and the means for classes is

$$\mu\_k = \sum\_{i \in \mathcal{C}\_k} \frac{i.pi}{w\_k} \tag{3}$$

In the case of bi-level thresholding, Otsu defined the between-class variance of the thresholded image as: Where

$$
\sigma\_{\rm B}^{\rm 2\*} = w\_1 (u\_1 - u\_T)^2 + w\_2 (u\_2 - u\_T)^2 \tag{4}
$$

$$
\mu\_T = \sum\_{k=1}^{M} \alpha\_k \mu\_k \tag{5}
$$

And

$$\sum\_{\substack{k=1\\k=1}}^{M} w\_k = 1\tag{6}$$

Assuming that there are M-1 thresholds, {t1, t2, …, tM-1}, which divide the original image into M classes: C1 for [1,…, t1], C2 for [t1+1, …, t2], … , Ci for [t i-1+1, …, t i], …, and CM for [tM-1+1, …, L], the optimal thresholds {t1\*, t2\*, …, tM-1\*} are chosen by maximizing <sup>2</sup> *<sup>B</sup>* as follows

$$\{t\_1 \text{\*}, t\_2 \text{\*}, \dots, t\_{M-1} \text{\*}\} = \text{arg}\,\,\,\text{Max}\{\sigma\_B^2(t\_1, t\_2, \dots, t\_{M-1})\}\tag{7}$$

$$1 \le t\_1 < \dots < t\_{M-1} < L$$

then

$$\mathbf{u}(\sigma\_B^2) = \sum\_{k=1}^{M} w\_k u\_k^2 - u\_T^2 = \sum\_{k=1}^{M} w\_k u\_k^{-2} \tag{8}$$

A threshold value tOtsu developed by Otsu is the one that maximizes vart between-class, or equivalently minimizes vart within-class.

$$\begin{aligned} t\_{\text{Otsu}} &= \arg \left\{ \text{Min}\_{1 \le t \le L} \text{(Var}\_{within-class}^{t} \text{)} \right\} \\ t\_{\text{Otsu}} &= \arg \left\{ \text{Max}\_{1 \le t \le L} \text{(Var}\_{between-class}^{t} \text{)} \right\} \end{aligned} \tag{9}$$

where *L* is the number of gray levels in each band, in our images *L* is 256 because each pixel is represented with eight bits.

#### **2.2.2 Unsupervised classification strategy by within-class and between-classes spectral variances**

There are three steps in the proposed classification strategy. First, the assignment process, that consists in assigning one of the possible classes to each pixel. Second, the codification of each cluster, which is identified by a label. Finally, a regrouping process so that very similar classes are merged into one.

#### **<IP_ADDRESS> Assignment process**

174 Deforestation Around the World

*<sup>w</sup> pi <sup>k</sup> i Ck*

The *wk* is regarded as the zeroth-order cumulative moment of the kth class Ck. and the

.

*k*

In the case of bi-level thresholding, Otsu defined the between-class variance of the

2\* <sup>2</sup> <sup>2</sup> ( )( ) 11 22 *wu u wu u BT T*

*M <sup>T</sup> k k <sup>k</sup>*

1

Assuming that there are M-1 thresholds, {t1, t2, …, tM-1}, which divide the original image into M classes: C1 for [1,…, t1], C2 for [t1+1, …, t2], … , Ci for [t i-1+1, …, t i], …, and CM for [tM-1+1, …, L], the optimal thresholds {t1\*, t2\*, …, tM-1\*} are chosen by maximizing <sup>2</sup>

<sup>2</sup> { \*, \*,..., \*} arg { ( , ,..., )} 12 1 12 1 *tt tM BM Max t t t*

1 ... 1 1 *ttL <sup>M</sup>*

2 22 2 ( ) 1 1

arg ( )

arg ( )

*t*

*t*

1

*t*

*Otsu t L within class*

*Min Var*

1

*t Max Varet*

*Otsu t L between class*

where *L* is the number of gray levels in each band, in our images *L* is 256 because each pixel

A threshold value tOtsu developed by Otsu is the one that maximizes vart

*M M wu u wu B T kk kk k k*

*M wk <sup>k</sup>*

1

1

*i C k i pi*

*k*

*u*

within-class.

means for classes is

thresholded image as:

Where

And

follows

then

equivalently minimizes vart

is represented with eight bits.

(2)

(5)

*<sup>B</sup>* as

between-class, or

(9)

*<sup>w</sup>* (3)

(6)

(7)

(8)

(4)

Given a pixel i located at *(x, y)* in the original RGB image. Its three spectral components in this space are obtained, namely *R(x, y)* = *ir*, *G(x, y)* = *ig* and *B(x, y)* = *ib*.

As already mentioned, the thresholding methods split the histogram into two regions. As there are three spectral components, six sub-regions are obtained. If necessary, successive thresholding can be applied to each spectral channel. The second thresholding produces three partitions per channel. If a third thresholding is applied, four regions per component are obtained and so on. Therefore, assuming that eventually the number of thresholds per channel is M, there will be *tR1, tR2, … tRM* , thresholds for channel R, and in the same way, *tG1*, *tG2*, …, *tGM* for component *G*, and *tB1, tB2, …, tBM*, for component *B*. Based on this, each pixel *i* can be coded as *si* according to its spectral components by Equation (10):

$$
\tilde{i}\_s = \begin{cases}
0 & \text{if} \quad i\_s \le t\_{s1} \\
1 & \text{if} \quad t\_{s1} < i\_s \le t\_{s2} \\
2 & \text{if} \quad t\_{s2} < i\_s \le t\_{s3} \\
& \vdots \\
M & \text{if} \quad i\_s > t\_{sM}
\end{cases}
\tag{10}
$$

where s denotes the spectral component, i.e., *s* = *R*, *G* or *B*, and *tsi* are the consecutive thresholds.

For example, it is known that in the *RGB* colour space values are in the range [0, 255]. So, considering the spectral component *R* with two thresholds, *tR1* = 120 and *tR2* = 199, a pixel will be coded as 0, 1, or 2, if its spectral value R is smaller than 120, between 120 and 199, or greater than 199, respectively.

#### **<IP_ADDRESS> Cluster labelling**

Once the whole image has been coded, the next step is the labelling of the existing classes. If M thresholds haven been obtained, there are *n* = *M + 1* histogram partitions per channel, and therefore the number of possible combinations is nd, where d is the number of spectral components, i.e., *d* = 3 in the RGB colour space. This number of combinations represents the number of classes. Each cluster is identified by its label. Every pixel is assigned its corresponding label according to Equation (11). So, given the pixel *i ≡ (x, y)* with codes �� �*,* ı� �, and ı� �, its label will be given by P� � as follows:

$$
\tilde{p}\_i = n^2 \tilde{i}\_R + n \tilde{i}\_G + \tilde{i}\_B \tag{11}
$$

#### **<IP_ADDRESS> Merging process**

Let *Ck* be the number of clusters obtained by the classification procedure, where *k* identifies a class between 1 and nd, each class containing *Nk* pixels of the original image. It could be

Unsupervised Classification of Aerial Images Based on the Otsu's Method 177

*c* rows and *n* columns. The values in row *i* give the membership of all *n* input data in cluster *i* for *k=1* to *n* ; the *k*-th column of *U* gives the membership *p* of vector *k* (which represents some object *k*) in all *c* clusters for *i=1* to *c*. Each of the entries in *U* lies in *[0,1]*;

2. The other set of unknowns in the FCM model is a set of *c* cluster centers or prototypes, arrayed as the *c* columns of a *p* x *c* matrix *V*. These prototypes are vectors (points) in the input space of *p*-tuples. Pairs *(U,V)* of coupled estimates are found by alternating optimization through the first order necessary conditions for *U* and *V*. The objective function minimized in the original version measured distances between data points and prototypes in any inner product norm, and memberships were weighted with an

As *X xx x* 1 2 , ,..., *<sup>n</sup>* and the set all *Vcn* real matrices of dimension *c* x *n*, with 2 *c n* .  The basic definition

(17)

(18)

*ik cn* with

1. A fuzzy c-partition of the data, which is a *c x n* membership matrix *U V*

each row sum is greater than zero; and each column sum equals 1.

be obtained a matrix representing the partition follow *U V*

min ( ; )

*z Uv*

2

2

cluster analysis, but it is often time-consuming and complicated work [16].

*<sup>i</sup> <sup>n</sup> ik k <sup>m</sup> <sup>k</sup> ik*

2 1

*m*

2 1

The exponent m is known as exponential weight and reduces the influence of noise when getting the centers of the clusters. The higher the m > 1, the greater this influence. More

Evaluation of clustering results (or cluster validation) is an important and necessary step in

1

1

*x v*

1

*<sup>j</sup> k j <sup>G</sup>*

*x v*

*k i G ik m*

*k*

FCM for m > 1 is to minimize the following objective function:

G is a matrix of dimension pxp symmetric positive definite

*v*

1

*c*

details on fuzzy c-means clustering [11, 12].

**2.4 Methods for cluster validation**

The unknowns in FCM clustering are:

exponent *m>1*

That is:

Where

said that each class is defined by a tri-dimensional vector (d = 3). The elements of that vector are the spectral components of the pixels according to the *RGB* colour model, i.e., i� ≡ ��� �� �� �� �� �) for the pixel *i ≡ (x, y),* if the pixel and its spectral components belong to class *Ck*. For each class, the average value of the membership degrees to that class is calculated by Equation (12):

$$\mu\_{\mathbf{k}} = \left(\mu\_{\mathbf{k}}^{\mathbf{k}}, \mu\_{\mathbf{G}'}^{\mathbf{k}}, \mu\_{\mathbf{B}}^{\mathbf{k}}\right) = \frac{1}{N\_{\mathbf{k}}} \sum\_{\mathbf{i}\_{\mathbf{k}} \in \mathcal{C}\_{\mathbf{k}}} \mathbf{i}\_{\mathbf{k}} \tag{12}$$

Based on the potential of Otsu's method, it is possible to estimate the within-class and the between-classes spectral variances, denoted by *<sup>k</sup>* and *kh* respectively, according to Equations (1) and (16). Obviously, is only related to class *Ck* and, as expected, *kh* involves the two classes *Ck* and *Ch*, *k* ≠ *h*: *k*

$$\sigma\_{k} = \frac{1}{d \cdot N\_{k}} \sum\_{i\_{k} \in \mathcal{C}\_{k}} \left[ \left( \dot{\mathbf{i}}\_{R}^{k} - \mu\_{R}^{k} \right)^{2} + \left( \dot{\mathbf{i}}\_{G}^{k} - \mu\_{G}^{k} \right)^{2} + \left( \dot{\mathbf{i}}\_{B}^{k} - \mu\_{B}^{k} \right)^{2} \right]^{1/2} \tag{13}$$

$$
\sigma\_{\rm kl} = \frac{1}{d} \left[ \left( \mu\_{\rm R}^{k} - \mu\_{\rm R}^{h} \right)^{2} + \left( \mu\_{\rm G}^{k} - \mu\_{\rm G}^{h} \right)^{2} + \left( \mu\_{\rm B}^{k} - \mu\_{\rm B}^{h} \right)^{2} \right] \tag{14}
$$

Based on those variances, some classes can be fused due to their spectral similarities. The similarity is a concept defined as follows. Given the clusters *Ck* and *Ch,* for *k* ≠ *h*, they are merged if *k kh h kh* . This is based on the hypothesis that if a good partition is already achieved, the classes obtained are properly separated, without overlapping, and then no further fusion is required. On the contrary, if classes overlap, the between-class variance *kh* is greater than the individual within-class variances, *<sup>k</sup>* and *<sup>j</sup>* . This re-clustering process is repeated until all the between-class variances are greater than their corresponding within-class variances. Without lost of generality, if two classes are merged, the resulting fused class will be re-labelled with the name of the class with the smaller variance value. This does not affect the classification process because only labels are modified.

After the fusion process, it must be checked if more clusters are necessary. This is carried out on the basis that if after the combination process no class has been fused, it means that more clusters are needed. A new clustering process starts again with the number of thresholds increased by one. This is repeated until a fusion occurs.

#### **2.3 Fuzzy C-Means clustering**

Fuzzy c-means clustering (FCM) is a data clustering technique wherein each data point belongs to a cluster to some degree that is specified by a membership grade. This technique was originally introduced by Jim Bezdek [11], as an improvement on earlier clustering methods.

The FCM algorithm attempts to partition a finite collection of elements *X xx x* 1 2 , ,..., *<sup>n</sup>* into a collection of c fuzzy clusters with respect to some given criterion.

The FCM algorithm, processes *n* vectors in *p*-space as data input, and uses them, in conjunction with first order necessary conditions for minimizing the FCM objective functional, to obtain estimates for two sets of unknowns.

The unknowns in FCM clustering are:

#### That is:

176 Deforestation Around the World

said that each class is defined by a tri-dimensional vector (d = 3). The elements of that vector are the spectral components of the pixels according to the *RGB* colour model, i.e., i� ≡

<sup>1</sup> , ,

Based on the potential of Otsu's method, it is possible to estimate the within-class and the

*kkk k RGB <sup>k</sup> Nk <sup>C</sup>*

Equations (1) and (16). Obviously, is only related to class *Ck* and, as expected,

*k RR GG BB*

*k*

*k k*

 *i*

*k C*

*d N*

greater than the individual within-class variances,

classification process because only labels are modified.

thresholds increased by one. This is repeated until a fusion occurs.

into a collection of c fuzzy clusters with respect to some given criterion.

functional, to obtain estimates for two sets of unknowns.

between-classes spectral variances, denoted by

involves the two classes *Ck* and *Ch*, *k* ≠ *h*:

 *h kh*

**2.3 Fuzzy C-Means clustering**

�) for the pixel *i ≡ (x, y),* if the pixel and its spectral components belong to class *Ck*. For each class, the average value of the membership degrees to that class is calculated by

*k k*

*i* (12)

1 2 <sup>2</sup>

*kh* respectively, according to

(13)

(14)

*<sup>j</sup>* . This re-clustering process is

*kh*

*kh* is

*i*

*<sup>k</sup>* and

*kk k k kk*

*<sup>k</sup>* and

. This is based on the hypothesis that if a good partition is already

1 2 2

*iii*

 <sup>1</sup> 2 22 *kh kh kh kh R R G G B B d*

Based on those variances, some classes can be fused due to their spectral similarities. The similarity is a concept defined as follows. Given the clusters *Ck* and *Ch,* for *k* ≠ *h*, they are

achieved, the classes obtained are properly separated, without overlapping, and then no further fusion is required. On the contrary, if classes overlap, the between-class variance

repeated until all the between-class variances are greater than their corresponding within-class variances. Without lost of generality, if two classes are merged, the resulting fused class will be re-labelled with the name of the class with the smaller variance value. This does not affect the

After the fusion process, it must be checked if more clusters are necessary. This is carried out on the basis that if after the combination process no class has been fused, it means that more clusters are needed. A new clustering process starts again with the number of

Fuzzy c-means clustering (FCM) is a data clustering technique wherein each data point belongs to a cluster to some degree that is specified by a membership grade. This technique was originally introduced by Jim Bezdek [11], as an improvement on earlier clustering

The FCM algorithm attempts to partition a finite collection of elements *X xx x* 1 2 , ,..., *<sup>n</sup>*

The FCM algorithm, processes *n* vectors in *p*-space as data input, and uses them, in conjunction with first order necessary conditions for minimizing the FCM objective

��� �� �� �� ��

Equation (12):

merged if

methods.

 *k kh*

As *X xx x* 1 2 , ,..., *<sup>n</sup>* and the set all *Vcn* real matrices of dimension *c* x *n*, with 2 *c n* . Can be obtained a matrix representing the partition follow *U V ik cn* . The basic definition FCM for m > 1 is to minimize the following objective function:

$$\min z\_m(\mathcal{U}; \boldsymbol{\upsilon}) = \sum\_{k=1}^{n} \sum\_{i=1}^{c} \mu\_{ik}^{m} \left\| \boldsymbol{x}\_k - \boldsymbol{\upsilon}\_i \right\|\_{\mathcal{G}}^2 \tag{15}$$

G is a matrix of dimension pxp symmetric positive definite

$$\left\|\mathbf{x}\_{k} - \boldsymbol{\upsilon}\_{i}\right\|\_{\rm G}^{2} = \left(\mathbf{x}\_{k} - \boldsymbol{\upsilon}\_{i}\right)^{t} \mathbf{G}\left(\mathbf{x}\_{k} - \boldsymbol{\upsilon}\_{i}\right) \tag{16}$$

Where

$$\upsilon\_{i} = \frac{1}{\sum\_{k=1}^{n} \left(\mu\_{ik}\right)^{m}} \sum\_{k=1}^{n} \left(\mu\_{ik}\right)^{m} \mathbf{x}\_{k} \qquad \text{i} = \mathbf{1}, \ldots, \mathbf{c} \tag{17}$$

$$\mu\_{ik} = \frac{\left(\frac{1}{\left\|\mathbf{x}\_k - \boldsymbol{\upsilon}\_i\right\|\_G^2}\right)^{2fm-1}}{\sum\_{j=1}^c \left(\frac{1}{\left\|\mathbf{x}\_k - \boldsymbol{\upsilon}\_j\right\|\_G^2}\right)^{2fm-1}} \qquad i = 1, \dots, c; \ k = 1, \dots, n \tag{18}$$

The exponent m is known as exponential weight and reduces the influence of noise when getting the centers of the clusters. The higher the m > 1, the greater this influence. More details on fuzzy c-means clustering [11, 12].

#### **2.4 Methods for cluster validation**

Evaluation of clustering results (or cluster validation) is an important and necessary step in cluster analysis, but it is often time-consuming and complicated work [16].

Unsupervised Classification of Aerial Images Based on the Otsu's Method 179

/( 1) ( ) /( ) *B k <sup>k</sup> CH k W nk <sup>k</sup>*

where k denotes the number of clusters, and BK and Wk denote the between and within cluster sums of squares of the partition, respectively. Therefore an optimal number of

1

*p p diff K W k W k kk* (23)

*B*

*Wk*

*diffk diffk*

KL(k)

2/ 2/ ( 1) <sup>1</sup>

and p denotes the number of features in the data set. Therefore a value of k is optimal if it

( ) log

respectively. Therefore a value of k is optimal if it minimizes Han(k).

*Han k k*

where, BK and Wk denote the between and within cluster sums of squares of the partition,

In accordance with the objectives and methodology used in this research encouraging results were obtained regarding the proposal to adopt the criterion used in Otsu's method to the process of clustering and unsupervised classification of colour images, and the application of cluster validity methods by five cluster validation indices, compare the results with those generated by the old pattern recognition procedure, Fuzzy c-means clustering. In the present paper three issues are addressed. First, our proposed RGB unsupervised classification method. Secondly, to compare results we apply an old pattern recognition procedure, the Fuzzy c-means clustering. Third, five of cluster validation indices will be proposed to evaluate the quality of clusterings. To demonstrate the effectiveness of our proposed RGB unsupervised classification method, using 16 digital images from colour aerial photographs in which they can observe different land cover objects such as buildings,

(21)

(22)

(24)

**2.4.3 Calinski and Harabasz index**

**2.4.4 Krzanowski and Lai index**

where;

maximizes KL(k).

**2.4.5 Hartigan index**

The index of Hartigan is defined by:

**3. Results and discussion**

The index of Calinski and Harabasz is defined by:

The index of Krzanowski and Lai is defined by:

clusters is then defined as a value of k that maximizes CH(k).

The procedure of evaluating the results of a clustering algorithm is known under the term cluster validity. In reference [15] two kinds of validity indices are showed: external indices and internal indices. A third is added in reference [29], based on relative criteria. The first is based on external criteria. This implies that we evaluate the results of a clustering algorithm based on a pre-specified structure, which is imposed on a data set and reflects our intuition about the clustering structure of the data set. The second approach is based on internal criteria. We may evaluate the results of a clustering algorithm in terms of quantities that involve the vectors of the data set themselves. The third approach of clustering validity is based on relative criteria. Here the basic idea is the evaluation of a clustering structure by comparing it to other clustering schemes, resulting by the same algorithm but with different parameter values.

To evaluate the proposed classification method, five cluster validation techniques are applied, based on internal criteria. These indices are used to measure the "goodness" of the result of the grouping; comparing the proposed classification method against the old pattern recognition procedure called Fuzzy c-means clustering.

#### **2.4.1 Dunn's index**

This index identifies sets of clusters that are compact and well separated. For any partition U X:X ...X <sup>1</sup> c where Xi represents the ith cluster of such partition, the Dunn's validation index, D, is defined as:

$$D(\mathcal{U}) = \min\_{1 \le i \le c} \left\{ \min\_{\substack{1 \le j \le c \\ j \ne i}} \left\{ \frac{\delta(X\_{i'}, X\_j)}{\max \{ \Delta(X\_k) \}} \right\} \right\} \tag{19}$$

where (, ) *X Xi j* defines the distance between clusters Xi and Xj (intercluster distance); ( ) *Xk* represents the intracluster distance of cluster Xk, and c is the number of clusters of partition U. The main goal of this measure is to maximize intercluster distances whilst minimizing intracluster distances. Thus large values of D correspond to good clusters. Therefore, the number of clusters that maximizes D is taken as the optimal number of clusters, c.

#### **2.4.2 Davies-Bouldin index**

As the Dunn's index, the Davies-Bouldin index aims at identifying sets of clusters that are compact and well separated. The Davies-Bouldin validation index, DB, is defined as:

$$DB(\mathcal{U}) = \frac{1}{c} \sum\_{i=1}^{c} \max\left\{ \frac{\Delta(X\_j) + \Delta(X\_{\hat{j}})}{\delta(X\_{i'} X\_{\hat{j}})} \right\} \tag{20}$$

where U, (, ) *X Xi j* , *Δ(X ),Δ(X ) i j* and c are defined as in equation (20). Small values of DB correspond to clusters that are compact, and whose centers are far away from each other. Therefore, the cluster configuration that minimizes DB is taken as the optimal number of clusters, c.

#### **2.4.3 Calinski and Harabasz index**

178 Deforestation Around the World

The procedure of evaluating the results of a clustering algorithm is known under the term cluster validity. In reference [15] two kinds of validity indices are showed: external indices and internal indices. A third is added in reference [29], based on relative criteria. The first is based on external criteria. This implies that we evaluate the results of a clustering algorithm based on a pre-specified structure, which is imposed on a data set and reflects our intuition about the clustering structure of the data set. The second approach is based on internal criteria. We may evaluate the results of a clustering algorithm in terms of quantities that involve the vectors of the data set themselves. The third approach of clustering validity is based on relative criteria. Here the basic idea is the evaluation of a clustering structure by comparing it to other clustering schemes, resulting by the same algorithm but with different

To evaluate the proposed classification method, five cluster validation techniques are applied, based on internal criteria. These indices are used to measure the "goodness" of the result of the grouping; comparing the proposed classification method against the old pattern

This index identifies sets of clusters that are compact and well separated. For any partition U X:X ...X <sup>1</sup> c where Xi represents the ith cluster of such partition, the Dunn's

( ) min min 1 1 max ( )

( ) *Xk* represents the intracluster distance of cluster Xk, and c is the number of clusters of partition U. The main goal of this measure is to maximize intercluster distances whilst minimizing intracluster distances. Thus large values of D correspond to good clusters. Therefore, the number of clusters that maximizes D is taken as the optimal number of

As the Dunn's index, the Davies-Bouldin index aims at identifying sets of clusters that are

() ( ) <sup>1</sup> ( ) max (, ) <sup>1</sup> *<sup>c</sup> X X <sup>i</sup> <sup>j</sup> DB U*

DB correspond to clusters that are compact, and whose centers are far away from each other. Therefore, the cluster configuration that minimizes DB is taken as the optimal number of

*<sup>c</sup> <sup>i</sup> X Xi j*

compact and well separated. The Davies-Bouldin validation index, DB, is defined as:

*ic jc Xk j i k c*

*X Xi j D U*

(19)

(, )

1

defines the distance between clusters Xi and Xj (intercluster distance);

(20)

, *Δ(X ),Δ(X ) i j* and c are defined as in equation (20). Small values of

recognition procedure called Fuzzy c-means clustering.

parameter values.

**2.4.1 Dunn's index**

where (, ) *X Xi j*

**2.4.2 Davies-Bouldin index**

where U, (, ) *X Xi j*

clusters, c.

clusters, c.

validation index, D, is defined as:

The index of Calinski and Harabasz is defined by:

$$CH(k) = \frac{B\_k \, / \, (k-1)}{W\_k \, / \, (n-k)} \tag{21}$$

where k denotes the number of clusters, and BK and Wk denote the between and within cluster sums of squares of the partition, respectively. Therefore an optimal number of clusters is then defined as a value of k that maximizes CH(k).

#### **2.4.4 Krzanowski and Lai index**

The index of Krzanowski and Lai is defined by: where;

$$\text{KL}(\mathbf{k}) = \left| \frac{diff\_k}{diff\_{k+1}} \right| \tag{22}$$

$$diff\_{\mathbf{k}} = (\mathbf{K} - \mathbf{1})^{\mathbf{2}/p} \mathbf{W}\_{\mathbf{k}-\mathbf{1}} - \mathbf{k}^{\mathbf{2}/p} \mathbf{W}\_{\mathbf{k}} \tag{23}$$

and p denotes the number of features in the data set. Therefore a value of k is optimal if it maximizes KL(k).

#### **2.4.5 Hartigan index**

The index of Hartigan is defined by:

$$Ham(k) = \log\left(\frac{B\_k}{\mathcal{W}\_k}\right) \tag{24}$$

where, BK and Wk denote the between and within cluster sums of squares of the partition, respectively. Therefore a value of k is optimal if it minimizes Han(k).

#### **3. Results and discussion**

In accordance with the objectives and methodology used in this research encouraging results were obtained regarding the proposal to adopt the criterion used in Otsu's method to the process of clustering and unsupervised classification of colour images, and the application of cluster validity methods by five cluster validation indices, compare the results with those generated by the old pattern recognition procedure, Fuzzy c-means clustering.

In the present paper three issues are addressed. First, our proposed RGB unsupervised classification method. Secondly, to compare results we apply an old pattern recognition procedure, the Fuzzy c-means clustering. Third, five of cluster validation indices will be proposed to evaluate the quality of clusterings. To demonstrate the effectiveness of our proposed RGB unsupervised classification method, using 16 digital images from colour aerial photographs in which they can observe different land cover objects such as buildings,

Unsupervised Classification of Aerial Images Based on the Otsu's Method 181

As a result of this process, the classifier automatically grouped the different objects of the landscape into three classes, generating the classified image showed in Fig. 3, and the number of pixels per class you can see in Table 2. With size of 1 542 288 pixels per band, where 23% of the surface of the image contains plant coverage, identified by the green colour; 36% of the surface of the image contains trees, identified by the blue colour; 28% of the surface of the image contains natural green grass, identified by the red colour; 9% of the surface of the image contains dry natural grass, identified by the yellow colour; and 4% of the surface of the image contains the accumulated deposits on the river bank, the effect of

Fig. 3. Image classified by Fuzzy c-means clustering; where the optimal number of class by

**Classes Pixels Color**  Plant Coverage 818,893 Blue Water Bodies 228,830 Gray Natural green grass 128,199 Red Dry natural grass 180,696 Yellow Area without vegetation coverage 185,670 Cyan

**TOTAL 1,542,288**

hurricane Pauline, the image represented by the cyan colour.

Table 1. distribution of the classification of Fig. 2

**3.2 The Fuzzy c-means clustering (results)**

DB is five.

streets, roads, tree crop plots, temporary plots of crops, pastures, water bodies etc.. Due to limited space, only the results of one experiment are included.

#### **3.1 Unsupervised classification method by theory of the Otsu's method (results)**

The proposed methodology is based on the method of Otsu. First a single thresholding of each of the bands of the RGB image, creating two classes per band as RGB in forming our startup account with eight classes. However, not all are representative, so that once segmented in this way the image is inserted through a sorting process exhaustive analysis of the variation between classes and within classes.

According to the characteristics of land cover images, which must be considered objects with different heterogeneous properties in size, shape and spectral behavior, we make the classification of the image labeled and grouped by simple thresholding, using the comprehensive analysis of variances between classes and within classes, to group and classify objects in the image.

Fig. 2. Image classified by the proposed classification Otsu method, where the optimal number of class is five.

As a result of this process, the classifier automatically grouped the different objects of the landscape into five classes, generating the classified image showed in Fig. 2, and the number of pixels per class, you can see in Table 1. With size of 1 542 288 pixels per band, where 53.1% of the surface of the image contains vegetation cover, the image represented by the blue color, and 14.8% contains bodies of water identified by the gray color. The 8.3% contain natural green grass, identified by the red color, 11.7% contains dry natural grass in the image identified by the yellow color, 12% contain areas without vegetation identified by the cyan color. Among the latter the accumulated deposits on the river bank, the effect of Hurricane Pauline, as well as streets and buildings are considered.

Table 1. distribution of the classification of Fig. 2

#### **3.2 The Fuzzy c-means clustering (results)**

180 Deforestation Around the World