1 Kazrasho

Land Use And Land Cover Change Thesis Statement

1. Introduction

Land use/land cover (LULC) changes influence climate and weather conditions from local to global scales [1]. They can have such impacts by affecting the composition of the atmosphere and the exchange of energy between continents and the atmosphere, which can lead to global warming [2]. Changes in LULC can also affect biological diversity, contribute to forest fragmentation, lead to soil erosion, alter ecosystem services, disrupt socio-cultural practices, and increase natural disasters, such as flooding [3,4]. This calls for global attention for continuous monitoring of the changes. Up-to-date datasets on LULC change provide critical inputs to evaluate complex causes and responses in order to project future trends better, ranging from local, regional, to global scales [5,6]. They are also prerequisites for making development plans [7,8]. However, the magnitude of LULC change differs with the time period being examined [9], geographical location [10], slope gradient, and elevation range [11,12].

With an area of 1,130,000 km2, and as one of the most populous countries in Africa, Ethiopia is experiencing huge LULC dynamics from natural vegetation to farming practices and human settlement [13,14]. The problem of land cover dynamics is more severe in the highlands, which account nearly 44% of the country’s landmass and have been cultivated for millennia [15,16]. Like other parts of the world, the use and management of natural resources, and returning the vast degraded landscapes to protective and/or productive systems, have substantial importance to attain the goal of sustainable development in Ethiopia [17]. This, in turn, requires an understanding of the dynamics in time and space of these resources. In this regard, the importance of spatial data monitoring and evaluation for proper management of natural resources is critical. Some studies have been conducted to estimate and monitor LULC changes in different parts of the Ethiopian highlands [18–26]. These reports have shown heterogeneity in direction, pattern, type, and/or magnitude of LULC changes in the country. For instance, Zeleke and Hurni [21] reported a sharp decrease of forest cover while Bewket [22] found the opposite, i.e., an increasing trend. In terms of magnitude for changes, Zeleke and Hurni [21] reported an increase in cultivated lands by 38% in 38 years (1957–1995). On the other hand, Tegene [23] reported an increase in croplands only by 5.5% in 43 years (1957–2000). Consequently, making generalizations of results to other areas of the same physical setting might lead to erroneous conclusions. In addition, except the reports by Zeleke and Hurni [21] and Tegene [23] about changes in relation to slope gradient, no studies have been conducted in comprehensive approach to systematically analyze changes within the study area along slope gradient and agro-ecological zone, which is usually divided based on elevation range [27]. Therefore, it can be concluded that, until now, few studies have undertaken an integrated analyses on LULC change in the Ethiopian Highlands.

Results of various studies have demonstrated the need for a study focusing on location specific LULC dynamics for sustainable management and decision-making processes related to the use and conservation of natural resources [22,26,28,29]. Two ways of capturing LULC dynamics are available: conventional ground- and remote sensing-based methods. The ground method is labor intensive, time consuming, and difficult for capturing data from inaccessible areas with ragged topographies like the case of most Ethiopian landscape. On the contrary, remote sensing is considered the most efficient technology to handle these problems since it can explicitly reveal spatial patterns of land cover change over a large geographic area in a regular and consistent way [30,31]. Remote sensing data of the earth’s surface could be made readily available in digital format [32]. These advantages have attracted great interest in the scientific community. Moreover, the rich archive and spectral resolution of satellite images are the most important reasons for their use [30,33]. Thus, change detection has become a major application of remotely-sensed data because of repetitive coverage at short time intervals, which is useful for tracking changes in LULC over longer periods of time and at more varied temporal scales than what is typically done with field experiments or ground inventory [9,34]. Various techniques are available to extract meaningful information of LULCs from remotely captured datasets.

Recently, object-based image analysis has been applied more frequently for remote sensing image classification than pixel-based analysis [35,36]. Pixel-based methods classify individual pixels mainly using spectral patterns. The use of spatial or contextual information from neighborhood pixels remains a critical drawback to pixel-based image processing [37]. On the other hand, object-based methods allow integration of different object features, such as spectral values, shape, and texture [38–40]. One of its strength is the ability to combine spectral information and spatial information for extracting target objects [36,38].

However, accuracies of object-based approach differ depending on the nature of landscape and type of images used for analysis [41]. The benefit of improving accuracies of image classifications using object-based approach is not tested up to now in the Ethiopian landscape during LULC change studies. Studies conducted so far in the country were pixel-based and their overall accuracies from recent reports were not more than 88% [25,26]. On the other hand, apart from the need for location specific LULC change study justified above in a landscape with diverse features like the Ethiopian highlands, the necessity for improved classification accuracies for LULC change studies in the country have been discussed by many researchers [23,25,26,42,43]. Thus, in this study, we classified the land use/land cover with the highest possible accuracy and evaluated changes over a period of 39 years (1973–2012) in a landscape of Munessa-Shashemene area, one of the typical highlands in Ethiopia. We also explored distribution and changes in LULC along the slope gradient and Agro-ecological zones of the study landscape.

2. Materials and Methods

2.1. Study Area

The study was conducted in the landscape of Munessa-Shashemene area, which is a typical highland found in Munessa and Arsi-Negele Districts. The area lies within 7°20′01.23″ and 7°35′13.3″N, and 38°39′43.3″ and 38°59′57.31″E at about 200 km south of Addis Ababa (Figure 1). It covers about 1,091 km2 and lies between the altitudes of 1,500 m above sea level at the Central Rift Valley lakes and over 3,400 m at the Arsi-Bale massif. The rainfall has bimodal distribution. The short and main rainy seasons occur from March–May and July–September, respectively. Meteorological station records show that annual rainfall is about 1,200 mm at Degaga town (2,000 m), which is found in the study area. Mean annual temperature is 15 °C. The soils of the area are rich in clay and classified as Mazic Vertisol in the lower altitude (1,500 m) and Humic Umbrisol at about 3,000 m [44]. It is a diverse landscape with both flat and sloped areas. Crop cultivation is common in all altitudinal ranges with various proportions. Apart from croplands, the study landscape comprises mosaics of LULC types, mainly natural forests, plantation forests, woodlands, settlements and water bodies. The natural forest in the area belongs to a tropical dry Afromontane forest [45]. The plantation forests are composed of exotic species, mainly Cupresses lusitanica Miller, Pinus patula Schlechtendal & Chamisso and Eucalyptus spp. Woodlands are dominated by Acacia spp and found in the lower part of the study landscape.

2.2. Data Used

Datasets from various sources were used in this study (Table 1). Landsat and RapidEye imagery were the main data for classification and change analysis. The Landsat imagery data include Landsat MSS, Landsat Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM+) scenes of the year 1973, 1986 and 2000, respectively. These datasets were acquired from the National Aeronautics and Space Administration (NASA) through their EOS Data Gateway Database. The RapidEye imagery data were used for the study year 2012. The images were obtained from RapidEye Science Archive (RESA) supported by German Aerospace Center (DLR). They were ortho-rectified level 3A and geometrically corrected. Both Landsat and RapidEye imageries were acquired in the same season. Images of the same season were selected to reduce the effect of seasonal discrepancies on the classification result [46].

A 30 m Digital Elevation Model (DEM), based on Aster imagery, was also employed in order to study the relationship between LULC types with that of the slope gradient and agro-ecological zones of the study landscape. In addition, ancillary data were also utilized during analysis, including topographic maps, field data, thematic layers (roads and towns), Kebele and Wereda boundaries. All data were projected to the Universal Transverse Mercator (UTM) projection system zone 37N and datum of World Geodetic System 84 (WGS84), ensuring consistency between datasets during analysis. The pre-processing was made using ArcGIS 10 software. Fieldwork was conducted between November 2011 and January 2012 using draft classified maps derived from satellite images with reference years, aerial photos and topographic maps as guides. GPS coordinates of target LULC types were collected, and information regarding each site was noted. Thematic layers of towns were used to facilitate the classification process. Roads with other datasets were utilized for the study site map preparation.

2.3. Methodology

2.3.1. Image Segmentation

Object-based image analysis requires the creation of objects or separated regions in an image. One established way to do so is image segmentation. Depending on its application, different approaches exist for image segmentation ranging from very simple to highly sophisticated algorithm [40]. We used the sophisticated segmentation algorithm, known as multi-resolution segmentation (MS), which is based on the Fractal Net Evolution Approach (FNEA) [38] and available in eCognition Developer 8.0 software. The MS algorithm is bottom-up region merging technique starting with a single image object of one pixel and repeatedly merges them in several loops in pairs to larger units. The MS algorithm is also an optimization procedure that minimizes the average heterogeneity for a given number of objects and maximizes their homogeneity based on defined parameters. These parameters, namely scale (Sc), shape (Sh), and compactness (Cm), are defined through trial and error to successfully segment objects in an image [40,47–49]. We used scale parameters ranging from 8 to 500 with three different levels depending on the type of images used for the analysis (Table 2). The images were segmented in to three levels to facilitate the object-based classification depending on the nature of LULC classes to be detected. For instance, level 1 was to handle those big size classes like water bodies, whereas level 3 was for small size classes like tree patches. Segmentation outputs were visually checked in relation to target class (e.g., forest area or cropland) to evaluate which parameter combinations best captured the objects of interest.

2.3.2. Object-Based Classification

We used various sources, including field survey, ancillary data and existing Afri-cover classification approaches to set and implement our object-based classification schemes. Nine LULC classes were considered for this purpose (Table 3). Considering the power of object-based methods, an attempt has been made to separate forest types, e.g., natural forests and woodlands, in our classification schemes. This is owing to their differences in providing services and goods, and the underlying pressures towards the resources. Classifying them separately can also facilitate conservation, utilization, and management approaches.

Using identified target LULC classes, object-based classification was applied to a segmented image in order to assign a class to each of the segments. Object-based image analysis attempts to assign objects that are generated through image segmentations into a specific class of interest. We used eCognition to perform an object-based image classification [40]. There are two approaches in eCognition to assign classes to segmented objects, which are fuzzy membership functions and the nearest neighbor (NN) classifier. The membership function classifier uses the user’s expert knowledge to define rules and constraints in the membership function to control the classification procedure. The membership function describes intervals of object features that determine whether the objects belong to a particular class or not. An object feature can be the spectral value, texture, size, shape, and context of that image object to surrounding image objects. On the other hand, NN classifier uses a defined feature space, e.g., using original bands or customized bands, and a set of samples that represent different classes in order to assign class values to segmented objects. The procedure consists of teaching the system by giving certain image objects as samples and classifying image objects in the image object domain based on their nearest sample neighbors. Employing NN classifier is advantageous when using spectrally similar classes that are not well separated using a few features or just one feature [40]. Whenever applicable, we used both approaches during the classification process.

A hierarchical scheme of three levels was implemented during object-based classification using eCognition 8.0 software. The classification was applied using a “top-down” approach. That is, the classification started from very general classes (level 1), which were further subdivided into more specific classes (level 2 and 3). We first broadly classified the whole study landscape into water and land classes by using the spectral features from the mean value of objects in near infrared band. The second and third levels were used to extract the remaining target LULC types from the class land (Figure 2).

The classification of target classes was achieved by using mainly thresholds of mean and/or standard deviation of spectral features (original bands of blue, green, red, red edge, and near infrared), customized bands (ratio of blue over green), thematic layers, DEM values, texture value of grey-level co-occurrence matrix (GLCM) homogeneity, and normalized difference vegetation index (NDVI). The NDVI was calculated using the following equation:

where NIR and RED are reflectance in the near infrared and the red bands, respectively. The NDVI values have been typically used to map spatial distributions of vegetation [50].

In this study, NDVI values were also used to further classify the class land into vegetation and no-vegetation classes. As the images were taken during the dry season, some dried vegetation with low NDVI was classified as no-vegetation class. We used our expert knowledge and developed a rule set using red and red edge mean values to refine such classes from no-vegetation to vegetation classes. The vegetation class was again subdivided into forest and no-forest classes. The mean value of objects in red band and a value from blue over green ratio were used to differentiate these two categories, e.g., higher values are forests where as lower values are no-forest classes. The forest class was again classified to achieve the final target LULC types, namely plantation forests, tree patches, natural forests, and woodlands. The standard deviation value of objects in red band, texture value of GLCM homogeneity, DEM, and size (area) of the class objects were utilized to separate these four classes. The DEM was particularly used to separate woodlands from natural forests. Woodlands are usually found below 1,900 m [51]. Plantation forests have fine texture than natural forests. As a result, the texture values of GLCM homogeneity with other values were used to differentiate these two classes. On the other hand, the mean value of NIR was utilized to separate grasslands and croplands from the class no-forest. Based on the training samples, the NN classifier was also employed to further classify the no-vegetation class into settlements, bare lands, harvested croplands, and dry grasslands. Again, associated to the dry season images acquisition, there were areas that were signed as bare lands although they were dry grasslands and harvested croplands. To avoid such confusion, the two classes (harvested croplands and dry grasslands) were temporally created under no-vegetation class. Mean values of blue, red, and NIR bands were used to define the feature space during the classification process using NN classifier. Additionally, mean value of red and blue bands and other features, such as thematic layer of towns in the study landscape, were also used to define rules and constraints in the membership function to refine the classification process of these particular classes. At the end, the harvested croplands and dry grasslands were re-assigned to the class croplands and grasslands, respectively, using the ‘assign class’ function available in eCognition Developer 8.0 software.

2.3.3. Accuracy Assessment

A classification is not complete until its accuracy is assessed [30

Convening Lead Authors

Daniel G. Brown, University of Michigan

Colin Polsky, Clark University

Lead Authors

Paul Bolstad, University of Minnesota

Samuel D. Brody, Texas A&M University at Galveston

David Hulse, University of Oregon

Roger Kroh, Mid-America Regional Council

Thomas R. Loveland, U.S. Geological Survey

Allison Thomson, Pacific Northwest National Laboratory

Introduction

In addition to emissions of heat-trapping greenhouse gases from energy, industrial, agricultural, and other activities, humans also affect climate through changes in land use (activities taking place on land, like growing food, cutting trees, or building cities) and land cover (the physical characteristics of the land surface, including grain crops, trees, or concrete).10 For example, cities are warmer than the surrounding countryside because the greater extent of paved areas in cities affects how water and energy are exchanged between the land and the atmosphere. This increases the exposure of urban populations to the effects of extreme heat events. Decisions about land use and land cover can therefore affect, positively or negatively, how much our climate will change and what kind of vulnerabilities humans and natural systems will face as a result.

Land-use and land-cover changes affect climate processes. Above, development along Colorado’s Front Range

©Ted Wood Photography

The impacts of changes in land use and land cover cut across all regions and sectors of the National Climate Assessment. Chapters addressing each region discuss land-use and land-cover topics of particular concern to specific regions. Similarly, chapters addressing sectors examine specific land-use matters. In particular, land cover and land use are a major focus for sectors such as agriculture, forests, rural and urban communities, and Native American lands. By contrast, the key messages of this chapter are national in scope and synthesize the findings of other chapters regarding land cover and land use.

Land uses and land covers change over time in response to evolving economic, social, and biophysical conditions.16 Many of these changes are set in motion by individual landowners and land managers and can be quantified from satellite measurements, aerial photographs, on-the-ground observations, and reports from landowners and users.11,2 Over the past few decades, the most prominent land changes within the U.S. have been changes in the amount and kind of forest cover due to logging practices and development in the Southeast and Northwest and to urban expansion in the Northeast and Southwest.

Because humans control land use and, to a large extent, land cover, individuals, businesses, non-profit organizations, and governments can make land decisions to adapt to and/or reduce the effects of climate change. Often the same land-use decision can serve both aims. Adaptation options (those aimed at coping with the effects of climate change) include varying the local mix of vegetation and concrete to reduce heat in cities or elevating homes to reduce exposure to sea level rise or flooding. Land-use and land-cover-related options for mitigating climate change (reducing the speed and amount of climate change) include expanding forests to accelerate removal of carbon from the atmosphere, modifying the way cities are built and organized to reduce energy and motorized transportation demands, and altering agricultural management practices to increase carbon storage in soil.

Despite this range of climate change response options, there are three main reasons why private and public landowners may choose not to modify land uses and land covers for climate adaptation or mitigation purposes. First, land decisions are influenced not only by climate but also by economic, cultural, legal, or other considerations. In many cases, climate-based land-change efforts to adapt to or reduce climate change meet with resistance because current practices are too costly to modify and/or too deeply entrenched in local societies and cultures. Second, certain land uses and land covers are simply difficult to modify, regardless of desire or intent. For instance, the number of homes constructed in floodplains or the amount of irrigated agriculture can be so deeply rooted that they are difficult to change, no matter how much those practices might impede our ability to respond to climate change. Finally, the benefits of land-use decisions made by individual landowners with specific adaptation or mitigation goals do not always accrue to those landowners or even to their communities. Therefore, without some institutional intervention (such as incentives or penalties), the motivations for such decisions can be weak.

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