The study used both quantitative and qualitative research methods applicable to the selected site to answer the stated research questions.
3.3.1 GIS based mapping of land use land cover changes
Image data pre-processing: Pre-processing of image data is important to correct errors that are introduced during scanning, transmission and recording of the data (Das 2009). Particularly, pre-processing of satellite images prior to actual change detection is essential and has its unique goals-
18
the establishment of a more direct linkage between the data and biophysical phenomena (Coppin et al. 2004). To perform pre-processing, the reflective bands are stacked into a single multi-band image excluding the thermal band. The images projected to the Universal Transverse Mercator System (UTM WGS84 Zone 37), were then geometrically corrected taking the scanned topographical maps as reference images. The subsets of corrected images were used for further analysis.
Classification scheme: Several institutions have developed classification systems for use with remote sensing data. However, there is no one ideal classification scheme that fits the land cover types and their nomenclature everywhere. Thus, the land cover classes in the study area were defined based on the specific nature of the landscape features and partly based on the Level I LULC classification system developed by Anderson et al. (1976).
Image classification: Classification was performed using a hybrid method, where spectral signatures for specific land cover classes were created using unsupervised training followed by supervised method to further discern the land cover types. Hybrid classification techniques offers more reliable and accurate results to assess land cover changes (Bakr et al. 2010). Before classification, the images were partitioned into smaller units to improve classification accuracy in the fragmented and heterogeneous African landscape. Partitioning was performed based on visually homogeneous land cover types, supported by ancillary data and knowledge of the study area. The partitioned image data were then classified and mosaicked to form the whole. The land cover types in the classified and mosaicked images were recoded to the same number of classes to produce thematic maps for 1973, 1985, 1995 and 2011 (Paper I) and 1987, 1999, and 2011 (Paper II).
Accuracy assessment: The accuracy assessment was performed using independent reference data created from aerial photographs, topographical maps, field data, and visual interpretation. One of the most common ways of representing accuracy assessment information is in the form of an error matrix or contingency table (Congalton 1991; Binaghi et al. 1999; Foody 2002). The tables produce many statistical measures of thematic accuracy including an overall accuracy, producer’s accuracy, and user’s accuracy. The kappa statistic, a metric that compares chance agreement between the remotely sensed classification and reference data, was also computed as a measure of thematic map accuracy.
19
Land cover change detection: This study applied a post-classification comparison method to perform pixel based land cover change detection analysis. Image pairs of consecutive dates were compared by overlaying thematic maps (1973-1985, 1985-1995, 1995-2011, and 1973-2011 in Paper I and 1987-1999, 1999-2011, and 1987-2011 in Paper II). To overcome the variation in pixel resolution and to enable the overlay process, the output cell size of Landsat MSS was resampled to 30m, which is the processed spatial resolution of TM images. The overlay procedure in ERDAS Imagine produced cross-tabulation matrices, which permitted to quantify changed and not changed land cover types between temporal instants over the study period.
3.2.2 Landscape mapping and modelling to quantify urban growth and sprawl
Pre-processing, classification, and accuracy assessment of images and detection of land cover types: These tasks were performed following similar procedures described in section 3.3.1. To retain the original pixel values, resampling of the Landsat image (from 1999) to match the spatial resolution of the SPOT images (from 1987 and 2011) was performed using the nearest-neighbor method. This permits overlaying and matching of images from different sensors to compare changes in LC types.
Analytical modelling of urban growth and sprawl: Remote sensing data extending over a period of 24 years were used to produce land use land cover maps. The maps identified seven LULC classes in the area under the jurisdiction of Hawassa City administration. However, built-up areas were used to quantify urban growth and sprawl. Pearson’s Chi-square statistics, Shannon entropy, and Degree-of-goodness models were applied to analyze the pattern (Galster et al. 2001), process (Sarvestani et al. 2011), and overall (Bhatta et al. 2010) growth and sprawl status of the city. To apply the selected models and investigate the degree of sprawl, the built-up land cover was subdivided into eight zones (see Fig. 3 in Paper II). The subdivision was done in a circular pattern equidistant from the city center assuming that the urban areas will expand equally much in every direction from the center.
3.2.3 Estimating AGB/C by integrating remote sensing and allometric equations
Delineation of forest cover: This study characterizes the LULC, above ground biomass (AGB), and carbon status of the moist tropical forest in the Lake Hawassa Watershed, Ethiopia using remote sensing data and allometric equations for the base year 2011. Remote sensing is an
20
effective and efficient tool in forestry studies (Roy and Ravan 1996; Kale et al. 2009), so analysis of the vegetation status and delineation of forest land cover was performed using a Landsat 5 TM image in ERDAS/ArcGIS. A brief description of the classification and production of the thematic maps is given in Paper I.
Forest inventory: The tallying of woody plants and measurement of tree variables such as diameter at breast height (DBH) and total height (H) were performed during the forest inventory.
The data were recorded for all trees with DBH ≥ 5cm found in the 48 randomly selected sample plots (35 m x 35 m) within the forest strata. DBH was measured from the conventional height of 1.3 m (Shirima et al. 2011) using tree calipers and diameter tape, while individual tree height was measured using Clinometers and graduated poles.
Selection of allometric equations and estimation of AGB/C: Several allometric equations developed for moist tropical forests to estimate AGB and carbon were reviewed. The equations constructed by Chave et al. (2005) and Brown (1997) for mixed species were found to be more appropriate for describing the relationship between biomass and tree variables for natural and plantation forests, respectively. The AGB density of each tree in a sample plot, the total AGB density per plot per forest type, and the mean AGB density (t/ha) for all the sample plots was computed. The total mean was later multiplied by the total forest cover (ha) to obtain the total AGB stock in tons. Above ground carbon (AGC) was estimated with a generic assumption that 50% of terrestrial dry biomass is carbon (Martin and Thomas 2011).
Importance value index (IVI): Density, dominance and frequency, with their relative values for tree species were computed. The importance value index, calculated as a composite of these three ecological parameters following Rotaquio et al. (2007), measures different features of a species in its habitat.
3.2.4 Analysis of LULC conversions and underlying driving forces
LULC conversions: This study focuses on the magnitude and rates of land cover conversions and its causative agents. The temporal and spatial analysis of LULC conversions relied on classified Landsat images extending over 38 years. Considerable evidence is available demonstrating the use of Landsat data for investigating LC conversions (Loveland et al. 1999). To detect and quantify LULC conversions, thematic maps of successive dates were overlaid in a matrix function
21
using ERDAS Imagine, which generates cross-tabulation matrices with systematic arrays of numbers. Each cell in the matrix contains the surface area converted from one land cover class to another and the surface area remained unchanged. Owing to unequal temporal intervals and uneven distribution of land cover conversions, the annual rates of changes in addition to the rates of changes in various temporal intervals were computed.
Underlying driving forces: Key informants interviews were conducted to identify the underlying driving forces of the LULC conversions that had been quantified using remote sensing methods.
The key informants included both men (92.6%) and women (7.4%). The respondents were selected based on purposive sampling because of their specific knowledge on the topic of interest.
The interviewees responded to the semi-structured list of questions, drawing on their own individual experience about the development and status of the natural resources in the Lake Hawassa Watershed ecosystem.