Data on spatiotemporal land use land cover changes in peri-urban West Arsi Zone, Ethiopia: Empirical evidences from Shashemene peri-urban areas

Urban expansion is one of the major problems in Ethiopia resulting in displacement of the rural people inhabiting areas bordering the cities/towns. It is also resulting in land use land cover (LULC) changes affecting the livelihoods of the people and the ecosystems (Messay et al., 2017; Ganamo, 2013) [[1], [2]]. The data presented in this article, therefore shows the spatiotemporal LULC changes of peri-urban expansion areas of Shashemene City. The data were generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images (with path/row numbers 168/055) from EarthExplorer.usgs.gow and the data was classified, interpreted and cross-tabulated using ERDAS IMAGIN 2013 and ArcGIS 10.4.1 software packages. The accuracy of the image classification was verified by geo-location data collected from ground control points by using Geo Positioning System (GPS) receiver and the spatial resolution (1 m) and very recent (2016) Imagery downloaded from Google Earth. The result indicates that the built-up areas have increased by 1938.71 ha (19.3871 km2) with 73.4%, and 17.6% decline in forest land and grassland respectively between 1973 and 2016.

Urban expansion is one of the major problems in Ethiopia resulting in displacement of the rural people inhabiting areas bordering the cities/towns. It is also resulting in land use land cover (LULC) changes affecting the livelihoods of the people and the ecosystems (Messay et al., 2017; Ganamo, 2013) [1,2]. The data presented in this article, therefore shows the spatiotemporal LULC changes of peri-urban expansion areas of Shashemene City. The data were generated from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM þ ) images (with path/row numbers 168/055) from EarthExplorer.usgs.gow and the data was classified, interpreted and cross-tabulated using ERDAS IMAGIN 2013 and ArcGIS 10.4.1 software packages. The accuracy of the image classification was verified by geo-location data collected from ground control points by using Geo Positioning System (GPS) receiver and the spatial resolution (1 m) and very recent (2016)

Value of the Data
The data is helpful to Shashemene City municipality to venture the extent of the spatiotemporal expansion of Shashemene and its potential effect on the City periphery.
The data provides information on the status of urban expansion towards rural peri-urban areas around Shashemene.
The data is vital to model urban expansion towards rural peri-urban areas surrounding Shashemene to mitigate its adverse effect on the livelihoods of the people inhibiting the area and the eco system.
The data is useful to researchers, urban planners and experts working in the field.

Data
The data in this article offers information on the spatiotemporal LULC changes in Shashemene urban expansion areas between 1973 and 2016.  (Fig. 2), plantation was tremendously expanded and crop land was considerably reduced. In 2016 (Fig. 3), built up area was extremely enlarged, crop land was almost disappeared and some part of the cropland was replaced by built up areas. Table 1

Experimental design, materials and methods
Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM þ) images (with path/ row numbers 168/055) as well as GPS-based ground survey records were vigorous data sources for this data article. The analysis, such as data extraction and LULC classification, interpretation and creation of change matrices were done by using ERDAS IMAGINE version 2013 and ArcGIS 10.4.1 software. The images were geo-referenced with World Geodetic System (WGS) 1984.
datum and Universal Transverse Mercator (UTM) projection system zone 37 North. Supervised and unsupervised image classifications techniques were applied to extract the data [3]. Supervised classification involved selecting pixels that represent land cover classes that are documented by the expert. Unsupervised image classification is computer-automated. It allows the expert to specify some parameters that the computer uses to disclose statistical patterns that are intrinsic in the data.  These patterns are bands of pixels with similar spectral features. Due to similar spectral appearances of grass, and crop, which were determined to be independent classes before classification, the application of unsupervised classification might not provide decent results. As a result, in the data extraction process, supervised image classification was used. After defining the land use features, the next step was deriving LULC change matrices. This was done through overlaying the classified satellite images and analyzing by image differencing algorithm. Lastly, the outputs of images classification were verified by conducting ground truth while recoding x and y co-ordinates of sample spatial features using GPS. Based on the scope of the study and pictorial interpretation of the satellite imageries, four classes were identified in Shashemene urban areas. These are Forest land/ Plantation, Grassland, Cropland and Built up in the vicinity.