Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve Wereda of Ethiopia using mono window, split window algorithm and spectral radiance model.

In the past, decadal time-series analysis has been done traditionally using meteorological data. In particular, decadal analysis of land surface temperature has been a major issue due to the unavailability of remote sensing techniques. But, nowadays, with the recent advances in remote sensing techniques and modern software Land Surface Temperature (LST) can be calculated through the thermal bands. LST can be estimated through many algorithms such as Split-window, Mono-Window (SW), Single-Channel (SH), among others. LST was estimated using Mono-Window algorithm on Landsat-5 TM, Landsat-7 ETM+ and split window algorithm on Landsat-8 OLI/TIRS Thermal Infrared (TIR) bands. Vegetation index was obtained by using Normalized Difference Vegetation Index (NDVI) from red and Near-Infrared (NIR) bands. NDVI has been effectively used in vegetation monitoring and to analyze the vegetation in responses to climate change such as surface temperature variation. The twelve Weredas (third-level administrative divisions) of Ethiopia which are highly prone to drought were selected to investigate decadal land surface temperature variations and its impact on the surrounding environment, especially on vegetation cover. Ten Landsat images of three different sensors from 1999 to 2018 were used as the basic data source. The processed data of surface temperature and vegetation indices showed a strong correlation. The higher LST values indicate the smaller NDVI and vice versa and it is also identified the areas with high temperature being barren regions and areas with low temperature covered with more vegetation.

Data on land use and land cover changes in Adama Wereda, Ethiopia, on ETM+, TM and OLI- TIRS landsat sensor using PCC and CDM techniques.

Land use and land cover changes are often referred for the anthropogenic modification of Earth's surface. The extents of land use and land cover (LULC) changes in Adama Wereda at three different periods (2002, 2010, and 2017) were generated using data from various Landsat sensors namely ETM+, TM and OLI TIRS. This work focused on a change detection analysis using post classification comparison (PCC) and change detection matrix (CDM). These images were geometrically corrected and image processing operations for instance: radiometric correction, using spectral radiance model was carried out, followed by land cover categorisation into water bodies, built up, bare land, sparse vegetation and dense vegetation employing Knowledge, pixel and indices based classification in ERDAS imagine software. The generated data of both change detection techniques from 2002 to 2017 revealed interesting aspect that build up, dense vegetation and sparse vegetation increased in area of approximately 160%, 30% and 78% respectively at the expense of barren land which decreased at 8.5%, but there is not much change in the water bodies. It was also noticed that both the algorithms gives similar values but with negligible deviation.