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5 years ago

Die Wirksamkeit von Boden

Die Wirksamkeit von Boden

Land use-land cover and

Land use-land cover and normalized difference vegetation index changes in Wello have been implemented in the past three decades. These include improvement of vegetation cover on marginal lands through exclosure, reforestation and tree planting. However, controversies exist on the spatial coverage and effectiveness of some interventions particularly concerning tree plantations. Although millions of tree seedlings have been planted every year, observers comment that forest cover is much below the reported plantings. Moreover, data on forest, woody vegetation and exclosure cover obtained from various levels of the government offices are inconsistent. This indicates existence of information and knowledge gaps on spatial and temporal LULC change due to the restoration measures. Therefore, the objective of this part of the study is to analyze LULC and normalized difference vegetation index (NDVI) change in space and time to assess the effectiveness of the conservation measures in reversing vegetation degradation thereby land degradation in Wello, northern Ethiopia, and using MODIS satellite data. 4.2 Materials and methods 4.2.1 Study area The study was conducted in the North and South Wello zones of the Amhara National Regional State, which is located between 10°12’ and 12°22’ north latitude and 38°30’ and 40°14’ east longitude. The study area covers a total of 30,000 km 2 . The two zones have similar socio-economic and biophysical characteristics such as geology, geomorphology, soils, climate, agricultural practices, and vegetation cover (FAO 1984; Tefera et al. 1996). See Chapter 3 for the overall description of the study area. 4.2.2 MODIS data acquisition and preparation In order to understand the impact of soil and water conservation (SWC) interventions, particularly the biological measures of land restoration, LULC and inter-annual normalized difference vegetation index (NDVI) changes were analyzed using moderate resolution imaging spectrometer (MODIS) data. NDVI is a standardized index to measure greenness. The MODIS surface reflectance image is available for free online on ftp://e4ftl01.cr.usgs.gov/MOLT/MOD09A1. MODIS surface reflectance image data are composited every 8 days and delivered in approximately 10-degree blocks in sinusoidal grid mapping projection. The MOD09A1 terra in 215,000 m 2 resolution sin 36

Land use-land cover and normalized difference vegetation index changes in Wello grid V005 for 2000 and 2009 were downloaded for the study. The data are available in hierarchical data format (HDF) processed by MODIS re-projection tool (MRT) into Geo TIFF data. The extracted data are single integer pixel type, which has 13 bands in 16-bit pixel depth. To reduce impacts due to differences in the time of data acquisition, images captured between January and March were found appropriate for this analysis because in this period the study area has a clear sky (more or less cloud free) and crops have already been harvested. The time after crop harvest was preferred to avoid the effect of sorghum and maize signatures, which may be confused with bushes and shrubs (Hill and Donald 2003). Finally, images captured in March 2000 and 2009 were selected and used for the analysis. Relevant pre-processing such as re-projection and geometric correction were performed to prepare the images for further analysis. NDVI data is calculated from satellite imagery as NDVI = (NIR – RED)/(NIR + RED), where NIR is reflectivity of plant materials in the near-infrared and RED is the chlorophyll pigment absorption in the red band (Lillesand 2004). However, like MODIS imagery, NDVI data of same resolution (250 m) is available for free online on ftp://e4ftl01.cr.usgs.gov/MOLT/MOD13A1. NDVI data are delivered in the same degree blocks and projection as the MODIS surface reflectance image except that they were composited only twice a month. NDVI data of the non-growing period that covers the period between January and April from 2000 to 2010 were acquired. The NDVI values between January and April for each year were averaged. Finally, the averaged data were compared spatially and temporally. The analysis used linear relationships of year (X) and NDVI (Y) to determine inter-annual change using ArcGIS adopted from Vlek et al. (2008). The model is given as: Y = AX + B +� (4.1) where A = slope, B = intercept, � = random error Xi = dry months (January to April): one output for each year, i = 2000 to 2010, Yi = mean NDVI values of the dry period (January to April) for each year, i = 2000 to 2010 NDVI slope (calculated slope [Acal] = A) was statistically tested using t-test in such a way that: If |to| � tP, df: then the calculated slope coefficient is significantly different from zero. Therefore, NDVI slope was tested for significance at 75%, 90% and 95% 37