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1	Assessment of rainfall and NDVI anomalies in semi-arid regions using distributed lag models Zewdie, Worku, Csaplovics, E. 05 August 2019 (has links) The semiarid regions of Ethiopia are exposed to anthropogenic and natural calamities. In this study, we assessed the relationship between Tropical Applications of Meteorology using Satellite data (TAMSAT) and MODIS Normalized Difference Vegetation Index (NDVI) data for the period 2000 to 2014 on decadal and annual basis using multivariate distributed lag (DL) models. Decadal growing season (June to September) values for kaftahumera were calculated from MODIS NDVI data. The growing season NDVI values are highly correlated with the precipitations during the whole study period. A lag of up to 30 days observed in most parts of our study region in which the rainfall has effects on vegetation growth after 40 days. The lag-time effects vary with the distribution of land use types and seasons. A lower correlation was observed in the woodland regions where significant deforestation occurred due to expansion of croplands. The loss in vegetation contributed to the low biomass production attributable to extended loss in vegetation cover. info:eu-repo/classification/ddc/620 ddc:620
2	Introducing a rain-adjusted vegetation index (RAVI) for improvement of long-term trend analyses in vegetation dynamics Wessollek, Christine, Osunmadewa, Babatunde, Karrasch, Pierre 29 August 2019 (has links) It seems to be obvious that precipitation has a major impact on greening during the rainy season in semi-arid regions. First results1 imply a strong dependence of NDVI on rainfall. Therefore it will be necessary to consider specific rainfall events besides the known ordinary annual cycle. Based on this fundamental idea, the paper will introduce the development of a rain adjusted vegetation index (RAVI). The index is based on the enhancement of the well-known normalized difference vegetation index (NDVI2) by means of TAMSAT rainfall data and includes a 3-step procedure of determining RAVI. Within the first step both time series were analysed over a period of 29 years to find best cross correlation values between TAMSAT rainfall and NDVI signal itself. The results indicate the strongest correlation for a weighted mean rainfall for a period of three months before the corresponding NDVI value. Based on these results different mathematical models (linear, logarithmic, square root, etc.) are tested to find a functional relation between the NDVI value and the 3-months rainfall period before (0.8). Finally, the resulting NDVI-Rain-Model can be used to determine a spatially individual correction factor to transform every NDVI value into an appropriate rain adjusted vegetation index (RAVI). info:eu-repo/classification/ddc/620 ddc:620
3	Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets Osunmadewa, Babatunde Adeniyi, Gebrehiwot, Worku Zewdie, Csaplovics, Elmar, Adeofun, Olabinjo Clement 12 June 2018 (has links) (PDF) Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities. Vegetations-Phänotyp saisonale Trendanalyse AVHRR NDVI3g TAMSAT Nigeria Trockene subhumide Region TU Dresden Publikationsfond Vegetation Phenology Seasonal Trend Analysis AVHRR NDVI3g TAMSAT Nigeria Dry sub-humid Region TU Dresden Publishing Fund ddc:550 ddc:UA 1000
4	Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets Osunmadewa, Babatunde Adeniyi, Gebrehiwot, Worku Zewdie, Csaplovics, Elmar, Adeofun, Olabinjo Clement 12 June 2018 (has links) Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities. info:eu-repo/classification/ddc/550 ddc:550 info:eu-repo/classification/ddc/UA 1000 ddc:UA 1000

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