M.Sc. thesis presentation by Ahmed Saadallah

M.Sc. thesis presentation by Ahmed Saadallah

Ahmed Saadallah will defend his M.Sc. thesis “The Potential of Earth Observation for Monitoring Agricultural Lands in Egypt (1984-2017)” on Wednesday 10th of April at 2pm in the student working room JMW 52.

The need for accurate and timely information on the extent and the use-intensity of agricultural lands is growing with the increasing pressure on agricultural systems to increase production, in order to narrow the food gap, while maintaining at the same time the sustainability of the agricultural resources and reducing the negative impacts on the environment. In Egypt, such information is urgently needed for generating effective policies and improving management practices in order to promote the agricultural sector to address current and future challenges. Over the past four decades, it has been proven through the use of Earth Observation (EO) applications that remote sensing data can provide this information accurately, quickly, and at low cost. In this study, the extent and the use-intensity of the agricultural land in Egypt were mapped on an annual basis from 1984 to 2017 using Landsat time series data. Pixel-based supervised classification approach, using Random Forest algorithm, was used to map the extent of cultivated land area annually. Harmonic analysis was used for smoothing and filling the gaps in the time series data of Landsat derived Normalized Difference Vegetation Index (NDVI) in order to identify the Peak of Season (POS) phenological parameter, which was used later to map cropping frequency and estimate Multiple Cropping Intensity (MCI) on annual basis. The overall average classification accuracy was 89%. In addition, the results indicate that the cultivated area in Egypt expanded from 25760 km2 in 1984 to 32539 km2 in 2017, with an overall average increase of 185 km2 per year. Moreover, 86% of the total cultivated area in Egypt on average was multi-cropped. The overall average MCI at the national level for the last 34 years since 1984 is 214%. 

M.Sc. defense by Johannes Löw

M.Sc. defense by Johannes Löw

You are all invited to join the M.Sc. presentation by Johannes Löw. He will defend his M.Sc. thesis on Wednesday 20th of March at 2pm in room 0.004 in OKW 86.

from his abstract:
Since Sentinel-1 A and B have become fully operational, it is now possible to generate dense time series (six-day interval) for areas, which have been difficult to monitor by optical data due to cloud cover issues. In addition, over the last ten to twenty years, the derivation of phenological information from Synthetic Aperture Radar (SAR) data has been researched intensively. This study utilised dense time series of interferometric (InSAR) and polarimetric (PolSAR) features, which were temporally smoothed by GAM or LOESS-algorithms to create crop specific signatures for wheat, sugar beet, rapeseed and grassland for the growing season of 2017. In a first step, by computing descriptive statistics for crop parameters and SAR-features the discriminatory potential as well as the temporal stability of the data basis were assessed. Secondly, a qualitative analysis linking phenological stages to the behaviour of these signatures was conducted. The last step consisted of a correlation and regression analysis of wheat fields encompassing SAR features and the following crop parameter: plant height, crop cover and BBCH-values. By investigating the descriptive statistics VV backscatter intensity, entropy and K0 exhibited the highest discriminatory potential. The qualitative analyses allowed for the distinction of vegetative and reproductive stages for wheat and rapeseed. Furthermore, certain BBCH-stages like the leaf and the rosette development of sugar beet or the main shoot and the ripening of wheat were detected. The regression and correlation analysis revealed moderate negative correlation coefficients
(Pearson) for VV and VH coherence and plant height (-0.57 and -0.61). By conducting a regression analysis for each time step a decline in R2 during midseason was discovered. In the first half of the season R2 above 0.9 occurred for a pairing of Alpha, VH coherence and K0 with plant height and crop cover, whereas in the second half R2 did not surpass value above 0.6. Especially the findings of the qualitative analysis demonstrated that a composite of InSAR and PolSAR data displays the potential for feasible crop phenology monitoring.

Maninder Singh Dhillon handed in his MSc

Maninder Singh Dhillon handed in his MSc

Maninder Singh Dhillon handed in his M.Sc. “Comparing the performance of crop growth models using synthetic remote sensing data at DEMMIN, Germany” supervised by Thorsten Dahms with the first and second supervisors Martin Wegmann and Christopher Conrad, respectively.

His M.Sc. defense will be on Monday 18th at 2pm in room 0.004 (Oswald-Kuelpe Weg). Read the abstract of his M.Sc. for more details about his research project.

 

 

 

Abstract: Climate change, natural resource degradation, and population growth are increasingly challenging food security. Climatic effects such as warming temperatures, and decrements in soil moisture have already stagnated the winter wheat yields in many parts of the world. The present study was conducted over three years from 2015 to 2017 on a main agricultural production region of North-East Germany. Five widely used crop models, namely, WOFOST. CERES, Aqua crop. LUE and CropSyst, are compared to predict the biomass of winter wheat crop. The study stresses the use of remote sensing with crop growth models, as they lack spatial information on the actual conditions of each field or region. It also elaborates the concept of data fusion, in which both Landsat (30 m) and Modis (500 m) are combined to get synthetic remote sensing data of 30-meter spatial and one-day temporal resolution, in crop growth modeling with its contribution to crop growth models. Measurements of the biophysical parameters such as Leaf Area Index (LAI), Canopy Cover (CC), Fraction of Photosynthetically Absorbed Radiation (FPAR) were grouped according to the phenology using the BBCH characterization (Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie). The analysis was carried out for winter wheat during the years 2015 to 2017 and included a detailed comparison of the simulated and measured crop biomass as well as an impact of climate parameters such as temperature stress, soil moisture stress and vapor pressure deficit (VPD) on crop growth, and an evaluation of the modelled crop biomass. The study shows that the models (Aqua crop, CropSyst, and LUE) that require fewer input parameters to simulate crop biomass are well suitable and easier to implement.