TY  - JOUR
A1  - Ayanu, Yohannes
A1  - Conrad, Christopher
A1  - Jentsch, Anke
A1  - Koellner, Thomas
T1  - Unveiling undercover cropland inside forests using landscape variables: a supplement to remote sensing image classification
JF  - PLoS ONE
N2  - The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.
KW  - climate change
KW  - land-cover classification
KW  - bale mountains national park
KW  - sub-saharan africa
KW  - agroforestry systems
KW  - biodiversity conservation
KW  - ecosystem services
KW  - topographic aspect
KW  - wheat-varieties
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151686
VL  - 10
IS  - 6
ER  - 
TY  - JOUR
A1  - Usman, Muhammad
A1  - Reimann, Thomas
A1  - Liedl, Rudolf
A1  - Abbas, Azhar
A1  - Conrad, Christopher
A1  - Saleem, Shoaib
T1  - Inverse parametrization of a regional groundwater flow model with the aid of modelling and GIS: test and application of different approaches
JF  - ISPRS International Journal of Geo-Information
N2  - The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying out the automated pilot point (PP) method. The results indicate that automated PP is more flexible and robust as compared to other approaches. Different statistical indicators show that this method yields reliable calibration as values of coefficient of determination (R-2) range from 0.98 to 0.99, Nash Sutcliffe efficiency (ME) range from 0.964 to 0.976, and root mean square errors (RMSE) range from 1.68 m to 1.23 m, for manual and automated approaches, respectively. Validation results of automated PP show ME as 0.969 and RMSE as 1.31 m. The results of output sensitivity suggest that hydraulic conductivity is a more influential parameter. Considering the limitations of the current study, it is recommended to perform global sensitivity and linear uncertainty analysis for the better estimation of the modelling results.
KW  - pilot-point-approach
KW  - inverse parameterization
KW  - groundwater
KW  - sensitivity analysis
KW  - tikhonov regularization
KW  - PEST
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-175721
VL  - 7
IS  - 1
ER  -