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Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years. We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements. The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8 m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features. Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree. Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide. • Auxiliary open datasets were evaluated for improving object-based land cover mapping. • Six random forest models with and without auxiliary features were compared. • Including auxiliary features significantly improved classification overall accuracy. • Topographic and spectral-temporal metrics were most important. • Open datasets are valuable data source for mapping heterogeneous savanna landscapes. [ABSTRACT FROM AUTHOR]

A series of (Dy-Al) 3+ co-substituted SrFe 12−x (DyAl) x O 19 (0 ≤ x ≤ 0.9) hexaferrite were prepared via auto-combustion method with subsequent heat treatment in air at 950 °C for 10 h. The phase identification of the powders performed using x-ray diffraction show presence of single phase hexaferrite for x < 0.3 and presence of secondary phase, Fe 2 O 3 , for x ≥ 0.3. Lattice parameter of SrFe 12−x (DyAl) x O 19 remains unaffected upon doping mainly due to Fe 3+ being replaced with a smaller size Al +3 and a larger size Dy +3 ions than Fe 3+ . (DyAl) +3 substitution modifies saturation magnetization, Ms, and coercivity, Hc. The room temperature Ms value decreased rather slowly with substitution while Hc increased to a maximum value of 5572 Oe at x = 0.9, which is ∼33% increase in coercivity as compare to that of pure Sr-ferrite. The Curie temperature, T C , was observed to decreases upon (DyAl) 3+ substitution. Dielectric permittivity was improved with (DyAl) 3+ substitution in 800 MHz to 14 GHz frequency range. The DC electrical resistivity of all samples decreased with temperature showing semiconducting behavior. The room temperature Mossbauer spectral analysis indicate that Al 3+ prefers 12 k , 2a, and Dy 3+ prefers 4 f1 sites. The weighted average hyperfine field and isomer shift decreases with (DyAl) 3+ content due to magnetic dilution effect and increased charged density per unit cell, respectively. These results indicate that co-doping rare-earth and non-magnetic ion can together maintain high coercivity with fairly high magnetization value of the doped Sr-ferrite. [ABSTRACT FROM AUTHOR]