Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Can I Trust My One-Class Classification?

Contrary to binary and multi-class classifiers, the purpose of a one-class classifier for remote sensing applications is to map only one specific land use/land cover class of interest. Training these classifiers exclusively requires reference data for the class of interest, while training data for other classes is not required. Thus, the acquisition of reference data can be significantly reduced. However, one-class classification is fraught with uncertainty and full automatization is difficult, due to the limited reference information that is available for classifier training. Thus, a user-oriented one-class classification strategy is proposed, which is based among others on the visualization and interpretation of the one-class classifier outcomes during the data processing. Careful interpretation of the diagnostic plots fosters the understanding of the classification outcome, e.g., the class separability and suitability of a particular threshold. In the absence of complete and representative validation data, which is the fact in the context of a real one-class classification application, such information is valuable for evaluation and improving the classification. The potential of the proposed strategy is demonstrated by classifying different crop types with hyperspectral data from Hyperion

Magnetic heat conductivity in CaCu2O3\rm\bf CaCu_2O_3: linear temperature dependence

We present experimental results for the thermal conductivity $\kappa$ of the pseudo 2-leg ladder material $\rm CaCu_2O_3$. The strong buckling of the ladder rungs renders this material a good approximation to a $S=1/2$ Heisenberg-chain. Despite a strong suppression of the thermal conductivity of this material in all crystal directions due to inherent disorder, we find a dominant magnetic contribution $\kappa_\mathrm{mag}$ along the chain direction. $\kappa_\mathrm{mag}$ is \textit{linear} in temperature, resembling the low-temperature limit of the thermal Drude weight $D_\mathrm{th}$ of the $S=1/2$ Heisenberg chain. The comparison of $\kappa_\mathrm{mag}$ and $D_\mathrm{th}$ yields a magnetic mean free path of $l_\mathrm{mag}\approx 22 \pm 5$ \AA, in good agreement with magnetic measurements.Comment: appears in PR

RANDOM FORESTS FOR CLASSIFYING MULTI-TEMPORAL SAR DATA

The accuracy of supervised land cover classifications depends on several factors like the chosen algorithm, adequate training data and the selection of features. In regard to multi-temporal remote sensing imagery statistical classifier are often not applicable. In the study presented here, a Random Forest was applied to a SAR data set, consisting of 15 acquisitions. A detailed accuracy assessment shows that the Random Forest significantly increases the efficiency of the single decision tree and can outperform other classifiers in terms of accuracy. A visual interpretation confirms the statistical accuracy assessment. The imagery is classified into more homogeneous regions and the noise is significantly decreased. The additional time needed for the generation of Random Forests is little and can be justified. It is still a lot faster than other state-of-the-art classifiers. 1

Structure and Giant Inverse Magnetocaloric Effect of Epitaxial Ni-Co-Mn-Al Films

The structural, magnetic, and magnetocaloric properties of epitaxial Ni-Co-Mn-Al thin films with different compositions have been studied. The films were deposited on MgO(001) substrates by co-sputtering on heated substrates. All films show a martensitic transformation, where the transformation temperatures are strongly dependent on the composition. The structure of the martensite phase is shown to be 14M. The metamagnetic martensitic transformation occurs from strongly ferromagnetic austenite to weakly magnetic martensite. The structural properties of the films were investigated by atomic force microscopy and temperature dependent X-ray diffraction. Magnetic and magnetocaloric properties were analyzed using temperature dependent and isothermal magnetization measurements. We find that Ni$_{41}$Co$_{10.4}$Mn$_{34.8}$Al$_{13.8}$ films show giant inverse magnetocaloric effects with magnetic entropy change of 17.5\,J\,kg$^{-1}$K$^{-1}$ for $\mu_0 \Delta H=5\,\text{T}$.Comment: 8 pages, 8 figure