Historicae Relationis Continuatio. Das ist/ Kurtze Beschreibung deß jenige[n]/ so sich seithero jüngster Fastenmeß biß an jetzo in den Niderlanden und Franckreich zugetragen. Insonderheit was fast von tag zu tag in der Belägerung der gewaltiger und starcker Statt Graue/ furgelauffen: Mit angehenckter erzehlung des Hertzogen von Biron vorgehabter Conspiration: dessen gefängkliche einziehung/ und wider ihn ergangenes Endurtheil. Alles fein ördentlich zusammen verfasset/ durch Conradt Löw ...

Vorlageform des Erscheinungsvermerks: Gedrückt zu Cölln/ Durch Wilhelm Lützenkirchen. Im jahr 1602

Meer oder Saehanen Buch/|| Darinn || Verzeichnet seind/ die Wun=||derbare/ Gedenckwuerdige Reise vnd Schiffarhten/ so || recht vnd billich geheissen Meer vnd Seehanen/ der Kœnigen von Hi=||spania/ Portugal/ Engellandt vnd Franckreich/ inwendig den letst vergangnen hun=||der Jahren/ gethan ... || Hierzu seind noch gesetzt zwey seltzame vnd gedenckwuerdige Stueck.|| Das eine ist/|| Die Erzehlung der Schiffart/ so im Jar 1594.gethan siben Schiff/|| welche die Vnierte Niderlændische Stænd geschickt gegen Mitternacht/ vmb von || dannen jren lauff nach China zu nemen ... || Das ander stueck ist.|| Ein Warhaffter/ klarer/ eigentlicher Bericht/ von der weiten vñ wun=||derbaren Reise oder Schifffahrt/ so drey/ Schiff vnd ein Pinaß/ auß Holland/ biß in || Jndien gegen Auffgang gethan ... || Dise Reisen vnd Schiffahrten seind zusamen/ auß an=||dern Spraachen ins Teutsch gebracht/|| Durch || Conrad Lœw der Historien Liebhaber.||

Vorlageform des Erscheinungsvermerks: Getruckt zu Cœlln/ auff der Burgmauren/ Bey || Bertram Buchholtz/ Jm Jahr || 1598.|

Analysis of uncertainty in multi-temporal object-based classification

Agricultural management increasingly uses crop maps based on classification of remotely sensed data. However, classification errors can translate to errors in model outputs, for instance agricultural production monitoring (yield, water demand) or crop acreage calculation. Hence, knowledge on the spatial variability of the classier performance is important information for the user. But this is not provided by traditional assessments of accuracy, which are based on the confusion matrix. In this study, classification uncertainty was analyzed, based on the support vector machines (SVM) algorithm. SVM was applied to multi-spectral time series data of RapidEye from different agricultural landscapes and years. Entropy was calculated as a measure of classification uncertainty, based on the per-object class membership estimations from the SVM algorithm. Permuting all possible combinations of available images allowed investigating the impact of the image acquisition frequency and timing, respectively, on the classification uncertainty. Results show that multi-temporal datasets decrease classification uncertainty for different crops compared to single data sets, but there was no “one-image-combination-fits-all” solution. The number and acquisition timing of the images, for which a decrease in uncertainty could be realized, proved to be specific to a given landscape, and for each crop they differed across different landscapes. For some crops, an increase of uncertainty was observed when increasing the quantity of images, even if classification accuracy was improved. Random forest regression was employed to investigate the impact of different explanatory variables on the observed spatial pattern of classification uncertainty. It was strongly influenced by factors related with the agricultural management and training sample density. Lower uncertainties were revealed for fields close to rivers or irrigation canals. This study demonstrates that classification uncertainty estimates by the SVM algorithm provide a valuable addition to traditional accuracy assessments. This allows analyzing spatial variations of the classifier performance in maps and also differences in classification uncertainty within the growing season and between crop types, respectively

The Impact of Phenological Developments on Interferometric and Polarimetric Crop Signatures Derived from Sentinel-1: Examples from the DEMMIN Study Site (Germany)

This study explores the potential of Sentinel-1 Synthetic Aperture Radar (SAR) to identify phenological phases of wheat, sugar beet, and canola. Breakpoint and extreme value analyses were applied to a dense time series of interferometric (InSAR) and polarimetric (PolSAR) features recorded during the growing season of 2017 at the JECAM site DEMMIN (Germany). The analyses of breakpoints and extrema allowed for the distinction of vegetative and reproductive stages for wheat and canola. Certain phenological stages, measured in situ using the BBCH-scale, such as leaf development and rosette growth of sugar beet or stem elongation and ripening of wheat, were detectable by a combination of InSAR coherence, polarimetric Alpha and Entropy, and backscatter (VV/VH). Except for some fringe cases, the temporal difference between in situ observations and breakpoints or extrema ranged from zero to five days. Backscatter produced the signature that generated the most breakpoints and extrema. However, certain micro stadia, such as leaf development of BBCH 10 of sugar beet or flowering BBCH 69 of wheat, were only identifiable by the InSAR coherence and Alpha. Hence, it is concluded that combining PolSAR and InSAR features increases the number of detectable phenological events in the phenological cycles of crops