Based on expeditionary work in the period 2003–2018 we compile a map of linden forests locations in the Jewish Autonomous Region (JAR). An inventory map of forest fires was created using data on forest fire registration of the Forest Management Department of the JAR Government for 2017–2020. The number, area, and configuration of the burned areas were determined by superimposing of these maps. The analysis of fire-damaged forests shows that during the study period fires were observed in all forest formation with linden. Their total number was 174. The most of fires (33%) noted in 2018 and the fewest (15%) – in 2017. The average annual number of fires in 2017–2020 was 44. The largest area affected by the pyrogenic factor was observed in 2018. The smallest area (7.5%) of linden forests were fire-transformed in 2020. The average area of one fire is 653 hectares. This corresponds to 64% of the total fire-affected area in the studied vegetation formations. The black birch and oak park-type forests, sometimes with linden, larch, with silverspike-and-forbs cover and forbs-and-pinegrass meadows were most affected by fire. We analyzed the spatial distribution of the fire-damaged forests and established its main patterns as well as identified the areas of repeated burnout. In the most of the JAR linden forests fires have a one-time character. Local areas are subject to multiple burnout; the causes of their high multiple ignition require further study. In this regard, forest fires is a factor that significantly contributes to the reduction of the honey-bearing lands of the JAR.

We carried out a search for unknown dwarf novae in a public data release of the Zwicky Transient Facility survey and suspected that the object ZTF18abdlzhd is a SU UMa-type star.Performed multicolor CCD observations permit us to follow its fading from an outburst in August and an entire superoutburst in October 2020.The duration of the superoutburst is 13 days.We detected superhumps with period P = 0. d 06918(3) that are characteristic of UGSU type stars.

Photometric measurements are prone to systematic errors presenting a challenge to low-amplitude variability detection. In search for a general-purpose variability detection technique able to recover a broad range of variability types including currently unknown ones, we test 18 statistical characteristics quantifying scatter and/or correlation between brightness measurements. We compare their performance in identifying variable objects in seven time series data sets obtained with telescopes ranging in size from a telephoto lens to 1m-class and probing variability on time-scales from minutes to decades. The test data sets together include lightcurves of 127539 objects, among them 1251 variable stars of various types and represent a range of observing conditions often found in ground-based variability surveys. The real data are complemented by simulations. We propose a combination of two indices that together recover a broad range of variability types from photometric data characterized by a wide variety of sampling patterns, photometric accuracies, and percentages of outlier measurements. The first index is the interquartile range (IQR) of magnitude measurements, sensitive to variability irrespective of a time-scale and resistant to outliers. It can be complemented by the ratio of the lightcurve variance to the mean square successive difference, 1/h, which is efficient in detecting variability on time-scales longer than the typical time interval between observations. Variable objects have larger 1/h and/or IQR values than non-variable objects of similar brightness. Another approach to variability detection is to combine many variability indices using principal component analysis. We present 124 previously unknown variable stars found in the test data.

Photographic plate archives contain a wealth of information about positions and brightness celestial objects had decades ago. Plate digitization is necessary to make this information accessible, but extracting it is a technical challenge. We develop algorithms used to extract photometry with the accuracy of better than 0.1m in the magnitude range 130.2m) variable stars. The algorithms are implemented in the free software VaST available at http://scan.sai.msu.ru/vast/