Oto Kaláb

Oto Kaláb
University of Ostrava · Department of Physical Geography and Geoecology

I am focused on Orthoptera and spatial data processing in ecology and conservation. Open to collaboration.

About

15
Publications
4,760
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
23
Citations
Citations since 2016
14 Research Items
22 Citations
201620172018201920202021202202468
201620172018201920202021202202468
201620172018201920202021202202468
201620172018201920202021202202468

Publications

Publications (15)
Book
Full-text available
Moderní kartografická učebnice popisující proces tvorby map od získání dat přes všechny fáze kartografického projektu po tvorbu finálního výstupu. Kromě teorie kartografie přináší i poznatky z dalších oborů, které kartograf při tvorbě map potřebuje – počítačové grafiky, typografie či designu. Každá kapitola je doplněna praktickými návody a postupy...
Article
In the past, insect species richness was high in Central European seminatural grasslands, which were characterized by low‐intensity land use. Currently, however, the hay in most of these grasslands is mechanically harvested, which negatively impacts insect biodiversity. One way to reduce this negative effect is to leave unmown patches as refuges. I...
Poster
Full-text available
Data o výskytu druhů v otevřených databázích jsou, spolu s dalšími otevřenými daty, nedílnou součástí ekologického, biogeografického a ochranářského výzkumu. Mohou být ale také využita při zpracování úředních dokumentů, vzdělávání nebo komunikaci s veřejností. Dominantním zdrojem volně dostupných dat o výskytu druhů v ČR je Nálezová databáze ochran...
Article
Full-text available
Recent changes in insect distribution are consistent with the expected interacting effects of climate and habitat change. The orthopteran Ruspolia nitidula has expanded its area of distribution in Western and Central Europe in recent decades. Because males emit a sound that is easily detected at a distance of up to 40 m, it is possible to detect sp...
Article
Full-text available
In this study, we describe an inexpensive and rapid method of using video analysis and identity tracking to measure the effects of tag weight on insect movement. In a laboratory experiment, we assessed the tag weight and associated context-dependent effects on movement, choosing temperature as a factor known to affect insect movement and behavior....
Article
Full-text available
In the period 2012–2015, an extensive mapping of the distribution of the Orthoptera in five regions of the Czech Republic (Jihočeský, Karlovarský, Středočeský, Vysočina and Ústecký regions) was carried out. The selected regions cover 43 % of the Czech Republic. The mapping unit used was a standardized square map of the grid mapping, in which the ha...
Conference Paper
Tracking animals using transmitters is a busy research branch in zoology. In this paper, we study the effect of a temperature and transmitter’s weight on a field cricket’s movement. We recorded the movement of 180 field crickets with a 4K camera and the captured movies were analyzed by a novel tracking algorithm based on a soft-computing technique...
Conference Paper
We introduce a problem of tracking small animals, especially insects. To solve this problem, we focus on visual tracking in recorded movies, propose our pattern tracking mechanism based on F-transform, and implement a user-friendly software to handle the movies. The tracking core is compared with five state-of-the-art tracking algorithms: KCF, MIL,...
Article
Full-text available
Using 35 presence-only data samples and five uncorrelated bioclimatic variables, we made species distribution models (SDMs) for 4 species of critically endangered (CR) liverworts from genus Jungermanniales and Marchantiales (Cephaloziella elegans, Leiocolea heterocolpos, Lophozia wenzelii and Riccia papillosa) using the maximum entropy modelling me...
Article
Full-text available
Bryological research carried out from 2008 in Tajikistan and Kyrgyzstan brought interesting data on the occurrence of epiphytic bryophytes which have not been recorded yet there. One of the species was recently described as a new ( Orthotrichum pamiricum ) and some of the other newly recorded species are considered as rare or endangered in the regi...
Poster
Full-text available
First pilot simple analysis was done only on relation mean NDVI index and abundances. Mean NDVI index was calculated from one Landsat image from period of field survey in different distances around sampling points. This shows negative effect of imprevious surfaces on presence of P. griseoaptera in all studied scales (50, 100, 250, 500, 1000 m)
Poster
Full-text available
Geografické informační systémy (GIS) mají již nějakou dobu své pevné místo v ekologii a ochraně přírody, a to jak ve výzkumu (design, analýza dat), tak i v praxi (plánování managementu, mapování druhů, publikacedat). Cílem příspěvku je seznámení s volně dostupnými nástroji (open source software) pro analýzu a vizualizaci geografických dat.Dle metaa...
Article
Full-text available
Travinné ekosystémy jsou významnou součástí středoevropské krajiny. Polopřirozená travinná společenstva dnes tvoří v Evropě místa s vysokou biodiverzitou, jsou velmi náchylná k degradaci a k jejich efektivní ochraně je nutné znát vlivy různých typů managementu. Neodmyslitelnými obyvateli těchto ekosystémů jsou také zástupci rovnokřídlého hmyzu (Ort...
Article
Full-text available
The study presents the distribution of the genera Orthotrichum s.l. and Nyholmiella in the phytogeographical district 98. Nízký Jeseník, and follows the project carried out between years 2005 and 2009 in nearby phytogeographical district 97. Hrubý Jeseník. The total number of 57 locations was visited during the year 2015, and 142 samples of mosses...

Questions

Questions (2)
Question
Is it reasonable to use predictions outputs (probability rasters 0-100) as variable in niche/distribution models? For example SoilGrids (https://soilgrids.org/) layer of individual soil type. I am especially interested in case when the probabilities are low in the study area, but species response is high. I will be thankful for any references, suggestions and comments
Question
I downloaded TM (2006-2011) and OLI (2013-2016) Surface Reflectance Higher-Level Data Products (atmospheric corrected) based on L1TP Landsat images (radiometrically calibrated, orthorectified). And I got problems wtih preparing data for time series change analysis. Thereafter in this analysis I'll want to detect changes in area damaged by bark beetle as only supporting information for our project. All images are taken from end of July to early September with respect to ecology and behaviour of given species. Study area is mountainous region in Georgia.
According to USGS documentation (https://landsat.usgs.gov/landsat-collections) :
"Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments."
I assume, that key pre-processing steps are done with this data and now I have absolute surface reflectance data. So if I create time series of some vegatation index (e.g. NDVI) from images from one sensor type (taking into account, that values may vary across sensors) and pick some time consistent pixel (e.g. impervious built-up surface), the values will be more or less similar, or vary sligthly. But it looks like, that it does not work this way.
I processed the images with R packages 'raster' and 'RSToolbox'
1. Batch clip all data to area of intertest.
2. Create individual raster stacks for each image
3. Topographic correction with topCor() (srtm data + image metadata)
4. Claud masking with BQA band
5. Calculate NDVI indices
6. Create raster stack with NDVI outputs
7. Checking NDVI values of given pixel in time-series stack (see results in attached files, first example is from build-up area which does not change over time)
As you can see the pixel value changes drastically across time within TM (2006-2011) and OLI (2013-2016) separately.
Although it may not make much sense for me, I also try matching images by pifMatch() or histMatch() and I also try the process without cloud masking and topographic correction steps, all without visible improvement.
My question is:
Can I suppose that Surface Reflectance Higher-Level L1TP is ready for time-series analysis in way I did it, than my processing steps are correct or I am missing something crucial?
I am new to remote sensing, so I will be grateful if someone points to what I am doing wrong, or for giving me any suggestions. I am comfortable with QGIS, R, Python, GRASS or bash/GDAL.
My pre-processing approach was based on this paper (and few case studies)
A survival guide to Landsat preprocessing (Young et al. 2017)

Network

Cited By

Projects

Project (1)
Project
The aim of the project is to improve the conservation status of hermit beetle (Osmoderma eremita) in SCI Poodří. The project limit or eliminate threatening factors for the species. The project will have also a positive impact on the conservation status of other species, which are protected within SCI (Misgurnus fossilis, Bombina bombina) or have a special status of protection in national legislation (cavity-nesting birds, Vespertilionidae). Use of biomass from pollarded willows will help the local community. Start of appropriate management and renewal of traditions (pollarding of willow trees, traditional varieties of pear trees cultivation) will improve the relation of local citizens to the region and increase the attractiveness of the region to tourists. Also, other ecosystem services in the project area will be strengthened. Specific goals: 1. To stop degradation and restore the typical habitat of hermit beetle in the area – pollarded willows Preserving management of 570 willows will be implemented in 6 localities with hermit beetle occurrence. 2. To connect isolated areas of hermit beetle occurrence by planting suitable species of trees. There will be 1 590 trees planted. 3. To create stepping stones connecting SCI with other localities of occurrence of hermit beetle in direction of natural migration corridor – Moravská Brána (Moravian Gate). We will provide preserving management for 60 willows and plant 430 trees as potential biotopes. 4. To mitigate invasion of mistletoe, which is endangering the second most common habitat of hermit beetle in the area – linden trees. We will treat 145 linden trees attacked by mistletoe. 5. To support the preservation of standing cavity-trees and dead trees. We will treat 180 grown oaks. 6. To set up appropriate management of biotopes and create socio-economic conditions for longterm preservation of favourable conservation status. We will raise awareness of stakeholders of benefits of biodiversity, appropriate management practices and use of ecosystem services. Management will be provided together with stakeholders in at least 5 localities, where it will continue after the end of the project. 7. To prove a positive impact on the status of biotopes of hermit beetle and general biodiversity in SCI, on socio-economic conditions and on the performance of ecosystem services. For at least 2 other species, the positive impact on habitats via elimination of at least one risk factor will be proven.