Author
Katarzyna Ostapowicz
Other affiliations: University of Wisconsin-Madison
Bio: Katarzyna Ostapowicz is an academic researcher from Jagiellonian University. The author has contributed to research in topics: Land use & Land cover. The author has an hindex of 22, co-authored 36 publications receiving 2055 citations. Previous affiliations of Katarzyna Ostapowicz include University of Wisconsin-Madison.
Papers
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TL;DR: In this article, the authors analyzed changes in forest types, forest disturbances, and forest recovery for the Carpathian ecoregion in Eastern Europe using the Landsat archive.
263 citations
University of Copenhagen1, Humboldt University of Berlin2, Leibniz Institute for Neurobiology3, Alpen-Adria-Universität Klagenfurt4, VU University Amsterdam5, Slovenian Academy of Sciences and Arts6, Ghent University7, Norwegian University of Science and Technology8, University of Eastern Finland9, Aix-Marseille University10, University of Edinburgh11, University of Luxembourg12, University of Malta13, Charles University in Prague14, Technical University of Madrid15, Slovak Academy of Sciences16, Stockholm University17, Jagiellonian University18, University of West Hungary19, University of Tartu20, University of Latvia21, Wageningen University and Research Centre22, Spanish National Research Council23, University of the Aegean24, University of Bucharest25, Potsdam Institute for Climate Impact Research26, University of Potsdam27, University of Tirana28
TL;DR: In this article, the authors examined the evolution of European land management over the past 200 years with the aim of identifying key episodes of changes in land management, and their underlying technological, institutional and economic drivers.
233 citations
01 Jan 2015
229 citations
University of Wisconsin-Madison1, Potsdam Institute for Climate Impact Research2, Humboldt University of Berlin3, University of Constantine the Philosopher4, Slovak Academy of Sciences5, Leibniz Association6, Swiss Federal Institute for Forest, Snow and Landscape Research7, Jagiellonian University8, University of West Hungary9, Taras Shevchenko National University of Kyiv10, Charles University in Prague11
TL;DR: In this paper, a meta-analysis of 66 publications describing 102 case study locations and quantified the main forest and agricultural changes in the Carpathian region since the 18th century.
227 citations
TL;DR: New methods based on mathematical morphology provide a generic, flexible, and automated approach for the definition of indicators based on the classification and mapping of spatial patterns of connectivity from observed or simulated movement and dispersal events.
202 citations
Cited by
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1,327 citations
Leibniz Association1, Technische Universität München2, University of Potsdam3, Colorado State University4, University of Oxford5, Wildlife Conservation Society6, University of California, Davis7, Indonesian Institute of Sciences8, Mulawarman University9, Universiti Malaysia Sabah10, Kyoto University11, International Union for Conservation of Nature and Natural Resources12, Mississippi State University13
TL;DR: It is concluded that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
Abstract: Aim
Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.
Location
Borneo, Southeast Asia.
Methods
We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.
Results
Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.
Main Conclusions
We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
822 citations
TL;DR: In this article, the authors present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.
Abstract: Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.
784 citations
TL;DR: In this article, an error-adjusted estimator of area can be easily produced once an accuracy assessment has been performed and an error matrix constructed, which can then be incorporated into an uncertainty analysis for applications using land change area as an input (e.g., a carbon flux model).
749 citations