A5014806603- Land Use and Ecosystem Services
- Geographic Information Systems Studies
- Remote Sensing in Agriculture
- Ecology and Vegetation Dynamics Studies
- Species Distribution and Climate Change
- Soil Geostatistics and Mapping
- Spatial and Panel Data Analysis
- Data Management and Algorithms
- Allergic Rhinitis and Sensitization
- Data Analysis with R
- Remote Sensing and LiDAR Applications
- demographic modeling and climate adaptation
- 3D Modeling in Geospatial Applications
- Remote-Sensing Image Classification
- Regional Economic and Spatial Analysis
- Soil erosion and sediment transport
- Lichen and fungal ecology
- Advanced Database Systems and Queries
- Ecosystem dynamics and resilience
- Remote Sensing and Land Use
- Forest ecology and management
- Theoretical and Computational Physics
- Water Quality Monitoring and Analysis
- Climate change and permafrost
- Botany and Plant Ecology Studies
Adam Mickiewicz University in Poznań
2014-2025
University of Cincinnati
2018-2019
United States Army Corps of Engineers
2019
University of Alabama
2019
U.S. Army Engineer Research and Development Center
2019
Rzeszów University
2015
Quantifying landscape characteristics and linking them to ecological processes is one of the central goals ecology. Landscape metrics are a widely used tool for analysis patch‐based, discrete land‐cover classes. Existing software calculate has several constraints, such as being limited single platform, not open‐source or involving complicated integration into large workflows. We present landscapemetrics , an R package that overcomes many constraints existing metric software. The includes...
Quantitative grouping of similar landscape patterns is an important part ecology due to the relationship between a pattern and underlying ecological process. One priorities in development theoretically consistent framework for quantifying, ordering classifying patterns. To demonstrate that information theory as applied bivariate random variable provides ordering, After presenting context landscapes, information-theoretical metrics were calculated exemplar set landscapes embodying all...
Freely available and reliable meteorological datasets are highly demanded in many scientific business applications. However, the structure of publicly databases is often difficult to follow, especially for users who only deal with this kind dataset on occasion. The “climate” R package aims fill gap an easy-to-use interface downloading global data a fast consistent way. provides access different sources in-situ data, including Ogimet website, atmospheric vertical sounding gathered at...
There is a keen interest in calculating spatial associations between two variables spanning the same study area. Many methods for such have been proposed, but case when both are categorical underdeveloped despite fact that many datasets of form either regionalizations or thematic maps. In this paper, we advance by adapting so-called -measure method from its original information-theoretical formulation to analysis variance which provides more insight analysis. We present step-by-step...
Changes in the timing of plant phenological phases are important proxies contemporary climate research. However, most commonly used traditional observations do not give any coherent spatial information. While consistent data can be obtained from airborne sensors and preprocessed gridded meteorological data, many studies robustly benefit these sources. Therefore, main aim this study is to create evaluate different statistical models for reconstructing, predicting, improving quality monitoring...
Converting an image to a set of superpixels is useful preprocessing step in many computer vision applications; it reduces the dimensionality data and removes noise. The most popular algorithm Simple Linear Iterative Clustering (SLIC). To use original SLIC with non-imagery (for example, rasters discrete probability distributions, time-series, or matrices describing local texture pattern), needs be converted false-color RGB constructed from first three principal components. Here we propose...
Last year, I received a grant from the Marie Skłodowska-Curie Actions Postdoctoral Fellowships (MSCA-PF) program for project called PRISM: <em> PReservation and RecognItion of Spatial patterns using Machine learning </em> . Between August 2024 2026, am in Remote Sensing Modeling group at University Muenster, Germany.