Mateo Gašparović

2.5k total citations
83 papers, 1.7k citations indexed

About

Mateo Gašparović is a scholar working on Environmental Engineering, Ecology and Geology. According to data from OpenAlex, Mateo Gašparović has authored 83 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Environmental Engineering, 28 papers in Ecology and 17 papers in Geology. Recurrent topics in Mateo Gašparović's work include Remote Sensing and LiDAR Applications (32 papers), Remote Sensing in Agriculture (26 papers) and 3D Surveying and Cultural Heritage (17 papers). Mateo Gašparović is often cited by papers focused on Remote Sensing and LiDAR Applications (32 papers), Remote Sensing in Agriculture (26 papers) and 3D Surveying and Cultural Heritage (17 papers). Mateo Gašparović collaborates with scholars based in Croatia, United States and India. Mateo Gašparović's co-authors include Dorijan Radočaj, Mladen Jurišić, Ivan Balenović, Luka Jurjević, Damir Medak, Xinlian Liang, Mislav Stepinac, Ivan Pilaš, Ivan Plašćak and Sudhir Kumar Singh and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and Journal of Hazardous Materials.

In The Last Decade

Mateo Gašparović

82 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mateo Gašparović Croatia 24 722 696 493 247 234 83 1.7k
Chongcheng Chen China 28 553 0.8× 801 1.2× 687 1.4× 138 0.6× 394 1.7× 145 2.3k
Amr Abd‐Elrahman United States 30 876 1.2× 992 1.4× 798 1.6× 433 1.8× 231 1.0× 84 2.4k
Manuel Á. Aguilar Spain 27 1.0k 1.4× 950 1.4× 728 1.5× 456 1.8× 345 1.5× 93 2.3k
Fernando J. Aguilar Spain 28 1.1k 1.5× 987 1.4× 752 1.5× 464 1.9× 364 1.6× 95 2.4k
Adriaan van Niekerk South Africa 25 660 0.9× 843 1.2× 592 1.2× 249 1.0× 257 1.1× 95 2.1k
Alfonso García-Ferrer Spain 23 936 1.3× 648 0.9× 353 0.7× 152 0.6× 120 0.5× 47 1.8k
Luis Ángel Ruiz Spain 29 1.0k 1.4× 804 1.2× 833 1.7× 302 1.2× 266 1.1× 100 2.3k
Veraldo Liesenberg Brazil 25 1.1k 1.5× 1.3k 1.9× 539 1.1× 277 1.1× 158 0.7× 105 2.3k
Justin Morgenroth New Zealand 27 962 1.3× 663 1.0× 948 1.9× 156 0.6× 218 0.9× 79 2.2k
Quanlong Feng China 23 673 0.9× 869 1.2× 693 1.4× 448 1.8× 349 1.5× 53 2.0k

Countries citing papers authored by Mateo Gašparović

Since Specialization
Citations

This map shows the geographic impact of Mateo Gašparović's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mateo Gašparović with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mateo Gašparović more than expected).

Fields of papers citing papers by Mateo Gašparović

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mateo Gašparović. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mateo Gašparović. The network helps show where Mateo Gašparović may publish in the future.

Co-authorship network of co-authors of Mateo Gašparović

This figure shows the co-authorship network connecting the top 25 collaborators of Mateo Gašparović. A scholar is included among the top collaborators of Mateo Gašparović based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mateo Gašparović. Mateo Gašparović is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gašparović, Mateo, et al.. (2024). Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review. Applied Sciences. 14(24). 12020–12020. 3 indexed citations
2.
Kaplan, Gordana, et al.. (2023). Soil salinity prediction using Machine Learning and Sentinel – 2 Remote Sensing Data in Hyper – Arid areas. Physics and Chemistry of the Earth Parts A/B/C. 130. 103400–103400. 25 indexed citations
3.
Gašparović, Mateo, et al.. (2023). Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia. Forests. 14(6). 1193–1193. 16 indexed citations
4.
Kaplan, Gordana, et al.. (2023). Machine Learning-Based Classification of Asbestos-Containing Roofs Using Airborne RGB and Thermal Imagery. Sustainability. 15(7). 6067–6067. 6 indexed citations
5.
Skejić, Davor, et al.. (2022). Fatigue tests of as-welded and HFMI treated S355 details with longitudinal and transverse attachments. Welding in the World. 66(12). 2549–2561. 2 indexed citations
6.
Gašparović, Mateo, et al.. (2022). Discrimination of Rock Units in Karst Terrains Using Sentinel-2A Imagery. Remote Sensing. 14(20). 5169–5169. 6 indexed citations
7.
Šiljeg, Ante, et al.. (2022). Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery. Remote Sensing. 14(3). 757–757. 29 indexed citations
8.
9.
Jurjević, Luka, Mateo Gašparović, Xinlian Liang, & Ivan Balenović. (2021). Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest. Remote Sensing. 13(11). 2063–2063. 14 indexed citations
10.
Gašparović, Mateo, et al.. (2020). Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods. Remote Sensing. 12(23). 3926–3926. 50 indexed citations
11.
Jurjević, Luka, Mateo Gašparović, Anita Šimić Milas, & Ivan Balenović. (2020). Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes. Remote Sensing. 12(3). 404–404. 14 indexed citations
12.
Gašparović, Mateo, et al.. (2019). Analysis of Solar Potential Spatial Data for Croatia. 25–44. 1 indexed citations
13.
Balenović, Ivan, et al.. (2019). Testing the Applicability of the Official Croatian DTM for Normalization of UAV-based DSMs and Plot-level Tree Height Estimations in Lowland Forests. SHILAP Revista de lepidopterología. 5 indexed citations
14.
Gašparović, Mateo, et al.. (2018). Analiza prostorne točnosti zračnih i satelitskih snimaka grada Zagreba. Hrčak Portal of scientific journals of Croatia (University Computing Centre). 1 indexed citations
15.
Gašparović, Mateo, et al.. (2017). Analiza urbanizacije grada Splita. University of Zagreb University Computing Centre (SRCE). 189–202. 1 indexed citations
16.
Gašparović, Mateo, et al.. (2017). Analysis of Urbanization of Split. 189–202. 4 indexed citations
17.
Gašparović, Mateo, et al.. (2016). Testing of Image Quality Parameters of Digital Cameras for Photogrammetric Surveying with. 253. 1 indexed citations
18.
Gašparović, Mateo, et al.. (2016). Analysis of the Gimbal Impact on the Determination of Camera External Orientation Elements on the UAV. 161–172. 2 indexed citations
19.
Gašparović, Mateo, et al.. (2016). Testing of Image Quality Parameters of Digital Cameras for Photogrammetric Surveying with Unmanned Aircrafts. University of Zagreb University Computing Centre (SRCE). 253–266. 5 indexed citations
20.
Gašparović, Mateo, et al.. (2015). Examination of the Influence of Lens Distortion of Non-Metric Digital Cameras on the Accuracy of Photogrammetric Survey. 27–40. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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