António Ferraz

2.5k total citations
40 papers, 1.4k citations indexed

About

António Ferraz is a scholar working on Nature and Landscape Conservation, Environmental Engineering and Ecology. According to data from OpenAlex, António Ferraz has authored 40 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Nature and Landscape Conservation, 27 papers in Environmental Engineering and 17 papers in Ecology. Recurrent topics in António Ferraz's work include Remote Sensing and LiDAR Applications (27 papers), Forest ecology and management (25 papers) and Remote Sensing in Agriculture (12 papers). António Ferraz is often cited by papers focused on Remote Sensing and LiDAR Applications (27 papers), Forest ecology and management (25 papers) and Remote Sensing in Agriculture (12 papers). António Ferraz collaborates with scholars based in United States, France and United Kingdom. António Ferraz's co-authors include Sassan Saatchi, Clément Mallet, Victoria Meyer, Stéphane Jacquemoud, L. Gomes Pereira, Gil Gonçalves, Margarida Tomé, Paula Soares, Frédéric Bretar and Nesrine Chehata and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

António Ferraz

35 papers receiving 1.3k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
António Ferraz 1.1k 744 596 353 350 40 1.4k
Carine Klauberg 1.2k 1.1× 690 0.9× 588 1.0× 421 1.2× 351 1.0× 49 1.5k
Cédric Vega 1.1k 1.0× 671 0.9× 734 1.2× 327 0.9× 222 0.6× 38 1.4k
Markus Holopainen 1.4k 1.3× 877 1.2× 631 1.1× 538 1.5× 284 0.8× 47 1.6k
Stephen E. Reutebuch 1.5k 1.4× 935 1.3× 637 1.1× 562 1.6× 491 1.4× 26 1.8k
Sylvie Durrieu 1.2k 1.1× 680 0.9× 790 1.3× 367 1.0× 383 1.1× 39 1.5k
Anssi Pekkarinen 970 0.9× 569 0.8× 806 1.4× 233 0.7× 443 1.3× 33 1.4k
Juan Carlos Pinilla Suárez 1.1k 1.0× 691 0.9× 729 1.2× 278 0.8× 388 1.1× 74 1.5k
Topi Tanhuanpää 902 0.8× 428 0.6× 574 1.0× 341 1.0× 284 0.8× 33 1.2k
Tristan R.H. Goodbody 1.5k 1.3× 731 1.0× 887 1.5× 464 1.3× 518 1.5× 30 1.8k
Juho Pitkänen 1.4k 1.3× 1.1k 1.4× 582 1.0× 705 2.0× 350 1.0× 29 1.6k

Countries citing papers authored by António Ferraz

Since Specialization
Citations

This map shows the geographic impact of António Ferraz'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 António Ferraz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites António Ferraz more than expected).

Fields of papers citing papers by António Ferraz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by António Ferraz. 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 António Ferraz. The network helps show where António Ferraz may publish in the future.

Co-authorship network of co-authors of António Ferraz

This figure shows the co-authorship network connecting the top 25 collaborators of António Ferraz. A scholar is included among the top collaborators of António Ferraz 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 António Ferraz. António Ferraz 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.
Longo, Marcos, Michael Keller, Lara M. Kueppers, et al.. (2025). Degradation and deforestation increase the sensitivity of the Amazon Forest to climate extremes. Environmental Research Letters. 20(5). 54024–54024. 1 indexed citations
2.
Takuo, Jean Michel, Matthew LeBreton, António Ferraz, et al.. (2025). Spaceborne and UAV-LiDAR reveal hammer-headed bat preference for intermediate canopy height and diverse structure in a Central African rainforest. Movement Ecology. 13(1). 30–30.
3.
Deblauwe, Vincent, António Ferraz, Matthew LeBreton, et al.. (2024). Divergent seed dispersal outcomes: Interactions between seed, disperser, and forest traits. Ecology. 105(10). e4409–e4409. 7 indexed citations
4.
Ferraz, António, Nicolas Barbier, Martin Wikelski, et al.. (2024). Three‐dimensional vegetation structure drives patterns of seed dispersal by African hornbills. Journal of Animal Ecology. 93(12). 1935–1946. 4 indexed citations
5.
Teitelbaum, Claire S., et al.. (2024). The potential of remote sensing for improved infectious disease ecology research and practice. Proceedings of the Royal Society B Biological Sciences. 291(2037). 20241712–20241712. 4 indexed citations
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Leitold, Veronika, Douglas C. Morton, Sebastián Martinuzzi, et al.. (2021). Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data. Ecosystems. 25(4). 892–910. 20 indexed citations
9.
Schneider, Fabian, António Ferraz, Steven Hancock, et al.. (2020). Towards mapping the diversity of canopy structure from space with GEDI. Environmental Research Letters. 15(11). 115006–115006. 99 indexed citations
10.
Meyer, Victoria, Sassan Saatchi, António Ferraz, et al.. (2019). Forest degradation and biomass loss along the Chocó region of Colombia. Carbon Balance and Management. 14(1). 2–2. 30 indexed citations
11.
Aubry‐Kientz, Mélaine, António Ferraz, Sassan Saatchi, et al.. (2019). A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests. Remote Sensing. 11(9). 1086–1086. 79 indexed citations
12.
Ferraz, António, S. S. Saatchi, Michael Keller, Marcos Longo, & Jean Pierre Ometto. (2019). Lidar tree crown detection reveals new patterns of tree density, height and volume in the Brazilian Amazon. Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research). 2019. 1 indexed citations
13.
Clark, David B., David B. Clark, António Ferraz, et al.. (2019). Diversity, distribution and dynamics of large trees across an old-growth lowland tropical rain forest landscape. PLoS ONE. 14(11). e0224896–e0224896. 19 indexed citations
14.
Meyer, Victoria, Sassan Saatchi, David B. Clark, et al.. (2018). Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes. Biogeosciences. 15(11). 3377–3390. 45 indexed citations
15.
Cawse‐Nicholson, Kerry, Joshua B. Fisher, Amy Braverman, et al.. (2018). Ecosystem responses to elevated CO 2 using airborne remote sensing at Mammoth Mountain, California. Biogeosciences. 15(24). 7403–7418. 9 indexed citations
16.
Silva, Carlos Alberto, Carine Klauberg, Andrew T. Hudak, et al.. (2017). Predicting Stem Total and Assortment Volumes in an Industrial Pinus taeda L. Forest Plantation Using Airborne Laser Scanning Data and Random Forest. Forests. 8(7). 254–254. 45 indexed citations
17.
Garcı́a, Mariano, Sassan Saatchi, António Ferraz, et al.. (2017). Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR. Carbon Balance and Management. 12(1). 4–4. 39 indexed citations
18.
Xu, Liang, Sassan Saatchi, Aurélie Shapiro, et al.. (2017). Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo. Scientific Reports. 7(1). 15030–15030. 62 indexed citations
19.
Ferraz, António, Sassan Saatchi, Clément Mallet, et al.. (2016). Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory. Remote Sensing. 8(8). 653–653. 48 indexed citations
20.
García‐Alonso, M. C., António Ferraz, S. S. Saatchi, et al.. (2015). Estimating forest biomass from LiDAR data: A comparison of the raster-based and point-cloud data approach. AGU Fall Meeting Abstracts. 2015. 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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