Daniel Gann

1.0k total citations
28 papers, 694 citations indexed

About

Daniel Gann is a scholar working on Ecology, Global and Planetary Change and Environmental Engineering. According to data from OpenAlex, Daniel Gann has authored 28 papers receiving a total of 694 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Ecology, 13 papers in Global and Planetary Change and 10 papers in Environmental Engineering. Recurrent topics in Daniel Gann's work include Coastal wetland ecosystem dynamics (10 papers), Land Use and Ecosystem Services (7 papers) and Remote Sensing in Agriculture (6 papers). Daniel Gann is often cited by papers focused on Coastal wetland ecosystem dynamics (10 papers), Land Use and Ecosystem Services (7 papers) and Remote Sensing in Agriculture (6 papers). Daniel Gann collaborates with scholars based in United States, Netherlands and South Korea. Daniel Gann's co-authors include Michael E. McClain, Shimelis Gebriye Setegn, Assefa M. Melesse, Keqi Zhang, Michael S. Ross, Bina Thapa, Jamie Rhome, Juan Pablo Sarmiento, Shimon Wdowinski and Jennifer H. Richards and has published in prestigious journals such as Remote Sensing of Environment, IEEE Transactions on Geoscience and Remote Sensing and Journal of Ecology.

In The Last Decade

Daniel Gann

26 papers receiving 674 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Gann United States 9 406 323 233 178 119 28 694
Ankit Gupta India 11 273 0.7× 261 0.8× 77 0.3× 250 1.4× 107 0.9× 23 626
Renata Vezzoli Italy 12 527 1.3× 215 0.7× 99 0.4× 86 0.5× 167 1.4× 34 769
Anzhou Zhao China 17 929 2.3× 305 0.9× 458 2.0× 167 0.9× 256 2.2× 46 1.2k
Ana I. Dogliotti Argentina 19 477 1.2× 304 0.9× 455 2.0× 161 0.9× 108 0.9× 49 1.4k
Adrian Fisher Australia 12 492 1.2× 210 0.7× 391 1.7× 306 1.7× 116 1.0× 16 826
Hailong Wang China 21 672 1.7× 412 1.3× 195 0.8× 169 0.9× 286 2.4× 46 967
W. Mehl Italy 10 297 0.7× 110 0.3× 337 1.4× 167 0.9× 111 0.9× 18 651
Ibrahim Mamadou France 8 426 1.0× 257 0.8× 63 0.3× 120 0.7× 165 1.4× 13 626
Huan Ma China 11 464 1.1× 386 1.2× 219 0.9× 130 0.7× 102 0.9× 18 753

Countries citing papers authored by Daniel Gann

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Gann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Gann

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Gann. A scholar is included among the top collaborators of Daniel Gann 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 Daniel Gann. Daniel Gann 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.
Gann, Daniel, et al.. (2024). Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations. Remote Sensing. 16(21). 3992–3992. 2 indexed citations
2.
Troxler, Tiffany G., Carlos Coronado‐Molina, Stephen E. Davis, et al.. (2024). Evaluating Hydrogeomorphic Condition Across Ecosystem States in a Non-tidal, Brackish Peat Marsh of the Florida Coastal Everglades, USA. Estuaries and Coasts. 47(5). 1209–1223. 1 indexed citations
3.
Zhang, Boya, Kaleb E Smith, Shimon Wdowinski, et al.. (2022). Space-Based Mapping of Mangrove Canopy Height with Multi-Sensor Observations and Deep Learning Techniques. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. 116. 3389–3392. 1 indexed citations
4.
5.
Zhang, Boya, et al.. (2022). Spatiotemporal variations of wetland backscatter: The role of water depth and vegetation characteristics in Sentinel-1 dual-polarization SAR observations. Remote Sensing of Environment. 270. 112864–112864. 37 indexed citations
6.
Hochmair, Hartwig H., et al.. (2022). Change Analysis of Urban Tree Canopy in Miami-Dade County. Forests. 13(6). 949–949. 5 indexed citations
7.
Gann, Daniel & Jennifer H. Richards. (2022). Scaling of classification systems—effects of class precision on detection accuracy from medium resolution multispectral data. Landscape Ecology. 38(3). 659–687. 5 indexed citations
8.
Tinsman, Jen, et al.. (2022). Habitat use by the island lemurs of Nosy Be, Madagascar. American Journal of Primatology. 84(3). e23362–e23362. 6 indexed citations
9.
Fadrique, Belén, Daniel Gann, Bruce Nelson, Sassan Saatchi, & Kenneth J. Feeley. (2020). Bamboo phenology and life cycle drive seasonal and long‐term functioning of Amazonian bamboo‐dominated forests. Journal of Ecology. 109(2). 860–876. 13 indexed citations
10.
Zhang, Keqi, et al.. (2020). Delineation of Tree Patches in a Mangrove-Marsh Transition Zone by Watershed Segmentation of Aerial Photographs. Remote Sensing. 12(13). 2086–2086. 13 indexed citations
11.
Zhang, Keqi, et al.. (2019). Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding. Remote Sensing of Environment. 225. 290–306. 112 indexed citations
12.
Gann, Daniel. (2019). Quantitative spatial upscaling of categorical information: The multi‐dimensional grid‐point scaling algorithm. Methods in Ecology and Evolution. 10(12). 2090–2104. 6 indexed citations
13.
Hochmair, Hartwig H., et al.. (2016). Miami-Dade County Urban Tree Canopy Assessment. Florida International University Digital Commons (Florida International University). 1 indexed citations
14.
Hochmair, Hartwig H., et al.. (2015). Miami- Dade Urban Tree Canopy Analysis. Florida International University Digital Commons (Florida International University). 1 indexed citations
15.
Richards, Jennifer H. & Daniel Gann. (2015). Vegetation Trends in Indicator Regions of Everglades National Park. Florida International University Digital Commons (Florida International University). 1 indexed citations
16.
Gann, Daniel & Jennifer H. Richards. (2015). Determine the Effectiveness of Plant Communities Classification from Satellite Imagery for the Greater Everglades Freshwater Wetlands & Community Abundance, Distribution and Hydroperiod Analysis for WCA 2A, Final Report. Florida International University Digital Commons (Florida International University).
17.
Gann, Daniel & Jennifer H. Richards. (2014). Quantitative Comparison of Plant Community Hydrology Using Large-Extent, Long-Term Data. Wetlands. 35(1). 81–93. 2 indexed citations
18.
Gann, Daniel, et al.. (2012). Consulting Services to Determine the Effectiveness of Vegetation Classification Using WorldView 2 Satellite Data for the Greater Everglades. Florida International University Digital Commons (Florida International University). 3 indexed citations
19.
Melesse, Assefa M., et al.. (2011). Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management. Hydrology and earth system sciences. 15(7). 2245–2258. 338 indexed citations
20.

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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