Pravesh Debba

1.0k total citations
38 papers, 797 citations indexed

About

Pravesh Debba is a scholar working on Ecology, Artificial Intelligence and Media Technology. According to data from OpenAlex, Pravesh Debba has authored 38 papers receiving a total of 797 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Ecology, 12 papers in Artificial Intelligence and 11 papers in Media Technology. Recurrent topics in Pravesh Debba's work include Remote Sensing in Agriculture (12 papers), Remote-Sensing Image Classification (11 papers) and Geochemistry and Geologic Mapping (11 papers). Pravesh Debba is often cited by papers focused on Remote Sensing in Agriculture (12 papers), Remote-Sensing Image Classification (11 papers) and Geochemistry and Geologic Mapping (11 papers). Pravesh Debba collaborates with scholars based in South Africa, Netherlands and United States. Pravesh Debba's co-authors include Moses Azong Cho, Renaud Mathieu, F.D. van der Meer, Gregory P. Asner, Jan van Aardt, Laven Naidoo, Alfred Stein, Emmanuel John M. Carranza, F.J.A. van Ruitenbeek and Abel Ramoelo and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Pravesh Debba

34 papers receiving 775 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pravesh Debba South Africa	12	387	273	247	172	144	38	797
G.B. Bénié Canada	17	185 0.5×	141 0.5×	319 1.3×	56 0.3×	191 1.3×	50	997
Julien Radoux Belgium	16	693 1.8×	348 1.3×	434 1.8×	68 0.4×	517 3.6×	43	1.4k
Shuai Gao China	22	899 2.3×	704 2.6×	104 0.4×	82 0.5×	432 3.0×	67	1.4k
Zhengchao Chen China	18	469 1.2×	347 1.3×	537 2.2×	107 0.6×	480 3.3×	100	1.8k
Yanchen Bo China	19	403 1.0×	334 1.2×	274 1.1×	43 0.3×	437 3.0×	64	1.2k
Kenneth Clarke Australia	13	444 1.1×	166 0.6×	76 0.3×	35 0.2×	282 2.0×	33	855
Satya Kalluri United States	15	725 1.9×	366 1.3×	210 0.9×	64 0.4×	760 5.3×	68	1.5k
Chinsu Lin Taiwan	19	578 1.5×	514 1.9×	184 0.7×	29 0.2×	302 2.1×	82	1.2k
Nadine Dessay France	18	314 0.8×	84 0.3×	49 0.2×	40 0.2×	524 3.6×	44	1.2k
Kenneth C. McGwire United States	16	396 1.0×	255 0.9×	51 0.2×	52 0.3×	441 3.1×	38	995

Countries citing papers authored by Pravesh Debba

Since Specialization

Citations

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

Fields of papers citing papers by Pravesh Debba

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pravesh Debba

This figure shows the co-authorship network connecting the top 25 collaborators of Pravesh Debba. A scholar is included among the top collaborators of Pravesh Debba 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 Pravesh Debba. Pravesh Debba is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Abdelatif, Nada, et al.. (2025). Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic. Heliyon. 11(11). e43171–e43171.

Mutanga, Onisimo, et al.. (2023). Distinguishing Tree Species from In Situ Hyperspectral and Temporal Measurements through Ensemble Statistical Learning. Remote Sensing. 15(17). 4117–4117. 1 indexed citations

Abdelatif, Nada, et al.. (2023). A spatial model with vaccinations for COVID-19 in South Africa. Spatial Statistics. 58. 100792–100792.

Archibald, Sally, et al.. (2022). A Spatial SEIR Model for COVID-19 in South Africa. 2(7). 14–45. 4 indexed citations

Reddy, Tarylee, Ziv Shkedy, Henry Mwambi, et al.. (2021). Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach. BMC Medical Research Methodology. 21(1). 15–15. 14 indexed citations

Debba, Pravesh, et al.. (2021). Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa. Frontiers in Big Data. 4. 718351–718351. 13 indexed citations

Abdelatif, Nada, et al.. (2020). Spatial variation in the basic reproduction number of COVID-19: A systematic review. arXiv (Cornell University). 1 indexed citations

Stein, Alfred, et al.. (2019). Uncertainty quantification for the extraction of informal roads from remote sensing images of South Africa. South African Geographical Journal. 102(2). 249–272. 3 indexed citations

Debba, Pravesh, et al.. (2018). Assessment of accuracy: systematic reduction of training points for maximum likelihood classification and mixture discriminant analysis (Gaussian and <i>t</i>-distribution). South African Journal of Geomatics. 7(2). 132–132. 4 indexed citations

10.

Holloway, Jennifer, Peter Schmitz, Alfred Stein, et al.. (2016). The applicability of the South African census 2011 data for evidence-based urban planning. University of Twente Research Information. 17(1). 67–132. 1 indexed citations

11.

Paasche, Hendrik, Sonali Das, Antony K Cooper, et al.. (2013). Are Earth Sciences lagging behind in data integration methodologies?. Environmental Earth Sciences. 71(4). 1997–2003. 3 indexed citations

12.

Cho, Moses Azong, Abel Ramoelo, Pravesh Debba, et al.. (2013). Assessing the effects of subtropical forest fragmentation on leaf nitrogen distribution using remote sensing data. Landscape Ecology. 28(8). 1479–1491. 32 indexed citations

13.

Cho, Moses Azong, Renaud Mathieu, Gregory P. Asner, et al.. (2012). Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sensing of Environment. 125. 214–226. 184 indexed citations

14.

Debba, Pravesh, et al.. (2009). Evaluation of discrimination measures to characterize spectrally similar leaves of African Savannah trees. 44(2). 140–142. 3 indexed citations

15.

Debba, Pravesh, et al.. (2009). Optimal spatial sampling scheme to characterize mine tailings. 11(1-2). 36–8. 1 indexed citations

16.

Debba, Pravesh. (2009). Methodology to unmix spectrally similar minerals using high order derivative spectra. 2 indexed citations

17.

Debba, Pravesh, Moses Azong Cho, & Renaud Mathieu. (2009). Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level. 1–4. 3 indexed citations

18.

Si, Yali, Andrew K. Skidmore, Tiejun Wang, et al.. (2009). Spatio-temporal dynamics of global H5N1 outbreaks match bird migration patterns. Geospatial health. 4(1). 65–65. 101 indexed citations

19.

Si, Yali, Pravesh Debba, Andrew K. Skidmore, & A.G. Toxopeus. (2008). Spatial and temporal patterns of global H5N1 outbreaks. University of Twente Research Information. 69–74. 11 indexed citations

20.

Debba, Pravesh, et al.. (2008). Geochemical sampling scheme optimization on mine wastes based on hyperspectral data. University of Twente Research Information. 1529–1532. 3 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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