Lonesome Malambo

1.3k total citations
30 papers, 1.0k citations indexed

About

Lonesome Malambo is a scholar working on Environmental Engineering, Ecology and Nature and Landscape Conservation. According to data from OpenAlex, Lonesome Malambo has authored 30 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Environmental Engineering, 21 papers in Ecology and 14 papers in Nature and Landscape Conservation. Recurrent topics in Lonesome Malambo's work include Remote Sensing and LiDAR Applications (24 papers), Remote Sensing in Agriculture (20 papers) and Forest ecology and management (14 papers). Lonesome Malambo is often cited by papers focused on Remote Sensing and LiDAR Applications (24 papers), Remote Sensing in Agriculture (20 papers) and Forest ecology and management (14 papers). Lonesome Malambo collaborates with scholars based in United States, Spain and United Kingdom. Lonesome Malambo's co-authors include Sorin Popescu, Lana L. Narine, William L. Rooney, David W. Horne, N. Ace Pugh, Seth C. Murray, C. D. Heatwole, Anjin Chang, Jinha Jung and Dale Cope 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

Lonesome Malambo

29 papers receiving 992 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lonesome Malambo United States 17 663 648 334 248 182 30 1.0k
Yuri Shendryk Australia 15 467 0.7× 456 0.7× 172 0.5× 227 0.9× 155 0.9× 22 820
Roope Näsi Finland 18 1.0k 1.5× 826 1.3× 306 0.9× 317 1.3× 111 0.6× 47 1.5k
A. Hornero Spain 18 1.2k 1.7× 474 0.7× 915 2.7× 406 1.6× 64 0.4× 39 1.6k
Niko Viljanen Finland 15 1.1k 1.7× 988 1.5× 290 0.9× 335 1.4× 165 0.9× 30 1.6k
Jonathan P. Dash New Zealand 17 586 0.9× 699 1.1× 180 0.5× 240 1.0× 282 1.5× 24 1.1k
Shuxin Pang China 10 370 0.6× 428 0.7× 415 1.2× 65 0.3× 87 0.5× 12 695
Guangcai Xu China 13 451 0.7× 691 1.1× 183 0.5× 210 0.8× 389 2.1× 31 987
Päivi Lyytikäinen‐Saarenmaa Finland 22 884 1.3× 518 0.8× 263 0.8× 408 1.6× 340 1.9× 56 1.4k
Julian Frey Germany 17 583 0.9× 547 0.8× 135 0.4× 416 1.7× 351 1.9× 41 1.2k
Nora Tilly Germany 14 513 0.8× 493 0.8× 250 0.7× 126 0.5× 57 0.3× 22 692

Countries citing papers authored by Lonesome Malambo

Since Specialization
Citations

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

Fields of papers citing papers by Lonesome Malambo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lonesome Malambo

This figure shows the co-authorship network connecting the top 25 collaborators of Lonesome Malambo. A scholar is included among the top collaborators of Lonesome Malambo 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 Lonesome Malambo. Lonesome Malambo 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.
Popescu, Sorin, et al.. (2025). Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning. Science of Remote Sensing. 12. 100251–100251.
2.
Popescu, Sorin, et al.. (2025). Mapping aboveground biomass in Oregon, Washington, Idaho and California with ICESat-2, GEDI and ancillary data. Forest Ecology and Management. 595. 123040–123040. 1 indexed citations
3.
Malambo, Lonesome & Sorin Popescu. (2024). Mapping vegetation canopy height across the contiguous United States using ICESat-2 and ancillary datasets. Remote Sensing of Environment. 309. 114226–114226. 11 indexed citations
4.
Popescu, Sorin, et al.. (2023). ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform. Sensors. 23(7). 3394–3394. 10 indexed citations
5.
Malambo, Lonesome, et al.. (2023). Regional Stem Volume Mapping: A Feasibility Assessment of Scaling Tree-Level Estimates. Forests. 14(3). 506–506. 1 indexed citations
6.
Narine, Lana L., Sorin Popescu, & Lonesome Malambo. (2023). A Methodological Framework for Mapping Canopy Cover Using ICESat-2 in the Southern USA. Remote Sensing. 15(6). 1548–1548. 9 indexed citations
7.
Malambo, Lonesome & Sorin Popescu. (2023). Image to Image Deep Learning for Enhanced Vegetation Height Modeling in Texas. Remote Sensing. 15(22). 5391–5391. 2 indexed citations
8.
Malambo, Lonesome, et al.. (2022). Woody Plant Encroachment: Evaluating Methodologies for Semiarid Woody Species Classification from Drone Images. Remote Sensing. 14(7). 1665–1665. 20 indexed citations
9.
Malambo, Lonesome & Sorin Popescu. (2021). Assessing the agreement of ICESat-2 terrain and canopy height with airborne lidar over US ecozones. Remote Sensing of Environment. 266. 112711–112711. 77 indexed citations
10.
Anderson, Steven L., Seth C. Murray, Yuanyuan Chen, et al.. (2020). Unoccupied aerial system enabled functional modeling of maize height reveals dynamic expression of loci. Plant Direct. 4(5). e00223–e00223. 32 indexed citations
11.
Narine, Lana L., Sorin Popescu, & Lonesome Malambo. (2020). Using ICESat-2 to Estimate and Map Forest Aboveground Biomass: A First Example. Remote Sensing. 12(11). 1824–1824. 88 indexed citations
12.
Anderson, Steven L., Seth C. Murray, Lonesome Malambo, et al.. (2019). Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems. 2(1). 1–15. 56 indexed citations
13.
Liu, Meng, Sorin Popescu, & Lonesome Malambo. (2019). Feasibility of Burned Area Mapping Based on ICESAT−2 Photon Counting Data. Remote Sensing. 12(1). 24–24. 34 indexed citations
14.
Malambo, Lonesome & C. D. Heatwole. (2019). Automated training sample definition for seasonal burned area mapping. ISPRS Journal of Photogrammetry and Remote Sensing. 160. 107–123. 23 indexed citations
15.
Malambo, Lonesome, Sorin Popescu, Nian-Wei Ku, et al.. (2019). A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting. Remote Sensing. 11(24). 2939–2939. 47 indexed citations
16.
Tolleson, Douglas R., et al.. (2019). Old School and High Tech: A Comparison of Methods to Quantify Ashe Juniper Biomass as Fuel or Forage. Rangelands. 41(4). 159–168. 4 indexed citations
17.
Malambo, Lonesome, Sorin Popescu, David W. Horne, N. Ace Pugh, & William L. Rooney. (2019). Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data. ISPRS Journal of Photogrammetry and Remote Sensing. 149. 1–13. 46 indexed citations
18.
Zhou, Tan, Sorin Popescu, Lonesome Malambo, Kaiguang Zhao, & Keith Krause. (2018). From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure. Remote Sensing. 10(12). 1949–1949. 7 indexed citations
19.
Han, Xiongzhe, J. Alex Thomasson, N. Ace Pugh, et al.. (2018). Measurement and Calibration of Plant-Height from Fixed-Wing UAV Images. Sensors. 18(12). 4092–4092. 73 indexed citations
20.
Malambo, Lonesome & C. D. Heatwole. (2015). A Multitemporal Profile-Based Interpolation Method for Gap Filling Nonstationary Data. IEEE Transactions on Geoscience and Remote Sensing. 54(1). 252–261. 29 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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