Michele Dalponte

6.6k total citations · 2 hit papers
95 papers, 4.3k citations indexed

About

Michele Dalponte is a scholar working on Environmental Engineering, Ecology and Nature and Landscape Conservation. According to data from OpenAlex, Michele Dalponte has authored 95 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Environmental Engineering, 61 papers in Ecology and 48 papers in Nature and Landscape Conservation. Recurrent topics in Michele Dalponte's work include Remote Sensing and LiDAR Applications (70 papers), Remote Sensing in Agriculture (52 papers) and Forest ecology and management (46 papers). Michele Dalponte is often cited by papers focused on Remote Sensing and LiDAR Applications (70 papers), Remote Sensing in Agriculture (52 papers) and Forest ecology and management (46 papers). Michele Dalponte collaborates with scholars based in Italy, Norway and United Kingdom. Michele Dalponte's co-authors include Damiano Gianelle, Lorenzo Bruzzone, David A. Coomes, Erik Næsset, Terje Gobakken, Hans Ole Ørka, Loris Vescovo, Liviu Theodor Ene, Lorenzo Frizzera and Tommaso Jucker and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and Global Change Biology.

In The Last Decade

Michele Dalponte

95 papers receiving 4.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas

2008 448 citations Michele Dalponte, Lorenzo Bruzzone et al. profile →
Topography shapes the structure, composition and function of tropical forest landscapes

2018 263 citations Tommaso Jucker, Michele Dalponte et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michele Dalponte Italy	33	2.8k	2.6k	1.5k	924	770	95	4.3k
Fabian Ewald Fassnacht Germany	36	2.5k 0.9×	3.2k 1.3×	1.2k 0.8×	1.5k 1.6×	449 0.6×	89	4.5k
Felix Morsdorf Switzerland	37	3.5k 1.2×	2.5k 1.0×	2.1k 1.4×	1.3k 1.4×	991 1.3×	96	4.7k
Hans Ole Ørka Norway	26	2.8k 1.0×	1.8k 0.7×	1.5k 1.0×	646 0.7×	962 1.2×	67	3.3k
Lindi J. Quackenbush United States	30	2.1k 0.7×	1.9k 0.7×	619 0.4×	1.6k 1.7×	309 0.4×	75	4.0k
Eija Honkavaara Finland	40	3.7k 1.3×	3.1k 1.2×	757 0.5×	994 1.1×	627 0.8×	183	6.2k
Donald G. Leckie Canada	24	1.9k 0.7×	1.7k 0.7×	1.1k 0.7×	990 1.1×	601 0.8×	64	2.9k
Lars T. Waser‬ Switzerland	24	1.7k 0.6×	1.7k 0.7×	701 0.5×	765 0.8×	315 0.4×	65	2.5k
Teemu Hakala Finland	35	2.7k 1.0×	2.4k 0.9×	478 0.3×	796 0.9×	431 0.6×	134	4.4k
Hooman Latifi Germany	26	1.9k 0.7×	2.0k 0.8×	991 0.7×	865 0.9×	457 0.6×	90	2.8k
Damiano Gianelle Italy	35	2.0k 0.7×	3.2k 1.3×	1.0k 0.7×	3.3k 3.6×	394 0.5×	119	6.1k

Countries citing papers authored by Michele Dalponte

Since Specialization

Citations

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

Fields of papers citing papers by Michele Dalponte

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michele Dalponte

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

All Works

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20 of 20 papers shown

Buchmann, Nina, Tommaso Jucker, Luca Belelli Marchesini, et al.. (2025). Diverging trends in plant phenology and productivity across European mountains in a warming world. Agricultural and Forest Meteorology. 375. 110874–110874. 1 indexed citations

Sakowska, Karolina, Damiano Gianelle, Duccio Rocchini, et al.. (2023). Assessing plant trait diversity as an indicators of species α‐ and β‐diversity in a subalpine grassland of the Italian Alps. Remote Sensing in Ecology and Conservation. 10(3). 328–342. 1 indexed citations

Hansen, Endre, et al.. (2023). Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data. European Journal of Remote Sensing. 56(1). 1 indexed citations

Dalponte, Michele, et al.. (2022). Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data. Remote Sensing. 14(8). 1892–1892. 4 indexed citations

Dalponte, Michele, et al.. (2022). Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15. 4755–4771. 10 indexed citations

Dalponte, Michele, Hans Ole Ørka, Erik Næsset, et al.. (2022). UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce. Remote Sensing. 14(15). 3830–3830. 8 indexed citations

Dalponte, Michele, et al.. (2022). Automated Machine Learning Driven Stacked Ensemble Modeling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 3442–3454. 11 indexed citations

Gianelle, Damiano, Michele Scotton, Duccio Rocchini, et al.. (2021). Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery. Remote Sensing. 13(14). 2649–2649. 30 indexed citations

Garfì, Vittorio, Michele Dalponte, Mirko Di Febbraro, et al.. (2021). Species interactions in pure and mixed-species stands of silver fir and European beech in Mediterranean mountains. iForest - Biogeosciences and Forestry. 14(1). 1–11. 11 indexed citations

10.

Dalponte, Michele, et al.. (2021). Prediction of Forest Aboveground Biomass Using Multitemporal Multispectral Remote Sensing Data. Remote Sensing. 13(7). 1282–1282. 43 indexed citations

11.

Stereńczak, Krzysztof, Gaia Vaglio Laurin, Gherardo Chirici, et al.. (2020). Global Airborne Laser Scanning Data Providers Database (GlobALS)—A New Tool for Monitoring Ecosystems and Biodiversity. Remote Sensing. 12(11). 1877–1877. 18 indexed citations

12.

Bollandsås, Ole Martin, Hans Ole Ørka, Michele Dalponte, Terje Gobakken, & Erik Næsset. (2019). Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data. Remote Sensing. 11(9). 1020–1020. 10 indexed citations

13.

Malek, Salim, F. Miglietta, Terje Gobakken, et al.. (2019). Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods. Remote Sensing. 11(8). 949–949. 4 indexed citations

14.

Dalponte, Michele, Lorenzo Frizzera, & Damiano Gianelle. (2019). Individual tree crown delineation and tree species classification with hyperspectral and LiDAR data. PeerJ. 6. e6227–e6227. 57 indexed citations

15.

Dalponte, Michele, Liviu Theodor Ene, Terje Gobakken, Erik Næsset, & Damiano Gianelle. (2018). Predicting Selected Forest Stand Characteristics with Multispectral ALS Data. Remote Sensing. 10(4). 586–586. 32 indexed citations

16.

Dalponte, Michele, Liviu Theodor Ene, Terje Gobakken, Erik Næsset, & Damiano Gianelle. (2018). Prediction of Forest Attributes with Multispectral Lidar Data. DORA WSL (Swiss Federal Institute for Forest, Snow and Landscape Research). 7528–7531. 2 indexed citations

17.

Dalponte, Michele, Liviu Theodor Ene, Mattia Marconcini, Terje Gobakken, & Erik Næsset. (2015). Semi-supervised SVM for individual tree crown species classification. ISPRS Journal of Photogrammetry and Remote Sensing. 110. 77–87. 53 indexed citations

18.

Sakowska, Karolina, Loris Vescovo, Damiano Gianelle, et al.. (2015). Modelling the Spatial Distribution of CO 2 Fluxes in a Subalpine Grassland Plateau of the Italian Alps Using Multiple Airborne AISA Eagle Hyperspectral Sensor Observations and Sentinel-2 Simulated Data.. Research Padua Archive (University of Padua). 2015. 1 indexed citations

19.

Dalponte, Michele, Lorenzo Frizzera, & Damiano Gianelle. (2014). Estimation of forest attributes at single tree level using hyperspectral and ALS data. CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach). 5 indexed citations

20.

Murphy, Brian, Massimo Poesio, Francesca Bovolo, et al.. (2011). EEG decoding of semantic category reveals distributed representations for single concepts. Brain and Language. 117(1). 12–22. 48 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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