Topics and Trends in Most Cited Forest ecology and management Papers

Ranked by citations 18 months after publication

Class of 2026 (Papers Published in 2024)

What topics and trends defined most-cited Forest ecology and management research in the Class of 2026?

Forest ecology research in 2024 centers on carbon sequestration, soil organic carbon, and high-resolution inventory modeling. Key emerging shifts showcase rapid adoption of Sentinel satellite data, XGBoost modeling, and explicit carbon sink assessments, alongside nationwide forest inventory integration and climate-driven species range shift modeling.

See details ↓

Updated July 23, 2026

At a glance

Field
Forest ecology and management
Cohort label
Class of 2026 (2024 publications)
Papers analyzed
4,510
Papers ranked
20
Top topics in ranked papers
Carbon sequestration, forest inventory, carbon sink, Sentinel satellite data, soil organic carbon
Publication window
Jan 1, 2024 – Dec 31, 2024
Eligibility
Research articles; reviews excluded
Citation window
18 months post-publication
18m citation range
35–100
Data source
OpenAlex · Retrieved Jul 2026
License
CC BY 4.0

Rankings

20 papers ranked by 18-month citation count

#1 of 4,510
10018m citations

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Shoaib Ahmad Anees et al.Ecological Informatics202410.1016/j.ecoinf.2024.102732

Shoaib Ahmad Anees, Mi LuoThe University of Agriculture, Pakistan

aboveground biomass (AGB)Landsat-9XGBoostSupport Vector Regression (SVR)Random ForestMultiple Linear Regression (MLR)Vegetation indicesShuttle Radar Topography Mission Digital Elevation Model (SRTM DEM)Landsat Operational Land Imager (OLI)spectral band B4spectral band B5TNDVINDVIRVIGNDVITopographic Wetness Index (TWI)temperate forestsHimalayan regionpredictor-algorithm combinations
Badge
#2 of 4,510
7618m citations

Carbon sequestration potential of tree planting in China

Ling Yao et al.Nature Communications202410.1038/s41467-024-52785-6

Jun Qin, Xi Chen, Shilong PiaoChinese Academy of Sciences, China

carbon sequestrationforest restorationCarbon neutralitymachine learning frameworktree growth suitabilitycarbon storageforest inventorytree densificationAfforestationChina's ecosystem management policyEnvironmental variablescarbon sink dynamicsindustrial CO2 emissions
Badge
#3 of 4,510
7318m citations

Maximizing carbon sequestration potential in Chinese forests through optimal management

Zhen Yu et al.Nature Communications202410.1038/s41467-024-47143-5

Zhen Yu, Shirong LiuNanjing University of Information Science and Technology, China

carbon sequestrationChina forestsforest demographicsforest harvestingprocess-based biogeochemical modelforest inventorypermanent plotscarbon storagewood product poolspatiotemporal optimizationforest management strategiescarbon sink dynamicslong-term projection biasstatistical model limitationsoptimal forest management
Badge
#5 of 4,510
6918m citations

Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

Tiago de Conto et al.Nature Communications202410.1038/s41467-024-52468-2

Ralph DubayahUniversity of Maryland, United States

forest structural complexityGEDI spaceborne lidartropical foreststemperate forestscanopy heightwaveform layersnear-global mappingbiodiversityhabitat qualitylight environmentsnutrient cyclingbiome-specific variationsAllometric modelscanopy attributesecosystem functioning
Badge
#6 of 4,510
6118m citations

SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

Maciej Wielgosz et al.Remote Sensing of Environment202410.1016/j.rse.2024.114367

Maciej WielgoszNorwegian Institute of Bioeconomy Research (NIBIO), Norway

individual tree detectionsensor-agnostic deep learning modelULS (unmanned laser scanning)terrestrial laser scanningmobile laser scanningPointGroup architecture3D convolutional neural networkdeep learning segmentationpoint cloud random subsamplingdata augmentation strategypoint cloudforest structural complexitycodominant and dominated treesModel transferabilityomission rates
Badge
#7 of 4,510
6018m citations

The global distribution and drivers of wood density and their impact on forest carbon stocks

Lidong Mo et al.Nature Ecology & Evolution202410.1038/s41559-024-02564-9

Lidong MoETH Zurich, Switzerland

tree biomass componentscarbon storagecarbon investment strategiesforest inventoryLatitudinal gradienttropical forestsboreal forestsangiospermsgymnospermshydrothermal conditionsMean annual temperaturesoil moistureenvironmental filtersevolutionary adaptationsabiotic factorshuman modificationfire riskterrestrial biomass distributionSpatially explicit modelingglobal forest carbon prediction
Badge
#8 of 4,510
5318m citations

Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning

Binbin Xiang et al.Remote Sensing of Environment202410.1016/j.rse.2024.114078

Binbin XiangETH Zürich, Switzerland

airborne lidarpoint cloud3D deep learningForAINetdeep learning segmentationindividual tree detectionFOR-Instance datasetcanopy heightDigital elevation model (DEM)stand densitysurveying droneslive branchesdead branchesforest inventorybiophysical parameters
Badge
#9 of 4,510
5218m citations

Dynamics of nitrogen mineralization and fine root decomposition in sub-tropical Shorea robusta Gaertner f. forests of Central Himalaya, India

Rachita Pandey et al.The Science of The Total Environment202410.1016/j.scitotenv.2024.170896

Surendra Singh Bargali, Rupesh ChaturvediKumaun University, India

nitrogen mineralizationfine root decompositionShorea robustaSubtropical forestsCentral Himalayanutrient cyclingtree biomass componentsnitrogen cyclingdecomposition rateslitter decompositionsoil organic carboncarbon-nitrogen ratiomicrobial mineralizationroot biomassforest floor dynamics
Badge
#10 of 4,510
4818m citations

A climate-induced tree species bottleneck for forest management in Europe

Johannes Wessely et al.Nature Ecology & Evolution202410.1038/s41559-024-02406-8

Johannes WesselyUniversity of Vienna, Austria

climate-induced bottlenecktree species selectionforest managementEuropeClimate change impactsSpecies range shiftsassisted migrationDrought tolerancetemperature thresholdsProvenance trialsforest restorationclimate envelope modelsadaptive forest managementsilvicultural practices
Badge
#11 of 4,510
4618m citations

More than 17,000 tree species are at risk from rapid global change

Coline C F Boonman et al.Nature Communications202410.1038/s41467-023-44321-9

Coline C. F. BoonmanAarhus University, Denmark

tree species extinction riskIUCN Red Listanthropogenic threatsthreat exposure assessmentData Deficient speciesextinction risk underestimationthreat hotspot mappingTree species diversitybiodiversity conservation prioritizationdata-driven risk assessmentrapid environmental changethreatened species evaluationconservation triagespecies re-evaluation prioritizationglobal change drivers
Badge
#12 of 4,510
4618m citations

Enhanced woody biomass production in a mature temperate forest under elevated CO2

Richard J. Norby et al.Nature Climate Change202410.1038/s41558-024-02090-3

Richard J. Norby, A. R. MacKenzieUniversity of Birmingham, United Kingdom

elevated CO2forest productivitymature temperate forestCO2 fertilization effectQuercus robur L.free-air CO2 enrichment (FACE)Dendrochronologycanopy laser scanningcarbon allocationtree biomass componentsClimate change mitigationatmospheric CO2 concentrationold-growth forestssoil organic carbon
Badge
#13 of 4,510
4318m citations

TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds

Jonathan Henrich et al.Ecological Informatics202410.1016/j.ecoinf.2024.102888

Jonathan HenrichUniversity of Göttingen, Germany

TreeLearnindividual tree detectionpoint cloudterrestrial laser scanningmobile laser scanningdeep learning segmentationLidar360 softwarebenchmark forest datasetmanual tree annotationWytham WoodsForAINetTLS2Treesfine-tuning with manual annotationsdata-driven segmentation
Badge
#15 of 4,510
4018m citations

Effects of plant diversity on productivity strengthen over time due to trait-dependent shifts in species overyielding

Liting Zheng et al.Nature Communications202410.1038/s41467-024-46355-z

Liting Zheng, En‐Rong YanEast China Normal University, China

biodiversity-ecosystem functionbiodiversity experimentsfunctional traitsecological strategiesresource-conservative speciesfast resource acquisition speciesaboveground resource acquisitionbelowground resource acquisitionLight interceptionsoil resource acquisition capacityComplementary resource useforest productivitytrait-dependent shiftsmonoculture comparisons
Badge
#16 of 4,510
4018m citations

Resilience of genetic diversity in forest trees over the Quaternary

Pascal Milesi, Chedly Kastally, Benjamin Dauphin, Sandra Cervantes, Martin Lascoux, Tanja Pyhäjärvi et al.Nature Communications202410.1038/s41467-024-52612-y

Pascal Milesi, Martin Lascoux, Tanja PyhäjärviUppsala University, Sweden

forest treesQuaternarygenetic diversitypopulation genomicsdemographic inferenceeffective population sizePleistocene glacial cyclesEuropean forest tree speciesphylogenetically distant speciesSpecies range shiftslife history traitsrange sizeevolutionary potentialcomparative population genomicslong-term demographic trajectories
Badge
#17 of 4,510
3918m citations

Forest aging limits future carbon sink in China

Yi Leng et al.One Earth202410.1016/j.oneear.2024.04.011

Wei LiTsinghua University, China

forest demographicsCarbon sinkChina forestscarbon sequestrationstand agecarbon storage capacityClimate change mitigationforest inventoryage-related declineforest managementmature foreststerrestrial carbon cycleforest productivity
Badge
#18 of 4,510
3918m citations

Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model

Fabien Wagner et al.Remote Sensing of Environment202410.1016/j.rse.2024.114099

Fabien WagnerCTrees, United States

canopy heightU-Net modelUSDA-NAIP aerial imagerySub-meter spatial resolutionRGB-NIR imageryCalifornia forest mappingforest structural complexityLiDAR-informed trainingdeep learning for forestrybiomass estimationnadir-looking optical imagery50 m height range without saturationtree cover mapping
Badge
#19 of 4,510
3618m citations

High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach

Martin A. Schwartz et al.International Journal of Applied Earth Observation and Geoinformation202410.1016/j.jag.2024.103711

Martin A. SchwartzUniversité Paris Saclay, France

canopy heightLandes de Gascogne forestmaritime pine plantationGEDI waveformsSentinel satellite dataU-Net modelmulti-sensor remote sensing10 m spatial resolutioneven-aged monospecific standsintensive forest managementcomposite time averagesforest inventorySkysat stereo 3D reconstructiondominant height retrievalmulti-band imagerySAR backscatterconiferous forest validation
Badge
#20 of 4,510
3518m citations

Vegetation restoration affects soil hydrological processes in typical natural and planted forests on the Loess Plateau

Xiru Liu et al.Journal of Hydrology202410.1016/j.jhydrol.2024.132465

Tianjiao Feng, Ping WangBeijing Forestry University, China

forest restorationsoil moistureLoess Plateaunatural secondary forestPinus tabuliformis planted forestPlatycladus orientalis planted forestRobinia pseudoacacia planted forestwater-holding capacitysoil organic carbonsoil bulk densitySoil porositysoil volumetric water contentStructural equation modelingnear-natural vegetation restorationunderstory attributesecohydrological remediationSemi-arid ecosystems
Badge

Dominant research themes and year-over-year shifts in Forest ecology and management

What Topics Define the Class of 2026?

The Class of 2026 in forest ecology and management is dominated by quantitative carbon accounting, high-resolution remote sensing, and structural biomass estimation. Carbon sequestration emerges as the most prominent canonical concept (20.0% normalized frequency), accompanied by related carbon dynamics including carbon storage (12.0%), soil organic carbon (12.0%), and carbon sink capacity (12.0%). Concurrently, methodological frameworks for assessing forest inventory (18.0%) and aboveground biomass (10.0%) form the foundational backbone of high-impact research. Remote sensing techniques, particularly those leveraging Sentinel satellite data (12.0%), canopy height models (10.0%), and point cloud LiDAR datasets (8.0%), continue to drive scalable forest monitoring across regional and global scales. Machine learning models like Random Forest (10.0%) and XGBoost (8.0%) are widely deployed for integrating multi-source spatial data with field measurements. Furthermore, climate change mitigation (8.0%) and forest restoration (8.0%) highlight the transition toward actionable ecological interventions, underscoring how modern forest ecology combines earth observation technology with ecosystem-service evaluation.

Leading research themes
Leading research themes

How Did Topics Shift from the Class of 2025 to the Class of 2026?

Comparing the Class of 2025 to the Class of 2026 reveals a significant shift toward operational satellite imagery, multi-source machine learning, and targeted climate resilience metrics. High-gain topics are led by Sentinel satellite data (increasing from 2 to 6 mentions) and explicit carbon sink modeling (rising from 1 to 6 mentions). Newly emerging topics gaining traction in 2024 include Carbon stock (5 mentions), National forest inventory (4 mentions), Species range shifts (4 mentions), and XGBoost algorithms (4 mentions), reflecting a push toward standardized national assessments and advanced predictive modeling. In contrast, several traditional broad descriptors saw relative declines in focus, including general biomass estimation (dropping from 11 to 3 mentions), forest productivity (from 13 to 5 mentions), and forest structural complexity (from 8 to 3 mentions). Rather than signaling reduced interest in these core ecological concepts, the trend points to methodological refinement, where general biomass and canopy profiling have evolved into specific remote-sensing techniques, standardized inventory data processing, and fine-scale carbon accounting.

How topics shifted year over year
How topics shifted year over year
Methodology

PRI identifies high-impact research using a transparent, topic-agnostic framework applied consistently across scientific domains. Bibliographic records are drawn from OpenAlex, including publication dates, citation relationships, and document types.

This ranking covers the Class of 2026 cohort: journal articles published in 2024. Reviews and other non-article document types are excluded to ensure comparability.

Research impact is quantified with an 18-month post-publication citation window—the number of citing works published within 18 months of each paper's publication date. This metric captures early impact while controlling for publication age.

An LLM-based relevance classifier then reviews each candidate's title and abstract to confirm substantive alignment with the target domain. Only papers classified as relevant appear in the final ranking.

Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459

Full methodologyGitHubbioRxiv preprint

Cite this ranking

Pepkio Research Index (PRI). Topics and Trends in Most Cited Forest ecology and management Papers, Class of 2026. https://pri.pepkio.com/top-papers/forest-ecology-and-management/2026. Accessed 2026-07-23.

Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459