John Armston

John Armston

University of Maryland, Baltimore

H-index: 49

North America-United States

About John Armston

John Armston, With an exceptional h-index of 49 and a recent h-index of 41 (since 2020), a distinguished researcher at University of Maryland, Baltimore, specializes in the field of Remote sensing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning

Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation

Exploring the influence of tree species richness on vertical structure variability in young plantations using terrestrial laser scanning

Can ICESat-2 estimate stand-level plant structural traits? Validation of an ICESat-2 simulator

Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

The effectiveness of global protected areas for climate change mitigation

Algorithm theoretical basis document for GEDI footprint aboveground biomass density

John Armston Information

University

Position

___

Citations(all)

8655

Citations(since 2020)

6386

Cited By

3948

hIndex(all)

49

hIndex(since 2020)

41

i10Index(all)

98

i10Index(since 2020)

80

Email

University Profile Page

Google Scholar

John Armston Skills & Research Interests

Remote sensing

Top articles of John Armston

Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning

Forest Ecosystems

2024/4/15

Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation

Authorea Preprints

2024/3/4

Exploring the influence of tree species richness on vertical structure variability in young plantations using terrestrial laser scanning

Forest Ecology and Management

2024/2/15

Can ICESat-2 estimate stand-level plant structural traits? Validation of an ICESat-2 simulator

Science of Remote Sensing

2023/6/1

John Armston
John Armston

H-Index: 30

Laura Duncanson
Laura Duncanson

H-Index: 18

Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI

Remote Sensing of Environment

2023/6/1

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

Global Change Biology

2023/6

The effectiveness of global protected areas for climate change mitigation

Nature Communications

2023/6/1

Algorithm theoretical basis document for GEDI footprint aboveground biomass density

Earth and Space Science

2023/4

John Armston
John Armston

H-Index: 30

Laura Duncanson
Laura Duncanson

H-Index: 18

Large-scale forest height mapping by combining TanDEM-X and GEDI data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

2023/2/22

Topology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds

International Journal of Applied Earth Observation and Geoinformation

2023/2/1

First validation of GEDI canopy heights in African savannas

Remote Sensing of Environment

2023/2/1

Addressing Underestimation in Global Forest Structure Mapping

Authorea Preprints

2023/1/3

Yan Yang
Yan Yang

H-Index: 13

John Armston
John Armston

H-Index: 30

NASA’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission: Science, Applications, and Future Directions II Oral

2021/12/1

GEDI L4B Country-level Summaries of Aboveground Biomass

ORNL DAAC

2023/12/6

On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake

Environmental Research Letters

2023/11/23

Corrigendum to" Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI"[Remote Sensing of Environment, volume 291 …

Remote Sensing of Environment

2023/10

StrucNet: a global network for automated vegetation structure monitoring

Remote Sensing in Ecology and Conservation

2023/10

Mapping Large-Scale Pantropical Forest Canopy Height by Integrating GEDI Lidar and TanDEM-X InSAR Data

2023/9/22

Global Forest Aboveground Carbon Stocks and Fluxes from GEDI and ICESat-2, 2018-2021

ORNL DAAC

2023/8/23

See List of Professors in John Armston University(University of Maryland, Baltimore)

Co-Authors

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