HelioWebMartin Brandt
flowchart
A[Martin Brandt]
AC["Associated Concepts (25)"]
AW["Authored Works (20)"]
CA["Linked Co-Authors (10)"]
CI["Linked Collaborating Institutions (17)"]
A== dcterms:relation ==>AC
AW== author ==>A
AW== author ==>CA
AW== affil ==>CI
click AC "#associated-concepts"
click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
NI["add incoming edge"]
NO["add outgoing edge"]
NI-- ? -->A
A-- ? -->NO
click NI "#add-incoming-edge"
click NO "#add-outgoing-edge"
style NI stroke-width:2px,stroke-dasharray: 5 5
style NO stroke-width:2px,stroke-dasharray: 5 5
- ORCiD
- https://orcid.org/0000-0001-9531-1239
- OpenAlex ID
- https://openalex.org/A5008072307 (API record)
Associated Concepts [?]
- Geography
- Biology
- Environmental science
- Ecology
- Geology
- Physics
- Oceanography
- Engineering
- Medicine
- Pathology
- Vegetation (pathology)
- History
- Climate change
- Archaeology
- Botany
- Computer science
- Agroforestry
- Agronomy
- Ecosystem
- Mathematics
- Forestry
- Quantum mechanics
- Physical geography
- Remote sensing
- Paleontology
Authored Works
sorted by decreasing year, and then by display-name
- Towards better understanding of sand harvesting and sustainability in Rwanda: an accessible deep learning approach with very high resolution imagery on a county scale
- Millions of Termite Mounds Enhance Herbaceous Productivity and Hinder Woody Productivity in West African Savannas
- Large Loss and Rapid Recovery of Vegetation Cover and Aboveground Biomass Over Forest Areas in Australia during 2019-2020
- Identifying Tree Systems in Agricultural Lands using Deep Learning
- Using large volumes of DigitalGlobe satellite data for machine learning tree mapping
- Using deep learning to map Adansonia digitata L. from high resolution satellite imagery in Senegal and South Africa at country scale
- Trees outside forests in global drylands
- The potential carbon sink of forests in southern China following two decades of afforestation
- Monitoring forest management and tree growth at field level in southern China
- Mapping the abundance of Faidherbia albida trees in Senegal with Sentinel-2 time series
- Mapping Continental African Tree Cover at Individual Tree Level With Planet Nanosatellites
- High Resolution Biomass and Height Maps of the Landes Forest (France) based on GEDI, Sentinel-1 and Sentinel-2 data : a Deep Learning Approach
- Detecting, mapping and characterizing agroforestry systems in Peru
- Deep Learning and High Resolution Satellite Imagery Reveal 10s of Millions of Termite Mounds in Senegal, West Africa
- Deep Learning Enables Country-wide Individual Tree Canopy Segmentation, Counting and Height Estimation
- Country-scale mapping of individual forest and non-forest trees and shrubs in Africa - the example of Rwanda
- Counting trees in African drylands
- Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon
- A global increase in tree cover extends the growing season length as observed from satellite records
- A Large but Transient Carbon Sink from Urbanization and Rural Depopulation in China
Linked Co-Authors
- B. M. Lind
- Catherine Ottlé
- Hao Tang
- Jean‐Pierre Wigneron
- Josep G. Canadell
- Laurent Kergoat
- Russell Doughty
- Sean Crowell
- Xiangming Xiao
- Yuanwei Qin
Linked Collaborating Institutions
- California Institute of Technology
- Chinese Academy of Sciences, Beijing
- Columbia University, New York
- Commonwealth Scientific and Industrial Research Organization, Australia
- Laboratoire des Sciences du Climat et de l'Environnement
- McGill University, Canada
- NASA Goddard Space Flight Center, Maryland
- National University of Singapore
- New Mexico State University
- Science Systems and Applications Inc., Maryland
- Southwest University, China
- University of Bremen, Germany
- University of California, Davis
- University of Copenhagen, Denmark
- University of Exeter, UK
- University of Illinois, Urbana-Champaign
- University of Oklahoma, Norman
Add Incoming Edge
Login via ORCiD to contribute.
Add Outgoing Edge
Login via ORCiD to contribute.