4.7 Article

Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System

Related references

Note: Only part of the references are listed.
Article Agriculture, Multidisciplinary

Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery

Hengbiao Zheng et al.

PRECISION AGRICULTURE (2019)

Article Agriculture, Multidisciplinary

Onion biomass monitoring using UAV-based RGB imaging

Rocio Ballesteros et al.

PRECISION AGRICULTURE (2018)

Article Environmental Sciences

Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012

Stuart E. Hamilton et al.

NATURE CLIMATE CHANGE (2018)

Article Environmental Sciences

Influence of introduced Sonneratia apetala on nutrients and heavy metals in intertidal sediments, South China

Ruili Li et al.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2017)

Article Remote Sensing

Forestry applications of UAVs in Europe: a review

Chiara Torresan et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)

Review Forestry

Applicability of different non-invasive methods for tree mass estimation: A review

S. Dittmann et al.

FOREST ECOLOGY AND MANAGEMENT (2017)

Article Remote Sensing

Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data

Shezhou Luo et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2017)

Article Remote Sensing

Mangrove above-ground carbon stock mapping of multi-resolution passive remote-sensing systems

Pramaditya Wicaksono

INTERNATIONAL JOURNAL OF REMOTE SENSING (2017)

Article Geography, Physical

Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms

Lien T. H. Pham et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2017)

Review Geography, Physical

Random forest in remote sensing: A review of applications and future directions

Mariana Belgiu et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)

Article Geography, Physical

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

Dengsheng Lu et al.

INTERNATIONAL JOURNAL OF DIGITAL EARTH (2016)

Article Biodiversity Conservation

Using lightweight unmanned aerial vehicles to monitor tropical forest recovery

Rakan A. Zahawi et al.

BIOLOGICAL CONSERVATION (2015)

Article Environmental Sciences

The potential of Indonesian mangrove forests for global climate change mitigation

Daniel Murdiyarso et al.

NATURE CLIMATE CHANGE (2015)

Review Geochemistry & Geophysics

Carbon Cycling and Storage in Mangrove Forests

Daniel M. Alongi

ANNUAL REVIEW OF MARINE SCIENCE, VOL 6 (2014)

Article Agriculture, Multidisciplinary

Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV

J. Torres-Sanchez et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2014)

Article Biodiversity Conservation

Improved allometric models to estimate the aboveground biomass of tropical trees

Jerome Chave et al.

GLOBAL CHANGE BIOLOGY (2014)

Article Environmental Sciences

Carbon stocks and potential carbon storage in the mangrove forests of China

Hongxiao Liu et al.

JOURNAL OF ENVIRONMENTAL MANAGEMENT (2014)

Article Environmental Sciences

L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia

O. Hamdan et al.

REMOTE SENSING OF ENVIRONMENT (2014)

Article Biodiversity Conservation

Predicting Global Patterns in Mangrove Forest Biomass

James Hutchison et al.

CONSERVATION LETTERS (2014)

Article Remote Sensing

High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm

Onisimo Mutanga et al.

INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION (2012)

Review Environmental Sciences

Carbon sequestration in mangrove forests

Daniel M. Alongi

CARBON MANAGEMENT (2012)

Article Remote Sensing

Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties

John Chadwick

INTERNATIONAL JOURNAL OF REMOTE SENSING (2011)

Review Geography, Physical

Support vector machines in remote sensing: A review

Giorgos Mountrakis et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2011)

Article Biodiversity Conservation

Wetland changes and mangrove restoration planning in Shenzhen Bay, Southern China

Hai Ren et al.

LANDSCAPE AND ECOLOGICAL ENGINEERING (2011)

Article Geosciences, Multidisciplinary

Mangroves among the most carbon-rich forests in the tropics

Daniel C. Donato et al.

NATURE GEOSCIENCE (2011)

Review Environmental Sciences

Remote Sensing of Mangrove Ecosystems: A Review

Claudia Kuenzer et al.

REMOTE SENSING (2011)

Article Computer Science, Interdisciplinary Applications

Feature Selection with the Boruta Package

Miron B. Kursa et al.

JOURNAL OF STATISTICAL SOFTWARE (2010)

Article Plant Sciences

Cryptochrome as a Sensor of the Blue/Green Ratio of Natural Radiation in Arabidopsis

Romina Sellaro et al.

PLANT PHYSIOLOGY (2010)

Article Environmental Sciences

Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

Takeshi Motohka et al.

REMOTE SENSING (2010)

Review Plant Sciences

Recent progresses in mangrove conservation, restoration and research in China

Luzhen Chen et al.

JOURNAL OF PLANT ECOLOGY (2009)

Article Environmental Sciences

Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data

J. Verrelst et al.

REMOTE SENSING OF ENVIRONMENT (2008)

Review Remote Sensing

The application of artificial neural networks to the analysis of remotely sensed data

J. F. Mas et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2008)

Letter Multidisciplinary Sciences

A world without mangroves?

N. C. Duke et al.

SCIENCE (2007)

Article Environmental Sciences

Novel algorithms for remote estimation of vegetation fraction

AA Gitelson et al.

REMOTE SENSING OF ENVIRONMENT (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)