Vegetation health evaluation using cost-effective aerial reflectance measurements

\nWith the increase of wildfires, posing a significant threat to ecosystems, human life, and infrastructure, early detection and management of vegetation becomes crucial. The Green-Red Vegetation Index (GRVI) and the Excess Green (ExG) index are often used as a health indicator for plants. While the ExG index excels in identifying vegetation, the GRVI can distinguish between healthy and dead vegetation by calculating the difference between reflected green and red light. This study utilized a cost-effective commercial-off-the-shelf (COTS) drone (DJI MAVIC 2 PRO) and in-house software to retrieve the forementioned GRVI and ExG indices. These enable the classification of vegetation in rural areas to identify biomass fuel that requires cleaning to prevent the spread of fires. Validation through comparison tests with field samples demonstrated that the drone and software can effectively identify and categorize vegetation types, as well as estimate biomass fuel.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-10-15
DOI
https://doi.org/10.1051/epjconf/202430500005/pdf
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Vegetation health evaluation using cost-effective aerial reflectance measurements

Springer Link (Chiba Institute of Technology)
Mycorrhizal Fungi and Plant Interactions
article

Vegetation health evaluation using cost-effective aerial reflectance measurements

article en

Abstract

\nWith the increase of wildfires, posing a significant threat to ecosystems, human life, and infrastructure, early detection and management of vegetation becomes crucial. The Green-Red Vegetation Index (GRVI) and the Excess Green (ExG) index are often used as a health indicator for plants. While the ExG index excels in identifying vegetation, the GRVI can distinguish between healthy and dead vegetation by calculating the difference between reflected green and red light. This study utilized a cost-effective commercial-off-the-shelf (COTS) drone (DJI MAVIC 2 PRO) and in-house software to retrieve the forementioned GRVI and ExG indices. These enable the classification of vegetation in rural areas to identify biomass fuel that requires cleaning to prevent the spread of fires. Validation through comparison tests with field samples demonstrated that the drone and software can effectively identify and categorize vegetation types, as well as estimate biomass fuel.\n

Springer Link (Chiba Institute of Technology)
Centro de Investigação em Materiais Cerâmicos e Compósitos, Fundação para a Ciência e a Tecnologia
Industry, innovation and infrastructure
Openalex Percentile: Top 56%
Mycorrhizal Fungi and Plant Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.