Indicating Saturation Limits of Multi-sensor Satellite Data in Estimating Aboveground Biomass of a Mangrove Forest DOI

Buddolla Jagadish,

Mukunda Dev Behera,

A. Jaya Prakash

и другие.

Journal of the Indian Society of Remote Sensing, Год журнала: 2024, Номер 52(11), С. 2483 - 2500

Опубликована: Авг. 10, 2024

Язык: Английский

Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI DOI Creative Commons

Indu Indirabai,

Mats Nilsson

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102712 - 102712

Опубликована: Июнь 30, 2024

Quantifying above ground biomass (AGB) and its spatial distribution can significantly contribute to monitor carbon stocks as well the storage dynamics in forests. For effective forest monitoring management case of complex tropical Indian forests, there is a need obtain reliable estimates amount sequestration at regional national levels, but estimation quite challenging. The main objective study validate usefulness gridded density (AGBD) (ton/ha) spaceborne LiDAR Global Ecosystem Dynamics Investigation data (GEDI L4B, Version 2) across two heterogeneous forests India, Betul Mudumalai Methodology includes, for each area, linear regression model which predicts AGB from Sentinel-2 MSI was developed using reference comparing it with GEDI AGBD values. Central India had RMSE 13.9 ton/ha, relative = 8.7% R2 0.88, bias −0.28 comparison between modelled 1 km resolution show relatively strong correlation (0.66) no or little bias. It also found that footprint value underestimated compared according model. southern an 29.1 10.8%, 0.79 −0.022. 0.84, field values lies 42.2 ton/ha 238.8 75.9 353.6 ton/ha. results indicates underestimates AGB, used produce product needs be adjusted provide information on balance changes over time type exists test areas.

Язык: Английский

Процитировано

12
Aboveground Biomass Estimation in Tropical Forests: Insights from SAR Data—A Systematic Review DOI

Anjitha A. Sulabha,

Smitha V. Asok, C. Sudhakar Reddy

и другие.

Journal of the Indian Society of Remote Sensing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Язык: Английский

Процитировано

0
A Synergistic Approach Using Machine Learning and Deep Learning for Forest Fire Susceptibility in Himalayan Forests DOI

Parthiva Shome,

A. Jaya Prakash,

Mukunda Dev Behera

и другие.

Journal of the Indian Society of Remote Sensing, Год журнала: 2025, Номер unknown

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

0
Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data DOI
Trinath Mahato, Bikash Ranjan Parida, Somnath Bar

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(3)

Опубликована: Фев. 29, 2024

Язык: Английский

Процитировано

2
Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications DOI Creative Commons
V. K. Dadhwal, Subrata Nandy

Journal of the Indian Society of Remote Sensing, Год журнала: 2024, Номер 52(4), С. 703 - 709

Опубликована: Апрель 1, 2024

Язык: Английский

Процитировано

0
Indicating Saturation Limits of Multi-sensor Satellite Data in Estimating Aboveground Biomass of a Mangrove Forest DOI

Buddolla Jagadish,

Mukunda Dev Behera,

A. Jaya Prakash

и другие.

Journal of the Indian Society of Remote Sensing, Год журнала: 2024, Номер 52(11), С. 2483 - 2500

Опубликована: Авг. 10, 2024

Язык: Английский

Процитировано

0