Mapping tree carbon density using sentinel 2A sensor on Google Earth Engine in Darjeeling Himalayas: Implication for tree carbon management and climate change mitigation

Singh, Manendra; Arshad, A.; Bijlwan, Amit; Tamang, Mendup; Shahina, N.N.; Biswas, Ankur; Bhowmick, Arpan; Vineeta,; Banik, Ganesh Chandra; Nath, Arun Jyoti; Shukla, Gopal; Chakravarty, Sumit

doi:10.1016/j.pce.2024.103569

Physics and Chemistry of the Earth, Parts A/B/C

2024

DOI: 10.1016/j.pce.2024.103569

|View full text |Cite

Mapping tree carbon density using sentinel 2A sensor on Google Earth Engine in Darjeeling Himalayas: Implication for tree carbon management and climate change mitigation

Manendra Singh,

A. Arshad,

Amit Bijlwan

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Sur,

Verma,

Panwar

et al. 2024

Front. For. Glob. Change

View full text Add to dashboard Cite

Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India.

show abstract

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Sur,

Verma,

Panwar

et al. 2024

Front. For. Glob. Change

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Mapping tree carbon density using sentinel 2A sensor on Google Earth Engine in Darjeeling Himalayas: Implication for tree carbon management and climate change mitigation

Cited by 1 publication

References 85 publications

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Contact Info

Product

Resources

About