Environmental computing · Journal article · March 2022

Carbon loss estimation: a case study of Little Andaman development plan

A geospatial study combining Bhuvan land-cover data and forest carbon inventories to estimate carbon stock affected by a development scenario.

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RecordIJARCCE 11(3) · pp. 196–203

Published2022-03

StatusJournal article

Authors: Sujit Raha, Avijit Chakraborty, Purbita Chatterjee, Tanmoy Chakraborty, Sayan Mondal.

Research question

Estimate the carbon implications of land-use change.

Study context

The paper examines the Little Andaman development proposal discussed at the time of publication in 2022. It asks how forest clearing within the proposed development area could affect stored carbon.

Contribution

The study combines satellite-derived land-cover areas with published forest carbon inventories in QGIS. It provides a secondary-data estimation workflow to support environmental assessment, rather than a direct field measurement of emissions.

Data and tools

Connect land cover with forest carbon inventories.

Inputs documented in the 2022 paper
Input	Reference period	Use
Bhuvan land use / land cover	2015–16	Resourcesat-2 LISS-III thematic data at 1:50,000 scale.
India State of Forest Report	2019	Forest-type and density-based carbon inventories for the Andaman and Nicobar Islands.
QGIS	Study workflow	Spatial processing and estimation of forest-class areas.

Methodology

Translate mapped forest areas into carbon-pool estimates.

Select areas of interest
Extract land-cover classes
Estimate class areas
Match carbon inventories
Scale each carbon pool
Aggregate stock estimates

Land-cover grouping

Four sampled areas inform estimates for evergreen/semi-evergreen forest, deciduous forest, swamp/mangrove, and plantation. The method maps their proportions onto the study area.

Carbon accounting

For each forest class, the regional inventory is scaled by the estimated class area. The five pools are above-ground biomass, below-ground biomass, dead wood, litter, and soil organic carbon; their contributions are then summed.

Reported findings

Soil and evergreen forests dominate the estimate.

The paper’s tables identify soil organic carbon as the largest pool and evergreen/semi-evergreen forest as the largest forest-class contribution. This highlights why an assessment of forest conversion needs to consider below-ground carbon as well as standing vegetation.

The results section reports a total of 2,996.286 tonnes. That number is reproduced as a reported value, with an important qualification: the source inventory uses thousands of tonnes, while later tables use tonnes, and study-area references alternate between 210 and 240 km². Those units and area assumptions need reconciliation before the total is reused for planning or comparison.

Source: Published paper, Tables 1–3 and Results section ↗. The qualification above follows from the labels and area values printed in the paper.

Scope and limitations

A historical estimation study.

Interpretation

The work uses historical imagery and regional inventory averages. It estimates carbon stock potentially affected under a development scenario; it does not measure when or how much of that carbon would reach the atmosphere, and it does not establish the current status of the development plan.

Further work

The authors suggest improving the estimate with alternative satellite datasets and machine-learning tools in GIS. Updated imagery, explicit unit checks, a consistent study boundary, and field validation would strengthen reuse of the workflow.

Publication record

IJARCCE · March 2022.

Authors: Sujit Raha, Avijit Chakraborty, Purbita Chatterjee, Tanmoy Chakraborty, and Sayan Mondal.

International Journal of Advanced Research in Computer and Communication Engineering, volume 11, issue 3, pp. 196–203. DOI: 10.17148/IJARCCE.2022.11336.

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Summary based on the CC BY 4.0 paper, verified against the publisher and the author’s Scholar profile on 27 October 2026.