Mr. Panji Mahyatar Profile

Panji Mahyatar

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 22 December 2021 Paper

Aboveground mangrove carbon stock mapping using WorldView-2 imagery

Panji Mahyatar, Muhammad Kamal

Proceedings Volume 12082, 120820R (2021) https://doi.org/10.1117/12.2619342

KEYWORDS: Carbon, Vegetation, Data modeling, Ecosystems, Statistical modeling, Remote sensing

Read Abstract +

Mangrove ecosystems have a significant role in absorbing carbon emissions in the atmosphere used as photosynthetic materials. Absorbed carbon emission is stored in the form of biomass in vegetation, of which 47% of the biomass is aboveground carbon stock (AGC). Monitoring the AGC needs to be carried out efficiently, consistently, and sustainably to increase efforts to prevent global warming, and remote sensing imagery has the potentials to address this issue. This study aims to (1) explore the relationship between the selected vegetation indices derived from the WorldView-2 image and the corresponding AGC measurement in the field and (2) estimate and map the spatial distribution of AGC of mangroves. This research was conducted in a mangrove forest in Clungup Mangrove Conservation area, East Java Province, Indonesia. We applied allometric equations to determine the value of vegetation biomass in the study area based on the tree diameter at breast height (DBH), which will then be converted into carbon stock value. For AGC estimation and mapping purposes, we used Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Rededge Simple Ratio (SRre), and Combined Mangrove Recognition Index (CMRI). We used correlation and regression analysis to evaluate the statistical relationship between these vegetation indices and field AGC data. Our findings suggested that the SR has the highest accuracy in modeling AGC with an R² value of 0.124. Thus, it results in a range of AGC from 0.127 tons/pixel to 0.414 tons/pixel in the study site.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years