Study Estimates Productivity of Mangrove Carbon Storage Using Remote Sensing Model

A model that has been created by researchers can calculate the mangrove forests' overall production. A productivity model based on remote sensing that took tidal inundation into account was created.

The model was able to predict the productivity of mangrove forests in China with high accuracy, according to comparisons with carbon readings from carbon flux towers.

The findings show how useful a model of this kind may be for determining how much carbon mangrove forests can store.

Monitoring carbon storage using remote sensing

(Photo : SIMON MAINA/AFP via Getty Images)

Mangrove forests hold a significant quantity of carbon, although it is difficult to estimate how much is stored globally, as per ScienceDaily.

The productivity of mangrove forests may now be calculated using a novel model created by Japanese researchers using remote sensing of the environment.

Researchers from the Institute of Industrial Science at The University of Tokyo created a model to evaluate the productivity of coastal mangroves in China for a new study published in Scientific Reports.

Tropical coasts are home to mangroves, which frequently experience ocean inundation.

These unusual plants have specific characteristics including aerial roots and salt-tolerant tissues that allow them to flourish in changing environments. They are also well adapted to tropical coastal areas.

As a result, a variety of environmental variables, including sea surface temperature, salinity, and photosynthetic active radiation, have an impact on the productivity of mangrove forests.

Light usage efficiency models have previously been used to evaluate the productivity of terrestrial forests, but more complex mangrove ecosystems lack such models.

Yuhan Zheng, the study's principal author, notes that earlier attempts to simulate mangrove productivity have relied on field data and generated estimates at a local level.

Global evaluations are necessary, and they call for considerably more extensive observations of environmental variables, in order to really comprehend the potential of mangroves to store carbon.

In order to do this, the researchers created a productivity model that is more suited for mangroves using satellite data.

Using remote sensing, data may be gathered about the environment over a wide region and over an extended period of time.

To evaluate the productivity of mangrove forests along China's coastline, researchers created a model that took into account the impacts of tidal inundation.

They then coupled the model with satellite data on photosynthetically active radiation.

Read more: Researches Into Remote Sensing Helps Storm Response 

Importance of remote sensing

One of the ways frequently used to get physical data for GIS integration is remote sensing. Without making immediate contact, remote sensors gather data from earthly things, as per Costello.

These devices, which are normally installed on satellites or airplanes, do this by sensing radiation reflected from the earth.

Recent years have seen a significant increase in the prevalence, accuracy, and accessibility of remote sensing technology, which has a wide range of technical applications.

The geography of our planet is first determined by engineers using remote sensing. Light Detection and Ranging, or LiDAR, is a technique that allows for the creation of three-dimensional photographs of the earth's surface.

Pulsed lasers and position and orientation information are combined by LiDAR sensors to create point clouds, which are dense collections of elevation data.

The form of the earth may then be represented by these point clouds' contours and digital elevation models (DEMs).

A DEM may be used by engineers to quickly and reasonably accurately estimate the amount of earth-moving required to develop a piece of land.

In order to help engineers and planners make decisions during a design project, remote sensing is also utilized to track land usage. Solar radiation that is reflected from the earth's surface is measured using optical sensors. When the various wavelengths are blended, an image that resembles a picture of the earth's surface is produced.

Related article: IUCN Red List Is Incomplete: Remote Sensing Data Shows Hundreds More Species in Danger