Jakarta – BRIN Public Relations. The total area of ​​mangroves worldwide continues to decline. In some areas, the decline is attributable to exploitation, land clearing, and pollution of mangrove ecosystems.

The National Research and Innovation Agency (BRIN) through the Aeronautics and Space Research Organization has developed a mangrove monitoring model using optical remote sensing technology.

However, ​​Indonesia, which is located at the equator, with several large islands and thousands of small islands that are covered by mangroves, is often covered by clouds. This is the problem that often arises when detecting mangroves.

Ratih Dewanti, a researcher in the field of Remote Sensing Technology, said the Cloud-Free Mosaic (MBA) model is considered efficient in the processing of optical remote sensing data, which contributes to generate data and information to support mangrove monitoring.

This model when integrated with the development of the latest concept of Analysis Ready Data (ARD) will have more significant weight in the processing of optical remote sensing data for mangroves. The combination of the Mosaic Tile Based (MTB) algorithm and the ARD model is referred to MBA model.

“The findings, in the form of MTB algorithm, can solve the problem of cloud coverage in optical remote sensing data, especially for coastal areas around the equator line which are often covered by clouds,” said Ratih in her academic oration for inauguration as Research Professor, at BRIN Building, Gatot Subroto, Jakarta, Thursday (10/10). 03).

While the ARD concept provides efficiency to users of remote sensing data in the required pre-processing.

“Efficient in this context means that it is faster and uses less resources to provide data, which can be used for mangrove monitoring compared to conventional approaches,” explains this mother of 2 children.

In more detail, Ratih explained the results of development of 5 models to provide remote sensing data for mangrove monitoring.

Model-1 is the development of MBA for optical remote sensing data, model-2 for determining mangrove coverage, model-3 for detecting the presence, density, and zoning of mangroves, model-4 for monitoring the rate of damage to mangrove land, and Model-5 for providing remote sensing ARD.

From the 5 model developments, continued Ratih, use of MBA for mangrove monitoring based on digital optical remote sensing data is considered more efficient than the conventional method. Conventional methods generally promote visual interpretation, scene-based interpretation, and expert justification.

“This efficiency is obtained from experiences in processing optical remote sensing data for 10 scenes for mangrove monitoring. Using model-2, model-3, and model-4 takes about 7 days, compared to 1 day when processing the same data using model-1,” explained the researcher who is also an active member of the Asian Association of Remote Sensing.

Some of these findings have been implemented in mangrove mapping carried out by the Ministry of Environment and Forestry, and reported in the National Forest Monitoring System (Simontana).

In addition, in the Web-GIS as collaboration between IPB and  BRIN (LAPAN)-Ecometrica, with funding from the United Kingdom Space Agency (UKSA), the MBA model has been used for Landsat data.

Ratih hopes that the development of an efficient model in processing optical remote sensing data can further strengthen the implementation of evidence-based policy principles.

Furthermore, the development of this model can support the implementation of one standard and one data as part of the One Map Policy, and in line with optimizing utilization of remote sensing data as mandated by the Law on Space (tnt).