This document discusses using remote sensing for mangrove conservation planning. It provides examples of using Landsat satellite imagery to analyze mangrove area and density changes over time in locations like the Philippines and India. Remote sensing techniques like NDVI analysis and image classification are used to measure and monitor mangrove degradation. The document concludes that while remote sensing provides a qualitative assessment, the quality depends on access, time, and funds, and understanding mangroves is key to conserving them.

1. Mangrove Conservation Planning: Finding a
Common Ground Using Remote Sensing
Emmanuel T. Olatunji
ENS 299, SESAM

2. Global Distribution of Mangroves

5. Google image

6. Comparison of areal estimates of mangrove forest for the Philippines. Dates indicate year
of estimate. (Long and Giri 2011)
Philippines Mangrove Status

7. Conservation

8. What is conservation?
“Man living in harmony with nature”- Leopold
T. Enders and J. Anderson theory of conservation
Local communities' interest in forest conservation depends at least to some
degree on how much they are still part of the ecosystem and how much their
management of resources directly affects their own survival.
David C. Korten theory of conservation- A learning process approach

9. What option do we have
REMOTE SENSING

10. What is Remote Sensing?
Magic.piktochat.com
Acquisition of information
about an object or
Phenomena without
making physical contact.
And WHY?

11. About Remote sensing

12. Common Applicable Data Acquisition
Technique
Earth Observation Satellite
• These thematic mappers take images in multiple wavelengths of
electro-magnetic radiation
• TMs generated can be used to prospect for minerals, detect or
monitor land usage, detect invasive vegetation,
deforestation, and examine the health of indigenous plants
and crops, including entire farming regions or forests.

13. Different Scenarios Using Remote Sensing for Conservation Planning

14. Degradation of Mangrove by using Landsat 5 TM and Landsat 8 OLI
image Mempawah Regency, West Kalimantan Province year 1989-2014
Khairuddin et al (2016)
Objective:
Analyze the area and density of the distribution of mangrove in the
Mempawah Regency of West Kalimantan Province using NDVI analysis

15. Image processing Overlay and display of image
Methodology
Landsat 5 TM 1989 and Landsat 8 OLI 2014 image

16. Landsat
Mangrove Area
Mangrove density
Preprocessing
Composite:
RGB:453 (Landsat 5)
RGB:564 (Landsat 8)
Histogram
Visual interpretation
(On screen digitization)
NDVI
Subset image
Data processing flow chart

17. Calculating the Normalized Difference vegetation index
1) NDVI = (Band 5-band 4) / (band 5+Band 4)
2) NIR = Near Infrared (Band 4 on Landsat 5 and Landsat Band 5 to 8)
3) Red = Red (Band 3 on Landsat 5 and Landsat Band 4 to 8)
Indonesian National Standard (SNI) 2011

18. Determining mangrove canopy density using NDVI. The value of the NDVI
classes in reclassification into 3 classes, high, medium and low.
KL = (xt-xr)/k
Where:
KL = class interval,
xt = maximum value
xr = minimum value
k = number of classes
This algorithm is applied to the satellite images, to highlight aspects of the density
of vegetation or other aspects relating to density, like biomass, Leaf Area Index
(LAI), the concentration of chlorophyll.

19. Landsat 5 TM 1989 and Landsat 8 OLI image

20. Changes in mangrove area and density in the Mempawah Regency

21. Conservation of Mangroves in Orissa: Role of Remote
Sensing and GIS Pattanaik et al 2010
Objective:
Assessment of mangrove forest and its change dynamics in
Orissa over 30 years using multi-temporal satellite data.

22. METHODOLOGY
• Survey of India topo-sheet of 1:50,000 scale and forest management maps were used as
reference data
• Data sets were co-registered to UTM projection and WGS 84 using ground control points
(GCP)
• Ground truth collection with the help of false color composite (FCC) hard copy prints, topo-
sheets, Global Positioning System (GPS) and magnetic compass
• The different tonal variation representing the different classes were marked and rectified on
the classified map
• The 2 images were overlaid to find the net change in mangrove and other land use

23. Mangrove and other land-use categories (ha) in Bhitarkanika
mangroves from 1973 to 2004

24. Land use land cover categories in Balasore mangroves from 1973 to 2004

25. Land use land cover categories in Mahanadi delta mangroves from 1973 to 2004

26. Conclusion
Most application of the use of Remote sensing are
Qualitative.
Quality of the result depends of the availability of access to
the site, time and funds

27. “In the end, we will conserve only what we love, we
will love only what we understand, and we will
understand only what we are taught.”
-Baba Dioum, a Senegalese conservationist

28. Gevana D., Garcia K., Malabrigo P., (November 2013) Philippines’ mangrove ecosystem: Status, Threats, and
Conservation
Kihairuddin B., Yulianda F., Kusmana C., Yonvitner (2016) Degradation mangrove by using landsat 5 TM and
Landsat 8 OLI image in mempawah regency, wet kalimanta province year, 1989-2014
Purnamasayangsukasih R. P., Norizah K., Adnan I. A. M., Shamsudin I. (2016) A review of uses of satellite
imagery in monitoring mangrove forests
Santos L. C. M., Bitencourt D. M., (2016) Remote sensing in the study of Brazilian Mangroves: review, gaps in
the knowledge, new perspectives and contributions of management. Journal of integrated coastal zone
management
Satyanarayana B., Thierry B., Seen Lo D., Raman A. V., Muthusankar G. (November 2001)Remote sensing in
mangrove research – Relationship between vegetation indices and dendrometric parameters: A case for coring,
East coast of India. National University of Singapore
Sudhakar C. R. (January 2010)Conservation of Mangrove in Orissa: Role of Remote sensing and GIS
References

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