**3. Mangroves mapping and monitoring in Malaysia**

#### **3.1 Production of seamless image mosaic**

F mask algorithm successfully removed almost 100% of cloud covers and their shadows on the images. The algorithm also managed to detect thin, low temperature clouds in the high altitude by thermal sensors onboard the Landsat TM, ETM+ and OLI. The algorithm somehow failed to detect small scattering clouds that occurred in small patches on the images. Nevertheless, the algorithm has facilitated the cloud removal process and make the mangroves mapping and monitoring work at landscape-level practical. **Figure 2** shows a portion of mangroves on two different images that were captured on different dates with clouds. These images were used to produce seamless mosaic of images without cloud covers.

#### **3.2 Identification of mangrove ecosystem and images classification**

The study indicated that the suitable spectral bands for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important spectrum across all scales and the visible bands (437–700 nm) were more important at pixel and crown scales. By using the RandomForest algorithm, the most important bands in the classification were represented by a mean decrease Gini values. The most important bands in mangroves discrimination, from most to least, are; MidIR, NIR-2, NIR, Green, Blue, Red. Spectral profile of the images also showed that the NIR channels separate the mangroves from the other land covers very well (**Figure 3**). On the other hands, the vegetation indices that were used in this study played similar important role in mangroves classification.

The image classification approach that has been applied in this study was found to be effective only at large coverage of mangroves. The accuracy for all classifications were ranging from 83 to 91%, which were acceptable and reliable for monitoring purpose. Mangroves are normally appear dark on any combination of spectral bands of multispectral image. This is due to the natural ecosystem of mangroves, which is covered by swamps and sometimes inundated by tidal water. The chlorophyll content of the mangrove leaves, which is higher than those of trees and crops, tends to make them appear darker on satellite images [25], as depicted in **Figure 4**. Each mangrove species has a unique configuration of trunks, prop roots and

#### **Figure 3.**

*Spectral profiles of several land covers extracted from the images. Channel 1 through 6 on the y-axis are blue, green, red, NIR, NIR-2 and MidIR, respectively.*

**107**

**Figure 5.**

*GIS and Remote Sensing for Mangroves Mapping and Monitoring*

pneumatophores that works as a different drag force therefore resulting in a different reduction rate of sea waves (**Figure 5**). Not only this, the wet floor of the forest gives special spectral characteristics on satellites images that can be differentiated

*Images showing (a) combination of bands 5, 6 and 4 of Landsat-8 OLI and (b) combination of vegetation* 

*indices, NDVI, GARI and NDII. These images were selected for the classification process.*

The classification results were further edited to refine the shapes and accuracy. This process was conducted manually on the vector shapefile by visual interpretation on GIS platform. Finally the spatial distribution of the mangroves were mapped properly (**Figure 7**). The mangroves in Malaysia were mostly found in Sabah (60%), followed by Sarawak (22%) and Peninsular Malaysia (18%). **Table 2** summarizes the total extents of mangroves in the respective regions that have been produced from the classification. It is notable that the total extents of mangroves have been decreasing throughout the monitoring period. **Figure 8** shows spatially explicit map of mangroves distribution in Malaysia as of year 2017. Mangroves are found mainly along the west coast of Peninsular Malaysia, west coast of Sarawak

**Table 3** reports the changes of mangroves that occurred over the 27 years of monitoring period. The total loss of mangroves was about 21,274 ha where majority of the mangroves loss were outside the Permanent Forest Reserve or within the

*Roots and successive stands of Rhizophora apiculata in a common mature mangrove forest.*

*DOI: http://dx.doi.org/10.5772/intechopen.81955*

easily from other features (**Figure 6**).

**Figure 4.**

and the east coast of Sabah.

**3.4 Monitoring of mangroves changes**

**3.3 Spatial data editing for mangroves mapping**

*GIS and Remote Sensing for Mangroves Mapping and Monitoring DOI: http://dx.doi.org/10.5772/intechopen.81955*

#### **Figure 4.**

*Geographic Information Systems and Science*

**3.1 Production of seamless image mosaic**

**3. Mangroves mapping and monitoring in Malaysia**

produce seamless mosaic of images without cloud covers.

**3.2 Identification of mangrove ecosystem and images classification**

this study played similar important role in mangroves classification.

F mask algorithm successfully removed almost 100% of cloud covers and their shadows on the images. The algorithm also managed to detect thin, low temperature clouds in the high altitude by thermal sensors onboard the Landsat TM, ETM+ and OLI. The algorithm somehow failed to detect small scattering clouds that occurred in small patches on the images. Nevertheless, the algorithm has facilitated the cloud removal process and make the mangroves mapping and monitoring work at landscape-level practical. **Figure 2** shows a portion of mangroves on two different images that were captured on different dates with clouds. These images were used to

The study indicated that the suitable spectral bands for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important spectrum across all scales and the visible bands (437–700 nm) were more important at pixel and crown scales. By using the RandomForest algorithm, the most important bands in the classification were represented by a mean decrease Gini values. The most important bands in mangroves discrimination, from most to least, are; MidIR, NIR-2, NIR, Green, Blue, Red. Spectral profile of the images also showed that the NIR channels separate the mangroves from the other land covers very well (**Figure 3**). On the other hands, the vegetation indices that were used in

The image classification approach that has been applied in this study was found to be effective only at large coverage of mangroves. The accuracy for all classifications were ranging from 83 to 91%, which were acceptable and reliable for monitoring purpose. Mangroves are normally appear dark on any combination of spectral bands of multispectral image. This is due to the natural ecosystem of mangroves, which is covered by swamps and sometimes inundated by tidal water. The chlorophyll content of the mangrove leaves, which is higher than those of trees and crops, tends to make them appear darker on satellite images [25], as depicted in **Figure 4**. Each mangrove species has a unique configuration of trunks, prop roots and

*Spectral profiles of several land covers extracted from the images. Channel 1 through 6 on the y-axis are blue,* 

**106**

**Figure 3.**

*green, red, NIR, NIR-2 and MidIR, respectively.*

*Images showing (a) combination of bands 5, 6 and 4 of Landsat-8 OLI and (b) combination of vegetation indices, NDVI, GARI and NDII. These images were selected for the classification process.*

pneumatophores that works as a different drag force therefore resulting in a different reduction rate of sea waves (**Figure 5**). Not only this, the wet floor of the forest gives special spectral characteristics on satellites images that can be differentiated easily from other features (**Figure 6**).

#### **3.3 Spatial data editing for mangroves mapping**

The classification results were further edited to refine the shapes and accuracy. This process was conducted manually on the vector shapefile by visual interpretation on GIS platform. Finally the spatial distribution of the mangroves were mapped properly (**Figure 7**). The mangroves in Malaysia were mostly found in Sabah (60%), followed by Sarawak (22%) and Peninsular Malaysia (18%). **Table 2** summarizes the total extents of mangroves in the respective regions that have been produced from the classification. It is notable that the total extents of mangroves have been decreasing throughout the monitoring period. **Figure 8** shows spatially explicit map of mangroves distribution in Malaysia as of year 2017. Mangroves are found mainly along the west coast of Peninsular Malaysia, west coast of Sarawak and the east coast of Sabah.

#### **3.4 Monitoring of mangroves changes**

**Table 3** reports the changes of mangroves that occurred over the 27 years of monitoring period. The total loss of mangroves was about 21,274 ha where majority of the mangroves loss were outside the Permanent Forest Reserve or within the

#### **Figure 6.**

*Mangroves as they appeared on Landsat-8 image. The dark green areas represent the mangrove areas. The image classification process, either automated or manual digitizing, is usually easier for mangrove areas than for other vegetation. The image is displayed using a combination of bands 543 (RGB) over the Kapar area in Klang, Selangor. The central bottom is Klang port complex and the bottom left is Pulau Klang, which is predominantly covered by mangroves.*

#### **Figure 7.**

*Mangroves appear dark green on the original image (left) and the classified mangroves, indicated as red polygons (right).*

stateland areas. These areas are actually the land bank for the states developments, which are principally included in the State's Structural Planning. Example of mangroves changes detected from the multi-temporal mapping process is shown in

**109**

**Table 3.**

*GIS and Remote Sensing for Mangroves Mapping and Monitoring*

**(ha)**

Peninsular Malaysia 116,746 114,353 110,953 Sabah 385,630 382,448 378,195 Sarawak 147,936 145,263 139,890 Total 650,311 642,063 629,038

**Mangroves 2000 (ha)**

**Mangroves 2017 (ha)**

**Figure 9**. From this information, it can be concluded that the annual decrease rate of mangroves was about 788 ha per year or about 0.13% per annum since year 1990. Major factors that contributed to these changes have been identified as: (i) direct conversion to other land uses (**Figure 10**), predominantly for commercial-scale

Sabah 3182 4253 318 | 0.08 250 | 0.07 275 | 0.07 Sarawak 2673 5373 267 | 0.18 316 | 0.22 298 | 0.21 Total 8227 13,190 823 | 0.13 776 | 0.12 793 | 0.13

**Rate of deforestation 1990–2000 (ha yr<sup>−</sup><sup>1</sup> ) | (% yr<sup>−</sup><sup>1</sup> )**

2393 3400 239 | 0.20 200 | 0.17 215 | 0.19

**Rate of deforestation 2000–2017 (ha yr<sup>−</sup><sup>1</sup> ) | (% yr<sup>−</sup><sup>1</sup> )**

**Average rate of deforestation 1990–2017 (ha yr<sup>−</sup><sup>1</sup> ) | (% yr<sup>−</sup><sup>1</sup> )**

*DOI: http://dx.doi.org/10.5772/intechopen.81955*

**Table 2.**

**Figure 8.**

Peninsular Malaysia

*Extents of mangroves in Malaysia.*

**Region Mangroves 1990** 

*Distribution of mangroves in Malaysia over the year 2017.*

**Mangrove loss 2000–2017 (ha)**

**loss 1990–2000 (ha)**

*Mangroves deforestation in Malaysia between years 1990 and 2017.*

**Region Mangrove** 


#### *GIS and Remote Sensing for Mangroves Mapping and Monitoring DOI: http://dx.doi.org/10.5772/intechopen.81955*

**Table 2.**

*Geographic Information Systems and Science*

**108**

**Figure 7.**

**Figure 6.**

*predominantly covered by mangroves.*

*polygons (right).*

stateland areas. These areas are actually the land bank for the states developments, which are principally included in the State's Structural Planning. Example of mangroves changes detected from the multi-temporal mapping process is shown in

*Mangroves appear dark green on the original image (left) and the classified mangroves, indicated as red* 

*Mangroves as they appeared on Landsat-8 image. The dark green areas represent the mangrove areas. The image classification process, either automated or manual digitizing, is usually easier for mangrove areas than for other vegetation. The image is displayed using a combination of bands 543 (RGB) over the Kapar area in Klang, Selangor. The central bottom is Klang port complex and the bottom left is Pulau Klang, which is* 

*Extents of mangroves in Malaysia.*

**Figure 8.** *Distribution of mangroves in Malaysia over the year 2017.*


#### **Table 3.**

*Mangroves deforestation in Malaysia between years 1990 and 2017.*

**Figure 9**. From this information, it can be concluded that the annual decrease rate of mangroves was about 788 ha per year or about 0.13% per annum since year 1990. Major factors that contributed to these changes have been identified as: (i) direct conversion to other land uses (**Figure 10**), predominantly for commercial-scale

#### **Figure 9.**

*Changes of mangroves that occurred between 1990 and 2017 overlaid on GIS platform.*

agriculture (**Figure 11**) and aquaculture (**Figure 12**), and (ii) coastal erosion (**Figure 13**). The other factors such as overharvesting and pollution affect the mangroves to a lesser degree.

Although coastal erosion was identified as one of the factors of mangroves loss, there were some accretions occurred in some other places. Erosion and accretion is a dynamic process and takes place along the coastlines and major estuaries, where suspended sediments are likely to settle. These phenomena also lead to species succession when the existing plant species die due to unsuitable soil and new species emerge. Besides, mangrove roots can act as wave breaker and promote flocculation and sedimentation, eventually forming mudflats that allow positive accretion (**Figure 14**). Coastal erosion occurs when the waves hit perpendicular to the coastlines and when the rapid flow of sea currents wash away the sand or soil particles. The frequency and height of waves hitting the coastlines contribute to the harshness of coastal erosion. Thus, the presence of mangroves can reduce the coastal erosion significantly. This condition is obvious particularly in the areas facing the sea [26, 27].

#### **Figure 10.**

*Land developments on mangroves. Reddish color represents newly opened areas for development purposes that were cleared from the original mangroves areas (dark green color).*

**111**

**Figure 13.**

**3.5 The estimated carbon emission**

*erosion ranging from 3.2 to 12.5 m per year.*

in mangroves in Malaysia is about 181 Mg C ha<sup>−</sup><sup>1</sup>

A study has indicated that the average C stock (aboveground and belowground)

*Shoreline changes that resulted from coastal erosion along the coast of south Pontian, Johor. The study indicated that 14.2 km stretches have been facing serious coastal erosion within the last two decades with the rate of* 

for each epoch were multiplied by this average carbon stocks. The study demonstrated that the total loss of carbon due to the loss of mangroves was about 2.6 million

[28]. The extents of mangroves loss

*GIS and Remote Sensing for Mangroves Mapping and Monitoring*

*Expansion of oil palm plantation on mangroves. Reddish color represents newly opened plantations from the* 

*Expansion of aquaculture industries on mangroves. Dark blue patches represents newly opened aquaculture* 

*original mangroves areas (dark green color). The bright green represents existing plantations.*

*DOI: http://dx.doi.org/10.5772/intechopen.81955*

*ponds from the original mangroves areas (dark green color).*

**Figure 11.**

**Figure 12.**

#### **Figure 11.**

*Geographic Information Systems and Science*

mangroves to a lesser degree.

**Figure 9.**

agriculture (**Figure 11**) and aquaculture (**Figure 12**), and (ii) coastal erosion (**Figure 13**). The other factors such as overharvesting and pollution affect the

*Changes of mangroves that occurred between 1990 and 2017 overlaid on GIS platform.*

condition is obvious particularly in the areas facing the sea [26, 27].

*were cleared from the original mangroves areas (dark green color).*

Although coastal erosion was identified as one of the factors of mangroves loss, there were some accretions occurred in some other places. Erosion and accretion is a dynamic process and takes place along the coastlines and major estuaries, where suspended sediments are likely to settle. These phenomena also lead to species succession when the existing plant species die due to unsuitable soil and new species emerge. Besides, mangrove roots can act as wave breaker and promote flocculation and sedimentation, eventually forming mudflats that allow positive accretion (**Figure 14**). Coastal erosion occurs when the waves hit perpendicular to the coastlines and when the rapid flow of sea currents wash away the sand or soil particles. The frequency and height of waves hitting the coastlines contribute to the harshness of coastal erosion. Thus, the presence of mangroves can reduce the coastal erosion significantly. This

*Land developments on mangroves. Reddish color represents newly opened areas for development purposes that* 

**110**

**Figure 10.**

*Expansion of oil palm plantation on mangroves. Reddish color represents newly opened plantations from the original mangroves areas (dark green color). The bright green represents existing plantations.*

#### **Figure 12.**

*Expansion of aquaculture industries on mangroves. Dark blue patches represents newly opened aquaculture ponds from the original mangroves areas (dark green color).*

#### **Figure 13.**

*Shoreline changes that resulted from coastal erosion along the coast of south Pontian, Johor. The study indicated that 14.2 km stretches have been facing serious coastal erosion within the last two decades with the rate of erosion ranging from 3.2 to 12.5 m per year.*

#### **3.5 The estimated carbon emission**

A study has indicated that the average C stock (aboveground and belowground) in mangroves in Malaysia is about 181 Mg C ha<sup>−</sup><sup>1</sup> [28]. The extents of mangroves loss for each epoch were multiplied by this average carbon stocks. The study demonstrated that the total loss of carbon due to the loss of mangroves was about 2.6 million

#### **Figure 14.**

*Positive accretion of mangroves at estuaries. The new formations at the river mouths were colonized by mangroves trees forming a naturally generated forest.*


#### **Table 4.**

*CO2 emission resulted from mangroves loss between years 1990 and 2017.*

Mg C. Subsequently, this has led to the CO2 emission at about 14.2 million Mg CO2, with an average of about 0.5 million Mg CO2 emission per year, along the monitoring period. **Table 4** summarizes the impact of mangroves loss in terms of CO2 emission. Although the figures are generally crude, the study provided some ideas for further studies, especially which related to carbon cycles and climate change.

## **4. Conclusion**

This study has successfully assessed the current state of mangroves and determined the rate of mangroves loss in Malaysia since the last decade. Total mangroves in Malaysia has decreased from 650,311 ha in 1990 to 629,038 ha in 2017. Total deforestation was accounted at 21,274 ha or 3.3% with the annual rate of deforestation of 788 ha yr<sup>−</sup><sup>1</sup> or 0.13% yr<sup>−</sup><sup>1</sup> , between 1990 and 2017. The study also quantified the C stock changes and estimated CO2 emission due to the loss of mangroves in Malaysia. Total emission caused by the mangroves deforestation was accounted at about 14 million Mg CO2 with annual emission rate of around 0.5 million Mg CO2 yr<sup>−</sup><sup>1</sup> .

The study found that the Landsat-based mapping and monitoring of mangroves was very practical. It provides a reliable information on mangroves distribution, both qualitatively and quantitatively. Landsat missions provide a very useful RS tool for monitoring changes of mangroves over time. The study suggests that appropriate actions should be taken by the Government of Malaysia to protect the mangroves and keep their ecosystem intact forever. The most effective way to conserve the mangroves is to gazette the remaining stateland forest as Permanent Reserved Forests (PRFs). These PRFs should then be maintained as amenity for current and future generations, while contributing to the mitigation of climate change impacts at the local level. Any development in PRFs should be prohibited or implemented with caution.

Overall, there is great potential in the application of Landsat-based data with appropriate GIS technique for mapping and monitoring of mangroves in Malaysia.

**113**

provided the original work is properly cited.

*GIS and Remote Sensing for Mangroves Mapping and Monitoring*

Although there are cloud covers problems on some of the images, this has not hindered the assessment of mangroves at landscape and regional levels. The accuracy and precision also vary depending on the objective of the application. However, the ability to detect major changes in the ecosystem that can cause profound and irreversible damage far outweighs a perfectly or highly accurate and precise RS

Currently, Malaysia has reserved about 85% (~535,000 ha) out of the total areas of mangroves as Permanent Forest Reserve and State/National Parks. The remaining 15% is under the state-lands and alienated lands. By far, the most effective way to preserve these mangroves is through gazzeting into permanent forest reserves.

This work has been carried out under the Research and Development Committee on Mangroves (JTRD) led by FRIM. Special thanks for the Forestry Department Peninsular Malaysia (JPSM), Sabah Forestry Department (SFD), and Forest Department Sarawak (FDS) for the supports on the ground data collection

The authors declare no 'conflict of interest' for this chapter.

Hamdan Omar\*, Muhamad Afizzul Misman and Samsudin Musa Forest Research Institute Malaysia, Kepong, Selangor, Malaysia

\*Address all correspondence to: hamdanomar@frim.gov.my

*DOI: http://dx.doi.org/10.5772/intechopen.81955*

based method at this point.

**Acknowledgements**

**Conflict of interest**

**Author details**

activities.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*GIS and Remote Sensing for Mangroves Mapping and Monitoring DOI: http://dx.doi.org/10.5772/intechopen.81955*

Although there are cloud covers problems on some of the images, this has not hindered the assessment of mangroves at landscape and regional levels. The accuracy and precision also vary depending on the objective of the application. However, the ability to detect major changes in the ecosystem that can cause profound and irreversible damage far outweighs a perfectly or highly accurate and precise RS based method at this point.

Currently, Malaysia has reserved about 85% (~535,000 ha) out of the total areas of mangroves as Permanent Forest Reserve and State/National Parks. The remaining 15% is under the state-lands and alienated lands. By far, the most effective way to preserve these mangroves is through gazzeting into permanent forest reserves.
