Mapping coastal landscapes in

Sri Lanka - Report -

contact : Jil Bournazel jil.bournazel@gmail.com

November 2013 (reviewed April 2014)

i

Table of Content List of Figures ...................................................................................................................ii List of Tables ....................................................................................................................ii Acronyms .........................................................................................................................ii Context ............................................................................................................................1 I- Aquaculture and mangrove forests in five areas of interest..........................................2 1-1 Study area ......................................................................................................2 1-2 Method ............................................................................................................3 1-3 Results ............................................................................................................5 II- Land cover changes in Puttalam lagoon .....................................................................7 2-1 1992/1994 data set .........................................................................................7 2-2 2012 data set ..................................................................................................7 2-3 Time series .....................................................................................................8 Brief conclusion .............................................................................................................12

ii

List of Figures Figure 1: Overview of the five study areas in Sri Lanka (SPOT 5 images). Page 2 Figure 2: OBIA decision tree. Page 4 Figure 3: Map of Sri Lanka showing the five areas of interests with subsets of each lagoon detailing land cover (aquaculture and mangrove forests) extracted from SPOT5 images. Page 6 Figure 4: Aerial photographs of the Puttalam lagoon (1992/1994) after georeferencing. Page 8 Figure 5: Pléiade images sub sampled in smaller images prior to classification. Page 8 Figure 6: Comparison of the proportion of mangrove, shrimp farm, salt pan and coconut plantation in Puttalam lagoon between 1992/1994 and 2007, 1992/1994 and 2012 and 2007 and 2012. Page 9 Figure 7: Time series for the four land cover in a specific area of the Puttalam lagoon, allowing observing changes between 1992/1994 and 2012. Page 10

List of Tables Table 1: Area (ha) for each land cover for the five study sites (2007/2008). Page 5 Table 2: Changes of land use areas in 1992/1994 and 2012 in Puttalam lagoon. Page 11 Table 3: Hectares of each land use lost or gained per year for the Puttalam lagoon, based on the analysis of image classification between 1992/1994-2007 and 2007-2012. Page 11

Acronyms MLC Maximum Likelihood Classification NDVI Normalised Difference Vegetation Indice NIR Near Infra-Red OBIA Object-Based Image Analysis

________________________________________Context____________________________________________________

1

As part of the iCoast project, the coastal landscape in Sri Lanka has been

mapped in order to establish the extent of the coverage of aquaculture infrastructure

(shrimp farms and salt pans), mangrove forests and coconut plantations (Puttalam

lagoon only).

This report details the remote sensing methods followed to classify these different types

of land cover from multi-spectral data (SPOT5 and Pléiade satellite images) or from

aerial photographs.

The first focus is on the mapping of five areas of interest located on the Western,

Southern and Eastern coast of Sri Lanka and details the extent of the four types of land

cover (data set from 2007/2008).

Two Pléiade images from 2012 were available covering the Puttalam lagoon (West

coast). Therefore the land cover for the Puttalam lagoon has been mapped for 2012.

The second part details the methods applied to classify these high-resolution images. In

order to get a more complete time series of coastal land cover in the Puttalam lagoon,

aerial images of the area from 1992 and 1994 have also been analysed.

____________________________Aquaculture and mangrove___________________________________________

2

I- Aquaculture and mangrove forests in five areas of interest

1-1 Study area

The remote sensing data available consisted of five SPOT5 images covering the area of

the Puttalam lagoon, the Chilaw lagoon, the Negombo area, the Rekawa area and the

Batticaloa area (Fig. 1). SPOT5 images have a spatial resolution of 10m in multispectral

mode. Prior to classification, satellite images were sub sampled to include only coastal

areas (Fig. 1).

B

Figure 1: Overview of the five study areas. A) Location of the study areas (adapted from Dahdouh-Guebas et al., 2001). B) SPOT5 images of the study areas (RGB = NIR –red –green), green boxes indicate that the image is from 2007, magenta boxes = 2008.

____________________________Aquaculture and mangrove___________________________________________

3

1-2 Method

The five SPOT5 images were classified using an Object-Based Image Analysis (OBIA)

within Definiens software (eCognition 8.7). All scenes were featured in a UTM map

projection (WGS_1984 datum, UTM_Zone_44N). The OBIA creates “objects” by

segmenting an image into groups of pixels showing similar characteristics based on

spectral (spectral band means or NDVI for instance) and spatial properties (image

texture). These segmented objects properties can then be used for further analysis such

as image classification (Heumann, 2011; Robin et al., 2010).

Two multi-resolution segmentations were applied to all five SPOT 5 images with the

same weight for each of their three bands. The first segmentation (scale parameter =30,

shape = 0.05, compactness = 0.5) was used to extract aquaculture ponds (shrimp farms

and salt pans). As pond areas are clearly demarcated by dykes, they exhibit a good

structure for segmentation. A second segmentation was performed in order to detect

mangrove forest patches (scale parameter = 10, shape = 0.05, compactness = 0.5).

The classification was then carried out on the two levels of objects: shrimp farms, salt

pans and mangrove forests were extracted. An extra class was added to the Puttalam

area, as it was the area of extra interest with most recent data (2012, Pléiade); the

abandoned shrimp farms reconversion capacity was studied in this area. A coconut

plantation class was created for the Puttalam lagoon. The classes were derived using a

decision tree (Fig. 2). When confusion between objects occurred, they were manually

edited using visual interpretation and existing maps of the area when available.

____________________________Aquaculture and mangrove___________________________________________

4

Figure 2: OBIA decision tree (rectangle=image, diamond = rule, oval = class). * coconut plantations are mapped only on the Puttalam lagoon image.

*

____________________________Aquaculture and mangrove___________________________________________

5

1-3 Results

Puttalam Chilaw Negombo Rekawa Batticaloa

Mangrove 783.02 278.16 115.3 715.48 859.44

Shrimp farm 1424.41 581.72 0 0 737.52

Salt pan 921.66 133.12 3.88 306.57 492.29

Coconut

plantation

3452.17

Table 1: Area (ha) for each land cover for the five study sites (in 2007 for Puttalam, Chilaw and Negombo; in 2008 for Rekawa and Batticaloa). Grey cells indicate the type of land cover has not been mapped.

____________________________Aquaculture and mangrove___________________________________________

6

Figure 3: Map of Sri Lanka showing the five areas of interests with subsets of each lagoon detailing land cover (aquaculture and mangrove forests) extracted from SPOT5 images. Green boxes = 2007, Magenta boxes= 2008

______________________________________Puttalam lagoon___________________________________________

7

II- Land cover changes in Puttalam lagoon

2-1 1992/1994 data set

Thirty-six scanned aerial photographs either from 1992, or from 1994, from Puttalam

lagoon were georeferenced using the georeferencing toolbar within ArcGIS version 9.3

(UTM map projection: WGS_1984 datum, UTM_Zone_44N). They were then compiled

together with the mosaic tool from ArcGIS (Fig. 4).

A multi-resolution segmentation (scale parameter =50, shape =0.5, compactness=0.5)

was applied to the combined aerial photographs within eCognition software. Manual

classification (4 classes: shrimp farm, salt pan, mangrove, coconut plantation) was

performed on the level object. The classification was based on visual analysis of the

aerial photographs using features such as tonality or texture.

Some areas of the lagoon could not be classified as photographs were missing.

2-2 2012 data set

Two Pléiade images of the Puttalam lagoon from 2012 were classified with the same

four classes than for SPOT images and the aerial photographs (shrimp farm, salt pan,

mangrove, coconut plantation). Pléiade images show a spatial resolution of 50 cm and

are composed of four bands (blue, green, red and NIR).

Using the Iso cluster tool within ArcGIS version 10, classes were defined. The Maximum

Likelihood Classification (in ArcGIS 10) was then applied to the image. MLC is an

unsupervised classification based on an automated routine. Therefore results were then

refined manually. As Pléiade images are high-resolution images, the processing was

time consuming so the main images were divided into sub images (Fig. 5).

______________________________________Puttalam lagoon___________________________________________

8

The coverage of each type of land cover for aerial images (92/94) and Pléiade images

(2012) are presented here (tab “PLEIADE” and “aerial images”).

2-3 Time series

As indicated in Fig. 1, 3 and 4, the images contours vary depending on the type of

image. In order to compare land cover areas between 92/94, 2007 and 2012, images

were clipped (clip tool, ArcGIS) to show the same area and thus be comparable.

Figure 5: Pléiade image sub sampled in smaller images to proceed to classification.

Figure 4: aerial photographs of the Puttalam lagoon (1992/1994) after georeferencing.

______________________________________Puttalam lagoon___________________________________________

9

Figure 6: Comparison of the proportion of mangrove, shrimp farm, salt pan and coconut plantation in Puttalam lagoon between 1992/1994 and 2012.

______________________________________Puttalam lagoon___________________________________________

10

Figure 7: Time series for the four land uses classified in this study in Puttalam lagoon, with a zoom in the Mee-Oya estuary, allowing observing shrimp farm expansion and coastal ecosystems deforestation between 1992/1994 and 2012.

______________________________________Puttalam lagoon___________________________________________

11

Values for area and percentage change between 1992-1994 and 2012 can be found in

table 2 and 3.

Land use 1992/1994 (ha) 2012 (ha) Changes (%)

Shrimp farms 46.38 (1%) 1009.45 (27%) +2076.5

Salt pans 539.28 (11%) 765.61 (20%) +42

Mangroves 1087.55 (23%) 633.28 (17%) - 41.8

Coconut

plantations 3091.26 (65%) 1374.09 (36%) - 55.6 %

SF: M ratio 1: 23 1: 0.6

SF: CP ratio 1: 67 1: 1.4

Land use 1992/1994-2007 (ha/yr) 2007-2012 (ha/yr)

Shrimp farms + 47 +87 Salt pans + 12 +13

Mangroves - 26 -45 Coconut plantations - 33 -282

Table 2: Land use areas in 1992/1994 and 2012 in Puttalam lagoon. Ratios of the area of shrimp farm (SF) to mangrove (M), and of shrimp farm (SF) to coconut plantation (CP) are indicated in the table, as well as the changes in land use areas between the beginning of the 90s and 2012. Percentages between brackets represent the percentage of total land areas.

Table 3: Hectares of each land use lost or gained per year for the Puttalam lagoon, based on the analysis of image classification between 1992/1994-2007 and 2007-2012.

______________________________________Puttalam lagoon___________________________________________

12

Brief conclusion:

- Time series data show which type of land cover abandoned shrimp farms could

be reconverted into. Indeed, it has been shown that only about 8% of the overall

shrimp ponds in the Puttalam lagoon are actually functioning (Jayatissa L.P.

(2013) Unpublished data).

- Results need to be validated with ground truth data, especially for mangrove

forests coverage as they could sometimes have been mistaken with wetland

vegetation. Therefore no estimate error for the classifications presented in this

report can be displayed yet.

__________________________________________References__________________________________________________

References

Dahdouh-Guebas F., Zetterstro T., Roonback P., Troell M., Wickramasinghe A &

Koedam N. (2002) Recent changes in land-use in the Pambala-Chilaw lagoon complex

(Sri Lanka) investigated using remote sensing and GIS: conservation of mangroves vs.

development of shrimp farming. Environment, Development and Sustainability. 4:185-

2000.

Heumann BW. (2011) An object-based classification of mangroves using a hybrid

decision tree – support vector machine approach. Remote Sensing. 3:2440-2460.

Robin M., Renoux E., Debaine F., Rakatonavalona Hobialisoa D. & Lamberts C. (2010)

Cartographie de la mangrove du delta de la Mahajamba par classification d’une image

SPOT selon une approche orientée objet. Revue télédétection. 9:195-208.