Mapping coastal landscapes in Sri Lanka - Report -

Transcription

1 Mapping coastal landscapes in Sri Lanka - Report - contact : Jil Bournazel [email protected] November 2013 (reviewed April 2014)

2 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 Study area Method Results...5 II- Land cover changes in Puttalam lagoon /1994 data set data set Time series...8 Brief conclusion...12 i

3 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 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 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/ and Page 11 Acronyms MLC NDVI NIR OBIA Maximum Likelihood Classification Normalised Difference Vegetation Indice Near Infra-Red Object-Based Image Analysis ii

4 Context 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 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. 1

5 Aquaculture and mangrove 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 =

6 Aquaculture and mangrove 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. 3

7 Aquaculture and mangrove * Figure 2: OBIA decision tree (rectangle=image, diamond = rule, oval = class). * coconut plantations are mapped only on the Puttalam lagoon image. 4

8 Aquaculture and mangrove 1-3 Results 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. Puttalam Chilaw Negombo Rekawa Batticaloa Mangrove Shrimp farm Salt pan Coconut plantation

9 Aquaculture and mangrove 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=

10 Puttalam lagoon II- Land cover changes in Puttalam lagoon /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 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). 7

11 Puttalam lagoon 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. 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. 8

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

13 Puttalam lagoon 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

14 Puttalam lagoon Values for area and percentage change between and 2012 can be found in table 2 and 3. 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 Percentages between brackets represent the percentage of total land areas. Land use 1992/1994 (ha) 2012 (ha) Changes (%) Shrimp farms (1%) (27%) Salt pans (11%) (20%) +42 Mangroves (23%) (17%) Coconut plantations (65%) (36%) % SF: M ratio 1: 23 1: 0.6 SF: CP ratio 1: 67 1: 1.4 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/ and Land use 1992/ (ha/yr) (ha/yr) Shrimp farms Salt pans Mangroves Coconut plantations

15 Puttalam lagoon 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. 12

16 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: Heumann BW. (2011) An object-based classification of mangroves using a hybrid decision tree support vector machine approach. Remote Sensing. 3: 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: