Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon, central Vietnam

Applied Geography 58 (2015) 48e64

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Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon, central Vietnam Leonardo Disperati a, b, Salvatore Gonario Pasquale Virdis c, d, *
di Siena, Dipartimento di Scienze Fisiche, della Terra e dell'Ambiente, Strada Laterina 8, 53100 Siena, Italy Universita
di Siena, Centro di GeoTecnologie, Via Vetri Vecchi 34, 52027 San Giovanni Valdarno, Arezzo, Italy Universita c Consiglio Nazionale delle Ricerche (CNR), Istituto di Biometeorologia (IBIMET), Traversa La Crucca, 3, Localita' Baldinca, Li Punti, 07100 Sassari, SS, Italy d Crops for the Future and School of Geography, The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Malaysia a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online

This study integrates the use of multi-source and multi-resolution remote sensing, topographic and fieldbased datasets to quantify land-use and land-cover (LULC) changes along a coastal stretch of Thua Thien Hue Province (central Vietnam). The LULC change analysis involves the Tam Giang-Cau Hai lagoon, the largest lagoon system in Southeast Asia, which is running nearly 70 km along the coast and having about 22,000 ha of water surface. The LULC change analysis was performed by computer-aided visual interpretation for 5 years (1965, 1989, 2000, 2006 and 2014) using satellite imagery from LANDSAT MSS, TM, ETMþ and 8, ASTER and SPOT5. National topographic maps were also used for the 1965 and 2000 years. To adequately represent the LULC features and peculiarities of central Vietnam coastal areas, an adapted CORINE Land Cover nomenclature was used where new 3rd and 4th level classes were adopted. Due to their intrinsic relative high spatial and radiometric resolution, SPOT5 images from 2006 were assumed as a reference for interpretation keys and first delineation. Changes were mapped by editing those vectors representing features which underwent LULC change prior or after 2006. Spatial and temporal changes were analyzed by post-classification approach and validated by ground truth information. High detail object-based classification was finally performed to infer the capability of medium spatial resolution imagery for extracting cadastral scale pond maps. The accuracy of classification was checked by a polygon by polygon comparison with an existing aquaculture facility inventory. Five LULC maps were obtained by applying a legend of 21 classes including two newly defined: “Aquaculture ponds” and “Mangrove forest”. The overall classification accuracy of the LULC map is 85% while the KHAT statistics 0.81 for the year 2006. Accuracy of the object-based aquaculture facilities classification is 84% or better for the SPOT5 imagery and 47.9% for the ASTER imagery. The study provides a synoptic LULC representation for the largest lagoon system of Southeast Asia and delivers quantitative estimates of main changes occurred during the last 50 years. Moreover, it reveals the adaptability of the CORINE Land Cover method outside European environment. Finally, SPOT5 provides good results to map aquaculture features at cadastral scale, even if in some circumstances (e.g. tidal areas), the integration with higher spatial resolution multispectral sensors should be envisaged. © 2015 Elsevier Ltd. All rights reserved.

Keywords: CORINE Land Cover Land use/land cover change LULC Object-based approach Accuracy assessment Aquaculture

Introduction

* Corresponding author. Consiglio Nazionale delle Ricerche (CNR), Istituto di Biometeorologia (IBIMET), Traversa La Crucca, 3, Localita' Baldinca, Li Punti, 07100 Sassari, SS, Italy. Tel.: þ39 079 2841 501; fax: þ39 079 2841 599. E-mail addresses: [email protected] (L. Disperati), [email protected], salvatore. [email protected] (S.G.P. Virdis). http://dx.doi.org/10.1016/j.apgeog.2014.12.012 0143-6228/© 2015 Elsevier Ltd. All rights reserved.

In Tam Giang-Cau Hai (TGCH) Lagoon, central Vietnam, agriculture has been the main source of income for centuries. Despite this long tradition of agricultural activity, aquaculture was quite recently introduced in the TGCH Lagoon. In the mid 1980s, following Vietnam's market-oriented economic reform known as doi moi (renovation), both the central government and local authorities supported and encouraged aquaculture development with

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

the aim of increasing profits. Consequently, starting in the early 1990s, the lagoon saw the rapid expansion of areas devoted to aquaculture activities (most notably shrimp ponds) and the reduction of traditional agricultural lands. According to the Department of Agriculture and Rural Development of the Thua
(TTH) Province, large areas of agricultural land and the Thien Hue lagoon have been converted to shrimp and fish aquaculture without land use conversion plans or environmental impact assessment. Combined with the rapid population growth, this process has had a negative impact on the lagoon's ecosystem: waste water from both urban areas and aquaculture ponds, the latter containing fish/shrimp faeces, uneaten feed and chemicals used to treat water and control fish disease, flows into the lagoon, leading to the deterioration of water quality (IMOLA, 2011). Accurate multitemporal representations of such coastal wetlands and water bodies are of utmost importance in monitoring land use/land cover (LULC) conversion from (semi)natural conditions to environmentally demanding activities such as aquaculture (Seto & Fragkias, 2007). Regional and/or local mapping of LULC changes is important because it can provide input data for environmental models dealing with topics such as climate change and sustainable development policies, or spatial planning and flood risk assessment (Castella, Pheng Kam, Dinh Quang, Verburg, & Thai Hoanh, 2007; Funkenberg, Binh, Moder, & Dech, 2014; Kuenzer, Leinenkugel, Vollmuth, & Dech, 2014; Leinenkugel, Kuenzer, Oppelt, & Dech, 2013; Zeidler, 1997). Recent environmental studies carried out in the TGCH wetlands (IMOLA, 2007; Nguyen, 2010; Nguyen & de Vries, 2009) highlight the increasing impact of both agriculture and aquaculture and lend support to lagoon resources management plans; however, results provide only a partial picture of present conditions because they are not based on accurate multitemporal representations of LULC in the whole area of the lagoon and its neighbouring regions. Earlier studies have combined remote sensing and GIS to map the extent of land-use conversion to shrimp farming and aquaculture s.l. in Vietnam (Beland, Goita, Bonn, & Pham, 2006; Binh, Vromant, Hung, Hens, & Boon, 2005; Giap, Yi, & Yakupitiyage, 2005; Sakamoto, Van Phung, Kotera, Nguyen, & Yokozawa, 2009; Seto & Fragkias, 2007; Tong et al., 2004; Vo, Oppelt, Leinenkugel, & Kuenzer, 2013) and elsewhere (DahdouhGuebas et al., 2002; Muttitanon & Tripathi, 2005; Ramasubramanian, Gnanappazham, Ravishankar, & Navamuniyammal, 2006; Tsai, Chang, Chang, & Chu, 2006). In order to fill the LULC monitoring gap existing at TGCH, we integrated, inventoried and collated local village-based knowledge of LULC changes in the TGCH lagoon over the last 49 years. By integrating remote sensing data, GIS tools and field surveys, we mapped the type and location of changes and quantified their extent and spatio-temporal dynamics. Note that, although the scientific community and spatial planners have had access to homogeneous sets of remotely sensed data covering the continents for some decades now, many different LULC nomenclatures (hierarchical or non-hierarchical, a-priori or aposteriori, land cover or land use oriented, etc.) have been proposed and implemented (Anderson, Hardy, Roach, & Witmer, 1976; Di Gregorio, 2005; Homer, Huang, Yang, Wylie, & Coan, 2004; Nippel & Klingl, 1998). This is one of the main problems currently hampering the quantitative comparison of LULC maps by region or time period, even after time-consuming attempts at harmonization (Vancutsem, Marinho, Kayitakire, See, & Fritz, 2012). Besides filling gaps in data, we applied the CORINE Land Cover (CLC) classification system to the study area, thereby putting to good use the expertise gained in 39 European and pan-European countries since the mid-1980s. The CLC system has proven to be an excellent decision-making support tool for environmental policy

49

makers and spatial planners in Europe (Feranec, Hazeu, Christensen, & Jaffrain, 2007; Feranec, Jaffrain, Soukup, & Hazeu, 2010). Taking into account the results of CLC nomenclature implementation in non-European biogeographical regions (mainly Africa, Central America and South America; Jaffrain, 2011), we harmonized and adapted the CLC system to the TGCH Lagoon, an important and peculiar coastal wetland of central Vietnam, in order to map and monitor LULC changes. Results indicate that the CLC nomenclature can be successfully implemented outside Europe, thereby extending the adoption of this a-priori hierarchical nomenclature and helping to make more objective (repeatable) quantitative LULC comparisons throughout the world. Lastly, considering the economic, social and environmental impact of conversion from both natural and agricultural lands to aquaculture areas, we focused on the spatio-temporal evolution of this phenomenon. We investigated the possible application of an object-oriented classification in the LULC analysis of optical satellite imagery with medium to high spatial resolution; the aim was to perform semi-automatic mapping of aquaculture ponds rather than traditional field surveys, which are both time-consuming and expensive. Results show that this straightforward approach can be used to keep the aquaculture database updated at the scale of single ponds. Study area The TTH Province of central Vietnam (Fig. 1) is bordered by the Quang Tri Province to the north, Da Nang city and Quang Nam Province to the south, Lao PDR to the west, and the Chinese Sea to the east. The coastal zone represents 20% of the total area of the TTH Province and is home to approximately 81% of the population.
, the ancient capital of imperial Vietnam and a The major city is Hue UNESCO World Cultural Heritage Site. The physiography of the TTH Province is heterogeneous. To the west, the Annamese Cordillera (or Nui Truong Son) is a Palaeozoic orogenic belt (Lepvrier, Van Vuong, Maluski, Truong Thi, & Van Vu, 2008) parallel to the coast and covered by dense forests. Farther east, a hilly area gives way to the lowlands dominated by the rivers Huong (Perfume), O Lau, Bo, Dai-Giang and Truoi. Rivers crosscut a plain characterised by wetlands, reservoirs (Lam, 2002) and lagoons, as well as a large inland area made up of two overlapping sand dune systems that give way to the sandy coast. To the north, these systems extend along the shore for about 100 km, with an average width of 4 km and an elevation of less than 10 m; to the south, the dune area is narrower (average width of 2 km) and increases in elevation from 10 m at the southern end to 32 m near the Thuan An Inlet. Coastal and inland sand dunes and banks are therefore an important physiographic feature of the study area. The TGCH Lagoon system comprises several smaller segments, namely (from north to south along the coast) Tam Giang, Thanh Lam, Sam Chuon, Ha Trung, Thuy Tu and Cau Hai. The lagoon has two connections to the sea: Thuan An in the centre and Tu Hien in the south. The depth of the lagoon generally ranges from 1 to 3 m, with a maximum depth of 11 m close to Thuan An Inlet (Fig. 1). TTH, one of the four provinces in central Vietnam, represents a cultural, touristic and educational site of national importance. According to Fezzardi (2006), between 2000 and 2006, in line with national development trends, the economy of the TTH Province grew considerably and poverty decreased from 25% to 8%. Most inhabitants rely on agriculture for their livelihoods, including forestry and animal farming, and the province ranks 39th out of 64 provinces in Vietnam in terms of rice production. Indeed, a large portion of the province's agricultural land is devoted to rice farming, producing about 240,000 tons of rice per year (ADB-MPI, 2005). The fishery sector is currently well developed and includes

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L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

Fig. 1. The TTH Province and study area.

aquaculture and capture fisheries (marine and inland). In recent years, tourism has grown rapidly, creating thousands of jobs and contributing indirectly to the development of transport, trade, industry and other services (PPC Thua Thien Hue, 2005a, 2005b).

Materials and methods Data acquisition and processing A set of five 1:50,000 topographic map sheets were scanned from the U.S. Army Map Service's Vietnam Series L7014 and georeferenced to VN2000, the national Vietnamese datum. The declared accuracy of this transformation is 25 m. These 1965 maps served as reference for LULC estimation. A 1999 topographic vector map at a nominal scale of 1:25,000 was used for further processing and for creating a Digital Terrain Model (DTM) with a 20 m cell size. Satellite images were acquired in order to investigate the rapid changes in LULC within the 1973e2014 period (Fig. 1 and Table 1). Images from October to January were excluded due to the recurrent

typhoons at that time of year, with widespread flooding in the plains and cloud cover that precludes the use of optical remote sensing imagery. Although the selected imagery covers a quite broad seasonal period (ca. 8 months: February to September), visual interpretation integrated with a dense database of ground truth information, allowed us to mitigate the effects of seasonality on LULC analysis. SPOT5 images were orthorectified using a photogrammetric block, rigorous sensor camera parameters, the 20 m raster DTM and 27 GPS ground control points. Landsat MSS, TM and ASTER images were coregistered using SPOT5 orthorectified images as a spatial reference. ETMþ and OLI imagery was not processed since it was delivered as standard Level-1 (orthorectified) data products (USGS, 2014). The accuracy of image orthorectification and coregistration was better than 0.5 pixels, allowing the multitemporal comparison of imagery (Coppin, Jonckheere, Nackaerts, Muys, & Lambin, 2004; Schowengerdt, 2007; Yuan, Elvidge, & Lunetta, 1998). Radiometric coregistration was not required because LULC analysis was carried out through visual interpretation (Liu et al., 2005; Ruelland, Levavasseur, & , 2010; Zhang, Zhengjun, & Xiaoxia, 2009). Tribotte LULC classification

Table 1 The satellite imagery dataset. Date of pass

Satellite

Sensor

Scene ID

Proc. level

Pixel size (m)

1973-05-26 1989-02-17

Landsat Landsat

MSS TM

Ortho Ortho

80 30

2000-05-10 2000-08-18 2001-02-06

ASTER

VNIR/SWIR

Path/row 134/49 Path/row 125/48 Path/row 125/49 e

L1B

15/30

Landsat

ETM+

Ortho

30

2005-08-23 2005-09-23 2006-06-05 2013-03-26

SPOT5

Path/row 125/48 Path/row 125/49 K-J 276-317 K-J 275-317 K-J 276-317 Path/row 125/49

L1A

5

Ortho

15/30

Landsat

OLI

Computer-assisted visual interpretation was used to generate a multitemporal (1965, 1989, 2000, 2006 and 2014) geographic database of LULC in the TGCH Lagoon and its surroundings at a scale of about 1:25,000. The surface area of the smallest mapped unit was 1.56 ha, in accordance with CLC implementation rules (Büttner et al., 2004; Heymann, Steenmans, Croissille, & Bossard, 1994; Steenmans & Perdigao, 2001). As for LULC nomenclature, the standard reference nomenclature was applied in Vietnam for the first time in this study. Different kinds of nomenclature have been used in published maps (Beland et al., 2006; Binh et al., 2005; Castella et al., 2007; Castella & Verburg, 2007; Chen, Son, Chang, & Chen, 2011; MacAlister & Mahaxay, 2009; Müller & Zeller, 2002; Nguyen, De Bie, Ali,

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

Smaling, & Chu, 2011; Tran, Marincioni, & Shaw, 2010; Ziegler et al., 2004), making it difficult or impossible to either perform spatial LULC comparisons or detect LULC change, hence diminishing the usefulness of previous LULC analyses for socio-economic, environmental and spatial planning purposes. Hierarchical a-priori nomenclatures are the most robust and useful tools for LULC mapping (Di Gregorio, 2005) over wide areas (i.e. at the scale of a country or larger) because they allow easier integration of data derived from different sources or different interpretation procedures. This concept also applies to multitemporal LULC analysis: maps related to different periods may accurately reveal changes only when they adopt the same nomenclature. For this reason we adopted an ad hoc hierarchical apriori legend (Appendix A1). The CLC nomenclature (Büttner et al., 2004; Heymann et al., 1994; Steenmans & Perdigao, 2001) served as reference, as it has been successfully used to develop a common multitemporal and multiscale LULC database for the whole of Europe. In addition, there is standard documentation for the CLC nomenclature, including the definition of LULC classes, the description of procedures for delineating polygon features, and examples of approaches for generalizing features smaller than the smallest mapped units. However, the CLC nomenclature was developed for European countries; it therefore does not include LULC categories peculiar to tropical environments, nor does it allow for the spatial organization and patterns typical of non-European countries such as Vietnam. In order to take into account these features, we introduced new third and fourth level categories without altering either the hierarchical organization or the first two levels of the nomenclature (Jaffrain, 2011). The nomenclature implemented in this study has four levels, with 5 headings for the first level and 11 for the second, 21 for the third and 12 for the fourth (Table 2 and Appendix A1). Highly detailed fourth level classes were used when interpreting subsets

51

of third level headings using either SPOT5 satellite images or historical topographic maps. The on-screen visual interpretation process was based on the physiognomic attributes (shape, size, colour, texture and pattern) of landscape objects (natural, modifiedecultivated and artificial) and spatial relationships among landscape objects or associations (Feranec, 1999; Feranec et al., 2007). SPOT5 images from 2006 were used to define LULC class interpretation keys and complete the first thematic output. This imagery was considered first because it has a relatively high spatial and radiometric resolution (Table 1) and because it was used as a reference base for both field surveys and ground truth surveys in 2006e2007, as well as for the final check in 2008. This allowed us to obtain a vector LULC dataset with the best possible spatial and thematic accuracy in terms of location, delineation and classification. Visual interpretation was supported by ancillary information collected in the field and extracted from the topographic vector map. In order to strictly follow the guidelines for the CLC a-priori nomenclature, interpretation keys were based on the standard criteria and recommendations in Büttner et al. (2004), Heymann et al. (1994) and Steenmans and Perdigao (2001). In the interpretation of Landsat OLI (2014), ASTER (2000), Landsat ETMþ (2000), Landsat TM (1989) and Landsat MSS images and of historical topographic maps (1965), we considered only those vectors representing LULC features that changed prior to or after 2006. The advantage of this approach was twofold: it enabled the application of a highly detailed, mixed (land-cover and landuse) a-priori nomenclature and it allowed us to forgo both the absolute and the relative radiometric correction of satellite imagery. The latter represents a big advantage, considering that the satellite image dataset comprises multisensor (6 sensors) and multitemporal (12 different epochs) data (Table 1).

Table 2 The a-priori LULC hierarchical nomenclature applied in this study. It includes 5 headings for the 1st level, 11 headings for the 2nd level, 21 headings for the 3rd level and 12 headings for the 4th level. 1st Level

2nd Level

3rd Level

1 Artificial Surfaces

11 Urban fabric

111 Continuous urban fabric 112 Discontinuous urban fabric 121 Industrial or commercial units (incl. hydraulic infrastructures) 124 Airports 125 Aquaculture ponds

12 Industrial, commercial and transport units

13 Mine, dump and construction sites 14 Artificial non-agricultural vegetated areas 2 Agricultural areas

21 Arable land

3 Forests and semi-natural areas

31 Forests

131 Mineral extraction sites 133 Construction sites 141 Sport and leisure facilities (incl. graves or cemeteries) 211 Non-irrigated arable land 213 Rice fields 311 Broad-leaved forest 312 Coniferous forest 313 Mixed forest

32 Shrub and/or herbaceous vegetation associations 33 Open spaces with little or no vegetation 4 Wetlands 5 Water bodies

41 Inland wetlands 51 Inland waters 52 Marine waters

314 Mangroves 324 Transitional woodland/shrub 331 Beaches, dunes, sands 411 511 512 521

Inland marshes Water courses Water bodies Coastal lagoon

523 Sea and ocean

4th Level

1251 Inland aquaculture ponds 1252 Lagoon aquaculture ponds

3111 3112 3121 3122 3131 3132

Broad-leaved sparse forest Broad-leaved dense forest Coniferous sparse forest Coniferous dense forest Mixed sparse forest Mixed dense forest

3311 Sandy inner areas 3232 Sandy coastal areas

5211 Deep waters 5212 Sandy or muddy shallow waters

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Post-classification change detection The different procedures proposed in the literature for LULC change detection are all based on one of the following two general approaches. The first considers a two-point timescale (bi-temporal change detection; i.e. post-classification comparison, univariate image differencing, change vector analysis, etc.), the second a multi-point timescale (temporal trajectory analysis; Coppin et al., 2004; Coppin, Lambin, Muys, & Jonckheere, 2002; Lam, 2008). Several recent reviews of both LULC mapping and change detection procedures provide a comprehensive summary of remote sensing tools for mapping and monitoring landscape change (Cihlar, 2000; Kennedy et al., 2009; Lu, Mausel, Brondizio, & Moran, 2004; Radke, Andra, Al-Kofahi, & Roysam, 2005; Rogan & Chen, 2004; Treitz & Rogan, 2004; Yuan et al., 1998). In this work, after multi-date visual interpretation, postclassification comparison was carried out to determine LULC changes in 1965e1989, 1989e2000, 2000e2006 and 2006e2014. Since this approach is based on a segment-per-segment thematic comparison, it provides a direct “frometo” indication of the type of change. Moreover, by modifying only those polygons representing areas of change, we avoided inconsistencies in the registration of stable polygon boundaries from different periods (sliver polygons), thereby avoiding change artefacts. This procedure allowed us to mitigate the major limitation of the postclassification comparison approach to change analysis, which some (Coppin et al., 2004) consider an unsatisfactory approach because the final thematic accuracy is the product of single-year accuracies. Another advantage is that this interpretation procedure is time-saving, although its application requires detailed a priori field-based knowledge (Disperati et al., 2002; Liu et al., 2005; Liu, Liang, Liu, & Zhuang, 2006; Zhang et al., 2009).

Classification of aquaculture ponds In order to make the multitemporal LULC database operational for both regulatory and planning purposes, as well as for environmental modelling requirements, aquaculture areas had to be characterized and mapped in greater detail than for a standard CLC LULC database with a reference scale of 1:25,000e1:100,000. The spatial description of and thematic information on single ponds had to be integrated into the LULC database so as to increase the detail of representation to the cadastral scale, i.e. 1:2000e1:5000 for agricultural lands and wetland areas of Vietnam (Dang & Palmkvist, 2001). Following the customary naming of aquaculture areas in the TGCH lagoon and in accordance with provincial legislation (PPCTTH decision No. 3014/2005/QD-UBND), pond areas were locally classed into two main types. “High-tide” ponds, created from the conversion of rice paddies (non sub-merged areas), are located inland with respect to the pristine lagoon shoreline. “Low-tide” ponds, obtained by damming marginal portions of the lagoon (submerged areas), are located beyond the pristine shoreline. This classification is important for assessing the impact of ponds on lagoon water quality; we therefore decided to introduce two fourth level headings in the class 125 LULC nomenclature, namely 1251 and 1252 (Table 2). For cadastral purposes, single ponds can be mapped with submeter spatial accuracy through GPS-based field surveys. However, this approach is not ideal because, i) field operations require a team of at least two surveyors; ii) GPS measurements require postprocessing; iii) GIS editing is necessary to integrate results within the aquaculture polygons of the LULC database obtained by satellite image interpretation; iv) given the extent of the lagoon, database

updating would be slow and difficult, even for limited pond changes. Medium to high resolution (5e30 m pixel size) and very high resolution (<2.5 m pixel size) satellite imagery are instead an ideal synoptic, multitemporal source of information for mapping single ponds and monitoring change, limiting fieldwork activities to thematic data collection. The elongated shape of pond embankments (tens of metres long, ca. 1e4 m wide) suggests that high spatial resolution imagery may be well suited for identifying and mapping these features. Drawbacks include the high price per scene unit area and the rather low areal coverage of each scene (i.e. ca. 10  10 km). Sensors with coarser spatial resolutions, such as SPOT5 and ASTER, may be a solution since they are cheaper and provide wide-area scenes (ca. 60  60 km or wider). Even though the pixel size of such imagery is generally greater than the width of pond embankments, the very high radiometric contrast with water and the one-dimensional continuity of these features is such that the use of SPOT and ASTER data to extract ponds for LULC mapping is well worth investigating. The test was developed for aquaculture features in three areas of interest (AOI) around the TGCH Lagoon (Fig. 4) characterized by different spatial settings. AOI1 includes a narrow area of low-tide ponds and a few high-tide ponds; it is bordered by rice fields regularly spaced along the former shoreline of the lagoon. AOI2 is characterized by net enclosures of variable size and shape for fishing within the lagoon. AOI3 includes, along the coastline, hightide ponds which are bordered inland by rice and mixed agricultural fields. We adopted the object-based image processing approach (Baatz & Schape, 1999, 2000; Blaschke, Lang, Lorup, Strobl, & Zeil, 2000; ^mara, Souza, Freitas, & Garrido, 1996; Hay, Castilla, Wulder, & Ca Ruiz, 2005; Huth et al., 2012), which has proven to be quite effective for image feature extraction (Dorren, Maier, & Seijmonsbergen, 2003; Hay et al., 2005; Whiteside, Boggs, & Maier, 2011; Willhauck, Schneider, De Kok, & Ammer, 2000). Moreover, it is easy to get outputs in vector format, which in principle better fit the LULC geographic database obtained by visual interpretation, especially if parameters such as object shape are taken into account for classification (Benz, Hofmann, Willhauck, Lingenfelder, & Heynen, 2004; Disperati, Rindinella, Salvi, & Agnelli, 2005; Walter, 2004). We completed the object-oriented classification by carrying out the following basic tasks using the Definiens Professional 5 software package (Definiens, 2006): multi-resolution segmentation, creation of a class hierarchical legend (class hierarchy) and definition of classification rules (class description and membership functions) (Benz et al., 2004; Hay et al., 2005). In order to identify aquaculture facilities, we first extracted water bodies. To this end, segmentation and classification parameters for aquaculture areas were chosen in order to obtain a good spatial fit with reference aquaculture polygons previously delineated by visual interpretation (classes 125 and 521). We extracted a two-level hierarchical network of segments from the SPOT5 images. The first level was obtained by choosing a scale parameter of 90 and a shape/colour criterion of 0.3, and by equally weighing compactness and smoothness (i.e. 0.5) (see Definiens, 2006 for a detailed description of parameters). The second level was obtained by choosing, for the same parameters, values of 40, 0.4 and 0.5 respectively. For the ASTER images, a single-level segment network was extracted by selecting values of 30, 0.9 and 0.5 respectively. We then classified both segment networks by establishing classification rules based on brightness values in the infrared bands, where water reflectance is close to zero and contrasts with the higher reflectance of terrestrial areas. At this stage of analysis aquaculture objects were easily identified using class hierarchy and membership functions.

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

Fig. 2. Map representation of the geographic LULC database for the years 1965, 1989, 2000, 2006 and 2014.

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SPOT5 images) were chosen with the aim of sampling all the LULC classes and taking into account site accessibility (Fig. 1). The large set of photos and videos acquired at most ground truth sites allowed us to assess the classification and positional accuracy of LULC polygons. The overall classification accuracy (OA) is 85%, the KHAT statistics 0.81 (Congalton & Green, 2009), as shown in Table 5.

Aquaculture pond database and accuracy assessment We implemented a post-classification comparison approach to assess the accuracy of this geographic database. The visually interpreted class 125 LULC polygons were used as reference features (Fig. 4A, B). Because the commission error of the ASTER-based classification outputs is about 50%, this dataset cannot be used for practical purposes as is. However, post-classification editing to improve accuracy is too complex and time consuming. The accuracy of outputs from SPOT5 is ca. 60%, whereas commission and omission errors are 16% and 24.5%, respectively. The interpreter can easily improve the quality of SPOT5 classification a posteriori by either deleting commission areas (i.e., area of interest AOI1) or editing single large polygons (AOI2). Such straightforward, non-invasive visual editing allowed us to improve accuracy to 84% (Figs. 4B and 5). The accuracy of the object-based classification was calculated in terms of the number of ponds classified correctly (Table 6), as well as the extension of pond water and embankments; an aquaculture inventory based on a GPS field survey completed in 2006 (IMOLA, 2007) served as a thematic reference (Table 7). As for the entire AOI dataset, almost 7 out of 10 ponds were classified correctly, even though the performance of the classification procedure was spatially heterogeneous and depended on the type of pond. The best results were obtained in those areas where the radiometric contrast between pond embankments and water is high, as generally occurs within high-tide ponds (accuracy within AOI3 reached 89.6%). Visual inspection revealed that some ponds were misclassified because they did not contain water at the time of image acquisition. In any case, the interpreter can easily assign these polygons to the right class during post-classification GIS editing. We obtained lower accuracies in areas characterized by

Fig. 3. Areal extent of LULC classes in the five different years.

Given the higher spatial resolution of SPOT imagery with respect to ASTER data, a third multiresolution segmentation step focussing on the aquaculture objects only was applied to the former. As segmentation parameters, we chose scale 6, shape/colour 0.1 and compactness/smoothness 0.5. This last segmentation allowed us to distinguish pond embankments from water by applying classification rules based on brightness values.

Results Multitemporal LULC database and accuracy assessment The classification process was used to produce a multitemporal LULC geographic database for the five years under study (Fig. 2). Fig. 3 and Table 3 summarize the areal extent of LULC classes for each year, whereas Table 4 shows the bi-temporal LULC changes in the years 1965e1989, 1989e2000, 2000e2006 and 2006e2014. To assess the accuracy of reference year 2006, we used ground truth data collected during fieldwork in 2006, 2007 and 2008. GIS point and polygon LULC features were delineated in the field using smartphones equipped with GPS. Ground truth sites (coeval with

Table 3 Summary of classification statistics for the years 2014, 2006, 2000, 1989 and 1965 (3rd level). LULC classes

111 112 121 124 125 131 133 141 211 213 311 312 313 314 324 331 411 511 512 521 523

Continuous urban fabric Discontinuous urban fabric Industrial or commercial units (incl. hydraulic infrastructures) Airports Aquaculture ponds Mineral extraction sites Construction sites Sport and leisure facilities (incl. graves or cemeteries) Non-irrigated arable land Rice fields Broad-leaved forests Coniferous forest Mixed forest Mangroves Transitional woodland/shrub Beaches, dunes, sands Inland marshes Water courses Water bodies Coastal lagoons Sea and ocean

2014

2006

2000

1989

1965

ha

%

ha

%

ha

%

ha

%

ha

%

442 11,652 81 13 4903 32 131 6294 2642 24,543 11,836 4914 2367 3 3225 3825 121 1395 983 18,626 27,703

0.4 9.3 0.1 0.0 3.9 0.0 0.1 5.0 2.1 19.5 9.4 3.9 1.9 0.0 2.6 3.0 0.1 1.1 0.8 14.8 22.0

435 11,344 81 13 4793 29 147 6331 2690 24,767 15,343 3358 333 3 3450 3778 98 1395 978 18,630 27,734

0.3 9.0 0.1 0.0 3.8 0.0 0.1 5.0 2.1 19.7 12.2 2.7 0.3 0.0 2.7 3.0 0.1 1.1 0.8 14.8 22.1

288 11,270 101 13 1729 0 7 6338 2844 26,306 12,941 3405 153 3 5260 4865 84 1413 944 20,004 27,765

0.2 9.0 0.1 0.0 1.4 0.0 0.0 5.0 2.3 20.9 10.3 2.7 0.1 0.0 4.2 3.9 0.1 1.1 0.8 15.9 22.1

205 11,244 99 13 324 0 7 6338 2741 26,989 12,354 3289 36 3 5835 5208 84 1437 926 20,865 27,734

0.2 8.9 0.1 0.0 0.3 0.0 0.0 5.0 2.2 21.5 9.8 2.6 0.0 0.0 4.6 4.1 0.1 1.1 0.7 16.6 22.1

0 6601 0 0 0 0 0 2434 124 32,620 11,999 0 0 7 8945 11,336 984 1287 231 21,764 27,399

0.0 5.3 0.0 0.0 0.0 0.0 0.0 1.9 0.1 25.9 9.5 0.0 0.0 0.0 7.1 9.0 0.8 1.0 0.2 17.3 21.8

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

55

Table 4 Matrices of LULC changes from 1965 to 2014 (2nd level). a. 2006e2014 2014

2006

2006 Total (ha)

11

12

11 12 13 14 21 31 32 33 41 51 52

11,781 1

7 4878 3

2014 Total (ha)

11,558

13

14

27

21 9 29

31

252 7 10 1 27,177 8

149 6293

0

32 5 9 2

33

41

4 33

3 17,725 1161 109 23

51

52 4

2358

21 46,319

12,093 4998 163 6294 27,185 19,121 3225 3825 121 2378 46,328

2357

47,768

125,732

5 1363 2051

25 6 3689

7 28 98

9 1843

7

6338

29,149

3 33

9

16,502

5260

4865

84

b. 2000e2006 2006

2000

2000 Total (ha)

11

12

11 12 13 14 21 31 32 33 41 51 52

11,486 48 14

6 1831 2

0 3

4

2006 Total (ha)

11,782

13

14

21

20 23 11 6284

7

31

190 1499 40 27,378 42

7

32

33

69 8 14 2

54 11 45

15,515 553 298

2305 2415 281

41

1

35 7

149

1 4888

176

6331

27,455

19,037

3450

11

12

13

11 12 13 14 21 31 32 33 41 51 52

11,449 0

436

2000 Total (ha)

11,449

52 0 75 0

3 1156 482 3057

72

0 15

10 14 2185

125

84 0

51

9 33 66

4 56 3738

98

46,299

11,780 4888 176 6331 27,458 19,037 3450 3778 98 2373 46,364

2373

46,403

125,732

51

52

1317 11

c. 1989e2000 2000

1989

1989 Total (ha) 14

21

31

32

87 496

33

41 23 1

2

2336

16 47,644

11,558 18,43 7 6338 29,149 16,502 5260 4865 84 2357 47,768

2363

48,599

125,732

51

52

27

880

0

47 2

7 6338 29,102

0 15,072 610

1384 4449

44 201 4855

10 84

5 40 436

7

6338

85

29,730

15,682

5835

5208

84

d. 1965e1989 1989

11 12 13 14 21 31 32 33

1965

1965 Total (ha)

11

12

6525 0

0

13

14 258 5

0 7 56 4 7

1822 167 50 46 57

21 2883 222 7 1022 26,498 487 310 154

31

32

33

41

55 3

801 0

751

52 0

2 194 8993 2665 6

729 736 3946 2125 444

2709 501 2058 638 4178

22 314 110 44 270

43 2 0 18 248 12 4 27

81 205 8 1016 23 3 65

11,449 436 7 6338 29,730 15,682 5835 5208

(continued on next page)

56

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

Table 4 (continued ) d. 1965e1989 1989

1965

1965 Total (ha)

11 41 51 52 1989 Total (ha)

12

13

14

3

6601

0

0

21

31

32

33

41

51

52

17 13

0 654 508

89 1

5 94 63

20 89 393

47 115 10

12 1143 8

159 47,602

84 2363 48,599

2434

32,744

12,007

8945

11,336

984

1517

49,163

125,732

2014) and demographic statistics (World Bank, World Development Indicators, 2013) at the national level: from 1965 to 2012 the population increased from ca. 38 million to ca. 89 million, with an almost constant yearly growth rate of 2.2% until 1989, and of 1.4% until 2012. A similar but less pronounced trend characterized the TTH Province, where the average yearly growth rate was 1.2% before 1999 and about 0.4% after 1999 (General Statistics Office of Vietnam, 2013). Demographic growth was accompanied by a spatial redistribution of the population: there was a simultaneous increase in the urban population (þ9.0%) and decrease in the rural population (14.3%) (General Statistics Office of Vietnam, 2013). Housing conditions improved in the former rural areas, as generally observed during fieldwork carried out around the TGCH Lagoon. The small rural villages close to the lagoon and sparse houses located within agricultural areas (previously falling within class 211) expanded and adopted more durable building materials (change to class 112), and traditional wood or bamboo huts were replaced by single-storey concrete houses after the eighties. Even
City built-up areas included in the within the subset of the Hue study area, from 1965 to 2014 there was a general increase in image reflectivity and in the spatial continuity of scattered urban patches corresponding to the conversion from widely-spaced concrete houses to dense multi-storey blocks (from CLC class 112 to 111). Indeed, economic growth has spurred investment in urban areas over the last 10 years (General Statistics Office of Vietnam, 2011), and in the early 2000s local authorities began to implement housing programmes (Phuc et al., 2014) to overcome the city's housing shortage and reduce unplanned and unregulated private intervention (Decision No. 1147/QÐ-UB). Lastly, the fact that the size of quarries and cemeteries increased considerably inland but only slightly in rural coastal areas provides further evidence of these demographic dynamics (Tables 3 and 4).

low-tide ponds due to the higher omission error (i.e. the extent of ponds was underestimated) (Fig. 5). This error is partly due to the fact that the width of low-tide pond embankments is about 2 m, which is smaller than the spatial resolution of both SPOT5 and ASTER imagery. Discussion As in other coastal areas of East and Southeast Asia, in the last decades the coasts of Vietnam have been subject to increasing environmental stress due to socioeconomic transformations (Primavera, 2006; Turner, Subak, & Adger, 1996). After the doi moi policy announced in 1986, the socioeconomic development of the TTH Province, and of the TGCH Lagoon in particular, was spurred primarily by an increase in population, the development of extensive agriculture, fisheries and aquaculture practices and, in the last 10 years, by tourism (Wong, 1998). In this work we integrated the CLC methodology (Heymann et al., 1994) to create multitemporal maps of LULC around the TGCH Lagoon from 1965 to 2014; objectbased classification was used to analyze aquaculture areas at the pond scale. Data sources were topographic maps, as well as multitemporal and multiresolution satellite imagery. LULC changes In agreement with the findings of ICEM (2003) and Phuc, Westen, and Zoomers (2014), urban areas in the TTH Province (classes 111 and 112 in Tables 3 and 4) have encroached on periurban agricultural areas. The change occurred throughout the studied 1965e2014 period (Fig. 6), although expansion rates have decreased over the last ten years (Table 4a and b). These results also agree with data (Leinenkugel, Esch, & Kuenzer, 2011; Saksena et al., Table 5 Accuracy matrix for the geographic LULC database, year 2006. Class code

Classification data 111 112 125 133 141 213 211 311 312 313 314 324 331 511 521 Total Producer's accuracy PA (%).

Reference 111

112

3 1

1 45

125

133

141

1 33

213

3 3

211

311

9

313

314

3

324

331

511

6

1

4

1 111 8

4 81

1

1

1 1

1

3 20 1

6 1

1

4

1 4 75

55 82

40 83

Overall accuracy (OA) 85%, KHAT statistics 0.81.

3 100

31 87

1

1

124 90

95 85

2 5 2

5 60

25 80

6 100

1 100

6 67

8 63

3 67

Total

User's accuracy UA (%)

4 63 33 4 29 125 100 3 20 7 1 7 5 5 2 408 346

75 71 100 75 93 89 81 100 100 86 100 57 100 40 100

521

1 27

2 5

312

2 2 100

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57

Fig. 4. Accuracy assessment of aquaculture areas obtained by the object-oriented classification of ASTER (year 2000) and SPOT5 (year 2005) images. The vector polygons of class 125 (Table 2), obtained by visual interpretation, were used as a reference geographic database.

Along the coastal zone facing the TGCH Lagoon, about 2000 ha of rice fields with low productivity due to high salinity groundwater were converted to aquaculture ponds from 1965 to 2014 (Fig. 7A); this corresponds to about 6.3% of the initial rice cultivation area. Similarly, after 1965 about 2400 ha of the lagoon (ca. 10.5%) were converted to aquaculture ponds. These significant changes highlight the local transition from an economy based on agriculture to a more profitable one (at least on the short-term) based on aquaculture (Huong & Berkes, 2011). High-tide ponds developed mainly inland, replacing the rice fields mapped in 1965, whereas low-tide ponds replaced the shallow waters of the lagoon (Fig. 7B). Ponds and other aquaculture/fishing facilities began to develop in 1989, mainly in areas inland of the shoreline of the northcentral lagoon, where existing transport infrastructures fostered the process. The maximum development rate occurred from 2000 to 2006, along with widespread pond growth in the southern lagoon (Cau Hai). After 2006 expansion almost ceased within the lagoon area, whereas new

ponds were built in the sandy northern coastal areas. Aquaculture expansion from 36 ha (2006) to 420 ha (2014) was the main driving force of development along the latter unproductive coastal areas of the TTH Province. Aquaculture activity in TGCH Lagoon is characterized by small holdings and intensive monoculture practices (Van Tuyen, Armitage, & Marschke, 2010). These conditions, along with the intensification of capture fisheries within the lagoon, greatly altered the whole TGCH Lagoon system (Carbonetti, Pomeroy, & Richards, 2014). Small holders quickly became vulnerable to market forces due to a gradual increase in production costs, the onset of disease, and the unprofessional management of their activity (Kleih et al., 2013; Lan, 2013; Pho Hoang Han, 2007). There was therefore a “spontaneous” decline in aquaculture development by 2006. In order to maximize productivity, reduce costs and guarantee sustainable aquaculture development within tidal-prone areas, Armitage and Marschke (2013) recently suggested new management solutions for these small-scale systems.

Fig. 5. Comparison between aquaculture ponds identified by visual interpretation (only the outer border of the area is represented) and by object-oriented classification for the AOIs represented in Fig. 4. In areas of weak radiometric contrast between water bodies and embankments (white arrows in AOI1 and AOI2) the interpreter's expertise ensures adequate accuracy, whereas object-oriented classification may produce high commission (AOI1) or high omission (AOI2) errors. Classification accuracy is quite good when ponds are of uniform shape and the contrast between water bodies and embankments is great (AOI3).

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Table 6 Estimated positional accuracy of aquaculture ponds obtained by object-oriented classification. a) Whole AOIs Classification

Pond Embankments and dry areas Empty Total P.A. (%)

a) Whole AOIs Reference (no. of polygons)

Classification

Pond

Embankments and dry areas

Empty

Total

U.A. (%)

2760 449

194 65

234 65

3188 579

86.6 11.2

217 3426 80.6

8 267 24.3

299 0.0

225 3992

Overall 70.8

b) AOI1 Classification

Pond Embankments and dry areas Empty Total P.A. (%)

Pond Embankments and dry areas Empty Total P.A. (%)

Pond

Embankments and dry areas

Empty

Total

U.A. (%)

514 159

40 18

77 15

631 192

81.5 9.4

35 708 72.6

3 61 29.5

92 0.0

38 861

Overall 61.8

Pond Embankments and dry areas Empty Total P.A. (%)

Pond

Embankments and dry areas

Empty

Total

476.7 275.5

119.5 297.5

148.9 260.5

745.1 833.6

127.2 879.5 54.2

189.2 606.3 49.1

409.4

316.4 1895.1

U.A. (%) 64.0 35.7

Overall 40.9

Classification low tide

Reference (ha) Pond

Embankments and dry areas

Pond Embankments and dry areas Empty Total P.A. (%)

8.4 14.4 1.6 24.4 34.6

Empty

Total

U.A. (%)

5.7 12.8

4.5 7.4

18.7 34.6

45.2 37.1

2.6 21.1 60.6

11.9

4.2 57.4

Empty

Total

U.A. (%)

9.3 15.0

42.3 44.5

Overall 37.0

c) AOI2 Reference (no. of polygons)

Classification

Pond

Embankments and dry areas

Empty

Total

U.A. (%)

215 35

32 14

29 7

276 56

77.9 25.0

124 374 57.5

3 49 28.6

36 0.0

127 459

Overall 49.9

d) AOI3 Classification

Pond Embankments and dry areas Empty Total P.A. (%)

Reference (ha)

b) AOI1 Reference (no. of polygons)

c) AOI2 Classification

Table 7 Estimated accuracy of the extent of aquaculture ponds obtained by object-oriented classification.

Reference (ha) Pond

Pond Embankments and dry areas Empty Total P.A. (%)

Embankments and dry areas

3.9 5.9

4.0 6.7

1.4 2.4

4.6 14.4 27.2

5.8 16.4 40.5

3.8 0.0

10.4 34.6

Overall 30.5

d) AOI3 Reference (no. of polygons)

Classification

Pond

Embankments and dry areas

Empty

912 27

42 8

27 7

4 943 96.7

50 16.0

34 0.0

Total 981 42 4 1027

U.A. (%) 93.0 19.0

Overall 89.6

P.A.: producer's accuracy. U.A.: user's accuracy.

Although small with respect to the major changes occurring in the area of the coastal plain and around the TGCH Lagoon, other anthropogenic LULC changes occurred in the period under investigation. Steep hills prevail in the southern sector of the study area, so that rice cultivation and aquaculture are possible only within a narrow strip of plain surrounding the lagoon. In accordance with nationwide trends (Clement & Amezaga, 2009; Dang, Turnhout, & Arts, 2012; General Statistics Office of Vietnam, 2006; Meyfroidt & Lambin, 2008), land cover in these hilly areas was first characterized by scattered vegetation that was replaced by woodlands in the investigated period of time. The forested areas increased from about 12,000 ha in 1965 to 19,000 ha in 2014, with deciduous broadleaves (class 311) and evergreen conifers (class 312) replacing barren areas (class 32; Table 4 and Fig. 8). During ground truth surveys we observed that the main species used for new plantations were Acacia spp., Pinus spp., Eucalyptus

Pond Embankments and dry areas Empty Total P.A. (%)

Reference (ha) Pond

Embankments and dry areas

Empty

Total

U.A. (%)

14.4 20.3

9.8 17.0

1.2 4.1

25.4 41.3

56.5 41.1

0.5 35.2 40.9

1.2 28.0 60.5

5.3

1.8 68.5

Overall 45.8

P.A.: producer's accuracy. U.A.: user's accuracy.

and Casuarina (Dinh, 1998); in 2000 these represented respectively 29%, 20%, 16% and 5% of the species planted as single-species stands in the TTH Province (Amat, Tuu, Robert, & Huu, 2010). The LULC changes detected in this work are in agreement with the master plan for socio-economic development of the TTH Province (ministerial decision No. 86/2009/QÐ-TTg), with national and international forestry programmes implemented since reunification in 1975 (FSSP, 2010), and with reforestation programmes launched in the province after 1989 (Tran et al., 2010). CLC method In answer to the need for reliable, homogenised and comparable multitemporal LULC outputs in different biogeographical regions (Kuenzer et al., 2013, 2014; Stibig et al., 2007; Yeung, 2007), we contributed to the implementation of the CLC nomenclature outside Europe in accordance with standard procedures. Following

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59

Fig. 6. Expansion of urban and aquaculture facilities in the northern part of TGCH Lagoon from 1965 to 2014.

Fig. 7. A) Change in the extent of aquaculture ponds (class 123) between the years 1965, 1989, 2000, 2006 and 2014; B) change in the extent of aquaculture ponds (class 123) from 1965 to 2014.

the CLC rules, we visually interpreted multiresolution satellite imagery, integrating field-based information and topographic maps as either ancillary or primary data. We adapted the a-priori standard classification system by adding new classes which describe features peculiar to central Vietnam: 3rd level class “125 Aquaculture ponds” and “314 Mangroves”

(Table 2). At the 4th level, class 125 was split into two subclasses in order to distinguish between “1251 Lagoon or low-tide ponds” and “1252 Inland or high-tide ponds”. We compared this adapted CLC nomenclature with the ones previously implemented outside Europe (Jaffrain, 2011). Class “314 Mangroves” had already been introduced in South America

Fig. 8. A) LULC change from 1965 to 2014 for classes 31 and 32; B) change of semi-natural areas (class 3) for the five investigated periods.

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L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

Fig. 9. LULC and aquaculture facilities in 2006 in the central TGCH Lagoon (AOI3 in Fig. 4): A) SPOT5 imagery FCC RGB143, 2005-08-23; B) LULC by visual interpretation and ponds by GPS field survey; C) LULC by visual interpretation and ponds by object-oriented classification. Numbers in B) and C) represent 3rd level LULC class.

(Colombia) and Central America (San Salvador, Honduras & Guatemala). Instead, prior to this study, ponds had never been explicitly promoted to the rank of 3rd or 4th level CLC class and were classified following various rules. In Heymann et al. (1994) only cement fish farming ponds were included in class “121 Industrial or commercial units”, whereas other fish farming ponds that developed in or close to abandoned salt pans were included in class “422 Salines”. In Bossard et al. (2000), only water bodies used for freshwater fish-breeding activities were included in class “512 Water bodies”, lagoons used for breeding shellfish in class “521 Coastal lagoons”. In 2011, the Eionet Action Group on Land monitoring in Europe (EAGLE) proposed that class “512 Water bodies” should include water bodies used for freshwater fish breeding activities and fish ponds temporarily without water. Ponds may be ascribed to either land cover class, so these features should be included in either CLC class 5 or 1. In agreement with Heymann et al. (1994), we chose the latter option in order to highlight their economic relevance and extent in Vietnam; ponds were therefore included in a new 3rd level class belonging to CLC class 12. We then split ponds into two 4th level classes. This adapted CLC classification may also be applied to other coastal areas of Vietnam, and it would be worthwhile testing it elsewhere in Southeast Asia. Aquaculture classification The main LULC change occurring in the study area refers to the development of aquaculture. In order to map single ponds at the cadastral scale we tested the performance of the object-based classification of both ASTER and SPOT5 satellite imagery used for multitemporal LULC analysis. ASTER data analysis generally yielded unsatisfactory results because of the low spatial resolution, whereas SPOT5 data analysis yielded valuable results (with some exceptions, to be discussed).

The extraction of classes 125 and 521 through the object-based classification of SPOT5 images led to an overestimation of aquaculture areas in the north by about 16%, and to an underestimation of those in the centre of the study area by about 24.5% (Fig. 3). Visual post-processing can quickly reduce these commission and omission errors (Congalton & Green, 2009) because errors generally involve segment classes, whereas segment shapes are consistent with polygons defined independently by visual interpretation. Post-processing increased accuracy to more than 84% and in some areas up to 95%. By implementing object-based classification runs and post-classification checks, the final vector database for classes 125 and 521 is comparable to that obtained by visual interpretation, both in terms of spatial extent and location of thematic features. This means that the procedure, although based only on a medium spatial resolution, may represent a powerful tool for mapping changes in aquaculture ponds. In particular, accuracy is higher (in terms of the number and extent of polygons) for high-tide ponds (AOI3 in Fig. 3), which are easier to detect because of the relatively high contrast between pond embankments and water. The vector database for AOI3 is very accurate (Fig. 9) in terms of the number of ponds identified correctly, with 89.6% overall accuracy (OA), 96.7% producer accuracy (PA) and 93.0% user accuracy (UA) (Table 6d), but much less accurate in terms of pond area (OA ¼ 45.8%, PA ¼ 40.9% and UA ¼ 56.6%; Table 7d). This contrasting result may be due to the low spatial resolution of SPOT5 images with respect to the size of pond perimeters, so that the boundary between ponds and embankments is blurred in the imagery. The size of ponds was therefore underestimated with respect to the area mapped by GPS. For AOI1 and AOI2, where low-tide aquaculture facilities prevail, the lower radiometric contrast among features decreased classification accuracy (Table 6bec; Table 76bec). For AOI1 the percentage of ponds classified correctly is fairly acceptable (OA ¼ 61.8%, PA ¼ 72.6% and UA ¼ 81.5%), whereas for AOI2 accuracy is much lower (OA ¼ 57.5%, PA ¼ 77.9% and UA ¼ 49.9%). The accuracy of the

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

estimated pond area is very low in both AOIs because of omission errors in low-contrast image clusters. Along this stretch of the lagoon, results cannot therefore be used to compile a vector database on aquaculture. In such zones advanced image processing by expert operators is required, thus reducing the usefulness of classification when compared to straightforward visual interpretation coupled with a final GPS check. Our results, in line with other studies (Hossain, Chowdhury, Das, Sharifuzzaman, & Sultana, 2009; Karthik, Suri, Saharan, & Biradar, 2005), confirm that SPOT5 imagery can be used to map aquaculture. However, in certain cases (e.g. in tidal areas), integration with higher spatial resolution multispectral sensors is required (Alexandridis, Topaloglou, Lazaridou, & Zalidis, 2008; Virdis, 2014). Nonetheless, SPOT5 is less expensive and has a wider swath than very high resolution imagery and can likely be used for inventory purposes in large areas. Moreover, given the natural slowdown of aquaculture expansion, legislative restrictions and new management plans, only small changes to ponds are expected in the near future (e.g. construction of nurseries, merging of ponds, etc.); in this case change detection through GPS field surveys would be time consuming and poorly effective. Once a vector database is built and its accuracy assessed, it can be updated through visual comparison with newly acquired remote sensing imagery. This approach is fast, requires no special knowledge-based procedures and can be implemented by non-expert GIS and remote sensing operators. Conclusions Anthropogenic pressures on the TGCH Lagoon system are increasing due to the socio-economic development promoted by the doi moi reform in Vietnam. Over the last 25 years, agriculture has spread, urban areas have expanded and, more significantly, aquaculture activities have developed along the TGCH Lagoon. By processing a multisource, multiresolution, multitemporal geographic dataset made up of topographic maps and satellite imagery, this work provides a quantitative estimation of the main LULC changes that occurred in the 1965e2014 period. We tried to implement the European CLC nomenclature outside Europe. We extracted multitemporal LULC thematic and spatial information by implementing a visual interpretation approach and following the a-priori CLC nomenclature. In order to represent adequately the LULC features and the peculiarities of central Vietnam coastal areas, we adapted the nomenclature by introducing new 3rd level classes peculiar to this area. Besides class “314 Mangroves”, already introduced in previous extension studies, class “125 Aquaculture ponds” was introduced in order to highlight the extent and economic relevance of aquaculture in the study area. The class was further split into two 4th level classes to distinguish between ponds obtained by progressively damming the TGCH Lagoon water close to the shoreline (1251), and inland ponds replacing rice fields (1252). This study has highlighted important processes occurring in the investigated period of time: i) the encroachment of the expanding urban system on the peri-urban agricultural areas around Hue and, ii) from 1989, the development and fast growth of aquaculture ponds. Encouraging results from accuracy assessment indicate that, in agreement with the literature, a-priori CLC nomenclature may be applied successfully in areas outside Europe, where physiographic, climate and socio-economic conditions may give rise to peculiar LULC features. Object-based processing was applied to the SPOT5 and ASTER datasets for LULC analysis in order to assess whether highly detailed (cadastral-scale) pond maps can be extracted from this medium spatial resolution imagery. Results indicate that only

61

SPOT5 yields outputs that can be integrated, after limited visual post-processing, within the CLC LULC database. Better results are obtained for high-tide ponds. This suggests that the object-based classification of medium spatial resolution multispectral imagery may be an effective tool for the periodic updating of LULC records and of high-tide ponds in cadastral maps. Accuracy assessments for low-tide ponds indicate that imagery with a higher spatial resolution (i.e. better than ca. 2 m) is required to adequately map these features. The highly detailed data on LULC changes from this study will provide valuable input for developing an integrated plan for the sustainable management of the landscape, environment and natural resources in the TGCH Lagoon or elsewhere in the region. The LULC data from this work have been successfully integrated within the IMOLA FAO project for developing aquaculture plans for selected target municipalities facing the TGCH Lagoon

Acknowledgements This research was carried out in the framework of the FAO project GCP/VIE/029/ITA “Integrated Management of Lagoon Activities in Thua Thien Hue Province” (IMOLA). Special thanks to the project’s Chief Technical Advisor, Prof. Massimo Sarti, and National Project Manager, Ms. Nguyen Thi Phuoc Lai, for their friendly support during our long stay in Vietnam and for providing free access to processed satellite imagery and reference aquaculture dataset. We thank the editor and the two anonymous reviewers for their comments, which helped us to improve the paper.

Appendix A1

Table A1 3rd Level LULC classes description. Class code

Class name

Short description

111

Continuous urban fabric

112

Discontinuous urban fabric

121

Industrial or commercial units (incl. hydraulic infrastructures)

124

Airports

Land covered by dense buildings, roads and artificially surfaced areas for more than 70% of the total surface. Mainly this class is represented in the
city premises. Hue Buildings, roads and artificially surfaced areas are associated with prevalent vegetated areas (household orchards and small agriculture paddies) and bare soil, which occupy discontinuous but significant surfaces. This class is prevalent along main and secondary
City urban fringes roads in Hue and rural areas. Industrial or commercial units, dams and channel Embankments, made of concrete or even sand. When feasible these features are used as transportation ways for motorcycles or even cars.
city airport installations: Hue runways, buildings and associated land. Infrastructure port areas, including quays, dockyards and marinas of Thuan An inlet in the central part of the lagoon.

125

Aquaculture ponds

62

L. Disperati, S.G.P. Virdis / Applied Geography 58 (2015) 48e64

Table A1 (continued ) Class code

131

Class name

Mineral extraction sites

133

Construction sites

141

Sport and leisure facilities (incl. graves or cemeteries)

211

Non-irrigated arable land

213

Rice fields

311

Broad-leaved forests

312

Coniferous forest

Table A1 (continued ) Short description

Class code

Class name

Short description

Area deployed to fish, shrimp and crab farming. Always occupied by water; without water for ordinary maintenance. At 4th level they are distinguished between “lagoon or low-tide ponds” (1251), having a direct connection with the lagoon, and “inland or high-tide ponds” (1252) having no direct connection with lagoon areas. Areas with open-pit extraction of construction material (sandpits, quarries) or other minerals (open-cast mines). This category of land cover has been identified with the help of ground truths and ancillary data. Bridges, roads and other spaces under construction development. Areas having high reflectance properties, visible with satellite imagery but univocally identified by ground truths. Land devoted to cemeteries, tombs and graves often encroaching urban and agriculture areas. Characterized by different land cover typologies (vegetated agriculture land, bushy and/or and bare sandy soil areas), in many cases they can be identified only with the aid of ancillary data and ground truths. Monumental areas area included in this class. Mainly orchards (vegetables, rice, cassava, sweet potatoes, potatoes, sugar can, cereals, legumes, fodder crops, root crops and fallow land; includes flowers and trees). This category appears as partially vegetated areas with an extremely diversified pattern because of the juxtaposition in short distances of different cultivation typologies. Land prepared for rice cultivation. Flat surfaces with irrigation channels. Surfaces periodically or permanently flooded according to the season. Given the regular geometric pattern, this category was identified using multitemporal images.

313

Mixed forest

314

Mangroves

324

Transitional woodland/shrub

331

Beaches, dunes, sands

411

Inland marshes

511

Water courses

512

Water bodies

521

Coastal lagoon

523

Sea and ocean

Similar to 311 but neither broad-leaved nor coniferous species predominate. Vegetation formation composed principally of plants where mangrove species predominate. Identified by means of ground truths since the small extent of this class. Bushy or herbaceous vegetation with scattered trees not taller than 3 m. It may represent either woodland degradation or forest regeneration/ colonisation where the bare soil occupy more than the half the total area. Sandy soil fraction is predominant. At 4th level they are distinguished between inner areas with no connection with lagoon or coastal areas (3311 “Sandy inner areas”, and beaches, dunes and expanses of sand or pebbles in coastal locations (3312 “Sandy coastal areas”). Low-reflectivity surfaces because of the presence of water. Sometimes small bushes may cause lighter tones in the images. Surfaces are under the water table for the main part of the year. Natural or artificial water courses serving as water drainage channels. Includes canals. Natural or artificial freshwater surfaces. Surfaces of salt or brackish water in coastal areas which are separated from the ocean by a tongue of land or similar topographic features. These water bodies are connected to the sea through a narrow inlet either permanently or for parts of the year only. At 4th level deep waters are classified as 5211, while sandy or muddy shallow waters as 5212 (topographic maps, year 1965). Zones seaward of the lowest tide coastal limit.

Vegetation formation composed principally of trees, including shrub and bush understoreys, where broadleaved species predominate. Young coppices and young plantations belong to this category. At 4th level this class includes “broad-leaved sparse forest” (3111) and “broadleaved dense forest” (3112) classes. Similar to 311 but coniferous species predominate.

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