Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and...

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 2, FEBRUARY 2015 503

Mangrove Mapping and Change Detection in Ca MauPeninsula, Vietnam, Using Landsat Data and

Object-Based Image AnalysisNguyen-Thanh Son, Chi-Farn Chen, Ni-Bin Chang, Cheng-Ru Chen, Ly-Yu Chang, and Bui-Xuan Thanh

Abstract—Mangrove forests provide important ecosystem goodsand services for human society. Extensive coastal developmentin many developing countries has converted mangrove forests toother land uses without regard to their ecosystem service values;thus, the ecosystem state of mangrove forests is critical for offi-cials to evaluate sustainable coastal management strategies. Theobjective of this study is to investigate the multidecadal changein mangrove forests in Ca Mau peninsula, South Vietnam, basedon Landsat data from 1979 to 2013. The data were processedthrough four main steps: 1) data preprocessing; 2) image process-ing using the object-based image analysis (OBIA); 3) accuracyassessment; and 4) multitemporal change detection and spatialanalysis of mangrove forests. The classification maps comparedwith the ground reference data showed the satisfactory agreementwith the overall accuracy higher than 82%. From 1979 to 2013,the area of mangrove forests in the study region had decreasedby 74%, mainly due to the boom of local aquaculture industry inthe study region. Given that mangrove reforestation and afforesta-tion only contributed about 13.2% during the last three decades,advanced mangrove management strategies are in an acute needfor promoting environmental sustainability in the future.

Index Terms—Change analysis, Landsat data, mangroves,object-based image analysis (OBIA).

I. INTRODUCTION

M ANGROVE forests are a unique habitat of ecosystemsalong the intertidal coastlines between land and sea

that provide important ecological services for coastal animalspecies. Rural population along coastlines relies on mangroveresources for their livelihood. Mangrove forests offer nurs-ery grounds for various aquatic species, such as juvenile fish,crabs, shrimp, and molluscs [1], [2]. They also act as a naturalconnection between terrestrial and marine ecosystems to main-tain the ecosystem integrity and buffer coastal communities

Manuscript received January 23, 2014; revised June 11, 2014; acceptedSeptember 18, 2014. Date of publication October 14, 2014; date of currentversion February 09, 2015.

N.-T. Son, C.-F. Chen, C.-R. Chen, and L.-Y. Chang are with the Centerfor Space and Remote Sensing Research, National Central University, Jhongli,Taoyuan County 32001, Taiwan (e-mail: [email protected]; [email protected]; [email protected]; [email protected]).

N.-B. Chang is with the Department of Civil, Environmental, andConstruction Engineering, University of Central Florida, Orlando, FL 32816USA (e-mail: [email protected]).

B.-X. Thanh is with the Department of Environmental Engineering, HoChi Minh City University of Technology, Ho Chi Minh, Vietnam (e-mail:[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2360691

from natural disasters and drastic sea–land interactions throughsediment stabilization and adsorption of pollutants [3]. Yet,mangrove forests have been threatening globally by defor-estation due to anthropogenic activities, especially urbaniza-tion and aquaculture activities. These activities convert man-grove forests to residential areas and aquaculture farms tomeet continuous economic development and increasing fooddemands [4], [5].

The world’s mangrove forests declined from 16.5 to 19.9million ha in the 1980s to 15.2 million ha in 2005 [4], [6],[7], a loss that continues today due to changes in land uses [6].The destruction of mangrove forests has reduced their viabil-ity and the quality of their ecological and economic services,consequently leading to environmental and ecological issues,such as loss of habitats and species, land degradation, decline inbiodiversity, increase in coastal erosion, and more storm surgeimpacts [8]–[10]. This issue is phenomenal along the coast-line of South Vietnam, where the area of mangrove forests hadbeen reduced from 190 812 ha in 1953 to 29 534 ha in 1995[11], a loss attributed to aquaculture development, especiallyshrimp farms [12]. Our study region, Ca Mau province locatedin the Mekong Delta, South Vietnam, encompasses the largestarea of mangrove forests in the country, where the mangroveforests have been suffering various losses due to alternativeland uses. Although shrimp farming plays a critical role inthe region’s economy, the rapid expansion of shrimp farms inlast decades had resulted in environmental problems [13]–[17].Understanding spatiotemporal changes in mangrove forests inthe region over the past three decades is thus critical to supportthe subsequent land-use planning and restoration of mangroveecosystems.

Remote sensing has a great potential for monitoring changesin mangrove coverage. Studies used satellite–sensor data formapping and monitoring mangrove forests at different scales,such as the data produced from the moderate resolutionimaging spectroradiometer (MODIS) [10], [18], Landsat [8],[9], [19]–[22], advanced space-borne thermal emission andreflection radiometer (ASTER) [11], [12], [14], Système Pourl’Observation de la Terre (SPOT) [13], [15], [17], [23], andQuickBird and IKONOS [24]–[26]. Because mangrove forestsin the study region are fragmental, the use of low-resolutionremote sensing data, such as MODIS data, is insufficient tocollectively map small patches of mangrove forests. The datafrom high-resolution satellites, such as QuickBird, IKONOS,and SPOT, indicated a higher potential to map small patchesof mangrove forests [13], [27], [28], with much higher costs,

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504 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 8, NO. 2, FEBRUARY 2015

limited range of area coverage, and temporal constraints forcapturing the changes in mangrove forests; therefore, Landsatimagery without having these disadvantages was chosen for thisstudy.

Many methods were developed for multitemporal changedetection of land-use/land-cover (LULC) from remote sensingimagery; yet no single approach can be universally applicableto all situations [29]. The pixel-based methods, being widelyused for investigating individual pixel changes, include changevector analysis [30]–[32], image differencing [33], [34], andunsupervised/supervised classifiers [35], [36]. The disadvan-tage of the pixel-based approaches is that it often produces“salt-and-pepper” outputs when the larger reflectance vari-ability is present in the image for each class of LULC fordifferentiation, potentially affecting the accuracy of changedetection results [37]–[39]. Because the real-world objectsprovide contextual information at different spatial scales forvarious classes of LULC under investigation, such a uniqueobject-oriented information is advantageous for specific envi-ronmental and ecological monitoring. It is worthwhile todevelop the object-based image analysis (OBIA) to bring spa-tially contextual relationships into LULC mapping and changedetection.

In post-classification change detection, OBIA performs theclassification by segmenting an image into homogeneousobjects, followed by a series of analyses for classification ofthese objects [40]. Compared with pixel-based methods, theadvantage of OBIA is that the algorithm drives the classificationbased on image objects that are deemed as basic units withinhomogeneous and spatially contiguous areas. However, OBIArequires an extensive prior knowledge of in situ LULC beforeclassification [41], [42], and thus the accuracy of image seg-mentation directly influences the classification [43]. This OBIAmethod has been widely used to successfully delineate patchesof mangrove forests from Landsat, SPOT, Worldview-2, andhyperspectral imageries [20], [44]–[47] and has demonstratedmore accuracy in mapping results than pixel-based approaches[48]–[52]. As different classifiers (i.e., pixel- and object-basedmethods) have their unique advantages and disadvantages,choosing a classifier for image classification may not always bedriven by the overall accuracy [52]. Unlike studies comparingonly the accuracy among classifiers, we chose OBIA for post-classification change detection of mangrove forests in the studyregion with the aid of Landsat data. This chosen approach pro-vides previous information associated with the spatial elementsof mangrove patches as we are very familiar with the local sit-uation. Mangrove forests in the study region are shrubs, mostlydistributed along shorelines and estuaries. Patches of mangroveforests in some small strips are smaller than 50 m; however, thesize of inland mangrove patches was generally multiple timeslarger than the size of a Landsat pixel, allowing our study todiscriminate mangrove forests from other land-cover types withLandsat data using OBIA [19], [20], [53].

This study extended the similar effort of OBIA for assess-ing changes in mangrove forests in Ca Mau peninsula ofSouth Vietnam during the periods 1979–1989, 1989–2003, and2003–2013 using Landsat imageries. The results may providenew insights of the multidecadal change in mangrove forests

Fig. 1. 2006 LULC map of the study area with a reference to the administrativeboundaries of provinces in Mekong Delta overlaid with the mangrove layer. Theground reference sites were used for accuracy assessment of the 2003 and 2013classification results.

that could be useful to simultaneously manage aquaculturedevelopment and mangrove conservation in the region.

II. STUDY REGION

The study region is located in Ca Mau province, southern-most Vietnam, between 9◦11′N and 105◦9′E (Fig. 1), coveringapproximately 4102 km2 with an inland area of 2542 km2. Theregion is characterized by seasonal saline-affected conditionssuitable for brackish aquaculture, and the area of mangroveforests was approximately 791 km2. Ca Mau encompasses thelargest areas of Vietnam’s mature mangrove ecosystems understressful environmental conditions [54]. Mangrove forests grownaturally on sand dust and mudflats, acting as a buffer to sta-bilize mudflats and protect inland communities. Due to thepressure of population growth from one million in 1995 to1.2 million in 2011 [55] and economic development driven bysocietal needs, the region experienced a significant conversionof mangrove forests to aquaculture, especially shrimp farms.Approximately, 77 389 ha of mangrove forests in this regionwas converted to aquaculture systems, especially shrimp farm-ing, during 1983–1995 [56], making this province the largestshrimp exporter in the country [55]. Although shrimp farmingbrought benefits to the region, the rapid conversion of man-grove ecosystems to shrimp farms without a proper land-useplanning has caused environmental impacts, including coastlineerosion and loss of habitats and breeding grounds of aquaticspecies [57].

III. DATA COLLECTION

Nine Landsat MSS, TM, ETM+, and OLI images acquiredfrom the U.S. geological survey (USGS) for 1979, 1989, 2003,and 2013 were used. The acquisition dates and characteristicsof Landsat data were described in Table I. We also collected the

SON et al.: MANGROVE MAPPING AND CHANGE DETECTION IN CA MAU PENINSULA, VIETNAM 505

TABLE ICHARACTERISTICS OF LANDSAT IMAGES USED IN THIS STUDY

2000 and 2006 LULC maps of the Vietnamese Mekong Delta(scale: 1/125 000) from the GIS and Remote Sensing ResearchCenter, Vietnamese Academy of Science and Technology; theSub-National Institute for Agricultural Planning and Projectionof Vietnam; and the 2005 LULC map of Ca Mau province(scale: 1/50 000) from the Department of Natural Resourcesand Environment for preparation of the ground reference data.These LULC maps were primarily constructed from Landsatimageries and validated with the field survey data.

IV. METHODS

A. Data Preprocessing

Landsat images of 1979, 1989, and 2013 were geometri-cally corrected using the 2003 Landsat image as a referencebasis. The geometric correction for each image was carried outusing 20 ground control points selected from distinct featuresthroughout the study region. The results confirmed that theroot-mean-squared error (RMSE) value is smaller than 15 m.The Landsat TM and ETM+ imageries (the surface reflectancedata product collected from USGS) have been radiometri-cally corrected to the level-1 standard. The Landsat TM andETM+ data product stored as digital numbers (DNs) were con-verted to the reflectance by multiplying with a scaling factor of0.0001 (http://glcf.umd.edu/data/gls_SR). We also performedthe image calibration and atmospheric correction for the 1979Landsat MSS images using the FLAASH module incorpo-rated in ENVI 5.1 to minimize spectral variations caused byatmospheric interference [58].

For the Landsat 8 images, the DN values were convertedto reflectance (ρ∗λ) using rescaling coefficients provided in theproduct metadata file and can be calculated using the followingequation:

ρ∗λ =ρλ′

sin θSE(1)

where ρλ′ is the top of atmosphere (TOA) planetary reflectance,with correction for solar angle, and θSE is the local sunelevation angle.

We also performed the reflectance normalization between thetwo Landsat tiles (covering the study region) for each year using

Fig. 2. Subset of the 2003 Landsat ETM+ false-color image (600× 600 pixels,RGB–432) overlaid with the refined segmentation results.

the image histogram matching method to make the distribu-tion of brightness values as close as possible and to minimizethe spectral variations within each land-cover type [59]. Theimages covering the study region of each target year weremosaicked and then cropped over the study area.

B. Image Processing With the OBIA

1) Segmentation: The OBIA used for image classificationis composed of two main steps, including image segmenta-tion and image classification. The multiresolution algorithm[60] embedded in eCognition 8.0 was used to perform imagesegmentation. The current study used only Landsat spectralbands (i.e., six bands for Landsat TM/ETM+/OLI data and fourbands for Landsat MSS data) for image segmentation. Basedon our prior knowledge of patch sizes of mangrove forests inthe region, the segmentation parameters (scale = 0.8, shape =0.1, and compactness = 0.5) and weights for layers (NIR = 2and other bands = 1) should be sufficient to collectively delin-eate mangrove patches in the region, and they were thus appliedfor the segmentation process in this analysis.

The spectral difference segmentation algorithm (in eCogni-tion 8.0) was then applied to merge finely segmented objects.The weights for layers in this step were set as those usedin the previous step. An illustration of segmentation resultsshow small objects of land-cover types that could be explic-itly identified with this approach (Fig. 2). Based on the existingLULC maps, visual interpretation of Google Earth imagery,and information from the field surveys, four LULC classeswere identified for image classification, including 1) mangroveforest; 2) mixed mangrove/aquaculture (i.e., areas where man-grove forest was mixed with aquaculture farms constructedinto scattered mangrove shrubs); 3) brackish aquaculture; and


4) others (i.e., areas allocated for agriculture such as rice andupland crops and orchards/fruit trees planted along roads).

2) Classification: The standard nearest neighbor algorithmin eCognition 8.0 was applied to perform the supervised imageclassification. This method computes the Euclidean distancefrom the observation to be classified to the nearest neighbor(i.e., training sample observation) and assigns it to that class[61]. The classification results yield values from 0 to 1 based onthe image object’s feature space distance to its nearest neighbor.The value of 1 indicates an image object identical to a trainingsample; otherwise, the feature space distance has a dependencyon the feature space distance to the nearest sample of a class.The means and standard deviations derived from the spectralbands obtained from the image segmentation were used forclassification.

The training data were chosen from the segmented imagesbased on the analysis of the existing LULC maps, Google Earthimagery, and field survey data. In eCognition 8.0, the “featureview” and the “feature space optimization” tools were used toselect training samples. The feature view was used to visuallyassess a feature of the segmented image in the grayscale. Thefeature space optimization tool is an algorithm that comparesthe features of the selected classes finding the combination offeatures that produces the largest average minimum distancebetween the training samples of different classes [62]. Thus,it allowed us to evaluate the best separation distance betweenthe samples of classes, which is important for selecting fea-ture combinations resulting in the best class separation distance.The training samples were carefully selected throughout thestudy region and evaluated using these tools to ensure the fea-ture space was accurately defined for each class. The featuredescribes spectral properties used in OBIA so that object statis-tics of the optimized feature space are calculated and usedfor classifying all image objects based on their nearest sampleneighbors (using the nearest neighbor algorithm).

In this study, the permanent water bodies (e.g., rivers, canals,and sea) were masked out using the normalized difference veg-etation index (NDVI), where its values are smaller than −0.1.Because the averaged size of mangrove patches in the studyregion was generally larger than 5 ha [46], a morphologicaloperation was thus applied to eliminate isolated small segments(<5 ha) from the classified maps.

3) Accuracy Assessment: Because LULC maps wereunavailable for the entire study period, this study relied onground reference data (Fig. 1) for accuracy assessment of the2003 and 2013 classification results. The ground reference mapwas prepared following the field surveys in 2013 by digitiz-ing homogenous sites for three classes: 1) mangrove forest,2) mixed mangrove/aquaculture, and 3) brackish aquacultureusing the existing LULC maps and Google Earth imagery asreference data sources. The map was converted to the rasterform (30 m resolution) and used for accuracy assessment.The accuracies of classification maps for 2003 and 2013 wereassessed using 2000 pixels randomly extracted from the groundreference map for each class to compare with those from theclassification maps. The error matrix was used to measure per-class classification accuracy using parameters including overallaccuracy, Kappa coefficient, and producer/user accuracies.

TABLE IIRESULTS OF ACCURACY ASSESSMENT FOR THE 2003 AND 2013 DATA

MF = mangrove forest; MMA = mixed mangrove/ aquaculture; BA = brac-kish aquaculture.

V. RESULTS AND DISCUSSION

A. Accuracies of the Classification Results

The classification accuracy assessment achieved by compar-isons between the classification maps and the ground referencedata for 2003 and 2013 indicated satisfactory results. The over-all accuracies and Kappa coefficients were, respectively, 84.2%and 0.76 for 2003, and 82.3% and 0.73 for 2013 (Table II).The higher level of producer accuracy (>95%) was generallyobserved for the mangrove forest class in the data for bothyears. The mixed mangrove/aquaculture class revealed the low-est producer accuracy probably due to spectral confusion duringthe process of image segmentation between the classes. Theeffects of mixed-pixel issues in areas where complex road andriver networks and aquaculture farms were developed couldlimit the image segmentation due spectral similarity of man-grove forests and associate land-cover types, especially whenvegetation is sparse or degraded, subsequently exaggeratingthe classification errors; however, this study used the groundreference data prepared from existing LULC maps and the high-resolution Google Earth imagery, so the resolution bias betweenthe classification maps and the ground reference data due toresolution difference could also be an intrinsic characteristicexaggerating the mapping accuracy.

B. Changes in Mangrove Forests

The classification results for 1979, 1989, 2003, and 2013indicated that mangrove forests generally shielded the river-banks in areas associated with the brackish-water marginbetween land and sea in the southern part of the study region(Fig. 3). The mangrove forests were broadly dominant in1979 [Fig. 3(a)]; however, impacts of coastal land-use changecaused a significant loss of the mangrove forests based on themaps of 1989 [Fig. 3(b)], 2003, and 2013 [Fig. 3(c) and (d)].Aquaculture development, especially shrimp farming widelyadopted in the 1980s, was the root cause of degradation anddestruction of mangrove forests.


Fig. 3. Distribution of mangrove forests in the study area in (a) 1979; (b) 1989;(c) 2003; and (d) 2013.

Fig. 4. Changes in mangrove forests within the study area during 1979–2013:(a) areas of mangrove forests and (b) change rate (%) in the extent of mangrove

forests calculated assj−si

si× 100, where sj and si are the areas of mangrove

forests in the years j and i, respectively.

Mangrove forests were heavily decreased from 1979 to 1989(Fig. 4). In this study, the 1979 map was obtained from theclassification of the Landsat MSS data. Considering the lowspatial and spectral resolution (four bands of visible and NIRonly), the classification accuracy would be lower than LandsatTM/ETM+ images. Change detection between dates cannoteliminate all uncertainties of these estimates of mangrove for-est areas based on the accuracy assessments. Nevertheless,the present approach provided alternative snapshots to signifythe multitemporal change detection of mangrove forests in thestudy region for each specified period. The results indicated thatthe areas of mangrove forests in 1979, 1989, 2003, and 2013were approximately 112 035, 58 744, 46 910, and 43 810 ha,respectively [Fig. 4(a)]. The overall conversion from mangroveforests to other land uses during this 34-year period (1979–2013) was remarkable, with a change rate of roughly −60.9%[Fig. 4(b)]. From 1979 to 1989, the change rate was highest(−47.6%) because shrimp culture was substantially adopted inthis region beginning in the 1980s [63] due to its economicbenefits [64]–[66]; however, the rate was changed to −20.1%between 1989 and 2003, partly driven by the environmen-tal concerns associated with brackish aquaculture, especiallyshrimp farming activities in the 1990s.

Fig. 5. Aquaculture and shrimp farm areas and shrimp production in Ca Mauprovince (1995–2011). The data were extracted from statistical yearbooksobtained from of the Department of Statistics of Ca Mau province.

The rapid development of small-scale shrimp farms duringthis period triggered shrimp disease outbreaks and water pol-lution [40], [67], leading to decreased shrimp production in1996, 1997, 1998, and 2003 (Fig. 5) [17], [55]. Interventionsby the government and support the agriculture-led growth pol-icy in the 1990s further tempered the degradation of mangroveforests. The goal of these interventions was to protect seawaterintrusion and promote intensive rice production [68] by con-structing a series of embankments and tidal sluices along thecoastline for saline water control and management [69]. Thisaction changed the salinity gradient of coastal shorelines andbrackish water environments to such an extent that a permanentfreshwater environment was created. In addition, an improvedmangrove management strategy was also adopted to reduce theland-use conversion rate of mangrove forests [70].

From 2003 to 2013, the change rate was approximately−6.6% because brackish water required for aquaculture pro-duction was cut down over the course of the government’sinterventions in the 1990s for agriculture-led growth in favor ofrice production. Shrimp farmers were forced to either abandontheir farms or convert the land to a less profitable rice produc-tion system. The ensuing social conflicts led to a revision of theagriculture-led growth policy in the 2000s, allowing farmers toagain develop shrimp farms, which explains the expansion ofshrimp culture after 2000 [71], [72] and the slight reduction inmangrove forests along the shorelines in the 2000s.

C. Deforestation and Reforestation/Afforestation of MangroveForests

The classification maps of each period were compared withinvestigate the mangrove deforestation, reforestation (i.e., prac-tice of planting mangrove forests in areas where they werepreviously cut down), and afforestation (i.e., establishment ofmangrove forests in areas where there were no forests) ratesbetween each of these paired years (i.e., 1979–1989, 1989–2003, 2003–2013, and 1979–2013) (Fig. 6 and Table III). Ingeneral, the overall mangrove deforestation rate during theperiod 1979–2013 was 74%, in which 30.8% and 36.1%,respectively, were observed due to the conversion of mangrove


Fig. 6. Changes in the extent of mangrove forests within the study area duringthe periods: (a) 1979–1989; (b) 1989–2003; (c) 2003–2013; and (d) 1979–2013.

TABLE IIICHANGE RATES OF MANGROVE DEFORESTATION, REFORESTATION, AND

AFFORESTATION DURING THE PERIODS 1979–1989 AND 2003–2013

The change rate calculated ascj−ci

si× 100, where cj and ci are the areas of

classes in the years i and j, respectively, and si is the total area of mangroveforests in the year j.MF, mangrove forest; MMA, mixed mangrove/aquaculture; BA, brackishaquaculture; WB, water body (mudflat).

forests to mixed mangrove/aquaculture and brackish aquacul-ture, especially extensive shrimp culture. A small portion ofmangrove forests (3.5%) was reforested, while the afforestedarea of mangrove forests was roughly 9.7%, mostly locatedin the western estuary mudflats in the region. The mangroveecosystems in this region were affected by tidal regimes. Thesoils were thus eroded along the shorelines of the eastern regionby water flows, making the eroded sedimentation of roughlyone million ton per annum deposited to the estuaries in thewestern part of the region [70].

From 1979 to 1989, the mangrove deforestation rate wasroughly 57.1% due to the rapid conversion to aquaculture(33.8%) and brackish aquaculture (12.7%). The reforestationand afforestation rates during this period were 1.6% and 8.1%,respectively. The fast-growing deforestation rate was explainedby the unprompted development of highly profitable aquacul-ture farming systems. From 1989 to 2003, the deforestationrate was, however, slightly reduced to be approximately 56.3%,in which 42.1% was attributed to the development of mixedmangrove/aquaculture farming systems. The reforestation andafforestation rates for this period reached 26.3% and 9.9%,respectively, partly due to the emergence of environmentalconcerns related to the shrimp culture and the interventionsimposed by the government in support of the agriculture-ledgrowth policy. From 2003 to 2013, the deforestation rate wasapproximately 35.7%, in which 18.1% was attributed to themangrove conversion to the mixed mangrove/aquaculture sys-tem. The reforestation and afforestation rates were only 26.4%and 2.7%, respectively.

VI. CONCLUSION

The OBIA was applied to process multitemporal Landsatimageries to investigate the decadal changes in the extent ofmangrove forests in Ca Mau peninsula of South Vietnam. Theresults further confirmed that OBIA is transferrable for changedetection of mangrove forests to any other study region. Theclassification results indicated an agreement with the groundreference data. The overall accuracies and the Kappa coeffi-cients achieved from the classification were 84.2% and 0.76 for2003, and 82.3% and 0.73 for 2013, respectively. The classifica-tion maps of each period were compared to derive change ratesin the extent of mangrove forests. From 1979 to 1989, approx-imately 64 000.6 ha of mangrove forests (57.1%) had beenconverted to other land uses. The values in the two subsequentperiods 1989–2003 and 2003–2013 were 33 098.9 ha (56.3%)and 16 753.4 ha (35.7%), respectively, mainly attributed tocontinued aquaculture activities. The overall conversion ofmangrove forests to other land uses from 1979 to 2013 was82 928 ha (74%) with little effort (3.5%) made to restoremangrove forests during this 34-year period. Overall, our anal-ysis demonstrates OBIA approach for mangrove mapping andchange detection in Ca Mau peninsula with Landsat data iscapable of providing quantitative information of long-termland-use change for coastal management.

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Nguyen-Thanh Son received the B.Sc. degree inenvironmental and natural resources managementfrom Can Tho University, Vietnam, the M.Sc. degreein natural resources management from the AsianInstitute of Technology (AIT), Thailand, and thePh.D. degree in geoinformation engineering from theDepartment of Civil Engineering, National CentralUniversity (NCU), Taiwan, in 2000, 2005, and 2011,respectively.

He is a Postdoctoral Fellow at the Center for Spaceand Remote Sensing Research (CSRSR), NCU,

Taiwan. His research interests include GIS and remote sensing applications foragricultural and environmental monitoring.

Chi-Farn Chen received the B.Sc. degree fromthe Department of Atmospheric Physics, NationalCentral University (NCU), Taiwan, the M.Sc. degreefrom the Department of Meteorology, University ofWisconsin-Madison, Madison, WI, USA, and thePh.D. degree in civil and environmental engineeringfrom the University of Wisconsin-Madison, in 1974,1981, and 1989, respectively.

Currently, he is a Professor with the CSRSR, NCU,Taiwan. His research interests include GIS, digitalimage processing, and integration of remote sensing

and GIS for environmental applications.

Ni-Bin Chang received the B.Sc. degree in civil engi-neering from the National Chiao-Tung University,Hsinchu, Taiwan, and the M.Sc. and Ph.D. degreesin environmental systems engineering from CornellUniversity, Ithaca, NY, USA, in 1983, 1989, and1991, respectively.

Currently, he is a Professor with the Universityof Central Florida, Orlando, FL, USA. His researchinterests include the areas of environmental, ecologi-cal, and hydrological remote sensing.

Cheng-Ru Chen received the B.Sc. and M.Sc.degrees in geography from the Department ofGeography, National Taiwan University, Taiwan, andthe Ph.D. degree in geo-information engineeringfrom the Department of Civil Engineering, NationalCentral University (NCU), Taiwan, in 1995, 1997,and 2014, respectively.

Currently, he is a Research Scientist with theCSRSR, NCU, Taiwan. His research interests includeGIS, multisensor data fusion, and phenology-basedremote sensing for agricultural and environmental

monitoring.

Ly-Yu Chang received the Master’s degree in spacescience and the Ph.D. degree in civil engineeringfrom the National Central University (NCU), Taiwan,in 1995 and 2013, respectively.

Currently, he is an Associate Research Engineerwith the CSRSR, NCU, Taiwan. His research inter-ests include photogrammetry, digital image process-ing, and geographic information systems.

Bui-Xuan Thanh received the Ph.D. degree inenvironmental engineering and management froman exchange program between the Asian Instituteof Technology (AIT), Thailand, and the InstituteNational des Sciences Appliquées, France.

Currently, he is an Associate Professor with theFaculty of Environment and Natural Resources, HoChi Minh City University of Technology, Vietnam.His research interests include membrane processes,wastewater treatment, and environmental remotesensing.

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