IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH ...tateishi-lab/pdf/2013/Tana.pdf · wetlands on a relatively large spatial extent with some success [27], [28]. In addition, well-designed

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 6, DECEMBER 2013 2391

Wetlands Mapping in North America by DecisionRule Classification Using MODIS and Ancillary Data

Gegen Tana, Husi Letu, Zhongkai Cheng, and Ryutaro Tateishi

Abstract—An up-to-date wetlands map based on remote sensingdata at a continental scale is urgently needed for estimating globalenvironmental change. In this study, a wetlands map of NorthAmerica was developed using Moderate Resolution ImagingSpectroradiometer (MODIS) data obtained in 2008 and ancillarydata. For this purpose, a decision rule classification method wasdeveloped relied upon the hierarchical characteristics of landtypes and prior knowledge about the geographical location of wet-lands. Two hierarchical levels of land types were used to extractwetlands. At the first level, non-vegetation land types includingwater, snow, urban, and bare areas were separately extractedfrom vegetation land types using threshold methods. At the secondlevel, wetlands were discriminated from non-wetland vegetationland types with the MODIS tasseled cap (brightness, greenness,and wetness) indices using the decision tree method. In addition,elevation data were used to build the elevation mask and a climatemap was used to subdivide the study area into five sub-regions.In the quantitative accuracy assessment, user’s and producer’saccuracies of wetlands for the whole study area were calculated as80.3% and 83.7%, respectively. In a comparison with two existingglobal land cover datasets, GLC2000 and IGBP DISCover, ourresults show significant improvement in extracting coastal andnarrow types of wetlands. This study indicates that decision ruleclassification, integrated with multi-temporal MODIS data andancillary data, is useful to develop an improved wetlands map at acontinental scale.

Index Terms—Ancillary data, climate map, elevation data, hier-archical classification, MODIS tasseled cap indices, wetlands map-ping.

I. INTRODUCTION

W ETLANDS are among the world’s most importantecosystems [1]. They are valued for their hydrological,

biogeochemical, and ecological functions [2]. For example, inhydrology, wetlands perform a very important role in flood con-trol, groundwater supplement and regulation ofwater quality [3].In biogeochemistry, wetlands have a critical role in processingmethane, carbon dioxide, and nitrogen, aswell as in sequesteringcarbon [4]. Inecology,wetlandsare considered tohave the richest

Manuscript received October 14, 2012; revised January 25, 2013; acceptedJanuary 29, 2013. Date of publication April 26, 2013; date of current versionNovember 21, 2013. This work was supported in part by JSPS Grant-in-Aidfor Scientific Research (KAKENHI) (Project No. 22220011). (Correspondingauthor: G. Tana.)G. Tana is with the Graduate Schools of Science, Chiba University, Chiba

2638522, Japan (e-mail: [email protected]).H. Letu is with the Research and Information Center, Tokai University, Tokyo

1510063, Japan (e-mail: [email protected]).Z. Cheng is with the Graduate Schools of Science, Chiba University, Chiba

2638522, Japan (e-mail: [email protected])R. Tateishi is with the Center for Environmental Remote Sensing, Chiba Uni-

versity, Chiba 2638522, Japan (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/JSTARS.2013.2249499

biodiversity and to provide a valuable habitat for fish and otherwildlife, includingendangeredand threatenedspecies [5].However, in recent years, global wetland systems have been

threatened in terms of both their function and spatial extent. Themost likely reason for this is thought to be climate change [6].Coastal wetlands have been affected by rising sea level and as aresult there is an increase in flood risk [7]. A warmer climate ac-companied by changes in precipitation patterns affects the watersupply of inland wetlands and changes their hydrological con-ditions and functions [8], [9]. The change in climate increasesmethane and carbon dioxide emissions [10], and wetlands pro-vide a positive feedback to climate change because of the in-creased amount of methane and carbon dioxide emitted fromthem [11]. In addition to climate change, changes in land coverdue to human activity are considered to be another reason forthe changes occurring in wetland ecosystems. Urbanization isthe major cause of loss of coastal wetlands, and it affects thestructure and function of coastal wetlands by modifying the hy-drological and sedimentation regimes, changing the dynamicsof nutrients and increasing levels of chemical pollutants [12].Urban sprawl due to population increases in developing coun-tries has also caused a degradation of wetlands and destroyedwetland ecosystems [13]. In low-lying wetlands, the degrada-tion that occurred is often associated with fragmentation of thelandscape by agriculture [14].As wetlands are among the most important, and also the

most threatened, ecosystems, the assessment of the geographicdistribution of wetlands together with their dynamic changesis becoming increasingly important. Satellite remote sensingprovides a considerable opportunity to monitor wetlands be-cause of its capability for data acquisition on a global scale,including relatively inaccessible areas, and its capability forcontinuous large area coverage [15], [16]. The spatial distri-bution and extent of wetlands at a regional scale have beenwell studied using satellite remote sensing technologies [17].However, compared with regional-scale wetlands studies whichconcentrate more on one particular wetland ecosystem and itssurrounding uplands, a wetlands study on a global scale ismore valuable for assessing global environmental change. Inthe early stages of global wetlands studies, due to the lack ofsatellite data, most global wetlands maps were derived fromfieldwork [4], [18], [19]. The first satellite data-based globalwetlands map was created with SSM/I, ERS-1, and AdvancedVery High Resolution Radiometer (AVHRR) data during theperiod from July 1992 to June 1993 at a 10-km spatial reso-lution [20]. However, these datasets were not quantitativelyvalidated and because the global extent of wetlands has beenchanging rapidly in recent years, these maps do not accuratelyrepresent the present global distribution of wetlands.

1939-1404 © 2013 IEEE

2392 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 6, DECEMBER 2013

Recently, a number of global land cover maps have beendeveloped using satellite remote sensing data at spatial resolu-tions ranging from 1-km to 300-m in which wetland is treatedas one of the individual land cover classes. The correspondingdatabases are International Geosphere- Biosphere ProgrammeData and Information System (IGBP-DIS) DISCover [21],Moderate-resolution Imaging Spectroradiometer (MODIS) landcover product [22], Global land cover datasets Global landCover 2000 (GLC2000) [23], Global Land Cover by NationalMapping Organizations (GLCNMO) [24] and Global landCover Map for the year 2005 (GLOBCOVER) [25]. Thesemaps classify permanent or temporary wetlands according to theInternational Geosphere-Biosphere Programme (IGBP) clas-sification and the Land Cover Classification System (LCCS).However, the spatial distributions of wetlands have not beenfully characterized inmost of these databases because of the dataand methodologies used. For example, in the IGBP DISCover,only wetlands with large area extent were extracted because itis difficult to extract them with NDVI values [21]. Similarly,the 300-mMERIS data used for GLOBCOVER are not suitableto identify wetlands because the data lack information froma mid-infrared channel which can be used to determine theinundation pattern [26]. In the GLCNMO, because single-stepsupervised classification method is not suitable for mappingwetlands over large spatial extents, the commonwetland areas inGLC2000 and IGBP-DISCover were used [24].Since it is difficult to map wetlands at a large geographic

scale with high accuracy from single-source satellite data,some researchers have used the multiple sources of data to mapwetlands on a relatively large spatial extent with some success[27], [28]. In addition, well-designed rule-based classificationtechniques have been developed to improve the accuracy ofwetlands map [29], [30]. In these studies, rule-based methodswere developed to map wetlands with satellite and ancillarydata, including Digital Elevation Models (DEM), soil maps,and existing digital land information. Li et al. (2005) concludedthat the rule-based methods integrated with other classificationmethods (e.g., decision tree) improved wetlands mapping ac-curacy when compared with traditional classification methods[30]. A decision rule model is one in which the classificationof a feature is guided by a set of decision rules [31]. Therefore,there is a significant potential for mapping wetlands at a conti-nental scale by integrating a decision rule classification methodwith spatial environmental data and satellite imagery.The main objective of this study is to develop a wetlands

map in North America using a combination of multi-temporalMODIS and ancillary data. To achieve this goal, we presentthe generation of the decision rule classification method, anddatabases being used to map wetlands. We then show the re-sulting map and validate it by using a confusion matrix. We fi-nally compare the results with existing global land cover mapsto confirm the improvement of our map over conventional wet-land datasets.

II. STUDY AREA

The study area covers the whole North American continentwith an area of approximately 24.7 million square kilometers.

It includes Canada, the United States, Mexico, the countries ofCentral America, and the islands of the Caribbean. The NorthAmerican continent is rich in biological resources and it con-tains nearly 30% of the world’s wetlands [32]. There are varioustypes of wetlands throughout the North American continent,from permanently inundated wetlands to seasonally inundatedwetlands, and from coastal saline water wetlands to inland fresh-water wetlands. Canada, the United States, and Mexico havemost of the wetland areas of the continent. Peatlands are widelydistributed in Alaska and the northern part of Canada [33]. Thetemperate regions of North America, including southern Canadaand most of the United States, contain many types of wetlands,such as bogs, fens, marshes, and forested swamps [34]. Coastaland estuarine wetlands are found in the entire Atlantic regionand some parts of the Pacific region of the North American con-tinent [32]. Mexico has most of the tropical coastal wetlands,including mangrove swamps and salt marshes.

III. DATA SOURCES

A. MODIS Land Cover Product

The MODIS 500-m reflectance product (MCD43A4 version5) was used as the main source data. MCD43A4 is a 16-daycomposite, level-3, nadir BRDF-adjusted reflectance datasetderived from the Terra and Aqua satellites in a sinusoidalprojection [35]. It includes seven spectral reflectance bands,which are designed for land remote sensing: band 1 (redreflectance, 630–670 nm), band 2 (near-infrared reflectance,841–876 nm), band 3 (blue reflectance, 459–479 nm), band 4(green reflectance, 545–600 nm), band 5 (middle-infrared re-flectance, 1230–1250 nm), band 6 (middle-infrared reflectance,1628–1652 nm), and band 7 (middle-infrared reflectance,2105–2155 nm). A full year of MCD43A4 data (23 periods)acquired for the study area (57 tiles) in 2008 were downloadedfrom the U.S. Geological Survey (USGS) Land ProcessesDistributed Active Archive Center (https://lpdaac.usgs.gov/).These original data were mosaicked and reprojected by usingMODIS Reprojection Tool (MRT). Landsat imagesavailable from the Global Land Cover Facility were used tocheck the geometric accuracy of MODIS data. The root meansquare error (RMSE) calculated from the geometric check waslower than one pixel (500 m). To get cloud-free data, linearinterpolation between two cloud-free pixels was used. Thistechnique was selected because it requires small amounts ofvalid data (a minimum of two pixels), and because it is easy toconduct using large-area satellite data. Linear interpolation ofMODIS data from 2007 and 2009 were used to get cloud-freedata for 2008.

B. MODIS Tasseled Cap Indices

The tasseled cap transformation is one of the spectral infor-mation enhancing technologies for multi-band satellite data. Itreduces the data volume with minimal information loss yet itsspectral features can also be directly associated with the impor-tant physical parameters of land surface [36], [37]. The transfor-mation formula has been developed for the use of different satel-lite sensors. The MODIS tasseled cap transformation, which is

TANA et al.: WETLANDS MAPPING IN NORTH AMERICA BY DECISION RULE CLASSIFICATION USING MODIS AND ANCILLARY DATA 2393

TABLE ITHE TASSELED CAP COEFFICIENTS FOR MODIS DATA

calculated by taking linear combinations of MODIS seven spec-tral bands, includes three orthogonal indices called brightness,greenness and wetness (BGW) [38].The transformation coefficients developed by Lobser and

Cohen [38] were shown in Table I. MODIS tasseled cap bright-ness, greenness, and wetness indices are highly correlatedwith albedo, photosynthetically active vegetation present, andmoisture content in both leaves and soils, respectively [37]. Thecombinations of multi-temporal tasseled cap indices have beenfound useful for vegetation classifications as well as recognitionof wetlands, because of their sensitivity to phenological changeof vegetations [28], [39].

C. Ancillary Data

In addition to the MODIS tasseled cap indices describedabove, digital elevation model and a climate map were used asthe ancillary data for assisting the mapping process.Digital elevation model: As most of the wetlands are located

in relatively low altitude areas, elevation data is useful for re-ducing spectral confusion from land cover classes of high alti-tude areas where wetlands do not probably exist [27]. Thus, theglobal digital elevation model (DEM) data GTOPO30, with ahorizontal grid spacing of (approximately 1 km), were usedto create an elevation mask. The data were available at the U.S.Geological Survey (USGS) Earth Resources Observation andScience (EROS) Center (http://eros.usgs.gov/).Climate map: Wetland is a complex land type because the

spectral reflectance of wetlands is related not only with the veg-etation structure but also the soil moisture. In addition, the studyarea spans tropical to polar climatic zones and includes manyland cover types as well as various types of wetlands. Thus, toprocess the whole study area at one time becomes extremely dif-ficult. Climate maps have been proven to have correlative rela-tionship with land cover types [40]. To map wetlands more effi-ciently, the Köppen-Geiger climate classification map [41] wasused to sub-divide the study area into several sub-regions. Thedata was downloaded from the Institute for Veterinary PublicHealth (http://koeppen-geiger.vu-wien.ac.at/). This is referredto as the K-G climate map in the following text.

D. Reference Data

The Ramsar Convention database, U.S. National WetlandsInventory (NWI) data and Canadian Wetland Inventory (CWI)data were used for creating the training data of wetlands. TheRamsar Convention is an intergovernmental treaty for the con-servation and sustainable utilization of wetlands [32]. It containsinformation on wetlands around the world and provides pointdata of wetlands from a Web GIS system. The NWI database,produced by the U.S. Fish and Wildlife Service, provides dig-ital wetland data for the United States (approximately 82% ofthe conterminous states) [42]. The vector data were downloaded

from the Fish andWildlife Service National Wetlands Inventorywebsite (http://www.nwi.fws.gov). The Environment Canada-Canadian Wildlife Service (CWS) produced the CWI to pro-vide digital wetland data for parts of Canada via the website ofDuck Unlimited Canada (http://maps.ducks.ca/cwi/). The NWIand CWI databases were used as complementary informationof Ramsar Convention database for creating the training data ofwetlands with small and narrow types.The existing global land cover datasets GLCNMO,

GLC2000, IGBP DISCover and GLOBCOVER were usedto create the training data for non-wetland vegetation landtypes. These maps were developed in response of the need forinformation about land cover and land cover dynamics. Thecharacteristics of these four land cover products are presentedin Table II. The Land Cover Classification System (LCCS) usedfor GLCNMO, GLC2000 and GLOBCOVER was developedby the Food and Agriculture Organization of the United Nation(FAO). The LCCS is a comprehensive, standardized, and apriori classification system which meets the needs of mappingexercises [43].

IV. METHODOLOGY

In this study, a decision rule classification method was used tomap wetlands in North America by MODIS data and ancillarydata. For this purpose, the Land Cover Classification SystemVersion 2 (LCCS2) was used to define wetlands. Wetlands inthe LCCS2 were described as follows:• The main layer consists of closed to open woody vegeta-tion with crown cover of 15–100%. The height of the veg-etation is in the range of 2–7 m. Water quality is fresh,brackish and saline.

• The main layer consists of closed to open herbaceous veg-etation with crown cover of 15–100%. The height is in therange of 0.03–3 m. Water quality is fresh, brackish andsaline.

To build the decision rule classifier model for extracting wet-lands from non-wetland land types, all land types were hierar-chically divided into two levels based on the land cover legendof LCCS2 (Fig. 1). At the first level, all land types were dividedas vegetation and non-vegetation (urban, bare area, water, andsnow/ice). At the next level, vegetation was classified by dom-inant vegetation type: wetland, forest, shrub, herbaceous, crop-land, or sparse.By applying the hierarchical structure of land types devel-

oped in this study and prior knowledge about the location ofwetlands, the decision rule classifier model was developed(Fig. 2). Benefits of this classification technology include that1) combining the advantages of different data sources, suchas satellite data and ancillary data, improves both the spatialand spectral resolution of classification data [29], and 2) themulti-level classification process is conducted by selectingoptimal data at each level to separate land types, and permitsexpansion or refinement of results without revising the entireclassification [30].The classification procedure includes the following steps:

(A) Separate non-vegetation land types (urban, bare area,water, and snow/ice) from vegetation land types by thethreshold method. (B) Collect training data for wetlands and


TABLE IIOVERVIEW OF EXISTING GLOBAL LAND COVER PRODUCTS

Fig. 1. Hierarchical structure of land types. and indicate levels of thehierarchy as referenced in the text.

non-wetland vegetation land types. (C) Use the DEM data togenerate an elevation mask using the wetland training data. (D)Discriminate wetland and non-wetland vegetation land typesby the decision tree method using the wetland and non-wetlandvegetation training data for each sub-region, subdivided by theK-G climate map. (E) Generate the final wetlands map by inte-grating the intermediate results of (A), (B), and (D). Details ofthe classification process are presented in Sections IV-A–IV-E.

A. Extraction of Non-Vegetation Land Types

The spectral characteristics of non-vegetation land types(urban, bare area, water, and snow/ice) show little changeamong seasons as compared with vegetation land types. There-fore, the masks for these land types were created independentlyusing the following thresholds and image bands: 1) For creatingthe urban and bare area mask, the global land cover productGLCNMO was used. In the GLCNMO, these two classes showhigher category accuracies and there are almost no commissionerrors with wetland [24]. 2) Water in low altitude areas hasunique spectral characteristics all year round, while the spectralcharacteristics of water in high altitude areas vary with theseason, because it is probably covered by snow or ice in winter.However, water shows a consistent value in the tasseled cap

brightness index images in the summer season. Therefore, thetasseled cap brightness image of Period 13 (July 13–July 28composite) was used to create the water mask. The threshold forwater is selected at 12. 3) The snow/ice threshold was definedfor the surfaces which are covered by snow/ice for more than 9months each year in all years. For this reason, the mean tasseledcap brightness image from all of the 23 periods was used tocreate the snow/ice mask. The threshold for snow/ice was setat 101.

B. Training Data

Training data (wetland and non-wetland vegetation) were col-lected for making the elevation mask and discriminating wet-lands from non-wetland vegetation land types.Collecting training data for wetlands at a continental scale

is difficult because of the widely ranged locations and hetero-geneous spectral characteristics of wetlands, as well as hydro-logic patterns (permanently inundated and seasonally inundatedareas). The Ramsar Convention database because of its com-prehensive information of wetlands at a global scale was usedfor creating training points for wetlands with large spatial ex-tent. However, the Ramsar definition of “wetlands” is broad, in-cluding not just vegetated wetlands but also lakes, coral reefs,and even underground caves [5]. Therefore, vegetated wetlandsites in the Ramsar Convention database that were from homo-geneous and representative locations (42 sites) were selected.To collect training sites for narrow and small types of wet-lands that are not included in the Ramsar database, NWI andCWI data were used. After determining the central coordinatesof the training sites, summer season (July–September) Landsat

data from 2005–2009 were used to delineate trainingpolygons. Finally, 486 training polygons (a total of 6,584 pixels)were determined for wetlands.Training data for non-wetland vegetation land types were

collected from the common area of existing global mapsGLCNMO, GLC2000, IGBP DISCover, and GLOBCOVER.


Fig. 2. Flow chart of the decision rule classification method. Characters in the green boxes indicate the separation of non-vegetation land types from vegetationtypes, characters in the yellow box indicate the separation of wetlands from non-wetland vegetation land types, and characters in the blue box indicate the elevationmask.

Although some vegetation types show lower accuracies inindividual maps, the common areas with the same land coverlabel in the four maps assumed to have high accuracy [45].For this reason, the generalized global land cover legend basedon LCCS that are developed by Herold et al. [45] was used toharmonize the common areas with the same land cover labelbetween the different maps. In addition, training data collectedfor the GLCNMO product were used. These data are availableat the website of Center for Environmental Remote Sensing,Chiba University (http://www.cr.chiba-u.jp/databaseGGI.htm).After that, the Landsat data and Google Earth wereused to interpret the homogeneity of collected training data.Finally, 1,334 training polygons (a total of 16,561 pixels) werecollected for non-vegetation land types.

C. Elevation Mask

Several studies have demonstrated that the use of DEM datahas a significant effect on wetlands mapping [30], [46]. In thisstudy, 1-km DEM data was used to mask high altitude areaswhere wetlands are not likely to occur. A threshold methodwas used to determine the mask area. The threshold for DEMdata was determined based on the highest elevation value ofthe training polygons of wetlands (1,236 m). The applicationof DEM data helps to reduce the spectral confusion from vege-tation land types of high altitude areas.

D. Discriminating Wetlands From Non-Wetland VegetationLand Types

In this study, the decision tree method was applied to discrim-inate wetlands from non-wetland vegetation land types by usingthe wetland and non-wetland vegetation training data.As vegetation communities are broadly homogeneous within

a single climate zone, the study area was sub-divided into fivesub-regions by using the K-G climate map and prior knowledgeabout the climate-vegetation relationship (Fig. 3). The polar cli-mate zone is not included, because the polar climate zone isalmost completely covered by snow and ice and any wetlandsthat do exist there are extremely ephemeral. Each sub-regionwas treated separately for applying the decision tree method.The use of sub-region improves the stability of the decision treemodel than conventional method which deals with the wholestudy area.A decision tree method successively partitions the input

training data into more and more homogeneous subsets byproducing optimal rules or decisions, which maximize theinformation gained and thus minimize the error rates in thebranches of the tree [47], [48]. In order to preserve the homo-geneity of the training data, the training data of each land type(wetland and non-wetland vegetation) were assigned to severalsubclasses using the seasonal tasseled cap greenness patterns.See5.0 and Cart [49] developed by the U.S. Geological Survey(USGS) were used to support the decision tree classification.


Fig. 3. Sub-regions determined from the Köppen-Geiger climate classification map.

Fig. 4. Wetlands map of North America developed by the decision rule classification method.

The main function of See5.0 is to find the feature characteristicsin the training samples and automatically create decision rulesbased on training data. Cart has been embedded in ERDAS8.6and the main function of it is to complete the decision treeclassifier. In order to capture the phenological and hydrologicaldifferences between wetlands and non-wetland vegetationtypes across seasons, 23 periods MODIS tasseled cap bright-ness, greenness and wetness indices of 2008 were applied toSee5.0 for building decision rules in each sub-region. Finally,the decision rules derived from See5.0 were imported to Cartmodule to discriminate wetlands from non-wetland vegetationland types.

E. Integration of the Intermediate Results

To get the final wetlands map, the classification results de-rived from each step were integrated into a single wetlands map

for the whole study area. The intermediate results include non-vegetation mask (urban, bare area, water, snow/ice), elevationmask and wetlands extracted from non-wetland vegetation landtypes. Then, the integrated map was filtered to remove isolatedpixels and replace them with the surrounding land cover typesto eliminate clumps smaller than the minimum resolution of thetraining polygons (3 3 MODIS pixels).

V. RESULTS AND DISCUSSION

The map includes wetlands, non-wetland land, and water isshown in Fig. 4. A quantitative accuracy assessment was con-ducted for evaluating the accuracy of the map. Additionally,spatial and area comparisons between our wetlands map andexisting global land cover maps (GLC2000, IGBP DISCover)were performed.


TABLE IIICONFUSION MATRIX FOR WETLANDS MAP DEVELOPED IN THIS STUDY. USER’S ACCURACY, PRODUCER’S ACCURACY, COMMISSION ERROR AND OMISSION

ERROR ARE SHOWN FOR EACH SUB-REGION

A. Accuracy Assessment of the Map

Evaluation of the quality of a map derived from remotesensing data is important not only for verifying the qualityof the map and its fitness for a particular purpose, but alsofor understanding error and its likely implications [50]. Manymethods have been used for evaluating the accuracies of themap. The most popular ones include the confusion matrix, usedfor maps derived from most types of classification methods[50], and the root mean square error (RMSE) and coefficientof determination , typically used for maps derived fromthe regression tree method [51]. In this study, the confusionmatrix was used to measure the categorical accuracies andto calculate the kappa coefficient of the map (Table III). Thekappa coefficient is a measure of chance-corrected agreementbetween the actual land cover classes and the classified landcover classes in remote sensing [52]. In this study, the kappacoefficient was calculated using the following equation:

(1)

where represents the number of rows in the matrix, repre-sents the number of observations in row and column andand are the marginal totals of row , column , respectively,and represents the total number of observations in the matrix.

For most purposes, the kappa values are interpreted as follows:(1) values indicate excellent agreement beyond chance,(2) values to indicate fair to good agreement be-yond chance, and (3) values indicate poor agreement be-yond chance [53].The stratified random sampling method, in which a simple

random sample is obtained in each stratum, was used to selectthe sample pixels. A total of 2,400 sample pixels, selected in-dependently from training pixels, were used for the accuracyassessment. Because collecting ground truth data for the wholestudy area is extremely difficult, reference data including U.S.National Wetlands Inventory data, Canadian Wetland Inventorydata and Google Earth imagery were used to visually interpretand determine the real land cover type (wetland or non-wetland)represented in the sample pixels.From Table III, the user’s and producer’s accuracies of wet-

lands, and the kappa coefficient for the whole study area, werecalculated as 80.3%, 83.7%, and 0.76, respectively. This sug-gests that the map shows an acceptable range of agreement withthe reference data used for the accuracy assessment [54]. Sub-region II shows the highest kappa coefficient (0.88) of all sub-re-gions. For this sub-region, the DEM data contributed most tominimizing the spectral confusion from the vegetation of moun-tain areas. Because of this sub-region’s arid climate, the vegeta-tion types were relatively homogeneous and wetlands were suc-cessfully extracted. In contrast, Sub-region V shows the lowestkappa coefficient of 0.64. This region is partly composed of a


TABLE IVWETLAND AREAS AND PERCENTAGES BY SUB-REGION IN GLC2000, IGBP DISCOVER AND OUR WETLANDS MAP

cold climate zone and has the largest area of all the sub-regions.In this sub-region, some regions of the Alaskan and Canadianarctic and sub-arctic tundra melt in the summer, which increasesthe amount of small wetlands [54], [55]. This makes the landtypes of this sub-region more heterogeneous with regard to col-lecting high quality training data from the available referencedata. Further, the high percentage of omission errors (27%) forwetlands shown in Table III for this sub-region could be ex-plained by the large amount of small wetlands in a heteroge-neous environment in regard to MODIS spatial resolution. Thekappa coefficients of Sub-regions I, III, and IV were 0.76, 0.82,and 0.72, respectively. Sub-region I is mainly a tropical climatezone and the main vegetation type is tropical forest. Therefore,most of the commission and omission errors are from forest.Most of Sub-region III and IV are temperate climate zones.These two sub-regions are rather flat compared with the otherthree sub-regions, and most of the agricultural lands in NorthAmerica are located in this region.

B. Comparisons With Wetlands in Global Land CoverProducts GLC2000 and IGBP DISCover

To provide numerical and spatial agreement or disagreementof existing wetlands databases, our wetlands map was comparedwith two global land cover products GLC2000 and IGBP DIS-Cover. In the spatial comparison, NWI vector data of the UnitedStates and LCC2000-V data for Canada were used as referencedata.In the total area comparison, GLC2000 shows a total area

of 101,278 km2; IGBP DISCover, 254,353 km2; and our wet-lands map, 486,665 km2. To clarify the distribution charac-teristics of wetlands, the areas and percentages of wetlands ineach sub-region were calculated for the three maps (Table IV).In each of the three maps, Sub-region V has the highest area ofwetlands, while Sub-region II has the smallest. All three mapswere also in good agreement with wetland areas for Sub-re-gions I and II. However, in Sub-regions III, IV, and V, our wet-lands map indicates much more wetland areas than the othertwo maps.To clarify the area difference in the three maps, spatial

comparisons at a coarse level were performed in Sub-region III(Fig. 5) and Sub-region V (Fig. 6). NWI vector data of 17 statesof the United States and LCC2000-V data of 3 provinces ofCanada were used for visual references. As shown in NWI map(Fig. 5(a)), there are many coastal wetlands and other long,narrow types of wetlands along the rivers (e.g., Mississippi andBrazos rivers). In this sub-region, our wetlands map (Fig. 5(b))as well as GLC2000 (Fig. 5(c)) and IGBP DISCover (Fig. 5(d))have a good spatial agreement with NWI data for wetlands

with large areas. However, for many coastal and other narrowwetlands along the rivers, our wetlands map has a better spatialagreement with NWI data than GLC2000 and IGBP DISCover.This confirms that our wetlands map provides significant im-provement over the other two maps for extracting coastal andnarrow types of wetlands.In Fig. 6, Hudson Bay lowlands included in Sub-region V

was compared in the three maps. As shown in the figure, ourwetlands map (Fig. 6(b)) has a better spatial agreement withLCC2000-V data (Fig. 6(a)) than GLC2000 (Fig. 6(c)) andIGBP DISCover (Fig. 6(d)). However, in the southern partof Hudson Bay lowlands, our wetlands map failed to extractsome isolated and even large wetlands. Possible causes forthe underestimation of wetlands in our map are as follows:(1) the spatial resolution of MODIS data was not be sufficientfor extracting some small and isolated wetlands, and (2) puretraining data for wetlands were difficult to collect because ofthe heterogeneity of this sub-region.

VI. CONCLUSIONS

Producing an accurate continental wetlands map based onsatellite data with automated classification procedures has sig-nificant meaning for global environmental studies. However,this procedure is difficult because 1) there are many types ofwetlands around the world, 2) wetlands exist in the transitionzone between aquatic and terrestrial areas, and thus containspectral characteristics of both vegetation and water, and 3) dataacquisition at a continental scale presents unique difficultiesand high data costs.In this study, we developed a decision rule classification

method to map wetlands in North America using the multi-tem-poral MODIS data of 2008 and ancillary data. Results fromaccuracy assessment and comparison with existing global landcover datasets demonstrated that the decision rule classificationmethod developed by integrating the advantages of differentdata improved the spectral and spatial resolution of the clas-sification data, and proved effective for mapping wetlandsat a continental scale. The multilevel classification methodconfirms the conclusions of a previous study that it helps re-duce the potential for confusion among land types because thehierarchical classification required the distinction of fewer landtypes at each node of the hierarchy [57]. The method is alsosuperior to the conventional method because each classificationlevel focuses on one particular sensor and land type, whichmakes modification possible even after classification to get amore accurate wetlands map. Furthermore, all input data forthe mapping process, including MODIS tasseled cap indices,elevation data, and the K-G climate map, are freely available


Fig. 5. Spatial comparisons of wetlands in Sub-region III of Fig. 3. (a): National Wetlands Inventory Wetland data; (b) Our wetlands map; (c) Wetland in theGLC2000; and (d) Wetland in the IGBP DISCover. Numbers in the figure (a) indicates the site areas. The stripe parts indicate that there are no digital wetland datain National Wetlands Inventory for comparison.

Fig. 6. Spatial comparisons of wetlands in Sub-region V of Fig. 3. (a): LandCover, Circa 2000-Vector data. (b), (c) and (d) are the same as Fig. 5.

and have been proved useful for discriminating wetlands fromother land types.However, our results also include some commission and

omission errors that arise from the spatial resolution of MODISdata, and from classification errors from the presence ofspectrally similar vegetation types such as forests, cropland,herbaceous areas, and water. Future work will include im-proving the individual class mask of non-vegetation landtypes (water, snow, urban, and bare areas) and reclassifying

the sub-regions which have lower categorical accuracies byhigh-quality training data collected from real-time groundtruth reference data. The seasonal characterization of wetlands,including inundation patterns, should also be considered. Morerepresentative validation points from the transition zone be-tween aquatic and terrestrial areas are required for evaluatingthe map.

ACKNOWLEDGMENT

The authors would like to thank Dr. B. Johnson for his lan-guage checking, and the anonymous reviewers for their veryhelpful suggestions.

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Gegen Tana received the B.S. degree in electronicinformation engineering from the Central Universityfor Nationalities, Beijing, China, in 2004, and theM.S. degree from the Graduate School of Scienceand Technology, Chiba University, Chiba, Japan,in 2008. She is currently working toward the Ph.D.degree in the Department of Earth Sciences, ChibaUniversity, Japan.Her research interests include land remote sensing

and image processing.

Husi Letu received the B.S. and M.S. degrees ingeography from Inner Mongolia Normal University,Hohhot, China, in 1999 and 2002, and the Ph.D.degree in geosciences and remote sensing fromCenter for Environmental Remote Sensing (CEReS),Chiba University, Chiba, Japan, in 2010.He is currently a post-doctor in the Research and

Information Center, Tokai University, Tokyo, Japan.His research interests include remote sensing appli-cations, image processing, and atmospheric remotesensing.

Zhongkai Cheng received the M.S. degree from theGraduate School of Science and Technology, ChibaUniversity, Japan, in 2012.His research interests include image processing

and remote sensing applications.

Ryutaro Tateishi received the Doctor of Engineeringdegree from the University of Tokyo, Tokyo, Japan,in 1982.He is a Professor at the Center for Environmental

Remote Sensing, Chiba University. His research ac-tivities are in the field of land remote sensing, cur-rently focusing on global land cover mapping andmonitoring.Prof. Tateishi served the International Society

for Photogrammetry and Remote Sensing (ISPRS)as a Commission Secretary and Working Group

Chairman from 1988 to 2004. He has remained Chairman of InternationalSteering Committee for Global Mapping (ISCGM) WG 4 on raster datadevelopment since 2002. He was a President of Remote Sensing Society ofJapan from 2008 to 2010.

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH ...tateishi-lab/pdf/2013/Tana.pdf · wetlands on a relatively large spatial extent with some success [27], [28]. In addition, well-designed