Journal of Global Change Data & Discovery2017.1(2):230-238

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Citation:Zhang,H.Y.,Niu,Z.G.,Xu,P.P,.et al.The Boundaries and Remote Sensing Classification Datasets on Large Wetlands of International Importance in 2001 and 2013[J]. Journal of Global Change Data & Discovery,2017.1(2):230-238 .DOI: 10.3974/geodp.2017.02.15 .

The Boundaries and Remote Sensing Classification Datasets on Large Wetlands of International
Importance in 2001 and 2013

Zhang, H. Y.1  Niu, Z. G.1*  Xu, P. P.1  Chen, Y. F.1  Hu, S. J.1  Gong, N.2

1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences, Beijing 100101, China
??

2. Department of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China

 

Abstract: Wetland is one of the ecosystems with the highest biodiversity and productivity in the world. In this paper, we developed a dataset of 100 large Wetlands of International Importance (the “Ramsar Sites”), which was developed by the data integration and mining from the World Database on Protected Areas (WDPA) and remote sensing images, expecially the high resolution imagery provided on the Google Earth and the Tianditu platforms. Each site covers at least an area of 200,000 hm2. Based on the Moderate Resolution Imaging Spectro radiometer (MODIS) 16-day composite products MOD13Q1 of these sites in 2001 and 2013, we applied the Savitzky-Golay filtering and time series reconstruction algorithms to Normalized Difference Vegetation Index (NDVI) data. The method of the Support Vector Machine (SVM) classifier was used to classify and map wetlands of these Ramsar sites. The time series data based wetland classification results were compared with high resolution remote sensing imagery and the relevant literatures. This method is more advantageous than single temporal imagery based wetland monitoring technique. The dataset consists of three components which are saved as .shp and .kmz file formats. The total data size is 102 MB in compressed file format.

Keywords: wetlands; boundary; remote sensing classification; wetland mapping; Ramsar sites

1 Introduction

Wetland is one of the ecosystems with the highest biodiversity and productivity in the world[1]. In recent years, large scale wetland monitoring and mapping emerged as a hot research topic and has been attracting great attention of many scholars because of its ecological significance. To understand the spatio-temporal change characteristics of wetland ecosystem is the key component of global change research[2]. The fast development of remote sensing technique provides an objective means to global wetlands monitoring research. The remotely sensed time series data play an irreplaceable role in the research of wetland changes in the growth cycle or in a calendar year of wetland vegetation[3]. It is the main data source of the remotely sensed monitoring on large wetlands of international importance at the global scale of this paper. Along with our related scientific reports published, we make this dataset available for public use.

2 Metadata of Dataset

The metadata of the boundaries and remote sensing classification datasets on large wetlands of international importance in 2001 and 2013 is summarized in Table 1[4]. It includes the dataset full name, short name, authors, year of the dataset, temporal resolution, spatial resolution, data format, data size, data files, data publisher, and data sharing policy, etc.

Table 1  Summary of the RamsarSites_Top100_WetlandCover_2001/2013 metadata.

Items

Description

Dataset full name

The wetland cover datasets on the large wetlands of international importance in 2001 and 2013 by remote sensing data integration

Dataset short name

RamsarSites_Top100_WetlandCover_2001/2013

Authors

Zhang, H. Y. L-4985-2016, Institute of Remote Sensing and Digital Earth, CAS, zhanghy@ radi.ac.cn

Niu, Z. G. L-4829-2016, Institute of Remote Sensing and Digital Earth, CAS, niuzg@ra di.ac.cn

Xu, P. P.   L-5064-2016, Institute of Remote Sensing and Digital Earth, CAS, 25486400 46 @qq. com

Chen, Y. F. L-5003-2016, Institute of Remote Sensing and Digital Earth, CAS, 935836745 @qq.com

Hu, S. J. L-6142-2016, Institute of Remote Sensing and Digital Earth, CAS, husj1989@y eah.net

Gong, N. L-6422-2016, Shandong Agricultural University, gongningbaobao@126.com

Geographic region

100 large wetlands of international importance in the world, distributed in different continents

Year

2001, 2013

Temporal resolution

Yearly                          

Spatial resolution

250 m

Data format

.kmz, .shp

Data size

1.54 MB in .kmz format. 100 MB in compressed

Data files

The dataset consists of three files[4]. They are as follows.

1) Top100_boundary.kmz.zip. This is the boundaries of 100 large wetlands of international importance in the world. It is stored in a .kmz file applicable to Google Earth, and the data size is 1.54 MB

2) Ramsar2001.rar. This is the remotely sensed classification results of these wetlands in 2001. Its data size is 51 MB

3) Ramsar2013.rar. This is the remotely sensed classification results of these wetlands in 2013. Its data size is 49.6 MB

Note: The classification results, Ramsar2001.rar and Ramsar2013.rar, are stored as ArcGIS shapefile vector format. Each vector file includes seven fields: (1) Area refers to the area of each patch in square meters; (2) RSN stands for Ramsar Site No., namely Ramsar number or the number of wetlands of international importance; (3) Lev3 indicates wetland code of that patch in the classification system[4] (Table 2); (4) the fields Name_EN and Name_CN refer to the English names and the Chinese names of the Ramsar sites respectively; and (5) the fields Country_EN and Country_CN respectively stand for the English names and the Chinese names of the country where the Ramsar sites lie in

Foundation(s)

National Natural Science Foundation of China (41271423)

Data publisher

Global Change Research Data Publishing & Repository, http://www.geodoi.ac.cn

(To be continued on the next page)

(Continued)

Items

Description

Address

No. 11A, Datun Road, Chaoyang District, Beijing 100101, China

Data sharing policy

Data from the Global Change Research Data Publishing & Repository includes metadata, datasets (data products), and publications (in this case, in the Journal of Global Change Data & Discovery). Data sharing policy includes: (1) Data are openly available and can be free downloaded via the Internet; (2) End users are encouraged to use Data subject to citation; (3) Users, who are by definition also value-added service providers, are welcome to redistribute Data subject to written permission from the GCdataPR Editorial Office and the issuance of a Data redistribution license; and (4) If Data are used to compile new datasets, the ‘ten per cent principal’ should be followed such that Data records utilized should not surpass 10% of the new dataset contents, while sources should be clearly noted in suitable places in the new dataset[5]

3 Methods

3.1 The Objective and Indicators of Wetland Monitoring by Remote Sensing

Based on some principles such as wetlands with an area larger than 200,000 hm2, with established geographic extent and map information, 100 wetlands of international importance all over the world distributed at different ecological-climatic zones and continents were selected from the database of Ramsar sites (https://rsis.ramsar.org/) (Figure 1).

Figure 1  Spatial distribution of large wetlands

of international importance

Indices for monitoring large wetlands of international importance mainly include the types and the area of land cover (Table 2). The conversion between wetlands and non-wetlands, the conversion in different wetland types and the changes on the area of different types, were used as important indicators to eva-

luate the ecological and environmental conditions of the wetlands. In general, if wetlands are converted into non-wetlands, or natural wetlands are converted into artificial wetlands, they usually lead to a deteriorated environmental condition and pose some threats to wetland biodiversity conservation. On the contrary, it often causes healthy development on the wetland environments.

3.2 Methodology of Wetland Boundaries and Classification Dataset Development

Most of the wetland boundaries were delineated by reference to the scanned paper maps published on the Ramsar website (https://rsis.ramsar.org/. Retrieved on July 23, 2014)[6]. Another small part of them were derived from World Database on Protected Areas (https://www.iucn.org/; http://www.protectedplanet.net/. Retrieved on July 23, 2014) and the other products of literature and document. The maps were georeferenced in ArcGIS. Because there are many differences of wetland management and monitoring techniques in each country, some inconsistencies of the boundaries often occur in such occasion. Therefore we

made modification and verification by using high resolution remote sensing images on Google Earth platform, based on the physiographic forms of wetlands such as walking along rivers, roads, and the ridge lines. The MODIS product MOD13Q1 (16-day composite, 250 m)

Table 2  Land cover classification system adopted in remote sensing based mapping

on wetlands of international importance

Level 1

Level 2

Level 3

Code

Characteristics of time series NDVI

Definition or spatial distribution characteristics

Wetlands

Water

11

NDVI 0.1

Including rivers, lakes, estuarine wa ter, reservoirs and urban entertainment landscape, wastewater treatment plant, etc. It mainly refers to permanent water body

Forested/shrub-dominated wetlands

Forested/shrub-

dominated wetlands (evergreen)

121

NDVI 0.4.   There is slight amplitude of variation on NDVI in a year

Including the forested/shrub-domin ated wetlands with evergreen vegetation located at the inland and coastal zone

Forested/shrub-

do min ated wetlands (deciduous)

122

NDVI 0.3. It presents a pattern of periodicity

Including the forested/shrub- dominated wetlands with deciduous vegetation located at the inland and coastal zone

Marshes

Permanent marshes

131

0.1 NDVI 0.5. There is a relative slight amplitude of variation on NDVI in a year. In high- water season, there may be minus value of NDVI

Wetlands with oversaturated soil and permanently flooded herbaceous plants

Seasonal marshes

132

0.1 NDVI 0.5. There is a periodic pattern of NDVI in a year. In high-water season,

NDVI < 0

Herbaceous marshes flooded regularly or seasonally in leaf-on season

Flooded flats/Intertidal zone / Estuarine deltas

14

NDVI 0.2. In high-water season,

NDVI < 0

They refer to inundated periodically and oversaturated soils distributed near lakes, rivers and estuary, with the vegetation coverage lower than 30%. They are often not covered with open water

Paddy fields

15

NDVI < 0, at the early stage of crop growth. There is a steep decreasing trend of NDVI after crops harvesting

Arable land used for growing rice

Tundra wetlands

16

In leaf-on season, NDVI < 0.6. They are usually distributed at high latitudes

Non-

wetlands

Snow

21

Perennial snow

Natural

vegetation

Forests/Scrublands

22

In the leaf-on season, NDVI>0.5

Forests and scrublands

Grasslands

23

In the leaf-on season,

0.3 NDVI0.6

Construction / Barren lands

24

0NDVI0.2

Including artificial construction such as buildings, roads, etc. and other forms of barren lands, e.g., rocks, barren sands

Drylands

25

The occurrence of the slow growth and the steep drop-off of the NDVI value in a calendar year

Farms cultivated with dry-land crops as a result of their scarcity of water

Cloud cover

32

These regions are covered by clouds

Ocean

33

in 2001 and 2013 downloaded from NASA (https://ladsweb.nascom.nasa.gov/data/) was used as the main data source. There are 23 periods of the images in each year. In this study, the images on the public service platform Tianditu (http://www.tianditu.com/map/index.html) powered by the National Geomatics Center of China (NGCC) and other geographic data were also used as auxiliary data. The Normalized Difference Vegetation Index (NDVI) can reflect the growth status of different vegetation types. By comparing the NDVI differences including the maximum, the minimum, and the range of annual NDVI, the emergence date of the positive NDVI value, and the growing season length, etc. between different vegetation types in the growth cycle or within a calendar year, and analyzing plants growth pattern[7], the accuracy of interpretation on different types of land cover types could be effectively improved. We applied Savitzky-Golay filtering to reconstruct NDVI time series dataset[8]. Based on the clustering results of time series data and the analysis on the features of different land cover types in high resolution remote sensing images, we collected 10-20 training samples for each type and used SVM classifier to delineate and map different types of wetlands. The minimum of the map unit is about 56.25 hm2 for 3×3 pixels. The main methods and the technical flow are described in Figure 2.

Figure 2  Flowchart for wetlands classification

4 Data Products

The dataset consists of three components, namely Top100_boundary kmz (1,470.14 KB), Ramsar2001.rar (52,245.46 KB) and Ramsar2013.rar (50,858.42 KB). They represent the boundaries of 100 large wetlands of international importance in the world, and the wetland classif­ication results in 2001 and 2013, respectively (http://www.geodoi.ac.cn/WebEn/doi.aspx?

Id=243).

4.1 Statistics on Large Wetlands of International Importance

The total area of 100 large wetlands of international importance is 138,392,121.05 hm2. Okavango Delta System (Ramsar Site No.879), located in Botswana, ranks first. The statistical results are listed in Table 3.

Table 3  Statistics on large wetlands of international importance

Name of Ramsar Sites

Area (hm2)

Country

Ramsar Site No.

 

Okavango Delta System

6,703,463.94

Botswana

 879

Queen Maud Gulf

6,263,006.27

Canada

 246

Ngiri-Tumba-Maindombe

6,194,581.31

Democratic Republic
of the Congo

1784

Plaines d’inondation des Bahr Aouk et Salamat

6,187,728.80

Chad

1621

Grands affluents

5,787,900.96

Republic of Congo

1742

Plaines d’inondation du Logone et les dépressions Toupouri

4,017,225.78

Chad

1560

Complejo de humedales del Abanico del río Pastaza

3,781,662.25

Peru

1174

Malagarasi-Muyovozi Wetlands

3,705,800.43

Tanzania

1024

Río Yata

3,448,554.92

Bolivia

2094

Palmar de las Islas y las Salinas de San José

3,434,589.67

Bolivia

1088

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 (Continued)

Name of Ramsar Sites

Area (hm2)

Country

Ramsar Site No.

Delta Intérieur du Niger

3,143,537.63

Mali

1365

Río Blanco 

2,909,863.42

Bolivia

2092

Plaine de Massenya

2,905,803.26

Chad

1839

Reentrancias Maranhenses

2,671,201.38

Brazil

 640

Area between the Pura & Mokoritto Rivers

2,620,671.33

Russia

 697

Sudd

2,470,516.39

South Sudan

1622

Gueltas et Oasis de l’Aïr

2,413,275.61

Niger

1501

Réserve Naturelle Nationale des Terres Australes Francaises

2,332,885.13

French

1837

Polar Bear Provincial Park

2,231,833.08

Canada

 360

Pacaya-Samiria

2,194,600.24

Peru

 546

Coongie Lakes

2,142,810.22

Australia

 376

Río Matos

2,116,721.12

Bolivia

2093

Kakadu National Park

1,939,734.28

Australia

 204

Parapolsky Dol

1,826,613.22

Russia

 693

Baixada Maranhense Environmental Protection Area

1,811,854.16

Brazil

1020

Los Lípez

1,729,203.22

Bolivia

 489

Lake Niassa and its Coastal Zone

1,697,149.57

Mozambique

1964

Partie tchadienne du lac Tchad

1,686,175.47

Chad

1134

Whooping Crane Summer Range

1,616,992.61

Canada

 240

Parc National du Banc d’Arguin

1,477,460.61

Mauritania

 250

Suakin-Gulf of Agig

1,450,427.12

Sudan

1860

Site Ramsar Odzala Kokoua

1,385,476.02

Republic of Congo

2080

Lago Titicaca

1,366,536.56

Bolivia

 959

Bañados del Río Dulce y Laguna de Mar Chiquita

1,351,673.35

Argentina

1176

Mamirauá

1,319,593.43

Brazil

 623

Lagunas altoandinas y puneñas de Catamarca

1,311,834.01

Argentina

1865

Tobol-Ishim Forest-steppe

1,278,538.61

Russia

 679

Gambie-Oundou-Liti

1,246,779.36

Guinea

1579

Volga Delta

1,199,249.85

Russia

 111

Lagunas de Guanacache, Desaguadero y del Bebedero

1,157,839.02

Argentina

1012

Zambezi Delta

1,140,940.11

Mozambique

1391

Lagos Poopó y Uru Uru

1,136,273.65

Bolivia

1181

Archipel Bolama-Bijagós

1,065,174.91

Guinea-Bissau

2198

Ili River Delta and South Lake Balkhash

 973,699.53

Kazakhstan

2020

Lake Uvs and its surrounding wetlands

 885,360.38

Mongolia

1379

Har Us Nuur National Park

 859,526.41

Mongolia

 976

Dinder National Park

 855,072.75

Sudan

1461

Alakol-Sasykkol Lakes System

 784,807.01

Kazakhstan

1892

Dewey Soper Migratory Bird Sanctuary

 782,498.00

Canada

 249

Le Lac Alaotra: Les Zones Humides et Bassins Versants

 781,472.69

Madagascar

1312

Everglades National Park

 754,573.06

United States

 374

Brekhovsky Islands in the Yenisei estuary

 737,894.04

Russia

 698

Área de Protección de Flora y Fauna Laguna de Términos

 734,350.69

Mexico

1356

Rufiji-Mafia-Kilwa Marine Ramsar Site

 734,194.39

Tanzania

1443

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         (Continued)

Name of Ramsar Sites

Area (hm2)

Country

Ramsar Site No.

Dalai Lake National Nature Reserve, Inner Mongolia

733,235.89

China

1146

Sian Ka'an

655,727.26

Mexico

1329

Aydar-Arnasay Lakes system

636,595.85

Uzbekistan

1841

Isyk-Kul State Reserve with the Lake Isyk-Kul

629,536.18

Kyrgyzstan

1231

Chott El Jerid

616,315.19

Tunisia

1699

Lake Chad Wetlands in Nigeria

611,134.90

Nigeria

1749

Rio Sabinas

606,876.07

Mexico

1769

Eqalummiut Nunaat and Nassuttuup Nunaa

594,195.32

Denmark

 386

Ilha do Bananal

592,910.39

Brazil

 624

Site Ramsar du Complexe W

581,917.66

Benin

1668

Sangha-Nouabalé-Ndoki

552,358.22

Republic of Congo

1858

Wadden Sea

541,257.30

Netherlands

 289

Cabo Orange National Park

523,199.02

Brazil

2190

Bafing-Falémé

522,191.82

Guinea

1719

Chott Ech Chergui

519,421.40

Algeria

1052

Bañados del Izozog y el río Parapetí

516,491.19

Bolivia

1087

Runn of Kutch

513,386.48

Pakistan

1285

Sundarbans Reserved Forest

499,921.44

Bangladesh

 560

Upper Dvuobje

499,157.08

Russia

 678

Jaaukanigás

491,725.87

Argentina

1112

Humedales Chaco

491,620.32

Argentina

1366

Schleswig-Holstein Wadden Sea and adjacent areas

481,776.92

Germany

 537

Lake Sevan

474,652.91

Armenia

 620

Lake Urmia [or Orumiyeh]

456,996.73

Iran

  38

Danube Delta

451,407.18

Romania

 521

Indus Delta

435,103.46

Pakistan

1284

Wasur National Park

411,337.03

Indonesia

1624

Chany Lakes

387,239.92

Russia

 680

Tanjung Puting National Park

378,350.88

Indonesia

2192

Zones Humides du Littoral du Togo

368,011.51

Togo

1722

Shadegan Marshes & mudflats of Khor-al Amaya
& Khor Musa

334,645.27

Iran

  41

Turkmenbashy Bay

324,761.47

Turkmenistan

1855

Hopen

316,453.18

Norway

1957

Lesser Aral Sea and Delta of the Syrdarya River

313,371.18

Kazakhstan

2083

Bear Island

296,711.28

Norway

1966

Lemmenjoki National Park

285,550.79

Finland

1521

Lakes of the lower Turgay and Irgiz

263,261.44

Kazakhstan

 108

Tengiz-Korgalzhyn Lake System

261,593.75

Kazakhstan

 107

Gansu Gahai Wetlands Nature Reserve

247,747.47

China

1975

Etangs de la Champagne humide

241,348.36

France

 514

Kama-Bakaldino Mires

223,709.33

Russia

 670

Lake Khanka

192,382.13

Russia

 112

Chott Melghir

192,282.62

Algeria

1296

Veselovskoye Reservoir

185,046.81

Russia

 672

Petit Loango

150,542.08

Gabon

 352

Old Crow Flats

 31,461.26

Canada

 244

           

4.2 Remote Sensing Classification Results on Large Wetland of International Importance in 2001 and 2013

Based on the remote sensing classification results on 100 large wetlands of international importance in 2001 and 2013, we calculate the subtotal area of different land cover types. The statistical results are shown in Table 4.

In 2001, the largest three wetland classes are forested/shrub-dominated wetlands (deciduous), seasonal marshes, and permanent marshes, respectively. Between 2001 and 2013, the total area of wetland reduced from 58,134,960 hm2 to 57,734,032 hm2, the big three losers were water, forested/shrub-dominated wetlands (deciduous), and tundra wetlands, respectively.

Table 4  Area of different land cover types in large wetlands of international importance

Code

Level 2

Level 3

Area (hm2)

2001

2013

Wetlands

58,134,960.36

57,734,032.42

11

Water

7,962,751.49

7,220,121.10

121

Forested/shrub-dominated wetlands

Forested/shrub-dominated wetlands (evergreen)

5,670,938.35

5,372,163.35

122

Forested/shrub-dominated wetlands (deciduous)

17,989,514.92

17,264,579.94

131

Marshes

Permanent marshes

8,852,939.87

9,019,623.32

132

Seasonal marshes

11,446,049.25

12,970,340.19

14

Flooded flats/Intertidal zone / Estuarine deltas

3,677,715.96

3,755,553.93

15

Paddy fields

161,536.44

118,356.86

16

Tundra wetlands

2,373,514.09

2,013,293.73

Non-wetlands

80,256,924.04

80,657,761.93

21

Snow

203,466.14

161,471.34

22

Natural vegetation

Forests/Scrublands

49,795,219.42

49,793,411.47

23

Grasslands

10,253,074.54

10,202,998.58

24

Construction / Barren lands

10,255,547.25

9,634,365.16

25

Drylands

 

2,071,076.67

3,200,432.37

32

Cloud cover

54,188.66

53,787.91

33

Ocean

7,624,351.36

7,611,295.11

5 Data Validation

Validation on the world’s large wetlands classification results accuracy was applied by

using high resolution remotely sensed images. We randomly selected 10 wetlands of international importance in different continents as the testing regions except for the Antarctic. About 2,386 validation samples randomly collected were interpreted based on high resolution images of China’s satellites GF-1, ZY-3. Accuracy of all land cover types was assessed. In general, the overall accuracy for all types is 88% and 89% in 2001 and 2013 respectively. And the Kappa coefficients are 0.86 and 0.87 respectively.

The dataset contains the boundaries of the world’s 100 large wetlands of international importance and wetland classification results on them in 2001 and 2013 based on 250 m spatial resolution, 16-day composite product MOD13Q1. It provides important reference data for research on global wetlands and climate change.

Author Contributions

Six coauthors were engaged in the design and development of the boundaries and remote sensing classification datasets on large wetlands of international importance in 2001 and 2013. Zhang, H. Y. and Niu, Z. G. participated in all the technological processes of the design and methodological development of boundaries extraction, data collection and processing, data analysis and validation, etc. Zhang, H. Y., Xu, P. P., Chen, Y. F., Hu, S. J. and Gong, N. took part in the boundaries extraction and wetlands classification. And the author list was arranged in their workload order. Zhang, H. Y. wrote this paper, and Niu, Z. G. was in charge of the paper proofreading, editing and revision.

Acknowledgements

We appreciate the National Geomatics Center of China, Google Earth, and the National Aeronautics and Space Administration (NASA), US for providing online public services of Tianditu, Google Earth and MODIS data.

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