Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Quantitative assessment of mangrove forest changes inChina from 1985 to 2018
Zheng Yuhan*, and Wataru Takeuchi
Institute of Industrial Science, the University of Tokyo, Japan

Abstract: Mangroves in China have experienced various changes in different time periods due to anthropogenic disturbances, climate change and nationalrestoration policy. However, few studies quantitatively analyzed these changes. This study detected the changes of mangroves in China from 1985 to 2018 bycombining Landsat and ALOS PALSAR images. The results demonstrated that the total areas of China’s mangrove in 1985, 1996, 2007, 2010 and 2018 were
20,086 ha, 18,033 ha, 22,428 ha, 23,639 ha and 24,602 ha respectively. Mangrove area began to increase after 1990s mainly due to the national conservation
actions. The land-cover conversions in mangrove forests are various in different regions and time periods. However, most mangroves gained from aquaculture andmudflat and lost to built-up and aquaculture. This is the first study to quantify the long-term dynamics of mangrove forests in China by remote sensing which has
potential importance to the mangrove protection and restoration programs in China, along with applicability to other developing countries.

2 Methodology

1 IntroductionWorld

1.80E+07(Lanly 1982)(Spalding et al., 1997)(FAO 2007)(Giri et al., 2011)

1.30E+07

8.00E+06
1975(Hamilton and Casey 2016)
1985

1995

45000

(He and Zhang 2001)

2005

2015

China

35000

(Jia et al., 2018)

25000

Fig. 1 In situ photos of mangroves inZhejiang province, China.

15000
1950

1960

1970

1980

1990

2000

2010

2020

Fig. 2 Mangrove area changes.
Fig. 3 Location of study area.
Objectives:➢ Quantify the long-term mangrove dynamics with high accuracy by combining Landsat and ALOS PALSAR
images and analyze the main driving force for each province;➢ Estimate the biocapacity changes in coastal ecosystems caused by the conversion between mangrove andother land covers.

3 Results
Table 1. Classification accuracy.
ProvinceZJ
FJ
GD
GX
HN
HK
Macao
TW
Total

Area
(ha)
24.48
908.559,214.63
9,095.71
4,269.78
533.34
10.62
542.3424,602.45

Overallaccuracy (%)
88.89
98.76
96.22
98.30
93.25
99.02
93.83
96.03

Kappa
coefficient
0.62000.9496
0.90420.95710.8646
0.97940.81150.8858Fig. 4 Overall flowchart of mangrove mapping and change detecting.

Fig. 5 The schematic diagram of mangrove change detection.

Fig. 6 Spatial distribution of China’s mangroves in 2018 and zoom views of
three mangrove natural reserves. The background of (a)-(c) are ALOS Fig. 7 Mangrove area changes in China.
PALSAR-2 mosaic in 2018 shown in R: G: B = HH: HV: HH-HV composite.

Fig. 9 Mangrove nature reserves in China during 1980 to 2020: (a) cumulative number;(b) protected area.

4 Conclusions and future work

Fig. 8 Percentage of mangrove gain and loss in terms of different land cover.

• First result of thequantitative mangrovechanges in national scale ofChina;• Mangrove gains and losses
are different in different
regions and different time
periods due to the various
surroundings and policy indifferent provinces.

• the China’s mangrove forests in 2018 is about 24,602 ha with over 90%
distributed along the southern coastline;• mangrove forests in most regions have a tendency of loss ﬁrst andrecovery later from 1985 to 2018;• most mangrove gains came from aquaculture and mudflat, whilst losses
were mainly due to the occupation of built-up and aquaculture;• the mangrove deforestations were mainly due to human-induced
destructions, while the mangrove recoveries were strongly associatedwith the conservation and restoration actions.
Future work: classifying mangrove species; refining the carbon footprint
and biocapacity for mangrove ecosystems.

For references and details, contact: Zheng Yuhan, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL:http://wtlab.iis.u-tokyo.ac.jp/E-mail: yuhan@g.ecc.u-tokyo.ac.jp)