2000-2020年广东省珠海淇澳岛红树林空间分布数据集

doi:10.19788/j.issn.2096-6369.230203

摘要/Abstract

摘要：

红树林分布于热带和亚热带海岸潮间带，对全球碳循环具有重要指标作用，为生态和生态经济发展提供服务价值。但随着人类活动的发展和自然环境的恶化，红树林资源出现急剧减少。文章采用2000年、2005年、2010年Landsat和2015年、2020年Sentinel-2影像，通过决策树分类与面向对象的分类方法，并结合实地踏勘调查，提取各年份红树林空间分布及面积，产出2000—2020年淇澳岛红树林空间分布数据集。文章数据集可解析红树林的空间动态演变，可为淇澳岛红树林动态变化规律、生态环境质量评价等科学研究提供重要参考；为红树林湿地的保护、恢复和管理提供决策支持；以及为珠海市和广东省环境监测提供基础数据支持。

关键词: 红树林, 遥感数据, 空间分布, 淇澳岛

Abstract:

Mangroves are distributed in the tropical and subtropical coastal intertidal zones, playing a critical role in the global carbon cycle and providing service value for ecological and ecological economic development. But with the development of human activities and the deterioration of the natural environment, mangrove resources have sharply decreased. Using Landsat images of 2000, 2005, and 2010, and Sentinel-2 images of 2015 and 2020, through decision tree classification and object-oriented classification methods, combined with field surveys, the study extracts the spatial distribution and area of mangroves each year, and produces the spatial distribution dataset of mangroves in Qi'ao Island from 2000 to 2020. The article dataset can analyze the spatial dynamic evolution of mangroves, providing important references for scientific research such as the dynamic changes of mangroves on Qi'ao Island and the evaluation of ecological environment quality; providing decision-making support for the protection, restoration, and management of mangrove wetlands; and providing basic data support for environmental monitoring in Zhuhai and Guangdong Province.

Key words: mangrove, remote sensing data, spatial distribution, Qi'ao Island

蔡惠娜, 王瑞源. 2000-2020年广东省珠海淇澳岛红树林空间分布数据集[J]. 农业大数据学报, 2023, 5(2): 9-15.

CAI HuiNa, WANG Ruei-Yuan. A Dataset on Spatial Distribution of Mangroves in Qi'ao Island, Zhuhai, Guangdong Province from 2000 to 2020[J]. Journal of Agricultural Big Data, 2023, 5(2): 9-15.

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参考文献 15

[1]	王文卿, 王瑁. 中国红树林[M]. 北京: 科学出版社, 2007: 5.
	Wang W Q, Wang M. Mangrove forests of China[M]. Beijing: Science Press, 2007: 5. (in Chinese)
[2]	Glaser M. Interrelations between mangrove ecosystem, local economy and social sustainability in Caete Estuary, North Brazil[J]. Wetlands Ecology and Management, 2003, 11(4): 265-272. doi: 10.1023/A:1025015600125
[3]	丁冬静, 廖宝文, 管伟, 等. 东寨港红树林自然保护区滨海湿地生态系统服务价值评估[J]. 生态科学, 2016, 35(6): 182-190.
	Ding D J, Liao B W, Guan W, et al. Evaluation on service value of coastal wetland ecosystem in Dongzhai harbor mangrove nature reserve[J]. Ecological Science, 2016, 35(6): 182-190. (in Chinese)
[4]	Song S S, Ding Y L, Li W, et al. Mangrove reforestation provides greater blue carbon benefit than afforestation for mitigating global climate change[J]. Nature Communications, 2023, 14(1):1-11. doi: 10.1038/s41467-022-34464-6
[5]	但新球, 廖宝文, 吴照柏, 等. 中国红树林湿地资源、保护现状和主要威胁[J]. 生态环境学报, 2016, 25(7):1237-1243. doi: 10.16258/j.cnki.1674-5906.2016.07.021
	Dan X Q, Liao B W, Wu Z B, et al. Resources, conservation status and main threats of mangrove wetlands in China[J]. Ecology and Environmental Sciences, 2016, 25(7): 1237-1243. (in Chinese) doi: 10.16258/j.cnki.1674-5906.2016.07.021
[6]	Pranchai A, Jenke M, Berger U. Well-intentioned, but poorly implemented: debris from coastal bamboo fences triggered mangrove decline in Thailand[J]. Marine Pollution Bulletin, 2019, 146:900-907. doi: S0025-326X(19)30593-4 pmid: 31426234
[7]	唐焕丽, 刘凯, 朱远辉, 等. 基于WorldView-2数据和支持向量机的红树林群落分类研究[J]. 中山大学学报(自然科学版), 2015, 54(4): 102-111.
	Tang H L, Liu K, Zhu Y H, et al. Mangrove community classification based on World View-2 image and SVM method[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2015, 54(4):102-111. (in Chinese)
[8]	邱霓, 徐颂军, 邱彭华, 等. 珠海淇澳岛红树林群落分布与景观格局[J]. 林业科学, 2019, 55(1):1-10.
	Qiu N, Xu S J, Qiu P H, et al. Community distribution and landscape pattern of the mangrove on the Qi’ao Island,Zhuhai[J]. Scientia Silvae Sinicae, 2019, 55 (1) :1-10. (in Chinese)
[9]	Giri C. Observation and monitoring of mangrove forests using remote sensing: opportunities and challenges[J]. Remote Sensing, 2016, 8(9): 783. doi: 10.3390/rs8090783
[10]	Giri C, Ochieng E, Tieszen L L, et al. Status and distribution of mangrove forests of the world using earth observation satellite data[J]. Global Ecology Biogeography, 2011, (20): 154-159.
[11]	Bunting P, Rosenqvist A, Lucas R, et al. The global mangrove watch—a new 2010 global baseline of mangrove extent[J]. Remote Sensing, 2018, 10(10): 1669. doi: 10.3390/rs10101669
[12]	Chen B, Xiao X, Li X, et al. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 131: 104-120. doi: 10.1016/j.isprsjprs.2017.07.011
[13]	孔令婷, 杨英宝, 章勇. 基于不同分辨率遥感影像的分类方法对比研究[J]. 测绘与空间地理信息, 2015, 38(3):40-43.
	Kong L T, Yang Y B, Zhang Y. Study on classification methods based on different resolution remote sensing image[J]. Geomatics & Spatial Information Technology, 2015, 38(3):40-43. (in Chinese)
[14]	Lucas I F J, Frans J M, Wel V D. Accuracy assessment of satellite derived land-cover data: A review[J]. Photogrammetric Engineering and Remote Sensing, 1994, 60(4):410-432.
[15]	Landis J R, Koch G G. The measurement of observer agreement for categorical data[J]. Biometrics, 1977, 33(1):159-174. pmid: 843571

序号	卫星	传感器	条带号/行号	分辨率(m)	成像日期
1	Landsat-7	ETM	122/044	30	2000.11.01
2	Landsat-5	TM	122/044	30	2005.10.22
3	Landsat-5	TM	122/044	30	2010.03.26
4	Sentinel-2	MSI	T49QGE	10	2015.10.23
5	Sentinel-2	MSI	T49QGE	10	2020.10.26

序号	名称	大小
1	2000_mangrove	86.5 KB
2	2005_mangrove	120 KB
3	2010_mangrove	124 KB
4	2015_mangrove	759 KB
5	2020_mangrove	900KB

Kappa 系数	分类精度
<0.00-0.20	较差
0.00-0.20	差
0.20-0.40	正常
0.40-0.60	好
0.60-0.80	较好
0.80-1.00	非常好

年份	卫星	OA(%)	Kappa
2000	Landsat-7	81%	0.79
2005	Landsat-5	80%	0.82
2010	Landsat-5	85%	0.83
2015	Sentinel-2	92%	0.90
2020	Sentinel-2	93%	0.91