浏览全部资源
扫码关注微信
Published： 07 June 2023 ，
扫 描 看 全 文
袁艺馨，温庆可，徐进勇，王晨，赵晓丽，刘朔，解睿.2023.1990年—2020年粤港澳大湾区红树林动态变化遥感监测.遥感学报，27（6）： 1496-1510
Yuan Y X，Wen Q K，Xu J Y，Wang C，Zhao X L，Liu S and Xie R. 2023. Remote sensing monitoring of mangrove forest changes from 1990 to 2020 in Guangdong-Hong Kong-Macao Greater Bay Area. National Remote Sensing Bulletin， 27（6）：1496-1510
粤港澳大湾区规划建设成为国际竞争力一流湾区和世界级城市群，资源集约利用、生态环境优美是国际湾区全面建成的重要方面之一。粤港澳大湾区拥有丰富的滨海红树林湿地，在消浪减风、保护生物多样性、净化海水及固碳等方面发挥重要生态作用。湾区红树林经历过人类开发活动的破坏，也受到了湿地保护政策指引下的良好恢复，但由于调查手段不一及监测时效性限制，目前仍缺乏标准一致的数据以科学客观地阐明粤港澳大湾区红树林湿地的历史变化与最新现状。本文利用现状监测和动态更新相结合的方法，构建了1990年—2020年期间共计4个时段标准一致、前后可比的红树林分布与变化数据库；利用高性能云计算GEE平台（Google Earth Engine），提出了一种红树林现状快速更新方法。研究表明：（1）利用GEE平台可快速提取红树林初步分类结果作为动态更新的参考底图，大大提高了目视解译动态更新方法的速度，为红树林高效管理提供及时的数据支持。（2）粤港澳大湾区过去30年的红树林面积净增长10.21 km
2
，红树林总体得到了较好的恢复和保护，但是在1990年 —2000年期间，红树林是净减少的时期，净减少4.60 km
2
，主要是被新增养殖坑塘、人工用地占用；2000年以来，粤港澳大湾区红树林面积持续增加，红树林公园和自然保护区的建设对红树林存量面积起到了积极作用。（3）虽然红树林自然分布多见于潮间带，但是粤港澳大湾区2010年之后通过建设红树林公园恢复的红树林，有向内陆方向延伸建设的态势。
The Guangdong-Hong Kong-Macao Greater Bay Area is a developing competitive international bay area and world-class city cluster. One of the most important aspects of such a comprehensive development goal is efficient and eco-friendly resource usage. Coastal mangrove wetlands are vast in this area
where mangroves play an important ecological role in reducing waves and wind
thereby protecting biodiversity
purifying the sea
and sequestering carbon. The mangrove forests in the bay area were damaged by human activities but have been well-restored under the guidance of the wetland protection policy. However
a consistent and standard dataset for scientifically and objectively clarifying the historical changes in and latest status quo of mangrove wetlands at the regional scale is lacking owing to inconsistent investigation methods and limitations in timely monitoring. By utilizing status quo monitoring
combined with the dynamic updating method
this study constructs a standard and consistent database that is scientifically comparable across different years. Specifically
this study proposes a dynamic updating method based on a high-performance cloud computing platform
namely
Google Earth Engine (GEE)
for last-time-period updating
which largely improves the updating efficiency. Using satellite remote sensing images
this study constructs a long-term mangrove distribution series for 1990
2000
2010
2018
and 2020. In addition
this study quantifies the mangrove changes during the four time periods. Results show that (1) an efficient mangrove dynamic updating method can be designed utilizing the GEE platform
making timely and constant mangrove database construction and yearly updating at the regional scale feasible. The timely database can contribute to the efficient management of mangroves by corresponding departments. (2) Over the past three decades
the mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area were well-restored and protected
with the total area increasing by 10.21 km
2
from 1990 to 2020. However
during the period of 1990—2000
the area decreased by 4.60 km
2
owing to the occupation of newly built fish/shrimp ponds and artificial construction. Since 2000
the mangrove area has increased steadily owing to the construction of mangrove parks and nature reserves. (3) Although natural-growing mangrove forests are mostly found in intertidal zones
the newly planted mangroves
restored as mangrove parks
demonstrated a tendency to extend inland slightly after 2010.
红树林变化监测动态更新Google Earth Engine（GEE）随机森林遥感
mangrovechange detectiondynamic updating methodGoogle Earth Engine (GEE)random forest methodremote sensing
Buitre M J C, Zhang H S and Lin H. 2019. The mangrove forests change and impacts from tropical cyclones in the Philippines using time series satellite imagery. Remote Sensing, 11(6): 688 [DOI: 10.3390/rs11060688http://dx.doi.org/10.3390/rs11060688]
Chen B Q, Xiao X M, Li X P, Pan L H, Doughty R, Ma J, Dong J W, Qin Y W, Zhao B, Wu Z X, Sun R, Lan G Y, Xie G S, Clinton N and Giri C. 2017. 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. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 104-120 [DOI: 10.1016/j.isprsjprs.2017.07.011http://dx.doi.org/10.1016/j.isprsjprs.2017.07.011]
Chen J N. 2016. Taking the improvement of the ecological environment as a new driving force, actively creating new advantages for the green development of the Bay Area. China Ecological Civilization, (2): 10-15
陈吉宁. 2016. 以改善生态环境为新动力 积极打造湾区绿色发展新优势——在湾区城市生态文明大鹏策会上的讲话. 中国生态文明, (2): 10-15
Chen Y J. 2012. Field Measurement and Simulation of Wave Attenuation Effects in Mangroves. Beijing: Chinese Academy of Forestry
陈玉军. 2012. 红树林消波效应观测与模拟. 北京: 中国林业科学研究院 [DOI: 10.7666/d.D603461http://dx.doi.org/10.7666/d.D603461]
Dan X Q, Liao B W, Wu Z B, Wu H J, Bao D M, Dan W Y and Liu S H. 2016. Resources, conservation status and main threats of mangrove wetlands in China. Ecology and Environmental Sciences, 25(7): 1237-1243
但新球, 廖宝文, 吴照柏, 吴后建, 鲍达明, 但维宇, 刘世好. 2016. 中国红树林湿地资源、保护现状和主要威胁. 生态环境学报, 25(7): 1237-1243 [DOI: 10.16258/j.cnki.1674-5906.2016.07.021http://dx.doi.org/10.16258/j.cnki.1674-5906.2016.07.021]
Donato D C, Kauffman J B, Murdiyarso D, Kurnianto S, Stidham M and Kanninen M. 2011. Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5): 293-297 [DOI: 10.1038/ngeo1123http://dx.doi.org/10.1038/ngeo1123]
Elmahdy S I, Ali T A, Mohamed M M, Howari F M, Abouleish M and Simonet D. 2020. Spatiotemporal mapping and monitoring of mangrove forests changes from 1990 to 2019 in the Northern Emirates, UAE using Random Forest, Kernel Logistic regression and Naive Bayes Tree Models. Frontiers in Environmental Science, 8: 102 [DOI: 10.3389/fenvs.2020.00102http://dx.doi.org/10.3389/fenvs.2020.00102]
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]
Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850-853 [DOI: 10.1126/science.1244693http://dx.doi.org/10.1126/science.1244693]
Hao B F, Han X J, Ma M G, Liu Y T and Li S W. 2018. Research progress on the application of Google Earth Engine in Geoscience and Environmental Sciences. Remote Sensing Technology and Application, 33(4): 600-611
郝斌飞, 韩旭军, 马明国, 刘一韬, 李世卫. 2018. Google Earth Engine在地球科学与环境科学中的应用研究进展. 遥感技术与应用, 33(4): 600-611 [DOI: 10.11873/j.issn.1004-0323.2018.4.0600http://dx.doi.org/10.11873/j.issn.1004-0323.2018.4.0600]
He B Y, Fan H Q, Wang M, Lai T H and Wang W Q. 2007. Species diversity in mangrove wetlands of China and its causation analyses. Acta Ecologica Sinica, 27(11): 4859-4870
何斌源, 范航清, 王瑁, 赖廷和, 王文卿. 2007. 中国红树林湿地物种多样性及其形成. 生态学报, 27(11): 4859-4870 [DOI: 10.3321/j.issn:1000-0933.2007.11.056http://dx.doi.org/10.3321/j.issn:1000-0933.2007.11.056]
Jhonnerie R, Siregar V P, Nababan B, Prasetyo L B and Wouthuyzen S. 2015. Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24: 215-221 [DOI: 10.1016/j.proenv.2015.03.028http://dx.doi.org/10.1016/j.proenv.2015.03.028]
Jia M M, Wang Z M, Wang C, Mao D H and Zhang Y Z. 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery. Remote Sensing, 11(17): 2043 [DOI: 10.3390/rs11172043http://dx.doi.org/10.3390/rs11172043]
Jia M M, Wang Z M, Zhang Y Z, Mao D H and Wang C. 2018. Monitoring loss and recovery of mangrove forests during 42 years: the achievements of mangrove conservation in China. International Journal of Applied Earth Observation and Geoinformation, 73: 535-545 [DOI: 10.1016/j.jag.2018.07.025http://dx.doi.org/10.1016/j.jag.2018.07.025]
Kanniah K D, Sheikhi A, Cracknell A P, Goh H C, Tan K P, Ho C S and Rasli F N. 2015. Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia. Remote Sensing, 7(11): 14360-14385 [DOI: 10.3390/rs71114360http://dx.doi.org/10.3390/rs71114360]
Kumar L and Mutanga O. 2018. Google Earth Engine applications since inception: usage, trends, and potential. Remote Sensing, 10(10): 1509 [DOI: 10.3390/rs10101509http://dx.doi.org/10.3390/rs10101509]
Liu J Y, Zhang Z X, Li X B, Zhuang D F and Zhang S W. 2005. Remote Sensing Information Study of Land Use Change in China in 1990s. Beijing: Science Press
刘纪远, 张增祥, 李秀彬, 庄大方, 张树文. 2005. 20世纪90年代中国土地利用变化的遥感时空信息研究. 北京: 科学出版社
Liu K, Peng L H, Li X, Tan M and Wang S G. 2019. Monitoring the inter-annual change of mangroves based on the Google Earth Engine. Journal of Geo-information Science, 21(5): 731-739
刘凯, 彭力恒, 李想, 谭敏, 王树功. 2019. 基于Google Earth Engine的红树林年际变化监测研究. 地球信息科学学报, 21(5): 731-739 [DOI: 10.12082/dqxxkx.2019.180354http://dx.doi.org/10.12082/dqxxkx.2019.180354]
Lou J, Wang X, Zhou G Y and Liao B W. 2009. Effect of the ecological system of mangrove S. caseolaris and Sonneratia apetala on the wind-prevention. Journal of Anhui Agricultural Sciences, 37(26): 12776-12781, 12784
楼坚, 王旭, 周光益, 廖宝文. 2009. 海南东寨港海桑+无瓣海桑红树林生态系统防风效应研究. 安徽农业科学, 37(26): 12776-12781, 12784 [DOI: 10.3969/j.issn.0517-6611.2009.26.167http://dx.doi.org/10.3969/j.issn.0517-6611.2009.26.167]
Ma C L, Ai B, Zhao J, Xu X P and Huang W. 2019. Change detection of mangrove forests in coastal Guangdong during the past three decades based on remote sensing data. Remote Sensing, 11(8): 921 [DOI: 10.3390/rs11080921http://dx.doi.org/10.3390/rs11080921]
Reiche J, Lucas R, Mitchell A L, Verbesselt J, Hoekman D H, Haarpaintner J, Kellndorfer J M, Rosenqvist A, Lehmann E A, Woodcock C E, Seifert F M and Herold M. 2016. Combining satellite data for better tropical forest monitoring. Nature Climate Change, 6(2): 120-122 [DOI: 10.1038/nclimate2919http://dx.doi.org/10.1038/nclimate2919]
Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A and Simard M. 2017. Distribution and drivers of global mangrove forest change, 1996-2010. PLoS ONE, 12(6): e0179302 [DOI: 10.1371/journal.pone.0179302http://dx.doi.org/10.1371/journal.pone.0179302]
Van Der Linden S, Rabe A, Held M, Jakimow B, Leitão P J, Okujeni A, Schwieder M, Suess S and Hostert P. 2015. The EnMAP-Box—A toolbox and application programming interface for EnMAP data processing. Remote Sensing, 7(9): 11249-11266 [DOI: 10.3390/rs70911249http://dx.doi.org/10.3390/rs70911249]
Wang Z Y, Liu K, Peng L H, Cao J J, Sun Y X, Qian Y X and Shi S Y. 2020. Analysis of mangrove annual changes in Guangdong Province during 1986-2018 based on Google Earth Engine. Tropical Geography, 40(5): 881-892
王子予, 刘凯, 彭力恒, 曹晶晶, 孙映雪, 钱雨昕, 史舒悦. 2020. 基于Google Earth Engine的1986-2018年广东红树林年际变化遥感分析. 热带地理, 40(5): 881-892 [DOI: 10.13284/j.cnki.rddl.003268http://dx.doi.org/10.13284/j.cnki.rddl.003268]
Xiong J, Thenkabail P S, Gumma M K, Teluguntla P, Poehnelt J, Congalton R G, Yadav K and Thau D. 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 225-244 [DOI: 10.1016/j.isprsjprs.2017.01.019http://dx.doi.org/10.1016/j.isprsjprs.2017.01.019]
Xiu X M, Zhou S F, Chen Q, Meng J H, Dong W Q, Yang G B and Li X S. 2019. Above-ground biomass estimation of provincial scattered grassland based on Google Earth Engine and machine learning. Bulletin of Surveying and Mapping, 3: 46-52, 75
修晓敏, 周淑芳, 陈黔, 蒙继华, 董文全, 杨广斌, 李晓松. 2019. 基于Google Earth Engine与机器学习的省级尺度零散分布草地生物量估算. 测绘通报, (3): 46-52, 75 [DOI: 10.13474/j.cnki.11-2246.2019.0076http://dx.doi.org/10.13474/j.cnki.11-2246.2019.0076]
Yu X L, Li C Q, Wang S C and Peng M. 2009. Adaptability of mangrove ecosystems and their role in water purification. Tropical Agricultural Engineering, 33(2): 19-23
于晓玲, 李春强, 王树昌, 彭明. 2009. 红树林生态适应性及其在净化水质中的作用. 热带农业工程, 33(2): 19-23
Zhang X Y and Shi C Y. 2019. The beautiful Greater Bay Area rides the wind and waves to set sail——actively constructing the ecological civilization strategic system of the GBA. Environmental Ecology, 1(5): 69-73
张修玉, 施晨逸. 2019. 美丽大湾区乘风破浪 扬帆起航——积极构建粤港澳大湾区生态文明战略体系. 环境生态学, 1(5): 69-73
Zhang Z X, Wang X, Zhao X L, Liu B, Yi L, Zuo L J, Wen Q K, Liu F, Xu J Y and Hu S G. 2014. A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images. Remote Sensing of Environment, 149: 142-154 [DOI: 10.1016/j.rse.2014.04.004http://dx.doi.org/10.1016/j.rse.2014.04.004]
Zhang Z X, Zhao X L, Wang X, et al. 2012. Remote Sensing Monitoring of Landuse in China. Beijing: Planet Map Publishing House
张增祥, 赵晓丽, 汪潇, 等. 2012. 中国土地利用遥感监测. 北京: 星球地图出版社
Zhao Y S. 2013. Principle and Method of Remote Sensing Application Analysis. 2nd ed. Beijing: Science Press
赵英时. 2013. 遥感应用分析原理与方法.2版. 北京: 科学出版社
相关文章
相关作者
相关机构
京公网安备11010802024621