粤港澳大湾区红树林长时间序列遥感监测

贾凯; 陈水森; 蒋卫国

doi:10.11834/jrs.20221451

红树林遥感 | 浏览量 : 0 下载量: 677 CSCD: 5 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

粤港澳大湾区红树林长时间序列遥感监测
Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area
2022年26卷第6期页码：1096-1111
纸质出版日期： 2022-06-07 ，
DOI： 10.11834/jrs.20221451

扫描看全文

贾凯，陈水森，蒋卫国.2022.粤港澳大湾区红树林长时间序列遥感监测.遥感学报，26（6）： 1096-1111

Jia K，Chen S S and Jiang W G. 2022. Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area. National Remote Sensing Bulletin， 26（6）：1096-1111
贾凯，陈水森，蒋卫国.2022.粤港澳大湾区红树林长时间序列遥感监测.遥感学报，26（6）： 1096-1111 DOI： 10.11834/jrs.20221451.

Jia K，Chen S S and Jiang W G. 2022. Long time-series remote sensing monitoring of mangrove forests in the Guangdong-Hong Kong-Macao Greater Bay Area. National Remote Sensing Bulletin， 26（6）：1096-1111 DOI： 10.11834/jrs.20221451.

摘要

随着遥感数据量的爆发式增长，对变化过程分析的精细化要求与本地算力不足之间的矛盾日益突出。GEE（Google Earth Engine）地理云平台的出现，解决了用户算力紧张的行业痛点。本文以粤港澳大湾区为研究区，在GEE的支持下，构建1987年—2020年年度湿地分类数据集，分析大湾区红树林的时间阶段性特征和空间扩张过程，结合连续长历时分析对变化时间点的准确识别，揭示了保护区设立与滩涂造林等工程在红树林保护与修复中的积极成效。主要结论有：（1）截止到2020年，大湾区共有红树林2174 ha，81%的红树林集中在深圳湾、淇澳岛和镇海湾；（2）大湾区的红树林经历了由平稳发展（1987年—2003年）到快速增长（2003年—2020年）的变化过程，其主要增量来源于镇海湾（40%）和淇澳岛（28%）；（3）淇澳岛和镇海湾的红树林仍处于快速增长期，但淇澳岛增速最快，从2002年至今面积翻了30倍，深圳湾则在早期的快速增长（1987年—2009年）后进入平稳期（2009年—2020年）；（4）由于保护区设立时间较早，深圳湾成为大湾区唯一一个形成稳定核心区的红树林分布区，镇海湾虽然拥有最大的红树林面积，但林道狭窄，景观破碎，生态系统反而更加脆弱；（5）设立自然保护区和滩涂造林都对红树林面积增长起到了积极作用。本研究为大湾区海岸带湿地生态系统保护与修复提供科学的证据支持，对沿海生态屏障建设具有一定的指导作用。

Abstract

With the explosive growth of remote sensing images

the contradiction between the refined requirements of the change processing analysis and the lack of local computing power has become increasingly prominent. The emergence of Google Earth Engine (GEE) geographic cloud platform has solved the pain points of the industry

where users are strained with computing power.

The Guangdong-Hong Kong-Macao Greater Bay Area (GHM) is considered the study area

and an annual wetland classification data set from 1987 to 2020 is constructed with the support of GEE. It is analyzed for the temporal phase characteristics and spatial expansion process of mangroves

and the result reveals the positive effects of the establishment of protected areas and tidal flat afforestation on the mangrove protection and restoration by combining the accurate time point of change identified by the continuous long time series analysis.

The imaging quality of optical images is greatly reduced because the study area is located in a tropical and subtropical cloudy and rainy area. To fully use the satellite remote sensing multitemporal features to effectively eliminate clouds/shadows

all Landsat images during the time period are collected to form a continuous time series. With the support of the GEE cloud platform to satisfy the requirements of computing power for the large amounts of data

the random forest algorithm is applied to obtain the annual wetland classification data set from 1987 to 2020. On this basis

the spatio-temporal characteristics of mangrove over long time are mined.

According to the accuracy assessment

the averaged out-of-bag error of wetland data set is 6.61%±0.08%

and the average identification accuracy for mangroves is 89.64%±0.13%. In 2020

the Greater Bay Area has 2

174 ha of mangrove forests

and 81% of the mangrove forests is concentrated in Shenzhen Bay

Qi’ao Island

and Zhenhai Bay. The mangrove forests in the GHM experience steady development (1987—2003) and then rapid growth (2003—2020)

the main increase is observed in Zhenhai Bay (40%) and Qi’ao Island (28%). The mangrove forests in Qi’ao Island and Zhenhai Bay are still in a period of rapid growth. However

Qi’ao Island has the fastest growth rate; it has doubled its area by 30 times since 2002. Shenzhen Bay has entered a stable period (2009—2020) after its early rapid growth (1987—2009). Shenzhen Bay became the only mangrove distribution area that formed a stable core area in the GHM due to the early establishment of the reserve. Although Zhenhai Bay has the largest area of mangrove forests

the ecosystem is more fragile because of the narrow width and the fragmented landscape.

The establishment of nature reserves and tidal flat afforestation has played an important role in the growth of mangrove area. Integrated monitoring

such as satellites

drones

and ground monitoring

should be incorporated into the mangrove protection and restoration assessment. This study provides scientific evidence support for the implementation of the sustainable development strategy goals of the GHM and has a certain guiding role in the construction of coastal ecological barriers.

关键词

遥感红树林连续长历时Google Earth Engine粤港澳大湾区时空信息挖掘空间扩张过程

Keywords

remote sensingmangrove forestscontinuous long time seriesGoogle Earth EngineGuangzhou-Hong Kong-Macao Greater Bay Areaspatio-temporal information miningspatial expansion processing

references

Ai J Q. 2018. Long-term Evolution Process and Mechanisms of Wetland Ecosystem in the Yangtze River Estuary Using Time-series Multi-sensor Remote Sensing Data. Shanghai: East China Normal University

艾金泉. 2018. 基于时间序列多源遥感数据的长江河口湿地生态系统长期演变过程与机制研究. 上海：华东师范大学

Bosire J O, Dahdouh-Guebas F, Walton M, Crona B I, Lewis III R R, Field C, Kairo J G and Koedam N. 2008. Functionality of restored mangroves: a review. Aquatic Botany, 89(2): 251-259 [DOI: 10.1016/j.aquabot.2008.03.010http://dx.doi.org/10.1016/j.aquabot.2008.03.010]

Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32 [DOI: 10.1023/A:1010933404324http://dx.doi.org/10.1023/A:1010933404324]

Che R G O. 1999. Concentration of 7 heavy metals in sediments and mangrove root samples from Mai Po, Hong Kong. Marine Pollution Bulletin, 39(1/12): 269-279 [DOI: 10.1016/S0025-326X(99)00056-9http://dx.doi.org/10.1016/S0025-326X(99)00056-9]

Dai Z Y, Liao L R, Liang J H, Wu M Y and Zuo P. 2022. Blue carbon stocks of mangrove from 1988 to 2018 in Beihai, Guangxi. Marine environmental science, 41(01): 8-15, 23

戴子熠, 廖丽蓉, 梁嘉慧, 武明月, 左平. 2022. 1988-2018年广西北海红树林蓝碳储量变化分析.海洋环境科学, 41(01): 8-15+23 [DOI:10.13634/j.cnki.mes.2022.01.018http://dx.doi.org/10.13634/j.cnki.mes.2022.01.018]

Dwyer, J L, Roy D P, Sauer B, Jenkerson C B, Zhang H K and Lymburner L. 2018. Analysis ready data: enabling analysis of the landsat archive. Remote Sensing, 10(9): 1363 [DOI: 10.3390/rs10091363] https://doi.org/10.3390/rs10091363https://doi.org/10.3390/rs10091363.

Farr T G, Rosen P A, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D and Alsdorf D. 2007. The shuttle radar topography mission. Reviews of Geophysics, 45(2): RG2004 [DOI: 10.1029/2005RG000183http://dx.doi.org/10.1029/2005RG000183]

Feng Z Y, Tan G M, Xia J Q, Shu C W, Chen P, Wu M W and Wu X M. 2020. Dynamics of mangrove forests in Shenzhen Bay in response to natural and anthropogenic factors from 1988 to 2017. Journal of Hydrology, 591: 125271 [DOI: 10.1016/j.jhydrol.2020.125271http://dx.doi.org/10.1016/j.jhydrol.2020.125271]

General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China and Standardization Administration of the People’s Republic of China. 2009. Wetland classification: GB/T 24708—2009. Beijing: Standards Press of China

中华人民共和国国家质量监督检验检疫总局和中国国家标准化管理委员会. 2009. 湿地分类：GB/T 24708—2009.北京：中国标准出版社

Gong M, Wu X Q and Yu L. 2019. Reclamation dynamics along the mainland coast of Shandong Province during 1974-2017. Journal of Geo-information Science, 21(12):1911-1922

宫萌, 吴晓青, 于璐. 2019. 1974-2017年山东省大陆海岸围填海动态变化分析.地球信息科学学报, 21(12): 1911-1922 [DOI: 10.12082/dqxxkx.2019.190175http://dx.doi.org/10.12082/dqxxkx.2019.190175]

Huang K L. 2020. Research on the construction of ecological security barrier in Guangdong-Hong Kong-macao greater bay area from the perspective of ecological security. Academic Search for Truth and Reality, 4: 60-68, 116

黄克亮. 2020. 生态安全视域下粤港澳大湾区生态安全屏障建设探究. 探求, (4): 60-68, 116 [DOI: 10.13996/j.cnki.taqu.2020.04.008http://dx.doi.org/10.13996/j.cnki.taqu.2020.04.008]

Huete A R, Liu H Q, Batchily K and van Leeuwen W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59(3): 440-451 [DOI: 10.1016/S0034-4257(96)00112-5http://dx.doi.org/10.1016/S0034-4257(96)00112-5]

Iverson L R, Prasad A M, Matthews S N and Peters M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management, 254(3): 390-406 [DOI: 10.1016/j.foreco.2007.07.023http://dx.doi.org/10.1016/j.foreco.2007.07.023]

Jaafar H H and Ahmad F A. 2020. Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: The Bekaa Valley, Lebanon. Remote Sensing of Environment, 238:111034 [DOI: 10.1016/j.rse.2018.12.033http://dx.doi.org/10.1016/j.rse.2018.12.033]

Kauth R J and Thomas G S. 1976. The tasselled cap -- A graphic description of the spectral-temporal development of agricultural crops as seen by landsat//Proceedings of the LARS 1976 Symposium on Machine Processing of Remotely-Sensed Data. West Lafayette: Purdue University: 41-51

Li H, Wan W, Fang Y, Zhu S Y, Chen X, Liu B J and Hong Y. 2019. A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images. Environmental Modelling and Software, 112: 16-22 [DOI: 10.1016/j.envsoft.2018.11.004http://dx.doi.org/10.1016/j.envsoft.2018.11.004]

Li J and Roy D P. 2017. A global analysis of sentinel-2A, sentinel-2B and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9(9): 902 [DOI: 10.3390/rs9090902http://dx.doi.org/10.3390/rs9090902]

Li J X, Wang J, Du Y H and Cai A L. 2019. Change Characteristics of CoastalWetlands in the Pearl River Delta under Rapid Urbanization. Wetland Science, 17(3): 267-276

李婧贤, 王钧, 杜依杭, 蔡爱玲. 2019. 快速城市化背景下珠江三角洲滨海湿地变化特征. 湿地科学, 17(3): 267-276 [DOI: 10.13248/j.cnki.wetlandsci.2019.03.002http://dx.doi.org/10.13248/j.cnki.wetlandsci.2019.03.002]

Li R Y, Li R L, Chai M W, Shen X X, Xu H L and Qiu G Y. 2015. Heavy metal contamination and ecological risk in Futian mangrove forest sediment in Shenzhen Bay, South China. Marine Pollution Bulletin, 101(1): 448-456 [DOI: 10.1016/j.marpolbul.2015.09.048http://dx.doi.org/10.1016/j.marpolbul.2015.09.048]

Lin H and Zhang H S. 2021. Tropical and subtropical remote sensing: needs, challenges, and opportunities. National Remote Sensing Bulletin, 25(1): 276-291

林珲, 张鸿生. 2021. 热带与亚热带遥感: 需求、挑战与机遇. 遥感学报, 25(1): 276-291 [DOI: 10.11834/jrs.20210237http://dx.doi.org/10.11834/jrs.20210237]

Liu K, Zhu Y H, Li Q, Li Y N, Xiao W H and Meng L. 2016. Analysis on mangrove resources changes of Zhenhai bay in Guangdong based on multi source remote sensing images. Tropical Geography, 36(5): 850-859

刘凯, 朱远辉, 李骞, 李越男, 肖望昊, 蒙琳. 2016. 基于多源遥感的广东镇海湾红树林演变分析. 热带地理, 36(5): 850-859 [DOI: 10.13284/j.cnki.rddl.002874http://dx.doi.org/10.13284/j.cnki.rddl.002874]

Lymburner L, Bunting P, Lucas R, Scarth P, Alam I, Phillips C, Ticehurst C and Held A. 2020. Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment, 238: 111185 [DOI: 10.1016/j.rse.2019.05.004http://dx.doi.org/10.1016/j.rse.2019.05.004]

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]

Mitchell M W. 2011. Bias of the random forest out-of-bag (OOB) error for certain input parameters. Open Journal of Statistics, 1(3): 205-211 [DOI: 10.4236/ojs.2011.13024http://dx.doi.org/10.4236/ojs.2011.13024]

Morton B. 2016. Hong Kong’s mangrove biodiversity and its conservation within the context of a southern Chinese megalopolis. A review and a proposal for Lai Chi Wo to be designated as a World Heritage Site. Regional Studies in Marine Science, 8: 382-399 [DOI: 10.1016/j.rsma.2016.05.001http://dx.doi.org/10.1016/j.rsma.2016.05.001]

Müller M F, Yoon J, Gorelick S M, Avisse N and Tilmant A. 2016. Impact of the Syrian refugee crisis on land use and transboundary freshwater resources. Proceedings of the National Academy of Sciences of the United States of America, 113(52): 14932-14937 [DOI: 10.1073/pnas.1614342113http://dx.doi.org/10.1073/pnas.1614342113]

Peng H Y, Zheng S F and Zhu H W. 2011. The practice of mangrove restoration in Qi’ao Island, Zhuhai. Wetland Science, 9(1): 97-100

彭辉武, 郑松发, 朱宏伟. 2011. 珠海市淇澳岛红树林恢复的实践. 湿地科学, 9(1): 97-100 [DOI: 10.13248/j.cnki.wetlandsci.2011.01.007http://dx.doi.org/10.13248/j.cnki.wetlandsci.2011.01.007]

Ramsar Convention Secretariat. 2006. The Ramsar Convention Manual: a guide to the Convention on Wetlands (Ramsar, Iran, 1971), edition4th. Gland, Switzerland: Ramsar Convention Secretariat: 6-10

Richardson A J and Wiegand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541-1552.

Ruan R Z, Feng X Z and She Y J. 2007. Fusion of radarsat SAR and ETM+ imagery for identification of fresh water wetland//Proceedings of SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information. Nanjing, China: SPIE [DOI: 10.1117/12.760748http://dx.doi.org/10.1117/12.760748]

Shen X X, Zhang Z, Zhai C Y and Li R L. 2022. A Meta-Analysis of the Overall Accuracy of Extent and Species of the Coastal Mangroves. Acta Scientiarum Naturalium Universitatis Pekinensis, 58(01):135-146

沈小雪, 张志, 翟朝阳, 李瑞利. 2022. 海岸带红树林范围与种类识别精度的荟萃分析. 北京大学学报(自然科学版), 58(01):135-146 [DOI:10.13209/j.0479-8023.2021.096http://dx.doi.org/10.13209/j.0479-8023.2021.096]

Sun N, Zhu W N and Cheng Q. 2017. Ｒemote sensing time-series analysis of wetland variations and driving factors in estuarine and coastal regions of Yangtze Ｒiver. Acta Scientiae Circumstantiae, 37(11): 4366-4373

孙楠, 朱渭宁, 程乾. 2017. 基于多年遥感数据分析长江河口海岸带湿地变化及其驱动因子. 环境科学学报, 37(11): 4366-4373 [DOI: 10.13671/j.hjkxxb.2017.0283http://dx.doi.org/10.13671/j.hjkxxb.2017.0283]

Tucker C J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150 [DOI: 10.1016/0034-4257(79)90013-0http://dx.doi.org/10.1016/0034-4257(79)90013-0]

Wang L, Jia M M, Yin D M and Tian J Y. 2019. A review of remote sensing for mangrove forests: 1956—2018. Remote Sensing of Environment, 231: 111223 [DOI: 10.1016/j.rse.2019.111223http://dx.doi.org/10.1016/j.rse.2019.111223]

Wang S G, Li X, Zhou Y Z, Liu K and Chen G Z. 2005. The change of mangrove wetland ecosystem and controlling countermeasures in the Qi’ao Island. Wetland Science, 3(1): 13-20

王树功, 黎夏, 周永章, 刘凯, 陈桂珠. 2005. 珠江口淇澳岛红树林湿地变化及调控对策研究. 湿地科学, 3(1): 13-20 [DOI: 10.3969/j.issn.1672-5948.2005.01.003http://dx.doi.org/10.3969/j.issn.1672-5948.2005.01.003]

Wang W Q, Shi J B and Chen L Z. 2021. Research of Conservation and Restoration Strategy of Mangrove Wetlands in China. Beijing: China Environment Publishing Group

王文卿, 石建斌, 陈鹭真. 2021. 中国红树林湿地保护与恢复战略研究. 北京：中国环境出版集团

Wang X X, Xiao X M, Zou Z H, Chen B Q, Ma J, Dong J W, Doughty R B, Zhong Q Y, Qin Y W, Dai S Q, Li X P, Zhao B and Li B. 2020. Tracking annual changes of coastal tidal flats in China during 1986—2016 through analyses of Landsat images with Google Earth Engine. Remote Sensing of Environment, 238: 110987 [DOI: 10.1016/j.rse.2018.11.030http://dx.doi.org/10.1016/j.rse.2018.11.030]

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]

Wen X, Jia M M, Li X Y, Wang Z M, Zhong C R and Feng E H. 2020. Identification of mangrove canopy species based on visible unmanned aerial vehicle images. Journal of Forest and Environment, 40(05): 486-496

闻馨, 贾明明, 李晓燕, 王宗明, 钟才荣, 冯尔辉. 2020. 基于无人机可见光影像的红树林冠层群落识别.森林与环境学报, 40(05): 486-496 [DOI: 10.13324/j.cnki.jfcf.2020.05.005http://dx.doi.org/10.13324/j.cnki.jfcf.2020.05.005]

Woodcock C E, Loveland T R and Herold M. 2020. Preface: time series analysis imagery special issue. Remote Sensing of Environment, 238: 111613 [DOI: 10.1016/j.rse.2019.111613http://dx.doi.org/10.1016/j.rse.2019.111613]

Wu Q H, Tam N F Y, Leung J Y S, Zhou X Z, Fu J, Yao B, Huang X X and Xia L H. 2014. Ecological risk and pollution history of heavy metals in Nansha mangrove, South China. Ecotoxicology and Environmental Safety, 104: 143-151 [DOI: 10.1016/j.ecoenv.2014.02.017http://dx.doi.org/10.1016/j.ecoenv.2014.02.017]

Xu H Q. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025-3033 [DOI: 10.1080/01431160600589179http://dx.doi.org/10.1080/01431160600589179]

Xu Y, Zhen J N, Jiang X P and Wang J J. 2021. Mangrove species classification with UAV-based remote sensing data and XGBoost. National Remote Sensing Bulletin, 25(3): 737-752

徐逸, 甄佳宁, 蒋侠朋, 王俊杰. 2021. 无人机遥感与XGBoost的红树林物种分类. 遥感学报, 25(3): 737-752 [DOI: 10.11834/jrs.20210281http://dx.doi.org/10.11834/jrs.20210281]

Yan W W, Gu D Q, Wang Y Z, Wu S Y, Feng A P and Ming J. 2012. Study of evolution of Yancheng coastal wetlands. Periodical of Ocean University of China, 42(12):130-317

闫文文, 谷东起, 王勇智, 吴桑云, 丰爱平, 明洁. 2012. 盐城海岸带湿地景观演变分析.中国海洋大学学报(自然科学版), 42(12): 130-137 [DOI:10.16441/j.cnki.hdxb.2012.12.020http://dx.doi.org/10.16441/j.cnki.hdxb.2012.12.020]

Ye J A, Wang S G, Liu K, Liu X P, Qian J P, Chen X Y, He Z J and Qin C F. 2006. Estimating mangrove wetland Biomass Using radar Remote sensing. Journal of remote sensing, 10(03): 387-396

黎夏, 叶嘉安, 王树功, 刘凯, 刘小平, 钱峻屏, 陈晓越, 何执兼, 覃朝锋. 2006. 红树林湿地植被生物量的雷达遥感估算. 遥感学报, 10(03): 387-396 [DOI: 1007-4619(2006)03-0387-10http://dx.doi.org/1007-4619(2006)03-0387-10]

Yu L Y, Lin S H, Jiao X Y, Shen X X and Li R L. 2019. Ecological problems and protection countermeasures of mangrove wetland in Guangdong-Hong Kong-macao greater bay area. Acta Scientiarum Naturalium Universitatis Pekinensis, 55(4): 782-790

于凌云, 林绅辉, 焦学尧, 沈小雪, 李瑞利. 2019. 粤港澳大湾区红树林湿地面临的生态问题与保护对策. 北京大学学报(自然科学版), 55(4): 782-790 [DOI: 10.13209/j.0479-8023.2019.051http://dx.doi.org/10.13209/j.0479-8023.2019.051]

Zhang R, Xia C L, Jia M M, Wang Z M, Yu X and Zhou Y M. 2019. Remote sensing analysis of greater pearl river delta’s mangrove forests dynamics based on object-oriented classification method. Geomatics and Spatial Information Technology, 42(12): 22-26

张蓉, 夏春林, 贾明明, 王宗明, 于欣, 周亚明. 2019. 基于面向对象的大珠三角红树林动态变化分析. 测绘与空间地理信息, 42(12): 22-26 [DOI: 10.3969/j.issn.1672-5867.2019.12.007http://dx.doi.org/10.3969/j.issn.1672-5867.2019.12.007]

Zhao J Y, Yu L, Xu Y D, Ren H Z, Huang X M and Gong P. 2019. Exploring the addition of Landsat 8 thermal band in land-cover mapping. International Journal of Remote Sensing, 40(12): 4544-4559 [DOI: 10.1080/01431161.2019.1569281http://dx.doi.org/10.1080/01431161.2019.1569281]

Zhi C, Wu W T and Su H. 2022. Mapping the intertidal wetlands of Fujian Province based on tidal dynamics and vegetational phonology. National Remote Sensing Bulletin, 26(02): 373-385

智超, 吴文挺, 苏华. 2022. 潮汐和植被物候影响下的潮间带湿地遥感提取.遥感学报, 26(02): 373-385 [DOI: 10.11834/jrs.20210586http://dx.doi.org/10.11834/jrs.20210586]

Zhu Z, Zhang J X, Yang Z Q, Aljaddani A H, Cohen W B, Qiu S and Zhou C L. 2020. Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238: 111116 [DOI: 10.1016/j.rse.2019.03.009http://dx.doi.org/10.1016/j.rse.2019.03.009]

文章被引用时，请邮件提醒。

提交

利用Sentinel-2影像超分辨率重建的红树林冠层氮含量反演