融合Landsat 8与Sentinel-2数据的红树林物候信息提取与分类
Fusion of Landsat 8 and Sentinel-2 data for mangrove phenology information extraction and classification
- 2022年26卷第6期 页码：1121-1142
纸质出版日期： 2022-06-07
DOI： 10.11834/jrs.20221448
扫 描 看 全 文
浏览全部资源
扫码关注微信
摘要
科学准确地监测红树林是保护海陆过渡性生态系统的基础和前提，但红树林分布于潮间带，难以进行大规模人工监测。遥感技术能够对红树林进行长时间、大面积监测，但已有研究尚存不足。一方面，红树林分布于热带、亚热带区域，受到天气条件限制难以获得长时间覆盖的有效光学遥感数据；另一方面，红树林极易与其他陆生植被混淆，仅利用多波段数据的光谱信息难以精确识别。本文以恒河三角洲孙德尔本斯地区为例，基于谷歌地球引擎GEE（Google Earth Engine）获取2016年全年的Landsat 8 OLI和Sentinel-2 MSI数据，利用物候信息进行红树林提取研究。首先，基于最小二乘回归构建两个传感器在相同指数之间的关系，重建时间序列数据，之后根据可分性判据选取增强型植被指数EVI（Enhanced Vegetation Index）和陆地表面水分指数LSWI（Land Surface Water Index）。其次，对两个指数的时间序列数据进行Savitzky-Golay滤波处理，并分别提取生长期始期等13种物候信息。最后，将两个指数的物候信息进行特征级联，采用随机森林RF（Random Forest）方法进行分类，提取研究区红树林范围。实验结果表明：Landsat 8 OLI和Sentinel-2 MSI数据融合可有效提升时间序列质量，与基于单一传感器数据的分类结果相比，总体精度提高1.58%；物候信息可以显著增强红树林与其他植被的可分性，与直接使用时间序列数据的分类结果相比，总体精度提高1.92%；同时考虑EVI和LSWI指数可极大地提升分类效果，与采用单一指数相比，总体精度分别提高14.11%和9.69%。因此，本文通过数据融合、物候信息提取和指数特征级联可以更好地提取红树林，总体精度达到91.02%，Kappa系数为0.892。研究验证了物候信息在红树林遥感监测中的应用潜力，提出的方法对科学准确地监测全球或区域红树林具有一定参考价值。
Abstract
Scientific and accurate monitoring of mangroves is the basis and premise for the protection of marine coastal and transitional ecosystem. However
mangroves are mainly distributed in the intertidal zone
result in large-scale manual monitoring a tough task. Although remote sensing technology can map mangrove within for long time and large area
the existing studies have some shortcomings. On the one hand
mangroves are often distributed in tropical and subtropical regions
where long-term coverage of effective optical remote sensing data is difficult to obtain due to weather conditions. On the other hand
mangroves are easily confused with other terrestrial vegetation by only using spectral information.
In this paper
we choose the Sundarbans located in the Ganges River Delta as the study area. Landsat 8 OLI and Sentinel-2 MSI data in 2016 are obtained based on GEE (Google Earth Engine) to conduct mangrove extraction in this research. Firstly
the relation between the two sensors for the same index is constructed based on a least square regression model
which is used to reconstruct the time series data. In this phase
EVI (Enhanced Vegetation Index) and LSWI (Land Surface Water Index) are selected according to the separability criterion. Secondly
Savitzky-Golay filtering is applied to the time series data of the two indices
and 13 phenology metrics are extracted. Finally
these metrics of the two indices are cascaded
and Random Forest (RF) is used to extract the area of mangrove.
Fusing the Landsat 8 OLI and Sentinel-2 MSI can effectively improve the quality of time series data. Compared with the classification results based on single sensor data
the overall accuracy is improved by 1.58%. Phenology information can significantly enhance the separability between mangrove and other vegetation
with a 1.92% improvement of overall accuracy compared with the classification results using time series data directly. Considering both EVI and LSWI indices can greatly improve
the classification effect
with 14.11% and 9.69% improvements compared with using a single index. Therefore
the method in this paper can effectively extract mangroves
with the overall accuracy and Kappa coefficient reaching to 91.02% and 0.892 respectively.
This research takes full account of the deficiency of optical remote sensing monitoring
biological characteristics and geographical characteristics of mangroves
which can extract the area of mangroves more objectively and accurately. Compared with other similar studies
the differences and characteristics of this study are: (1) Jointly using EVI and LSWI time series to describe the phenological information of mangroves can effectively differentiate mangroves and evergreen forests; (2) We introduce phenology information into mangrove classification using remote sensing for the first time
and verify the feasibility of using phenology information to monitor the range of mangrove. The method proposed in this paper may be benefit for scientific and accurate monitoring of global or regional mangroves.
关键词
遥感红树林数据融合物候信息时间序列数据GEE随机森林
Keywords
remote sensingmangrovedata fusionphenology informationtime seriesGEErandom forests
references
Baloloy A B, Blanco A C, Sta. Ana R R C and Nadaoka K. 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 95-117 [DOI: 10.1016/j.isprsjprs.2020.06.001http://dx.doi.org/10.1016/j.isprsjprs.2020.06.001]
Berlanga-Robles C A and Ruiz-Luna A. 2020. Assessing seasonal and long-term mangrove canopy variations in Sinaloa, northwest Mexico, based on time series of enhanced vegetation index (EVI) data. Wetlands Ecology and Management, 28(2): 229-249 [DOI: 10.1007/s11273-020-09709-0http://dx.doi.org/10.1007/s11273-020-09709-0]
Bunting P, Rosenqvist A, Lucas R M, Rebelo L M, Hilarides L, Thomas N, Hardy A, Itoh T, Shimada M and Finlayson C M. 2018. The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sensing, 10(10): 1669 [DOI: 10.3390/rs10101669http://dx.doi.org/10.3390/rs10101669]
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]
Dan T T, Chen C F, Chiang S H and Ogawa S. 2016. Mapping and change analysis in mangrove forest by using Landsat imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-8: 109-116 [DOI: 10.5194/isprsannals-III-8-109-2016http://dx.doi.org/10.5194/isprsannals-III-8-109-2016]
Descals A, Verger A, Yin G F and Peñuelas J. 2021. A threshold method for robust and fast estimation of land-surface phenology using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 601-606 [DOI: 10.1109/jstars.2020.3039554http://dx.doi.org/10.1109/jstars.2020.3039554]
Dong D, Zeng J S, Wei Z and Yan J H. 2020. Integrating spaceborne optical and SAR imagery for monitoring mangroves and Spartina alterniflora in Zhangjiang Estuary. Journal of Tropical Oceanography, 39(2): 107-117
董迪, 曾纪胜, 魏征, 严金辉. 2020. 联合星载光学和SAR影像的漳江口红树林与互花米草遥感监测. 热带海洋学报, 39(2): 107-117 [DOI: 10.11978/2019063http://dx.doi.org/10.11978/2019063]
Dutta D, Das P K, Paul S, Sharma J R and Dadhwal V K. 2015. Assessment of ecological disturbance in the mangrove forest of Sundarbans caused by cyclones using MODIS time-series data (2001-2011). Natural Hazards, 79(2): 775-790 [DOI: 10.1007/s11069-015-1872-xhttp://dx.doi.org/10.1007/s11069-015-1872-x]
Fan H Q and Wang W Q. 2017. Some thematic issues for mangrove conservation in China. Journal of Xiamen University (Natural Science), 56(3): 323-330
范航清, 王文卿. 2017. 中国红树林保育的若干重要问题. 厦门大学学报(自然科学版), 56(3): 323-330 [DOI: 10.6043/j.issn.0438-0479.201612003http://dx.doi.org/10.6043/j.issn.0438-0479.201612003]
Fu D J, Xiao H, Su F Z, Zhou C H, Dong J W, Zeng Y L, Yan K, Li S W, Wu J, Wu W Z and Yan F Q. 2021. Remote sensing cloud computing platform development and Earth science application. National Remote Sensing Bulletin, 25(1): 220-230
付东杰, 肖寒, 苏奋振, 周成虎, 董金玮, 曾也鲁, 闫凯, 李世卫, 吴进, 吴文周, 颜凤芹. 2021. 遥感云计算平台发展及地球科学应用. 遥感学报, 25(1): 220-230 [DOI: 10.11834/jrs.20210447http://dx.doi.org/10.11834/jrs.20210447]
Gao F and Zhang X Y. 2021. Mapping crop phenology in near real-time using satellite remote sensing: challenges and opportunities. Journal of Remote Sensing, 2021: 8379391 [DOI: 10.34133/2021/8379391http://dx.doi.org/10.34133/2021/8379391]
Ghosh A, Schmidt S, Fickert T and Nüsser M. 2015. The Indian sundarban mangrove forests: history, utilization, conservation strategies and local perception. Diversity, 7(2): 149-169 [DOI: 10.3390/d7020149http://dx.doi.org/10.3390/d7020149]
Ghosh M K, Kumar L and Roy C. 2016. Mapping long-term changes in mangrove species composition and distribution in the Sundarbans. Forests, 7(12): 305 [DOI: 10.3390/f7120305http://dx.doi.org/10.3390/f7120305]
Giri C, Ochieng E, Tieszen L L, Zhu Z, Singh A, Loveland T, Masek J and Duke N. 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1): 154-159 [DOI: 10.1111/j.1466-8238.2010.00584.xhttp://dx.doi.org/10.1111/j.1466-8238.2010.00584.x]
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]
Gupta K, Mukhopadhyay A, Giri S, Chanda A, Datta Majumdar S, Samanta S, Mitra D, Samal R N, Pattnaik A K and Hazra S. 2018. An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5: 1129-1139 [DOI: 10.1016/j.mex.2018.09.011http://dx.doi.org/10.1016/j.mex.2018.09.011]
Islam M, Borgqvist H and Kumar L. 2019. Monitoring Mangrove forest landcover changes in the coastline of Bangladesh from 1976 to 2015. Geocarto International, 34(13): 1458-1476 [DOI: 10.1080/10106049.2018.1489423http://dx.doi.org/10.1080/10106049.2018.1489423]
Jia M M. 2014. Remote Sensing analysis of China’s mangrove forests dynamics during 1973 to 2013. Changchun: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences
贾明明. 2014. 1973~2013年中国红树林动态变化遥感分析. 长春: 中国科学院研究生院(东北地理与农业生态研究所)
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]
Jönsson P and Eklundh L. 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers and Geosciences, 30(8): 833-845 [DOI: 10.1016/j.cageo.2004.05.006http://dx.doi.org/10.1016/j.cageo.2004.05.006]
Kumar T, Mandal A, Dutta D, Nagaraja R and Dadhwal V K. 2019. Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans. Geocarto International, 34(4): 415-442 [DOI: 10.1080/10106049.2017.1408699http://dx.doi.org/10.1080/10106049.2017.1408699]
Li H Y, Jia M M, Zhang R, Ren Y X and Wen X. 2019. Incorporating the plant phenological trajectory into mangrove species mapping with dense time series sentinel-2 imagery and the Google earth engine platform. Remote Sensing, 11(21): 2479 [DOI: 10.3390/rs11212479http://dx.doi.org/10.3390/rs11212479]
Liang H, Ma Y and Ren G B. 2016. Remote sensing monitoring of the mangrove forests resources of Kra Isthmus in Thailand. Marine Environmental Science, 35(5): 725-731
梁浩, 马毅, 任广波. 2016. 泰国克拉地峡东西海岸带红树林变化遥感分析. 海洋环境科学, 35(5): 725-731 [DOI: 10.13634/j.cnki.mes.2016.05.014http://dx.doi.org/10.13634/j.cnki.mes.2016.05.014]
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 D Z, Han Z W and Shen C Y. 2019. Remote sensing analysis of the distribution and change of mangrove forest in Lianjiang from 1991 to 2001. Marine Sciences, 43(4): 22-28
刘大召, 韩泽文, 沈春燕. 2019. 1991—2011年廉江红树林分布及变化的遥感分析. 海洋科学, 43(4): 22-28 [DOI: 10.11759/hykx20180625001http://dx.doi.org/10.11759/hykx20180625001]
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]
Liu X, Fatoyinbo T E, Thomas N M, Guan W W, Zhan Y N, Mondal P, Lagomasino D, Simard M, Trettin C C, Deo R and Barenblitt A. 2021. Large-scale high-resolution coastal mangrove forests mapping across West Africa with machine learning ensemble and satellite big data. Frontiers in Earth Science, 8: 560933 [DOI: 10.3389/feart.2020.560933http://dx.doi.org/10.3389/feart.2020.560933]
Long J B and Giri C. 2011. Mapping the Philippines' mangrove forests using Landsat imagery. Sensors, 11(3): 2972-2981 [DOI: 10.3390/s110302972http://dx.doi.org/10.3390/s110302972]
Lu C Y, Gao Y B, Chen Y L, Jia M M, Fu W W and Xiong Y L. 2019. Dynamic change analysis of mangrove swamps based on RS/GIS in Quanzhou Bay. Journal of Forest and Environment, 39(2): 143-152
路春燕, 高弋斌, 陈远丽, 贾明明, 傅玮韦华, 熊怡林. 2019. 基于RS/GIS的泉州湾红树林湿地时空动态变化分析. 森林与环境学报, 39(2): 143-152 [DOI: 10.13324/.cnki.jfcf.2019.02.005http://dx.doi.org/10.13324/.cnki.jfcf.2019.02.005]
Mandal M S H, Kamruzzaman M and Hosaka T. 2020. Elucidating the phenology of the Sundarbans mangrove forest using 18-year time series of MODIS vegetation indices. Tropics, 29(2): 41-55 [DOI: 10.3759/tropics.MS19-11http://dx.doi.org/10.3759/tropics.MS19-11]
Pastor-Guzman J, Dash J and Atkinson P M. 2018. Remote sensing of mangrove forest phenology and its environmental drivers. Remote Sensing of Environment, 205: 71-84 [DOI: 10.1016/j.rse.2017.11.009http://dx.doi.org/10.1016/j.rse.2017.11.009]
Peng L H, Liu K, Cao J J, Zhu Y H, Li F S and Liu L. 2020. Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods. International Journal of Remote Sensing, 41(3): 813-838 [DOI: 10.1080/01431161.2019.1648907http://dx.doi.org/10.1080/01431161.2019.1648907]
Savitzky A and Golay M J E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627-1639
Sheng N, Xin K and Liao B W. 2021. Literature analysis concerning studies of ecological functions and values of mangrove wetland. Wetland Science and Management, 17(1): 47-50
生农, 辛琨, 廖宝文. 2021. 红树林湿地生态功能及其价值研究文献学分析. 湿地科学与管理, 17(1): 47-50 [DOI: 10.3969/j.issn.1673-3290.2021.01.10http://dx.doi.org/10.3969/j.issn.1673-3290.2021.01.10]
Talukdar S, Singha P, Mahato S, Shahfahad, Pal S, Liou Y A and Rahman A. 2020. Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sensing, 12(7): 1135 [DOI: 10.3390/rs12071135http://dx.doi.org/10.3390/rs12071135]
Tamura M and Kikushima K. 2008. Extraction of mangrove forests using a satellite image and a digital elevation model//Proceedings of SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X. Cardiff: SPIE: 710403
Thomas N, Bunting P, Lucas R, Hardy A, Rosenqvist A and Fatoyinbo T. 2018. Mapping mangrove extent and change: a globally applicable approach. Remote Sensing, 10(9): 1466 [DOI: 10.3390/rs10091466http://dx.doi.org/10.3390/rs10091466]
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]
Tieng T, Sharma S, MacKenzie R A, Venkattappa M, Sasaki N K and Collin A. 2019. Mapping mangrove forest cover using Landsat-8 imagery, Sentinel-2, Very High Resolution Images and Google Earth Engine algorithm for entire Cambodia. IOP Conference Series: Earth and Environmental Science, 266: 012010 [DOI: 10.1088/1755-1315/266/1/012010http://dx.doi.org/10.1088/1755-1315/266/1/012010]
Vo Q T, Oppelt N, Leinenkugel P and Kuenzer C. 2013. Remote sensing in mapping mangrove ecosystems—an object-based approach. Remote Sensing, 5(1): 183-201 [DOI: 10.3390/rs5010183http://dx.doi.org/10.3390/rs5010183]
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 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]
Xia Q, Qin C Z, Li H, Huang C and Su F Z. 2018. Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sensing, 10(9): 1343 [DOI: 10.3390/rs10091343http://dx.doi.org/10.3390/rs10091343]
Xu F, Zhang Y, Zhai L, Liu J and Gu X H. 2020. Extraction method of intertidal mangrove by using Sentinel－2 images. Bulletin of Surveying and Mapping, (2): 49-54
徐芳, 张英, 翟亮, 刘佳, 谷祥辉. 2020. 基于Sentinel-2的潮间红树林提取方法. 测绘通报, (2): 49-54 [DOI: 10.13474/j.cnki.11-2246.2020.0043http://dx.doi.org/10.13474/j.cnki.11-2246.2020.0043]
Yang S C, Lu W X, Zou Z and Li S. 2017. Mangrove wetlands: distribution, species composition and protection in China. Subtropical Plant Science, 46(4): 301-310
杨盛昌, 陆文勋, 邹祯, 李思. 2017. 中国红树林湿地: 分布、种类组成及其保护. 亚热带植物科学, 46(4): 301-310 [DOI: 10.3969/j.issn.1009-7791.2017.04.001http://dx.doi.org/10.3969/j.issn.1009-7791.2017.04.001]
Zhang H K, Roy D P, Yan L, Li Z B, Huang H Y, Vermote E, Skakun S and Roger J C. 2018. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215: 482-494 [DOI: 10.1016/j.rse.2018.04.031http://dx.doi.org/10.1016/j.rse.2018.04.031]
Zhang L Y, Lei G P, Guo Y Y and Lu Z. 2021. Object-oriented land use classification based on Landsat images: a case study of the lower Liaohe Plain. Journal of Basic Science and Engineering, 29(2): 261-271
张露洋, 雷国平, 郭一洋, 路中. 2021. 基于Landsat影像的面向对象土地利用分类——以下辽河平原为例. 应用基础与工程科学学报, 29(2): 261-271 [DOI: 10.16058/j.issn.1005-0930.2021.02.002http://dx.doi.org/10.16058/j.issn.1005-0930.2021.02.002]
Zhang W, Chen Z H and Wang J K. 2015. Monitoring the areal variation of mangrove in Beibu Gulf coast of Guangxi China with remote sensing data. Journal of Guangxi University (Natural Science Edition), 40(6): 1570-1576
张威, 陈正华, 王纪坤. 2015. 广西北部湾海岸带红树林变化的遥感监测. 广西大学学报(自然科学版), 40(6): 1570-1576 [DOI: 10.13624/j.cnki.issn.1001-7445.2015.1570http://dx.doi.org/10.13624/j.cnki.issn.1001-7445.2015.1570]
Zhang X H. 2016. Decision tree algorithm of automatically extracting mangrove forests information from Landsat 8 OLI imagery. Remote Sensing for Land and Resources, 28(2): 182-187
张雪红. 2016. 基于决策树方法的Landsat8 OLI影像红树林信息自动提取. 国土资源遥感, 28(2): 182-187 [DOI: 10.6046/gtzyyg.2016.02.28http://dx.doi.org/10.6046/gtzyyg.2016.02.28]
Zhao C P and Qin C Z. 2021. A detailed mangrove map of China for 2019 derived from Sentinel-1 and -2 images and Google Earth images. Geoscience Data Journal [DOI: 10.1002/gdj3.119http://dx.doi.org/10.1002/gdj3.119]
Zhen J N, Liao J J and Shen G Z. 2018. Mapping mangrove forests of Dongzhaigang nature reserve in China using Landsat 8 and radarsat-2 Polarimetric SAR data. Sensors, 18(11): 4012 [DOI: 10.3390/s18114012http://dx.doi.org/10.3390/s18114012]
Zhou Z C. 2019. Research on the identification of mangrove forest based on multi-source remote sensing data —A case study of Zhanjiang Mangrove reserve. Changchun: Jilin University
周振超. 2019. 基于多源遥感数据的红树林遥感信息识别研究——以湛江自然保护区为例. 长春: 吉林大学
Zhou Z C, Li H, Huang C, Liu Q S, Liu G H, He Y and Yu H. 2018. Review on dynamic monitoring of mangrove forestry using remote sensing. Journal of Geo-information Science, 20(11): 1631-1643
周振超, 李贺, 黄翀, 刘庆生, 刘高焕, 何云, 于涵. 2018. 红树林遥感动态监测研究进展. 地球信息科学学报, 20(11): 1631-1643 [DOI: 10.12082/dqxxkx.2018.180247http://dx.doi.org/10.12082/dqxxkx.2018.180247]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
北京师范大学 地理科学学部 遥感科学国家重点实验室
湖南师范大学 地理科学学院
北京师范大学 地理科学学部 环境遥感与数字城市北京市重点实验室
南宁师范大学 地理与规划学院
中国科学院空天信息创新研究院 国家遥感应用工程技术研究中心
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部