Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (1) : 95-102     DOI: 10.6046/zrzyyg.2022482
|
Remote sensing information extraction for mangrove forests based on multi-feature parameters: A case study of Guangdong Province
WANG Yumiao1,2(), LI Sheng1,3, DONG Chunyu2, YANG Gang2()
1. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China
2. Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
3. Shenzhen Data Management Center of Planning and Natural Resource, Shenzhen 518000, China
Download: PDF(13911 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Accurate mangrove forest distribution information is critical to the conservation and management of mangrove forests. Despite extensive studies on the remote sensing mapping of mangrove forests, it is necessary to improve their mapping accuracy by effectively utilizing multi-source remote sensing features. First, this study designed 15 feature associations using temporal features, including spectral, scattering, texture, and terrain features, which were extracted from multi-source remote sensing data. Then, using a random forest model, it analyzed the accuracy of different feature associations in mangrove forest identification, obtaining the optimal feature association. Finally, this study mapped the 10-m-resolution mangrove forest distribution of Guangdong Province in 2021 based on platform Google Earth Engine (GEE). The results show that spectral features in winter exhibited the highest importance, with richer feature types corresponding to higher mapping accuracy. The optimal feature association yielded overall accuracy of 92.25% and a Kappa value of 0.91. Overall, this study extracted information on mangrove forests in Guangdong based on multi-feature parameters and the optimal feature association. The results of this study will provide a scientific reference for accurate mapping of mangrove forests on a large scale.
Keywords information extraction of mangrove forests      multi-source remote sensing data      GEE      machine learning      Guangdong Province     
ZTFLH:  TP79  
Issue Date: 13 March 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Yumiao WANG
Sheng LI
Chunyu DONG
Gang YANG
Cite this article:   
Yumiao WANG,Sheng LI,Chunyu DONG, et al. Remote sensing information extraction for mangrove forests based on multi-feature parameters: A case study of Guangdong Province[J]. Remote Sensing for Natural Resources, 2024, 36(1): 95-102.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022482     OR     https://www.gtzyyg.com/EN/Y2024/V36/I1/95
Fig.1  Location of the study area and distribution of sample data
指数名称 计算公式①
归一化植被指数 NDVI= N I R - R e d N I R + R e d
地表水指数 LSWI= N I R - S W I R 1 N I R + S W I R 1
修正归一化差异水体指数 MNDWI= G r e e n - S W I R 1 G r e e n + S W I R 1
淹没红树林指数 IMFI= B l u e + G r e e n - 2 N I R B l u e + G r e e n + 2 N I R

红边归一化植被指数		 RENDVI= R E 2 - R E 1 R E 2 + R E 1
Tab.1  Vegetation index formulas
Fig.2  Relationship between classification accuracy and number of features
Fig.3  Seasonal distribution of feature importance
组合序号 特征类型 具体特征
1 地形特征 Elevation,Slope,Aspect,Shade
2 纹理特征 Contrast4,Var4,Contrast1,Var1,Var3,Contrast3,Idm4,Corr4
3 散射特征 VH3,VV4,VH4,VH2,VH1,VV3,VV1,VV2
4 光谱特征 IMFI4,MNDWI4,LSWI4,RENDVI4,LSWI1,NDVI2,NDVI4,LSWI3,RENDVI2,MNDWI3,RENDVI3,IMFI1,MNDWI1,NDVI3,LSWI2,RENDVI1,NVI1,IMFI3,MNDWI2,IMFI3
Tab.2  Specific features of combinations 1—4
Fig.4  Classification accuracy of different feature combinations
Fig.5  Confusion matrix for different combinations of features on the testing dataset
指标 类别 组合15 组合14 组合13 组合12 组合11
用户
精度		 其他 91.19 89.34 89.26 88.16 76.28
红树林 95.86 95.53 96.73 94.84 86.23
农田 85.17 83.52 81.58 83.24 78.25
不透水面 95.10 94.67 92.82 94.74 78.49
其他植被 90.70 89.91 89.68 88.76 78.21
水体 96.45 95.45 96.75 95.76 83.30
制图
精度		 其他 91.19 89.62 91.51 88.99 74.84
红树林 95.25 94.62 93.67 93.04 91.14
农田 94.82 93.52 93.20 93.20 83.82
不透水面 96.77 93.84 94.72 95.01 79.18
其他植被 84.10 83.83 81.94 83.02 70.62
水体 92.52 92.86 91.16 92.18 84.01
Tab.3  User accuracy and mapping accuracy for combinations 11—15 (%)
Fig.6  Mangrove distribution and statistics in coastal cities of Guangdong Province
Fig.7  Comparison of local mangrove mapping in Zhanjiang City
[1] Pastor-Guzman J, Dash J, Atkinson P M. Remote sensing of mangrove forest phenology and its environmental drivers[J]. Remote Sensing of Environment, 2018, 205:71-84.
doi: 10.1016/j.rse.2017.11.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717305333
[2] 何斌源, 范航清, 王瑁, 等. 中国红树林湿地物种多样性及其形成[J]. 生态学报, 2007, 27(11): 4859-4870.
[2] He B Y, Fan H Q, Wang M, et al. Species diversity in mangrove wetlands of China and its causation analyses[J]. Acta Ecologica Sinic, 2007, 27(11): 4859-4870.
[3] 章恒, 王世新, 周艺, 等. 多源遥感影像红树林信息提取方法比较[J]. 湿地科学, 2015, 13(2):145-152.
[3] Zhang H, Wang S X, Zhou Y, et al. Comparison of different metho-ds of mangrove extraction from multi-source remote sensing images[J]. Wetland Science, 2015, 13(2):145-152.
[4] 麦少芝, 徐颂军. 广东红树林资源的保护与开发[J]. 海洋开发与管理, 2005, 22(1):44-48.
[4] Mai S Z, Xu S J. Protection and development of mangrove resources in Guangdong Province[J]. Ocean Development and Management, 2005, 22(1):44-48.
[5] 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 url: http://www.mdpi.com/2072-4292/8/9/783
[6] Kirui K B, Kairo J G, Bosire J, et al. Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery[J]. Ocean and Coastal Management, 2013, 83:19-24.
doi: 10.1016/j.ocecoaman.2011.12.004 url: https://linkinghub.elsevier.com/retrieve/pii/S0964569111001943
[7] Warfield A D, Leon J X. Estimating mangrove forest volume using terrestrial laser scanning and UAV-derived structure-from-motion[J]. Drones, 2019, 3(2):32.
doi: 10.3390/drones3020032 url: https://www.mdpi.com/2504-446X/3/2/32
[8] 徐逸, 甄佳宁, 蒋侠朋, 等. 无人机遥感与XGBoost的红树林物种分类[J]. 遥感学报, 2021, 25(3):737-752.
[8] Xu Y, Zhen J N, Jiang X P, et al. Mangrove species classification with UAV-based remote sensing data and XGBoost[J]. National Remote Sensing Bulletin, 2021, 25(3):737-752.
doi: 10.11834/jrs.20210281 url: http://www.ygxb.ac.cn/zh/article/doi/10.11834/jrs.20210281/
[9] 王子予, 刘凯, 彭力恒, 等. 基于Google Earth Engine的1986—2018 年广东红树林年际变化遥感分析[J]. 热带地理, 2020, 40(5):881-892.
doi: 10.13284/j.cnki.rddl.003268
[9] Wang Z Y, Liu K, Peng L H, et al. Analysis of mangrove annual changes in Guangdong Province during 1986—2018 based on Google Earth Engine[J]. Tropical Geography, 2020, 40(5):881-892.
[10] Jia M, Wang Z, Wang C, et al. A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery[J]. Remote Sensing, 2019, 11(17):2043.
doi: 10.3390/rs11172043 url: https://www.mdpi.com/2072-4292/11/17/2043
[11] Jia M, Wang Z, Li L, et al. Mapping China’s mangroves based on an object-oriented classification of Landsat imagery[J]. Wetlands, 2014, 34(2):277-283.
doi: 10.1007/s13157-013-0449-2 url: https://link.springer.com/10.1007/s13157-013-0449-2
[12] Ma C, Ai B, Zhao J, et al. Change detection of mangrove forests in coastal Guangdong during the past three decades based on remote sensing data[J]. Remote Sensing, 2019, 11(8):921.
doi: 10.3390/rs11080921 url: https://www.mdpi.com/2072-4292/11/8/921
[13] Xiao H, Su F Z, Fu D J, et al. 10-m global mangrove classification products of 2018—2020[DS/OL]. Science Data Bank, 2021[2022-12-09].http://doi.org/10.11922/sciencedb.01019.
url: http://doi.org/10.11922/sciencedb.01019
[14] 邓静雯, 田义超, 张强, 等. 机载LiDAR在红树林林分平均高估算中的应用[J]. 自然资源遥感, 2022, 34(3):129-137.doi: 10.6046/zrzyyg.2021237.
[14] Deng J W, Tian Y C, Zhang Q, et al. Application of airborne LiDAR in the estimation of the mean height of mangrove stand[J]. Remote Sensing for Natural Resources, 2022, 34(3):129-137.doi: 10.6046/zrzyyg.2021237.
[15] Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine:Planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202:18-27.
doi: 10.1016/j.rse.2017.06.031 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425717302900
[16] Zhao C, Qin C Z. 10-m-resolution mangrove maps of China derived from multi-source and multi-temporal satellite observations[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 169:389-405.
doi: 10.1016/j.isprsjprs.2020.10.001 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271620302732
[17] Peng Y, Zheng M, Zheng Z, et al. Virtual increase or latent loss? A reassessment of mangrove populations and their conservation in Guangdong,southern China[J]. Marine Pollution Bulletin, 2016, 109(2):691-699.
doi: 10.1016/j.marpolbul.2016.06.083 url: https://linkinghub.elsevier.com/retrieve/pii/S0025326X16304878
[18] 吴培强, 马毅, 李晓敏, 等. 广东省红树林资源变化遥感监测[J]. 海洋学研究, 2011, 29(4):16-24.
[18] Wu P Q, Ma Y, Li X M, et al. Remote sensing monitoring of the mangrove forests resources of Guangdong Province[J]. Journal of Marine Sciences, 2011, 29(4):16-24.
[19] 江娜, 陈超, 韩海丰. 海岸带地类统计模型中DEM空间尺度优选方法[J]. 自然资源遥感, 2022, 34(1):34-42.doi: 10.6046/zrzyyg.2022005.
[19] Jiang N, Chen C, Han H F. An optimization method of DEM resolution for land type statistical model of coastal zones[J]. Remote Sensing for Natural Resources, 2022, 34(1):34-42.doi: 10.6046/zrzyyg.2022005.
[20] Farr T G, Rosen P A, Caro E, et al. The shuttle Radar topography mission[J]. Reviews of Geophysics, 2007, 45(2):361.
[21] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2):127-150.
doi: 10.1016/0034-4257(79)90013-0 url: https://linkinghub.elsevier.com/retrieve/pii/0034425779900130
[22] Xiao X, Boles S, Liu J, et al. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images[J]. Remote Sensing of Environment, 2005, 95(4):480-492.
doi: 10.1016/j.rse.2004.12.009 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425705000209
[23] Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006, 27(14):3025-3033.
doi: 10.1080/01431160600589179 url: https://www.tandfonline.com/doi/full/10.1080/01431160600589179
[24] 贾明明. 1973—2013年中国红树林动态变化遥感分析[D]. 长春: 中国科学院东北地理与农业生态研究所, 2014.
[24] Jia M M. Remote sensing analysis of China’s mangrove forests dynamics during 1973 to 2013[D]. Changchun: Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences, 2014.
[25] Lin S, Li J, Liu Q, et al. Evaluating the effectiveness of using vegetation indices based on red-edge reflectance from Sentinel-2 to estimate gross primary productivity[J]. Remote Sensing, 2019, 11(11):1303.
doi: 10.3390/rs11111303 url: https://www.mdpi.com/2072-4292/11/11/1303
[26] 何昭欣, 张淼, 吴炳方, 等. Google Earth Engine支持下的江苏省夏收作物遥感提取[J]. 地球信息科学学报, 2019, 21(5):752-766.
doi: 10.12082/dqxxkx.2019.180420
[26] He Z X, Zhang M, Wu B F, et al. Extraction of summer crop in Jiangsu based on Google Earth Engine[J]. Journal of Geo-Information Science, 2019, 21(5):752-766.
[27] Naboureh A, Li A, Bian J, et al. A hybrid data balancing method for classification of imbalanced training data within Google Earth Engine:Case studies from mountainous regions[J]. Remote Sensing, 2020, 12(20):3301.
doi: 10.3390/rs12203301 url: https://www.mdpi.com/2072-4292/12/20/3301
[28] Ghorbanian A, Kakooei M, Amani M, et al. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 167:276-288.
doi: 10.1016/j.isprsjprs.2020.07.013 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271620302008
[29] Song Q, Hu Q, Zhou Q B, et al. In-season crop mapping with GF-1/WFV data by combining object-based image analysis and random forest[J]. Remote Sensing, 2017, 9(11):1184.
doi: 10.3390/rs9111184 url: http://www.mdpi.com/2072-4292/9/11/1184
[30] Van Beijma S, Comber A, Lamb A. Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR,elevation and optical RS data[J]. Remote Sensing of Environment, 2014, 149:118-129.
doi: 10.1016/j.rse.2014.04.010 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425714001485
[1] SONG Yingxu, ZOU Yujia, YE Runqing, HE Zhixia, WANG Ningtao. Dynamic analysis of landslide hazards in the Three Gorges Reservoir area based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2024, 36(1): 154-161.
[2] PARIHA Helili, ZAN Mei. Spatio-temporal changes and influencing factors of ecological environments in oasis cities of arid regions[J]. Remote Sensing for Natural Resources, 2023, 35(3): 201-211.
[3] YU Sen, JIA Mingming, CHEN Gao, LU Yingying, LI Yi, ZHANG Bochun, LU Chunyan, LI Huiying. A study of the disturbance to mangrove forests in Dongzhaigang, Hainan based on LandTrendr[J]. Remote Sensing for Natural Resources, 2023, 35(2): 42-49.
[4] SHI Min, LI Huiying, JIA Mingming. Spatio-temporal variations in mangrove forests in the Shankou Mangrove Nature Reserve based on the GEE cloud platform and Landsat data[J]. Remote Sensing for Natural Resources, 2023, 35(2): 61-69.
[5] HUANG Xiaoyu, WANG Xuemei, KAWUQIATI Baishan. Inversion of soil salinity of an oasis in an arid area based on Landsat8 OLI images[J]. Remote Sensing for Natural Resources, 2023, 35(1): 189-197.
[6] LI Xianfeng, YUAN Zhengguo, DENG Weihua, YANG Liyuan, ZHOU Xueying, HU Lili. Spatial downscaling methods for the 2-meter air temperature grid data based on multiple machine learning models[J]. Remote Sensing for Natural Resources, 2023, 35(1): 57-65.
[7] LI Yefan, WANG Lin, ZHANG Dongzhu. Information extraction of coastal aquaculture ponds based on spectral features and spatial convolution[J]. Remote Sensing for Natural Resources, 2022, 34(4): 42-52.
[8] LI Yi, CHENG Lina, LU Yingying, ZHANG Bochun, YU Sen, JIA Mingming. A study on the changes in coastal tidal flats in the Laizhou Bay based on MSIC and OTSU[J]. Remote Sensing for Natural Resources, 2022, 34(4): 68-75.
[9] SU Wei, LIN Yangyang, YUE Wen, CHEN Yingbiao. Identification of mariculture areas in Guangdong Province and remote sensing monitoring of their spatial and temporal changes based on the U-Net convolutional neural network[J]. Remote Sensing for Natural Resources, 2022, 34(4): 33-41.
[10] CHAO Zhenhua, CHE Mingliang, HOU Shengfang. Brief review of vegetation phenological information extraction software based on time series remote sensing data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 19-25.
[11] WANG Zheng, JIA Gongxu, ZHANG Qingling, HUANG Yue. Impacts of COVID-19 epidemic on the spatial distribution of GDP contributed by secondary and tertiary industries in Guangdong Province in the first quarter of 2020[J]. Remote Sensing for Natural Resources, 2021, 33(3): 184-193.
[12] Wei ZHANG, Jianwei QI, Ying CHEN, Xu HAN. A study of block adjustment of domestic multi-source high resolution satellite images[J]. Remote Sensing for Land & Resources, 2019, 31(1): 125-132.
[13] Xun ZHOU, Yuebin WANG, Suhong LIU, Peixin YU, Xikai WANG. A machine learning algorithm for automatic identification of cultivated land in remote sensing images[J]. Remote Sensing for Land & Resources, 2018, 30(4): 68-73.
[14] Jiasi YI, Xiangyun HU. Extracting impervious surfaces from multi-source remote sensing data based on Grabcut[J]. Remote Sensing for Land & Resources, 2018, 30(3): 174-180.
[15] Yangming WANG, Jingfa ZHANG, Zhirong LIU, Xuhui SHEN. Active faults interpretation of Shannan area in Tibet based on multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2018, 30(3): 230-237.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech