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Received：29 July 2020，
Published：07 March 2021
移动端阅览
无人机遥感数据会衍生大量的光谱、纹理与结构特征，如何提取优势特征是提高红树林物种分类效率和精度的关键问题。针对深圳福田红树林自然保护区缓冲区获取的无人机高光谱影像和LiDAR点云数据，本研究旨在利用极端梯度提升算法（XGBoost）的“特征重要性”属性筛选出适合红树林物种分类的8类优势特征：基于无人机高光谱影像的单一特征（光谱波段、植被指数和纹理特征：F1—F3）及其融合特征（F4）、基于LiDAR点云的单一特征（高度和强度特征：F5和F6）及其融合特征（F7）、高光谱影像与LiDAR点云的融合特征（F8）；基于以上优势特征构建8个XGBoost分类模型。结果表明：综合物种分类精度及其制图结果，基于F8特征的模型分类性能最佳（总体精度为96.41%，莫兰指数为0.5520）；基于单一数据源融合特征（总体精度，F4：96.74%；F7：90.64%）的分类性能优于基于单一特征（总体精度，F1—F3：90.31%、92.20%和91.96%；F5和F6：87.66%和81.99%）；基于融合特征（F4、F7和F8）和纹理特征（F3）分类图的莫兰指数比基于单一特征（F1、F2、F5和F6）的更大。本文论证了无人机遥感数据和XGBoost方法在基于像元的红树林物种精准分类上具备可行性，可为红树林生态系统健康、保护与恢复的立体监测提供科学依据和技术支撑。
Mangrove forest provides huge value of ecosystem services
such as beach protection
siltation promotion
flood and wave prevention
preventing waves
and biodiversity maintenance. Species composition and diversity are key parameters for assessing the health of forest ecosystem
and the loss of species diversity often accelerates degradation of structure and function of forest ecosystem. Therefore
accurately monitoring species composition and spatio-temporal distribution of mangrove forest are helpful for timely and effective management and restoration measures
which can further retain the quality of the mangrove ecosystem and biodiversity. The traditional means of obtaining mangrove species information requires time-consuming
labor-intensive and costly field survey
however
it is difficult to further understand the continuous distribution of forest health. In contrast
remote sensing technology is more cost-effective and can achieve spatially continuous monitoring of mangrove species composition and health status. With the fast-developing fine-resolution multispectral satellites
the images are used for classifying mangrove species due to their rich spatial geometric information. However
compared to multispectral images that contain limited spectral information
hyperspectral data are more effective for tree species discrimination or classification due to hundreds or even thousands of continuous bands that can reflect vegetation functional traits (e.g. pigment content
specific leaf area and nitrogen content). Moreover
LiDAR (Light Detection and Ranging) point cloud can acquire details related to three-dimensional features of the vegetation structure.
Traditionally
there are many data dimension reduction or feature extraction methods
such as Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
Minimum Noise Fraction (MNF) and Successive Projections Algorithm (SPA). However
these methods should combine with other classifiers in classification of plant species. To improve the classification accuracy and efficiency
this paper introduces a new machine learning method with both feature selection and classification functions—eXtreme Gradient Boosting (XGBoost). The rapidly growing Unmanned Aerial Vehicles (UAV) and portable sensors of hyperspectral imagery and LiDAR have provided higher quality remote sensing data. UAV-based remote sensing data can derive massive features of spectra
texture and structure
therefore
how to extract dominant features is a key issue to improve the efficiency and accuracy of mangrove species classification.
With UAV-based hyperspectral imagery and LiDAR point cloud of the buffer area in Shenzhen Futian Mangrove Nature Reserve
this study aims to extract eight types of dominant features suitable for mangrove species classification using “
feature_importance
” property of XGBoost. The dominant features include: single feature derived from UAV-based hyperspectral imagery (spectral bands
vegetation indices and texture: F1—F3) and their fused feature (F4)
single feature derived from UAV-based LiDAR point cloud (height and intensity feature: F5
F6) and their fused feature (F7)
and fused feature coupling hyperspectral imagery and LiDAR point cloud.
Synthetically considering species classification accuracy and mapping results
the classification model based on F8 feature held the best performance (overall accuracy was 96.41%
Moran’s
I
was 0.5520). The classification performance based on fused feature of single data source (F4 and F7
overall accuracy was separately 96.74% and 90.64%) was superior than that of single feature (F1—F3
F5 and F6
overall accuracy was 90.31%
92.20%
91.96%
87.66% and 81.99% respectively). The Moran’s
I
of classification maps based on fused feature (F4
F7 and F8) and texture feature (F3) were greater than that of single feature (F1
F2
F5 and F6). Moreover
mangrove species classification models based on different dominant features have their own advantages on spatial mapping. The introduction of F3 effectively solved the common salt-and-pepper effect in the mapping results based on F1 and F2; moreover
the salt-and-pepper effect in the edge of classification images (near tidal flat area) was significantly improved in the mapping results based on F5
F7 and F8.
We conclude that: (1) The combination of UAV-based remote sensing data and XGBoost is feasible to pixel-oriented accurate classification of mangrove species
the fusion feature of UAV-based hyperspectral image and LiDAR point cloud has the best classification effect when comprehensively comparing classification accuracy (OA) and mapping effect (Moran’s
I
); (2) when fusing the different types of features derived from UAV-based hyperspectral or LiDAR data alone
the corresponding classification accuracy and mapping effect behaved better than the single feature derived from the same UAV data source; (3) XGBoost
a machine learning method with both feature selection and classification functions
has great potential in remote sensing image classification; (4) the intensity features derived from LiDAR point clouds are greatly affected by UAV flight strips
however
the height features are robust in mangrove species classification. Future research will focus on unmixing of hyperspectral data
fuzzy classification and radiation transfer models (such as PROSAIL) to improve the accuracy and interpretability of mangrove species classification. This paper demonstrated the feasibility of UAV-based remote sensing data and XGBoost in the pixel-oriented precise classification of mangrove species
which can provide scientific basis and technical support for three-dimensional monitoring of health
conservation and restoration for mangrove ecosystem.
Alonzo M , Bookhagen B and Roberts D A . 2014 . Urban tree species mapping using hyperspectral and lidar data fusion . Remote Sensing of Environment , 148 : 70 - 83 [ DOI: 10.1016/j.rse.2014.03.018 http://dx.doi.org/10.1016/j.rse.2014.03.018 ]
Bajjali W . 2017 . ArcGIS for environmental and water issues . Springer . [ DOI: 10.1007/978-3-319-61158-7 http://dx.doi.org/10.1007/978-3-319-61158-7 ]
Bullock E L , Fagherazzi S , Nardin W , Vo-Luong P , Nguyen P and Woodcock C E . 2017 . Temporal patterns in species zonation in a mangrove forest in the Mekong Delta, Vietnam, using a time series of Landsat imagery . Continental Shelf Research , 147 : 144 - 154 [ DOI: 10.1016/j.csr.2017.07.007 http://dx.doi.org/10.1016/j.csr.2017.07.007 ]
Cai L F , Wu D S , Fang L M and Zheng X Y . 2019 . Tree species identification using XGBoost based on GF-2 images. Forest Resources Management, (5): 44 - 51
蔡林菲 , 吴达胜 , 方陆明 , 郑辛煜 . 2019 . 基于XGBoost的高分二号影像树种识别. 林业资源管理, (5): 44 - 51 [DOI: 10.13466/j.cnki.lyzygl.2019.05.009]
Cao J J , Leng W C , Liu K , Liu L , He Z and Zhu Y H . 2018 . Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models . Remote Sensing , 10 ( 1 ): 89 [ DOI: 10.3390/rs10010089 http://dx.doi.org/10.3390/rs10010089 ]
Chan J C W and Paelinckx D . 2008 . Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery . Remote Sensing of Environment , 112 ( 6 ): 2999 - 3011 [ DOI: 10.1016/j.rse.2008.02.011 http://dx.doi.org/10.1016/j.rse.2008.02.011 ]
Chen T Q and Guestrin C . 2016 . XGBoost: a scalable tree boosting system // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . San Francisco : Association for Computing Machinery : 785 - 794 [ DOI: 10.1145/2939672.2939785 http://dx.doi.org/10.1145/2939672.2939785 ]
Clark M L , Clark D B and Roberts D A . 2004 . Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape . Remote Sensing of Environment , 91 ( 1 ): 68 - 89 [ DOI: 10.1016/j.rse.2004.02.008 http://dx.doi.org/10.1016/j.rse.2004.02.008 ]
Clark M L , Roberts D A and Clark D B . 2005 . Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales . Remote Sensing of Environment , 96 ( 3/4 ): 375 - 398 [ DOI: 10.1016/j.rse.2005.03.009 http://dx.doi.org/10.1016/j.rse.2005.03.009 ]
Dalponte M , Ørka H O , Ene L T , Gobakken T and Næsset E . 2014 . Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data . Remote Sensing of Environment , 140 : 306 - 317 [ DOI: 10.1016/j.rse.2013.09.006 http://dx.doi.org/10.1016/j.rse.2013.09.006 ]
Ferreira M P , Wagner F H , Aragão L E O C , Shimabukuro Y E and de Souza Filho C R . 2019 . Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis . ISPRS Journal of Photogrammetry and Remote Sensing , 149 : 119 - 131 [ DOI: 10.1016/j.isprsjprs.2019.01.019 http://dx.doi.org/10.1016/j.isprsjprs.2019.01.019 ]
Franklin S E , Hall R J , Moskal L M , Maudie A J and Lavigne M B . 2000 . Incorporating texture into classification of forest species composition from airborne multispectral images . International Journal of Remote Sensing , 21 ( 1 ): 61 - 79 [ DOI: 10.1080/014311600210993 http://dx.doi.org/10.1080/014311600210993 ]
Freeman E A , Moisen G G , Coulston J W and Wilson B T . 2015 . Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance . Canadian Journal of Forest Research , 46 ( 3 ): 323 - 339 [ DOI: 10.1139/cjfr-2014-0562 http://dx.doi.org/10.1139/cjfr-2014-0562 ]
García M , Riaño D , Chuvieco E and Danson F M . 2010 . Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data . Remote Sensing of Environment , 114 ( 4 ): 816 - 830 [ DOI: 10.1016/j.rse.2009.11.021 http://dx.doi.org/10.1016/j.rse.2009.11.021 ]
Govender M , Chetty K , Naiken V and Bulcock H . 2008 . A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation . Water SA , 34 ( 2 ): 147 - 154 [ DOI: 10.4314/wsa.v34i2.183634 http://dx.doi.org/10.4314/wsa.v34i2.183634 ]
Guo Q H , Su Y J , Hu T Y , Zhao X Q , Wu F F , Li Y M , Liu J , Chen L H , Xu G C , Lin G H , Zheng Y , Lin Y Q , Mi X C , Fei L and Wang X G . 2017 . An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China . International Journal of Remote Sensing , 38 ( 8/10 ): 2954 - 2972 [ DOI: 10.1080/01431161.2017.1285083 http://dx.doi.org/10.1080/01431161.2017.1285083 ]
Haralick R. M. , Shanmugam K. , & Dinstein I . H . 1973 . Textural features for image classification . IEEE Transactions on systems , man , and cybernetics , ( 6 ), 610 - 621 . [ DOI: 10.1109/TSMC.1973.4309314 http://dx.doi.org/10.1109/TSMC.1973.4309314 ]
Hauser L T , Nguyen Vu G , Nguyen B A , Dade E , Nguyen H M , Nguyen T T Q , Le T Q , Vu L H , Tong A T H and Pham H V . 2017 . Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004-2013) . Applied Geography , 86 : 197 - 207 [ DOI: 10.1016/j.apgeog.2017.06.019 http://dx.doi.org/10.1016/j.apgeog.2017.06.019 ]
Heumann B W . 2011 . Satellite remote sensing of mangrove forests: recent advances and future opportunities . Progress in Physical Geography : Earth and Environment , 35 ( 1 ): 87 - 108 [ DOI: 10.1177/0309133310385371 http://dx.doi.org/10.1177/0309133310385371 ]
Hirano A , Madden M and Welch R . 2003 . Hyperspectral image data for mapping wetland vegetation . Wetlands , 23 ( 2 ): 436 - 448 [ DOI: 10.1672/18-20 http://dx.doi.org/10.1672/18-20 ]
Hovi A , Korhonen L , Vauhkonen J and Korpela I . 2016 . LiDAR waveform features for tree species classification and their sensitivity to tree- and acquisition related parameters . Remote Sensing of Environment , 173 : 224 - 237 [ DOI: 10.1016/j.rse.2015.08.019 http://dx.doi.org/10.1016/j.rse.2015.08.019 ]
Huang X . 2013 . Multiscale Texture and Shape Feature Extraction and Object-Oriented Classification for Very High Resolution Remotely Sensed Imagery . Wuhan : Wuhan University
黄昕 . 2013 . 高分辨率遥感影像多尺度纹理、形状特征提取与面向对象分类研究 . 武汉 : 武汉大学
Huang X , Zhang L P and Wang L . 2009 . Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS imagery . IEEE Geoscience and Remote Sensing Letters , 6 ( 3 ): 393 - 397 [ DOI: 10.1109/LGRS.2009.2014398 http://dx.doi.org/10.1109/LGRS.2009.2014398 ]
Jensen R R , Hardin P J and Hardin A J . 2012 . Classification of urban tree species using hyperspectral imagery . Geocarto International , 27 ( 5 ): 443 - 458 [ DOI: 10.1080/10106049.2011.638989 http://dx.doi.org/10.1080/10106049.2011.638989 ]
Jia M M , Zhang Y Z , Wang Z M , Song K S and Ren C Y . 2014 . Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data . International Journal of Applied Earth Observation and Geoinformation , 33 : 226 - 231 [ DOI: 10.1016/j.jag.2014.06.006 http://dx.doi.org/10.1016/j.jag.2014.06.006 ]
Johansen K and Phinn S . 2006 . Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs . Photogrammetric Engineering and Remote Sensing , 72 ( 1 ): 71 - 80 [ DOI: 10.14358/PERS.72.1.71 http://dx.doi.org/10.14358/PERS.72.1.71 ]
Kamal M and Phinn S . 2011 . Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach . Remote Sensing , 3 ( 10 ): 2222 - 2242 [ DOI: 10.3390/rs3102222 http://dx.doi.org/10.3390/rs3102222 ]
Latifi H , Fassnacht F and Koch B . 2012 . Forest structure modeling with combined airborne hyperspectral and LiDAR data . Remote Sensing of Environment , 121 : 10 - 25 [ DOI: 10.1016/j.rse.2012.01.015 http://dx.doi.org/10.1016/j.rse.2012.01.015 ]
Li Q S , Wong F K K and Fung T . 2019a . Classification of mangrove species using combined wordview-3 and LiDAR data in Mai Po nature reserve, Hong Kong . Remote Sensing , 11 ( 18 ): 2114 [ DOI: 10.3390/rs11182114 http://dx.doi.org/10.3390/rs11182114 ]
Li Y C , Li C , Li M Y and Liu Z Z . 2019b . Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms . Forests , 10 ( 12 ): 1073 [ DOI: 10.3390/f10121073 http://dx.doi.org/10.3390/f10121073 ]
Li Z , Zan Q J , Yang Q , Zhu D H , Chen Y J and Yu S X . 2019c . Remote estimation of mangrove aboveground carbon stock at the species level using a low-cost unmanned aerial vehicle system . Remote Sensing , 11 ( 9 ): 1018 [ DOI: 10.3390/rs11091018 http://dx.doi.org/10.3390/rs11091018 ]
Liu L X , Coops N C , Aven N W and Pang Y . 2017 . Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data . Remote Sensing of Environment , 200 : 170 - 182 [ DOI: 10.1016/j.rse.2017.08.010 http://dx.doi.org/10.1016/j.rse.2017.08.010 ]
Lyons M B , Keith D A , Phinn S R , Mason T J and Elith J . 2018 . A comparison of resampling methods for remote sensing classification and accuracy assessment . Remote Sensing of Environment , 208 : 145 - 153 [ DOI: 10.1016/j.rse.2018.02.026 http://dx.doi.org/10.1016/j.rse.2018.02.026 ]
Mallinis G , Koutsias N , Tsakiri-Strati M and Karteris M . 2008 . Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site . ISPRS Journal of Photogrammetry and Remote Sensing , 63 ( 2 ): 237 - 250 [ DOI: 10.1016/j.isprsjprs.2007.08.007 http://dx.doi.org/10.1016/j.isprsjprs.2007.08.007 ]
Maltamo M , Eerikäinen K , Pitkänen J , Hyyppä J and Vehmas M . 2004 . Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions . Remote Sensing of Environment , 90 ( 3 ): 319 - 330 [ DOI: 10.1016/j.rse.2004.01.006 http://dx.doi.org/10.1016/j.rse.2004.01.006 ]
Maltamo M , Peuhkurinen J , Malinen J , Vauhkonen J , Packalén P and Tokola T . 2009 . Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data . Silva Fennica , 43 ( 3 ): 203 [ DOI: 10.14214/sf.203 http://dx.doi.org/10.14214/sf.203 ]
Manjunath K R , Kumar T , Kundu N and Panigrahy S . 2013 . Discrimination of mangrove species and mudflat classes using in situ hyperspectral data: a case study of Indian Sundarbans . GIScience and Remote Sensing , 50 ( 4 ): 400 - 417 [ DOI: 10.1080/15481603.2013.814275 http://dx.doi.org/10.1080/15481603.2013.814275 ]
Mather P M and Koch M . 2011 . Computer processing of remotely-sensed images: an introduction . 4th ed . Chichester : John Wiley and Sons [ DOI: 10.1002/9780470666517 http://dx.doi.org/10.1002/9780470666517 ]
Maxwell A E , Warner T A and Fang F . 2018 . Implementation of machine-learning classification in remote sensing: an applied review . International Journal of Remote Sensing , 39 ( 9 ): 2784 - 2817 [ DOI: 10.1080/01431161.2018.1433343 http://dx.doi.org/10.1080/01431161.2018.1433343 ]
Means J E , Acker S A , Fitt B J , Renslow M , Emerson L and Hendrix C J . 2000 . Predicting forest stand characteristics with airborne scanning LIDAR . Photogrammetric Engineering and Remote Sensing , 66 ( 11 ): 1367 - 1372
Melville B , Lucieer A and Aryal J . 2019 . Classification of lowland native grassland communities using hyperspectral unmanned aircraft system (UAS) imagery in the Tasmanian midlands . Drones , 3 ( 1 ): 5 [ DOI: 10.3390/drones3010005 http://dx.doi.org/10.3390/drones3010005 ]
Murdiyarso D , Purbopuspito J , Kauffman J B , Warren M W , Sasmito S D , Donato D C , Manuri S , Krisnawati H , Taberima S and Kurnianto S . 2015 . The potential of Indonesian mangrove forests for global climate change mitigation . Nature Climate Change , 5 ( 12 ): 1089 - 1092 [ DOI: 10.1038/nclimate2734 http://dx.doi.org/10.1038/nclimate2734 ]
Næsset E . 2002 . Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data . Remote Sensing of Environment , 80 ( 1 ): 88 - 99 [ DOI: 10.1016/S0034-4257(01)00290-5 http://dx.doi.org/10.1016/S0034-4257(01)00290-5 ]
Næsset E and Gobakken T . 2008 . Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser . Remote Sensing of Environment , 112 ( 6 ): 3079 - 3090 [ DOI: 10.1016/j.rse.2008.03.004 http://dx.doi.org/10.1016/j.rse.2008.03.004 ]
Neukermans G , Dahdouh-Guebas F , Kairo J G and Koedam N . 2008 . Mangrove species and stand mapping in Gazi bay (Kenya) using quickbird satellite imagery . Journal of Spatial Science , 53 ( 1 ): 75 - 86 [ DOI: 10.1080/14498596.2008.9635137 http://dx.doi.org/10.1080/14498596.2008.9635137 ]
Nishii R and Tanaka S . 1999 . Accuracy and inaccuracy assessments in land-cover classification . IEEE Transactions on Geoscience and Remote Sensing , 37 ( 1 ): 491 - 498 [ DOI: 10.1109/36.739098 http://dx.doi.org/10.1109/36.739098 ]
Peñuelas J , Filella I , Lloret P , Muñoz F and Vilajeliu M . 1995 . Reflectance assessment of mite effects on apple trees . International Journal of Remote Sensing , 16 ( 14 ): 2727 - 2733 [ DOI: 10.1080/01431169508954588 http://dx.doi.org/10.1080/01431169508954588 ]
Pontius Jr R G and Millones M . 2011 . Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment . International Journal of Remote Sensing , 32 ( 15 ): 4407 - 4429 [ DOI: 10.1080/01431161.2011.552923 http://dx.doi.org/10.1080/01431161.2011.552923 ]
Prasad K A and Gnanappazham L . 2016 . Multiple statistical approaches for the discrimination of mangrove species of Rhizophoraceae using transformed field and laboratory hyperspectral data . Geocarto International , 31 ( 8 ): 891 - 912 [ DOI: 10.1080/10106049.2015.1094521 http://dx.doi.org/10.1080/10106049.2015.1094521 ]
Richter R , Reu B , Wirth C , Doktor D and Vohland M . 2016 . The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area . International Journal of Applied Earth Observation and Geoinformation , 52 : 464 - 474 [ DOI: 10.1016/j.jag.2016.07.018 http://dx.doi.org/10.1016/j.jag.2016.07.018 ]
Sankey T , Donager J , McVay J and Sankey J B . 2017 . UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA . Remote Sensing of Environment , 195 : 30 - 43 [ DOI: 10.1016/j.rse.2017.04.007 http://dx.doi.org/10.1016/j.rse.2017.04.007 ]
Shahshahani B M and Landgrebe D A . 1994 . The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon . IEEE Transactions on Geoscience and Remote Sensing , 32 ( 5 ): 1087 - 1095 [ DOI: 10.1109/36.312897 http://dx.doi.org/10.1109/36.312897 ]
Shen X and Cao L . 2017 . Tree-species classification in subtropical forests using airborne hyperspectral and LiDAR data . Remote Sensing , 9 ( 11 ): 1180 [ DOI: 10.3390/rs9111180 http://dx.doi.org/10.3390/rs9111180 ]
Shi Y F , Wang T J , Skidmore A K and Heurich M . 2018 . Important LiDAR metrics for discriminating forest tree species in Central Europe . ISPRS Journal of Photogrammetry and Remote Sensing , 137 : 163 - 174 [ DOI: 10.1016/j.isprsjprs.2018.02.002 http://dx.doi.org/10.1016/j.isprsjprs.2018.02.002 ]
Thenkabail P S , Enclona E A , Ashton M S , Legg C and De Dieu M J . 2004a . Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests . Remote Sensing of Environment , 90 ( 1 ): 23 - 43 [ DOI: 10.1016/j.rse.2003.11.018 http://dx.doi.org/10.1016/j.rse.2003.11.018 ]
Thenkabail P S , Enclona E A , Ashton M S and Van Der Meer B . 2004b . Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications . Remote Sensing of Environment , 91 ( 3/4 ): 354 - 376 [ DOI: 10.1016/j.rse.2004.03.013 http://dx.doi.org/10.1016/j.rse.2004.03.013 ]
Thenkabail P S , Smith R B and De Pauw E . 2002 . Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization . Photogrammetric Engineering and Remote Sensing , 68 ( 6 ): 607 - 622
Vaglio Laurin G , Puletti N , Hawthorne W , Liesenberg V , Corona P , Papale D , Chen Q and Valentini R . 2016 . Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data . Remote Sensing of Environment , 176 : 163 - 176 [ DOI: 10.1016/j.rse.2016.01.017 http://dx.doi.org/10.1016/j.rse.2016.01.017 ]
Wallace L , Lucieer A , Watson C and Turner D . 2012 . Development of a UAV-LiDAR system with application to forest inventory . Remote Sensing , 4 ( 6 ): 1519 - 1543 [ DOI: 10.3390/rs4061519 http://dx.doi.org/10.3390/rs4061519 ]
Wang D Z , Wan B , Qiu P H , Su Y J , Guo Q H , Wang R , Sun F and Wu X C . 2018a . Evaluating the performance of sentinel-2, Landsat 8 and pléiades-1 in mapping mangrove extent and species . Remote Sensing , 10 ( 9 ): 1468 [ DOI: 10.3390/rs10091468 http://dx.doi.org/10.3390/rs10091468 ]
Wang D Z , Wan B , Qiu P H , Su Y J , Guo Q H and Wu X C . 2018b . Artificial mangrove species mapping using pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms . Remote Sensing , 10 ( 2 ): 294 [ DOI: 10.3390/rs10020294 http://dx.doi.org/10.3390/rs10020294 ]
Wang T , Zhang H S , Lin H and Fang C Y . 2016 . Textural–spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery . Remote Sensing , 8 ( 1 ): 24 [ DOI: 10.3390/rs8010024 http://dx.doi.org/10.3390/rs8010024 ]
Wang Y J . 2002 . Research on The bird community and ecological evaluation of Sonneratia apetala + Sonneratia caseolaris in Futian, Shenzhen //Joining the WTO and China's Technology and Sustainable Development-Challenges and Opportunities, Responsibilities and Countermeasures. Chengdu: Science and Technology of China Press (王勇军 . 2002 . 深圳福田无瓣海桑+海桑人工林鸟类群落研究及生态评价 // 加入WTO和中国科技与可持续发展——挑战与机遇、责任和对策(上册) . 成都 : 中国科学技术出版社)
Wang Y R . 2018 . Estimation of Mangrove Biomass in Shenzhen Bay Based on Multi-Source Remote Sensing Data . Chongqing : Southwest University
王月如 . 2018 . 基于多源遥感数据的深圳湾红树林生物量估算 . 重庆 : 西南大学
Warrens M J . 2015 . Properties of the quantity disagreement and the allocation disagreement . International Journal of Remote Sensing , 36 ( 5 ): 1439 - 1446 [ DOI: 10.1080/01431161.2015.1011794 http://dx.doi.org/10.1080/01431161.2015.1011794 ]
Wong F K K and Fung T . 2014 . Combining EO-1 Hyperion and Envisat ASAR data for mangrove species classification in Mai Po Ramsar Site, Hong Kong . International Journal of Remote Sensing , 35 ( 23 ): 7828 - 7856 [ DOI: 10.1080/01431161.2014.978034 http://dx.doi.org/10.1080/01431161.2014.978034 ]
Xia J S , Du P J , He X Y and Chanussot J . 2014 . Hyperspectral remote sensing image classification based on rotation forest . IEEE Geoscience and Remote Sensing Letters , 11 ( 1 ): 239 - 243 [ DOI: 10.1109/LGRS.2013.2254108 http://dx.doi.org/10.1109/LGRS.2013.2254108 ]
Xiao H Y , Zeng H , Zan Q J , Bai Y and Cheng H H . 2007 . Decision tree model in extraction of mangrove community information using hyperspectral image data . Journal of Remote Sensing , 11 ( 4 ): 531 - 537
肖海燕 , 曾辉 , 昝启杰 , 白钰 , 程好好 . 2007 . 基于高光谱数据和专家决策法提取红树林群落类型信息 . 遥感学报 , 11 ( 4 ): 531- 537 [ DOI: 10.3321/j.issn:1007-4619.2007.04.014 http://dx.doi.org/10.3321/j.issn:1007-4619.2007.04.014 ]
Xu Y , Wang J J , Xia A Q , Zhang K Y , Dong X Y , Wu K P and Wu G F . 2019 . Continuous wavelet analysis of leaf reflectance improves classification accuracy of mangrove species . Remote Sensing , 11 ( 3 ): 254 [ DOI: 10.3390/rs11030254 http://dx.doi.org/10.3390/rs11030254 ]
Yin D M and Wang L . 2019 . Individual mangrove tree measurement using UAV-based LiDAR data: possibilities and challenges . Remote Sensing of Environment , 223 : 34 - 49 [ DOI: 10.1016/j.rse.2018.12.034 http://dx.doi.org/10.1016/j.rse.2018.12.034 ]
Zan Q J , Wang B S , Wang Y J and Li M G . 2003 . Ecological Assessment on the Introduced Sonneratia caseolaris and S . apetala at the Mangrove Forest of Shenzhen Bay, China . Acta Botanica Sinica , 45 ( 5 ): 544 - 551 [ DOI: 10.3321/j.issn:1672-9072.2003.05.007 http://dx.doi.org/10.3321/j.issn:1672-9072.2003.05.007 ]
Zhao Y J , Zeng Y , Zheng Z J , Dong W X , Zhao D , Wu B F and Zhao Q J . 2018 . Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China . Remote Sensing of Environment , 213 : 104 - 114 [ DOI: 10.1016/j.rse.2018.05.014 http://dx.doi.org/10.1016/j.rse.2018.05.014 ]
Zhong L H , Hu L and Zhou H . 2019 . Deep learning based multi-temporal crop classification . Remote Sensing of Environment , 221 : 430 - 443 [ DOI: 10.1016/j.rse.2018.11.032 http://dx.doi.org/10.1016/j.rse.2018.11.032 ]
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