Advanced search
Publication Cover
Geocarto International Volume 38, 2023 - Issue 1
Submit an article Journal homepage
1,520
Views
2
CrossRef citations to date
0
Altmetric
Research Article

A comparative study of the performances of joint RFE with machine learning algorithms for extracting Moso bamboo (Phyllostachys pubescens) forest based on UAV hyperspectral images

Yi-fan Lia College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Zhang-hua Xua College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, China;b Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, ChinaCorrespondence[email protected]
View further author information
,
Zhen-bang Haob Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, China;c College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, ChinaView further author information
,
Xiong Yaob Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, China;d College of Architecture and Planning, Fujian University of Technology, Fuzhou, ChinaView further author information
,
Qi Zhange Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, The Academy of Digital China, Fuzhou University, Fuzhou, ChinaView further author information
,
Xu-ying Huanga College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, China;f International Institute for Earth System Science, Nanjing University, Nanjing, ChinaView further author information
,
Bin Lia College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
An-qi Hea College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, ChinaView further author information
,
Zeng-lu Lib Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, China;g SEGi University, Kota Damansara, MalaysiaView further author information
&
Xiao-yu Guob Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming, ChinaView further author information
 show all
Article: 2207550 | Received 24 Oct 2022, Accepted 21 Apr 2023, Published online: 16 Jun 2023

References

  • Alharan AFH, Fatlawi HK, Ali NS. 2019. A cluster-based feature selection method for image texture classification. IJEECS. 14(3):1433–1442. doi: 10.11591/ijeecs.v14.i3.pp1433-1442.
  • Anderson K, Gaston KJ. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ. 11(3):138–146. doi: 10.1890/120150.
  • Aparicio N, Villegas D, Royo C, Casadesus J, Araus JL. 2004. Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions. Int J Remote Sens. 25(6):1131–1152. doi: 10.1080/0143116031000116967.
  • Atmoko D, Lin TH. 2022. Sea salt aerosol identification based on multispectral optical properties and its impact on radiative forcing over the ocean. Remote Sens. 14(13):3188. doi: 10.3390/rs14133188.
  • Berni JAJ, Zarco-Tejada PJ, Suarez L, Fereres E. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens. 47(3):722–738. doi: 10.1109/TGRS.2008.2010457.
  • Breiman L. 2001. Random forest. Mach Learn. 45(1):5–32. doi: 10.1023/A:1010933404324.
  • Buschmann C, Nagel E. 1993. In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. Int J Remote Sens. 14(4):711–722. doi: 10.1080/01431169308904370.
  • Cai LF, Wu DS, Fang LM, Zheng XY. 2019. Tree species identification using XGBoost based on GF-2 images. Forest Resour Manag. (5):44–51.
  • Cao J, Leng W, Liu K, Liu L, He Z, Zhu Y. 2018. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens. 10(2):89. doi: 10.3390/rs10010089.
  • Chen TQ, Guestrin C. 2016. XGBoost: a scalable tree boosting system. In Proceeding of 30th International Conference on Machine Learning. p. 13–17.
  • Colomina I, Molina P. 2014. Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens. 92:79–97. doi: 10.1016/j.isprsjprs.2014.02.013.
  • Cui L, Du HQ, Zhou GM, Li XJ, Mao FJ, Xu XJ, Fan WL. 2019. Combination of decision tree and mixed pixel decomposition for extracting bamboo forest information in China. J Remote Sens. 23(1):166–176.
  • Elarab M, Ticlavilca AM, Torres-Rua AF, Maslova I, McKee M. 2015. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. Int J Appl Earth Observ Geoinf. 43:32–42. doi: 10.1016/j.jag.2015.03.017.
  • Erudel T, Fabre S, Houet T, Mazier F, Briotte X. 2017. Criteria comparison for classifying peatland vegetation types using in situ hyperspectral measurements. Remote Sens. 9(7):748. doi: 10.3390/rs9070748.
  • Fernández-Manso A, Fernández-Manso O, Quintano C. 2016. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int J Appl Earth Observ Geoinf. 50:170–175. doi: 10.1016/j.jag.2016.03.005.
  • Feyisa GL, Meilby H, Fensholt R, Proud SR. 2014. Automated water extraction index: a new technique for surface water mapping using landsat imagery. Remote Sens Environ. 140:23–35. doi: 10.1016/j.rse.2013.08.029.
  • Freeman EA, Moisen GG, Coulston JW, Wilson BT. 2016. Random forests and stochastic gradient boosting for predicting tree canopy cover: comparing tuning processes and model performance. Can J For Res. 46(3):323–339.
  • Fu S, Zhang YH, Li JY, Wang MZ, Peng L, Feng QS, Liang TG. 2021. Influence of different vegetation indices and heights of UAVs on the accuracy of grassland coverage estimation. Pratacult Sci. 38(1):11–19.
  • Gitelson AA, Gritz Y, Merzlyak MN. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol. 160(3):271–282. doi: 10.1078/0176-1617-00887.
  • Gitelson AA, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J Photochem Photobiol B Biol. 22(3):247–252. doi: 10.1016/1011-1344(93)06963-4.
  • Gitelson AA, Merzlyak MN, Zur Y, Stark R, Gritz U. 2001. Non-destructive and remote sensing techniques for estimation of vegetation status. Pap Nat. 273:205–210.
  • Gratani L, Crescente MF, Varone L, Fabrini G, Digiulio E. 2008. Growth pattern and photosynthetic activity of different bamboo species growing in the botanical garden of Rome. Flora. 203(1):77–84. doi: 10.1016/j.flora.2007.11.002.
  • Griffiths F, Kuemmerle T, Baumann M, Radeloff VC, Abrudan IV, Lieskovsky J, Munteanu C, Ostapowicz K, Hostert P. 2014. Forest disturbances, forest recovery, and changes in forest types across the Carpathian Ecoregion from 1985 to 2010 based on landsat image composites. Remote Sens Environ. 151:72–88. doi: 10.1016/j.rse.2013.04.022.
  • Guo CF, Guo XY. 2016. Estimation of wetland plant leaf chlorophyll content based on continuum removal in the visible domain. Acta Ecol Sin. 36(20):6538–6546.
  • Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ. 81(2–3):416–426. doi: 10.1016/S0034-4257(02)00018-4.
  • Han N, Du HQ, Zhou GM, Sun XY, Ge HL, Xu XJ. 2014. Object-based classification using SPOT-5 imagery for moso bamboo forest mapping. Int J Remote Sens. 35(3):1126–1142. doi: 10.1080/01431161.2013.875634.
  • Houdanon RD, Mensah S, Gnanglè C, Yorou NS, Houinato M. 2018. Ecosystem services and biomass stock from bamboo stands in central and southern Benin, West Africa. Energ Ecol Environ. 3(3):185–194. doi: 10.1007/s40974-018-0084-0.
  • Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 25(3):295–309. doi: 10.1016/0034-4257(88)90106-X.
  • Jiang Z, Huete AR, Didan K, Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ. 112(10):3833–3845. doi: 10.1016/j.rse.2008.06.006.
  • Jiang YF, Zhang L, Yan M, Qi JG, Fu TM, Fan SX, Chen BW. 2021. High-resolution mangrove forests classification with machine learning using worldview and UAV hyperspectral data. Remote Sens. 13(8):1529. doi: 10.3390/rs13081529.
  • Joyce KE, Anderson K, Bartolo RE. 2021. Of course we fly unmanned-we’re women!. Drones. 5(1):21. doi: 10.3390/drones5010021.
  • Karoui MS, Djerriri KA. 2018. New unmixing-based approach for shadow correction of hyperspectral remote sensing data. In IEEE Geosci. Remote Sens. Symp. (IGARSS), p. 2725–2728.
  • Li YG, Du HQ, Mao FJ, Li XJ, Cui L, Han N, Xu XJ. 2019. Information extracting and spatiotemporal evolution of bamboo forest based on landsat time series data in Zhejiang Province. Sci Silvae Sin. 55(3):88–96.
  • Li Z, Kobayashi M. 2004. Plantation future of bamboo in China. J For Res. 15(3):233–242. doi: 10.1007/BF02911032.
  • Li YC, Li CM, Li Y, Liu ZZ. 2019. Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms. Forests. 10(12):1073. doi: 10.3390/f10121073.
  • Lin TH, Tsay SC, Lien WH, Lin NH, Hsiao TC. 2021. Spectral derivatives of optical depth for partitioning aerosol type and loading. Remote Sens. 13(8):1544. doi: 10.3390/rs13081544.
  • Liu HP, Hui-Jun AN. 2019. Analysis of T&N band advantage in tree species classification based on Worldview-2. J West China For Sci. 48(3):41–46.
  • Liu S, Jiang QG, Ma Y, Xiao Y, Li YH, Cui C. 2017. Object-oriented wetland classification based on hybrid feature selection method combining with relief F, multi-objective genetic algorithm and random forest. Trans Chin Soc Agric Mach. 48(1):119–127.
  • Li Z, Zhang QY, Qiu XC, Peng DL. 2019. Temporal stage and method selection of tree species classification based on GF-2 remote sensing image. Chin J Appl Ecol. 30(12):4059–4070.
  • Li PH, Zhou GM, Du HQ, Lu DS, Mo LF, Xu XJ, Shi YJ, Zhou YG. 2015. Current and potential carbon stocks in moso bamboo forests in China. J Environ Manage. 156:89–96. doi: 10.1016/j.jenvman.2015.03.030.
  • Lu XX, Pan LP. 2019. Comparing ELMs and SVMs generalization performance on multi-class classification problem. Comput Appl Softw. 36(10):262–267.
  • Lu M, Pebesma E, Sanchez A, Verbesselt J. 2016. Spatio-temporal change detection from multidimensional arrays: detecting deforestation from MODIS Time Series. ISPRS J Photogramm Remote Sens. 117:227–236. doi: 10.1016/j.isprsjprs.2016.03.007.
  • Lv WJ, Zhou GM, Chen GS, Zhou YF, Ge ZP, Niu ZW, Xu L, Shi YJ. 2020. Effects of different management practices on the increase in phytolith-occluded carbon in moso bamboo forests. Front Plant Sci. 11:591852. doi: 10.3389/fpls.2020.591852.
  • Maschler J, Atzberger C, Immitzer M. 2018. Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sensing. 10(8):1218–1247. doi: 10.3390/rs10081218.
  • Matese A, Toscano P, Gennaro SF, Genesio L, Vaccari FP, Primicerio J, Belli C, Zaldei A, Bianconi R, Gioli B. 2015. Intercomparison of Uav, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 7(3):2971–2990. doi: 10.3390/rs70302971.
  • Maxwell AE, Warner TA, Fang F. 2018. Implementation of machine-learning classification in remote sensing: an applied review. Int J Remote Sens. 39(9):2784–2817. doi: 10.1080/01431161.2018.1433343.
  • Maxwell AE, Warner TA, Strager MP, Conley JF, Sharp AL. 2015. Assessing machine-learning algorithms and image-and Lidar-derived variables for GEOBIA classification of mining and mine reclamation. Int J Remote Sens. 36(4):954–978. doi: 10.1080/01431161.2014.1001086.
  • Maxwell AE, Warner TA, Strager MP, Pal M. 2014. Combining RapidEye Satellite Imagery and LiDAR for mapping of mining and mine reclamation. Photogramm Eng Remote Sens. 80(2):179–189. doi: 10.14358/PERS.80.2.179-189.
  • Milosevic M, Jankovic D, Peulic A. 2014. Thermography based breast cancer detection using texture features and minimum variance quantization. Excli J. 13:1204–1215.
  • Osco LP, Ramos APM, Faita Pinheiro MM, Moriya ÉAS, Imai NN, Estrabis N, Ianczyk F, Araújo FFd, Liesenberg V, Jorge LAdC, et al. 2020. A machine learning framework to predict nutrient content in Valencia-orange leaf hyperspectral measurements. Remote Sens. 12(6):906. doi: 10.3390/rs12060906.
  • Ozkan UY, Ozdemir I, Saglam S, Yesil A, Demirel T. 2016. Evaluating the woody species diversity by means of remotely sensed spectral and texture measures in the urban forests. J Indian Soc Remote Sens. 44(5):687–697. doi: 10.1007/s12524-016-0550-0.
  • Pal M. 2005. Random forest classifier for remote sensing classification. Int J Remote Sens. 26(1):217–222. doi: 10.1080/01431160412331269698.
  • Pargal S, Fararoda R, Rajashekar G, Balachandran N, Réjou-Méchain M, Barbier N, Jha C, Pélissier R, Dadhwal V, Couteron P. 2017. Inverting aboveground biomass-canopy texture relationships in a landscape of forest mosaic in the Western Ghats of India using very high resolution Cartosat imagery. Remote Sens. 9(3):228. doi: 10.3390/rs9030228.
  • Peñuelas J, Gamon JA, Fredeen AL, Merino J, Field CB. 1994. Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote Sens Environ. 48(2):135–146. doi: 10.1016/0034-4257(94)90136-8.
  • Qi SH, Song B, Liu C, Gong P, Luo J, Zhang MA, Xiong TW. 2022. Bamboo forest mapping in china using the dense Landsat 8 image archive and Google earth engine. Remote Sens. 14(3):762. doi: 10.3390/rs14030762.
  • Ruwaimana M, Satyanarayana B, Otero V, Muslim AM, Syafiq AM, Ibrahim S, Raymaekers D, Koedam N, Dahdouh GF. 2018. The advantages of using drones over space-borne imagery in the mapping of mangrove forests. PLoS One. 13(7):e0200288. doi: 10.1371/journal.pone.0200288.
  • Sakamoto T, Gitelson AA, Nguy-Robertson AL, Arkebauer TJ, Wardlow BD, Suyker AE, Verma SB, Shibayama M. 2012. An alternative method using digital cameras for continuous monitoring of crop status. Agric For Meteorol. 154–155:113–126. doi: 10.1016/j.agrformet.2011.10.014.
  • Shang, Z. Z., G. M. Zhou, H. Q. Du, X. J. Xu, Y. J. Shi, Y. L. Lu, Y. F. Zhou and C. Y. Gu. 2013. Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images. Int J Remote Sens 34 (15–16): 5351–5368. doi: 10.1080/01431161.2013.788260.
  • Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ. 81(2–3):337–354.
  • Stagakis S, Vanikiotis T, Sykioti O. 2016. Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery. ISPRS J Photogramm Remote Sens. 119(9):79–89. doi: 10.1016/j.isprsjprs.2016.05.013.
  • Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. 8(2):127–150. doi: 10.1016/0034-4257(79)90013-0.
  • Vincini M, Frazzi E. 2011. Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in Planophile crop canopies. Precision Agric. 12(3):334–344. doi: 10.1007/s11119-010-9204-3.
  • Vogelmann JE, Rock BN, Moss DM. 1993. Red edge spectral measurements from sugar maple leaves. Int J Remote Sens. 14(8):1563–1575. doi: 10.1080/01431169308953986.
  • Wang Q,Zhao Y,Yang F,Liu T,Xiao W,Sun H. 2021. Simulating heat stress of coal gangue spontaneous combustion on vegetation using alfalfa leaf water content spectral features as indicators. Remote Sens. 13(13):2634.
  • Wang T, Zhang H, Lin H, Fang C. 2015. Textural-spectral feature-based species classification of mangroves in Mai Po nature reserve from worldview-3 imagery. Remote Sens. 8(1):24. doi: 10.3390/rs8010024.
  • Wen XL, Zhong A, Hu XJ. 2018. The classification of urban greening tree species based on feature selection of random forest. J Geo-Inf Sci. 20(12):1777–1786.
  • Xie ZL, Chen YL, Lu DS, Li GY, Chen EX. 2019. Classification of land cover, forest, and tree species classes with ZiYuan-3 multispectral and stereo data. Remote Sens. 11(2):164. doi: 10.3390/rs11020164.
  • Xu ZH, Liu J, Yu KY, Gong CH, Xie WJ, Tang MY, Lai RW, Li ZL. 2013. Spectral features analysis of Pinus massoniana with Pest of Dendrolimus Punctatus walker and levels detection. Spectrosc Spectral Anal. 33(2):428–433.
  • Xu Y, Zhen JN, Jiang XP, Wang JJ. 2021. Mangrove species classification with UAV-based remote sensing data and XGBoost. J Remote Sens. 25(3):737–752.
  • Zhang L, Gong ZN, Wang QW, Jin DD, Wang X. 2019. Wetland mapping of Yellow River delta wetlands based on multi-feature optimization of sentinel-2 images. J Remote Sens. 23(2):313–326.
  • Zhang CY, Xie ZX. 2013. Object-based vegetation mapping in the Kissimmee River watershed using HyMap Data and machine learning techniques. Wetlands. 33(2):233–244. doi: 10.1007/s13157-012-0373-x.
  • Zhang CH, Yang ZP, Xie QY, Deng YB, Yu KY, Liu J. 2020. Study of effective height of the living bamboo density identification based on unmanned aerial vehicle (UAV) remote sensing. Remote Sens Technol Appl. 35(6):1436–1446.
  • Zhao QZ, Jiang P, Wang XW, Zhang LH, Zhang JX. 2021. Classification of protection forest tree species based on UAV hyperspectral data. Trans Chin Soc Agric Mach. 52(11):190–199.
  • Zheng D, Shen GC, Wang BJ, Dai GH, Lin F, Hu JR, Ye J, Fang S, Hao ZQ, Wang XG. 2022. Classification of dominant species in coniferous and broadleaf mixed forest on Changbai Mountain based on UAV-based hyperspectral image and deep learning algorithm. Trans Chin Soc Agric Mach. 41(5):1024–1032.
  • Zhong LH, Hu LN, Zhou H. 2019. Deep learning based multi-temporal crop classification. Remote Sens Environ. 221:430–443. doi: 10.1016/j.rse.2018.11.032.
  • Zhou GM, Meng CF, Jiang PK, Xu QF. 2011. Review of carbon fixation in bamboo forests in China. Bot Rev. 77(3):262–270. doi: 10.1007/s12229-011-9082-z.
  • Zhou XC, Zheng L, Huang HY. 2021. Classification of forest stand based on multi-feature optimization of UAV visible light remote sensing. Sci Silvae Sin. 57(6):24–36.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.