AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Forest Ecosystems Article
PDF (23.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China

School of Resource and Environmental Sciences, Wuhan University, Hubei, 430079, China
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China
College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing, 100714, China
School of Ecology and Environment, Ningxia University, Yinchuan, 750021, China
Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China, Ningxia University, Yinchuan, 750021, China
Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwest China of Ministry of Education, Ningxia University, Yinchuan, 750021, China

1 These authors (W. Huang and W. Min) contributed equally to the work.

Show Author Information

Abstract

Background

Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging. It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems. Yet current regional to national scale forest height maps were mainly produced at coarse-scale. Such maps lack spatial details for decision-making at local scales. Recent advances in remote sensing provide great opportunities to fill this gap.

Method

In this study, we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China. A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models. Specifically, the relationships between three types of in-situ measured tree heights (maximum-, averaged-, and basal area-weighted- tree heights) and plot-level remote sensing metrics (multispectral, radar, and topo variables from Landsat, Sentinel-1/PALSAR-2, and SRTM) were analyzed. Three types of models (multilinear regression, random forest, and support vector regression) were evaluated. Feature variables were selected by two types of variable selection approaches (stepwise regression and random forest). Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach. Then, tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.

Results

The best estimation of plot-level tree heights (R2 ranged from 0.47 to 0.52, RMSE ranged from 3.8 to 5.3 ​m, and rRMSE ranged from 28% to 31%) was achieved using the random forest model. A comparison with existing forest height maps showed similar estimates of mean height, however, the ranges varied under different definitions of forest and types of tree height.

Conclusions

Primary results indicate that there are small biases in estimated heights at the province scale. This study provides a framework toward establishing regional to national scale maps of vertical forest structure.

Keywords

Forest canopy heightHunan provinceLandsat ARDPALSAR-2Sentinel-1

References

 

Breiman, L., 2001. Random forests. Machine Learn 45, 5-32
Crossref Google Scholar
 

Chen, Q., 2010. Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry. Remote Sens. Environ. 114, 1610-1627
Crossref Google Scholar
 

Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., Mills, J., 2015. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7-27
Crossref Google Scholar
 
CristE.P.A TM Tasseled Cap equivalent transformation for reflectance factor dataRemote Sens. Environ.19851730130610.1016/0034-4257(85)90102-6

Crist, E.P., 1985. A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 17, 301-306
Crossref Google Scholar
 

Dubayah, R., Blair, J.B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P.L., Qi, W., Silva, C., 2020. The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. http://doi.org/10.1016/j.srs.2020.100002
Crossref Google Scholar
 

Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., Crowther, T.W., Falkowski, M., Kellner, J.R., Labriere, N., Lucas, R., MacBean, N., McRoberts, R.E., Meyer, V., Naesset, E., Nickeson, J.E., Paul, K.I., Phillips, O.L., Rejou-Mechain, M., Roman, M., Roxburgh, S., Saatchi, S., Schepaschenko, D., Scipal, K., Siqueira, P.R., Whitehurst, A., Williams, M., 2019. The importance of consistent global forest aboveground biomass product validation. Survey Geophys. http://doi.org/10.1007/s10712-019-09538-8
Crossref Google Scholar
 

Garcia, M., Saatchi, S., Ustin, S., Balzter, H., 2018. Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. Intl. J. Appl. Earth Observ. Geoinform. 66, 159-173
Google Scholar
 
GenuerR.PoggiJ.-M.Tuleau-MalotC.VSURF: an R package for variable selection using random forestsR. J.20157193310.32614/rj-2015-018

Genuer, R., Poggi, J.-M., Tuleau-Malot, C., 2015. VSURF: An R package for variable selection using random forests. R. J. 7, 19-33
Crossref Google Scholar
 

Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J., Chen, J., 2013. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Intl. J. Remote Sens. 34, 2607-2654
Crossref Google Scholar
 

Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping, S.H., Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu, L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., Song, L., 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull. 64, 370-373
Crossref Google Scholar
 
HansenM.C.RoyD.P.LindquistE.AduseiB.JusticeC.O.AltstattA.A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo BasinRemote Sens. Environ.20081122495251310.1016/j.rse.2007.11.012

Hansen, M.C., Roy, D.P., Lindquist, E., Adusei, B., Justice, C.O., Altstatt, A., 2008. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sens. Environ. 112, 2495-2513
Crossref Google Scholar
 

Herold, M., Carter, S., Avitabile, V., Espejo, A.B., Jonckheere, I., Lucas, R., McRoberts, R.E., Naesset, E., Nightingale, J., Petersen, R., Reiche, J., Romijn, E., Rosenqvist, A., Rozendaal, D.M.A., Seifert, F.M., Sanz, M.J., De Sy, V., 2019. The role and need for space-based forest biomass-related measurements in environmental management and policy. Survey Geophys. http://doi.org/10.1007/s10712-019-09510-6
Crossref Google Scholar
 

Hu, Y., Wu, F., Sun, Z., Lister, A., Gao, X., Li, W., Peng, D., 2019. The laser vegetation detecting sensor: a full waveform, large-footprint, airborne laser altimeter for monitoring forest resources. Sensors. http://doi.org/10.3390/s19071699
Crossref Google Scholar
 

Hu, Y., Xu, X., Wu, F., Sun, Z., Xia, H., Meng, Q., Huang, W., Zhou, H., Gao, J., Li, W., Peng, D., Xiao, X., 2020. Estimating forest stock volume in Hunan Province, China, by integrating in situ plot data, sentinel-2 images, and linear and machine learning regression models. Remote Sens. 12, 186
Crossref Google Scholar
 

Huang, W., Sun, G., Dubayah, R., Cook, B.D., Montesano, P.M., Ni, W., Zhang, Z., 2013. Mapping biomass change after forest disturbance: applying LiDAR footprint-derived models at key map scales. Remote Sens. Environ. 134, 319-332
Crossref Google Scholar
 
Huang, W., Swatantran, A., Duncanson, L., Johnson, K., Watkinson, D., Dolan, K., O’Neil-Dunne, J., Hurtt, G., Dubayah, R., 2017. County-scale biomass map comparison: a case study for Sonoma, California. Carbon Manag. http://doi.org/10.1080/17583004.2017.1396840https://doi.org/10.1080/17583004.2017.1396840
Crossref
 

Hurtt, G., Zhao, M., Sahajpal, R., Armstrong, A., Birdsey, R.A., Campbell, K., Dolan, K., Dubayah, R., Fish, J.P., Huang, C., Huang, W., Johnson, K., Lamb, R., Ma, L., Marks, R., O’Leary III, D., O’Neil-Dunne, J., Swantaran, A., Tang, H., 2019. Beyond MRV: High-resolution forest carbon modeling for climate mitigation planning over MD, USA. Environm. Res. Lett. http://doi.org/10.1088/1748-9326/ab0bbe
Crossref Google Scholar
 

Hyde, P., Dubayah, R., Peterson, B., Blair, J.B., Hofton, M., Hunsaker, C., Knox, R., Walker, W., 2005. Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sens. Environm. 96, 427-437
Crossref Google Scholar
 
JAXA, 2018. Global 25 m resolution PALSAR-2/PALSAR mosaic and forest/non-forest map dataset description. https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/DatasetDescription_PALSAR2_Mosaic_FNF_revH.pdf (accessed 01 May 2021)
 

Kellndorfer, J., Walker, W., Pierce, L., Dobson, C., Fites, J.A., Hunsaker, C., Vona, J., Clutter, M., 2004. Vegetation height estimation from shuttle radar topography mission and national elevation datasets. Remote Sens. Environ. 93, 339-358
Crossref Google Scholar
 
KellndorferJ.M.WalkerW.S.LaPointE.KirschK.BishopJ.FiskeG.Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: a regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data setsJ. Geophys. Res.2010115G00E08

Kellndorfer, J.M., Walker, W.S., LaPoint, E., Kirsch, K., Bishop, J., Fiske, G., 2010. Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets. J. Geophys. Res. 115, G00E08
Google Scholar
 

Li, C., Song, J., Wang, J., 2021. New approach to calculating tree height at the regional scale. Forest Ecosyst. 8, 24. http://doi.org/10.1186/s40663-021-00300-4
Crossref Google Scholar
 
Liu, L., Zhang, X., Chen, X., Gao, Y., Mi, J., 2020. GLC_FCS30-2020:Global land cover with fine classification system at 30 m in 2020 (v1.2). Zenodo. http://doi.org/10.5281/zenodo.4280923
 

Mahoney, C., Hopkinson, C., Held, A., Simard, M., 2016. Continental-scale canopy height modeling by integrating national, spaceborne, and airborne lidar data. Can. J. Remote Sens. 42, 574-590
Crossref Google Scholar
 

Ni, X., Zhou, Y., Cao, C., Wang, X., Shi, Y., Park, T., Choi, S., Myneni, R., 2015. Mapping forest canopy height over continental China using multi-source remote sensing data. Remote Sens. 7, 8436
Crossref Google Scholar
 

Ni, W., Sun, G., Pang, Y., Zhang, Z., Liu, J., Yang, A., Wang, Y., Zhang, D., 2018. Mapping three-dimensional structures of forest canopy using UAV stereo imagery: evaluating impacts of forward overlaps and image resolutions with LiDAR data as reference. IEEE J. Select Topics Appl. Earth Observ. Remote Sens. 11, 3578-3589
Crossref Google Scholar
 
PangY.ZhaoF.LiZ.ZhouS.DengG.LiuQ.ChenE.Forest height inversion using airborne lidar technologyJ. Remote Sens.2008121152158

Pang, Y., Zhao, F., Li, Z., Zhou, S., Deng, G., Liu, Q., Chen, E., 2008. Forest height inversion using airborne lidar technology. J. Remote Sens., 12(1), 152-158
Google Scholar
 

Pascual, C., Garcia-Abril, A., Cohen, W.B., Martin-Fernandez, S., 2010. Relationship between LiDAR-derived forest canopy height and Landsat images. Intl. J. Remote Sens. 31, 1261-1280
Crossref Google Scholar
 

Potapov, P., Tyukavina, A., Turubanova, S., Talero, Y., Hernandez-Serna, A., Hansen, M.C., Saah, D., Tenneson, K., Poortinga, A., Aekakkararungroj, A., Chishtie, F., Towashiraporn, P., Bhandari, B., Aung, K.S., Nguyen, Q.H., 2019. Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000-2017 Landsat time-series. Remote Sens. Environ. 232, 111278
Crossref Google Scholar
 

Potapov, P., Hansen, M.C., Kommareddy, I., Kommareddy, A., Turubanova, S., Pickens, A., Adusei, B., Tyukavina, A., Ying, Q., 2020. Landsat analysis ready data for global land cover and land cover change mapping. Remote Sens. 12, 426. http://doi.org/10.3390/rs12030426
Crossref Google Scholar
 

Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M.C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C.E., Armston, J., Dubayah, R., Blair, J.B., Hofton, M., 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165
Crossref Google Scholar
 

Qi, W., Lee, S.-K., Hancock, S., Luthcke, S., Tang, H., Armston, J., Dubayah, R., 2019. Improved forest height estimation by fusion of simulated GEDI Lidar data and TanDEM-X InSAR data. Remote Sens. Environ. 221, 621-634
Crossref Google Scholar
 
R Development Core Team, 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria
 

Shimada, M., Itoh, T., Motooka, T., Watanabe, M., Shiraishi, T., Thapa, R., Lucas, R., 2014. New global forest/non-forest maps from ALOS PALSAR data (2007-2010). Remote Sens. Environ. 155, 13-31
Crossref Google Scholar
 
SimardM.PintoN.FisherJ.B.BacciniA.Mapping forest canopy height globally with spaceborne lidarJ. Geophys. Res.201110.1029/2011JG001708

Simard, M., Pinto, N., Fisher, J.B., Baccini, A., 2011. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. http://doi.org/10.1029/2011JG001708
Crossref Google Scholar
 
Statistics, HPBo, 2019. Hunan Statistical Yearbook. Beijing: China Statistics Press (in Chinese)
 

Sun, G., Ranson, K.J., Guo, Z., Zhang, Z., Montesano, P., Kimes, D., 2011. Forest biomass mapping from lidar and radar synergies. Remote Sens. Environ. 115, 2906-2916
Crossref Google Scholar
 
Thomas, E.A., Harold, B., 2001. Forest Measurements. McGraw-Hill Education, 5th edition
 

Wang, Y., Ni, W., Sun, G., Chi, H., Zhang, Z., Guo, Z., 2019. Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass. Remote Sens. Environ. 224, 386-400
Crossref Google Scholar
 

Zhang, Z., Ni, W., Sun, G., Huang, W., Ranson, K.J., Cook, B.D., Guo, Z., 2017. Biomass retrieval from L-band polarimetric UAVSAR backscatter and PRISM stereo imagery. Remote Sens. Environ. 194, 331-346
Crossref Google Scholar
 

Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., Mi, J., 2021. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth Syst. Sci. Data 13, 2753-2776
Crossref Google Scholar
 

Zhao, K., Suarez, J.C., Garcia, M., Hu, T., Wang, C., Londo, A., 2018. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sens Environ 204:883-897
Crossref Google Scholar
Forest Ecosystems
Volume 9 Issue 1,
February 2022
Article number: 100006
DOI: 10.1016/j.fecs.2022.100006
Cite this article:
Huang W, Min W, Ding J, et al. Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China. Forest Ecosystems, 2022, 9(1): 100006. https://doi.org/10.1016/j.fecs.2022.100006

978

Views

38

Downloads

18

Crossref

14

Web of Science

17

Scopus

0

CSCD

Altmetrics
Published: 25 February 2022
© 2022 Beijing Forestry University.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by/4.0/).
Return