593
Views
1
CrossRef citations to date
0
Altmetric
Research Article

An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China

ORCID Icon, ORCID Icon, , & ORCID Icon
Article: 2271246 | Received 08 Jul 2023, Accepted 11 Oct 2023, Published online: 10 Nov 2023

References

  • Amato, U., R. M. Cavalli, A. Palombo, S. Pignatti, and F. Santini. 2008. “Experimental Approach to the Selection of the Components in the Minimum Noise Fraction.” IEEE Transactions on Geoscience and Remote Sensing 47 (1): 153–27. https://doi.org/10.1109/TGRS.2008.2002953.
  • Asuero, A. G., A. Sayago, and A. G. González. 2006. “The Correlation Coefficient: An Overview.” Critical Reviews in Analytical Chemistry 36 (1): 41–59. https://doi.org/10.1080/10408340500526766.
  • Bagozzi, R. P., and Y. Yi. 2012. “Specification, Evaluation, and Interpretation of Structural Equation Models.” Journal of the Academy of Marketing Science 40 (1): 8–34. https://doi.org/10.1007/s11747-011-0278-x.
  • Belgiu, M., and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” ISPRS Journal of Photogrammetry and Remote Sensing 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
  • Blennow, K., and E. Olofsson. 2008. “The Probability of Wind Damage in Forestry Under a Changed Wind Climate.” Climatic Change 87 (3–4): 347–360. https://doi.org/10.1007/s10584-007-9290-z.
  • Bobbink, R., K. Hicks, J. Galloway, T. Spranger, R. Alkemade, M. Ashmore, M. Bustamante, et al. 2010. “Global Assessment of Nitrogen Deposition Effects on Terrestrial Plant Diversity: A Synthesis.” Ecological Applications 20 (1): 30–59. https://doi.org/10.1890/08-1140.1.
  • Calviño-Cancela, M., M. L. Chas-Amil, E. D. García-Martínez, and J. Touza. 2017. “Interacting Effects of Topography, Vegetation, Human Activities and Wildland-Urban Interfaces on Wildfire Ignition Risk.” Forest Ecology and Management 397:10–17. https://doi.org/10.1016/j.foreco.2017.04.033.
  • De Vries, W., E. Du, and K. Butterbach-Bahl. 2014. “Short and Long-Term Impacts of Nitrogen Deposition on Carbon Sequestration by Forest Ecosystems.” Current Opinion in Environmental Sustainability 9-10 (nov): 90–104. https://doi.org/10.1016/j.cosust.2014.09.001.
  • Du, C., W. Fan, Y. Ma, H. Jin, and Z. Zhen. 2021. “The Effect of Synergistic Approaches of Features and Ensemble Learning Algorithms on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8.” Sensors 21 (17): 5974. https://doi.org/10.3390/s21175974.
  • Du, E., M. E. Fenn, W. De Vries, and Y. S. Ok. 2019. “Atmospheric Nitrogen Deposition to Global Forests: Status, Impacts and Management Options.” Environmental Pollution 250:1044–1048. https://doi.org/10.1016/j.envpol.2019.04.014.
  • Estornell, J., J. M. Martí-Gavilá, M. T. Sebastiá, and J. Mengual. 2013. “Principal Component Analysis Applied to Remote Sensing.” Modelling in Science Education and Learning 6:83–89. https://doi.org/10.4995/msel.2013.1905.
  • Everingham, M., L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. 2010. “The Pascal Visual Object Classes (Voc) Challenge.” International Journal of Computer Vision 88:303–338. https://doi.org/10.1007/s11263-009-0275-4.
  • Fedrigo, M., G. J. Newnham, N. C. Coops, D. S. Culvenor, D. K. Bolton, and C. R. Nitschke. 2018. “Predicting Temperate Forest Stand Types Using Only Structural Profiles from Discrete Return Airborne Lidar.” ISPRS Journal of Photogrammetry and Remote Sensing 136:106–119. https://doi.org/10.1016/j.isprsjprs.2017.11.018.
  • Genuer, R., J. Poggi, and C. Tuleau-Malot. 2010. “Variable Selection Using Random Forests.” Pattern Recognition Letters 31 (14): 2225–2236. https://doi.org/10.1016/j.patrec.2010.03.014.
  • Georganos, S., T. Grippa, S. Vanhuysse, M. Lennert, E. Wolff, and E. Wolff. 2018. “Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting.” IEEE Geoscience and Remote Sensing Letters 15 (4): 607–611. https://doi.org/10.1109/LGRS.2018.2803259.
  • Ghosh, A., and P. K. Joshi. 2014. “A Comparison of Selected Classification Algorithms for Mapping Bamboo Patches in Lower Gangetic Plains Using Very High Resolution WorldView 2 Imagery.” International Journal of Applied Earth Observation and Geoinformation 26:298–311. https://doi.org/10.1016/j.jag.2013.08.011.
  • Gorelick, N., Hancher, M., and Dixon, M. 2017. “Google Earth Engine: Planetary-scale geospatial analysis for everyone[J].” Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031.
  • Grabska, E., D. Frantz, and K. Ostapowicz. 2020. “Evaluation of Machine Learning Algorithms for Forest Stand Species Mapping Using Sentinel-2 Imagery and Environmental Data in the Polish Carpathians.” Remote Sensing of Environment 251:112103. https://doi.org/10.1016/j.rse.2020.112103.
  • Hansen, M. C., Potapov, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–853. https://doi.org/10.1126/science.1244693.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR),770–778. https://doi.org/10.1109/CVPR.2016.90.
  • Hofstra, N., M. Haylock, M. New, P. Jones, and C. Frei. 2008. “Comparison of Six Methods for the Interpolation of Daily, European Climate Data.” Journal of Geophysical Research Atmospheres 113 (D21). https://doi.org/10.1029/2008JD010100.
  • Ioffe, S., and C. Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:448–456.
  • Jia, Y., Q. WANG, J. ZHU, Chen, Z., He, N.P., and Yu, G.R. 2019a. “Spatial and Temporal Patterns of Atmospheric Inorganic Nitrogen Dry Deposition in China.” 2006–2015. [DB/OL. https://doi.org/10.11922/sciencedb.921.
  • Jia, Y., Q. WANG, J. ZHU, Chen, Z., He, N. P., and Yu, G.R. 2019b. Spatial Pattern of Atmospheric Inorganic Nitrogen Wet Deposition in China from 1996 to 2015. https://doi.org/10.11922/csdata.2018.0031.zh.
  • Jia, L., Z. Zhou, and B. Li. 2012. “Study of SAR Image Texture Feature Extraction Based on GLCM in Guizhou Karst Mountainous Region”. In 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China, 1–4.
  • Kattenborn, T., J. Leitloff, F. Schiefer, and S. Hinz. 2021. “Review on Convolutional Neural Networks (CNN) in Vegetation Remote Sensing.” Isprs Journal of Photogrammetry & Remote Sensing 173:24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010.
  • Konrad Turlej, C., M. Ozdogan, and V. C. Radeloff. 2022. “Mapping Forest Types Over Large Areas with Landsat Imagery Partially Affected by Clouds and SLC Gaps.” International Journal of Applied Earth Observation and Geoinformation 107:102689. https://doi.org/10.1016/j.jag.2022.102689.
  • Li, W., R. Dong, H. Fu, J. Wang, L. Yu, and P. Gong. 2020. “Integrating Google Earth Imagery with Landsat Data to Improve 30-M Resolution Land Cover Mapping.” Remote Sensing of Environment 237:111563. https://doi.org/10.1016/j.rse.2019.111563.
  • Lin, T., M. Maire, S. Belongie, J. H. C, and L. Zitnick. 2014. “Microsoft COCO: Common Objects in Context.” European Conference on Computer Vision8693:740–755.
  • Liu, Q., G. Liu, C. Huang, S. Liu, and J. Zhao. 2014. “A Tasseled Cap Transformation for Landsat 8 OLI TOA Reflectance Images.” IEEE Geoscience and Remote Sensing Symposium 541–544. https://doi.org/10.1109/IGARSS.2014.6946479.
  • Ma, L., Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson. 2019. “Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152:166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015.
  • Mäyrä, J., S. Keski-Saari, S. Kivinen, T. Tanhuanpää, P. Hurskainen, P. Kullberg, L. Poikolainen, et al. 2021. “Tree Species Classification from Airborne Hyperspectral and LiDar Data Using 3D Convolutional Neural Networks.” Remote Sensing of Environment 256:112322. https://doi.org/10.1016/j.rse.2021.112322.
  • Mohammadpour, P., D. X. Viegas, and C. Viegas. 2022. “Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal.” Remote Sensing 14 (18): 4585. https://doi.org/10.3390/rs14184585.
  • Olofsson, P., G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder. 2014. “Good Practices for Estimating Area and Assessing Accuracy of Land Change.” Remote Sensing of Environment 148:42–57. https://doi.org/10.1016/j.rse.2014.02.015.
  • Pham, L. T. H., and L. Brabyn. 2017. “Monitoring Mangrove Biomass Change in Vietnam Using SPOT Images and an Object-Based Approach Combined with Machine Learning Algorithms.” ISPRS Journal of Photogrammetry & Remote Sensing 128:86–97. https://doi.org/10.1016/j.isprsjprs.2017.03.013.
  • Pu, R. 2021. “Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-Of-The-Art Review and Perspective.” Journal of Remote Sensing 2021:9812624. https://doi.org/10.34133/2021/9812624.
  • Ripley, B. D. 2007. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
  • Rohith, G., and L. S. Kumar. 2020. Remote Sensing Signature Classification of Agriculture Detection Using Deep Convolution Network Models Machine Learning, Image Processing, Network Security and Data Sciences. Singapore: Springer.
  • Sayemuzzaman, M., and M. K. Jha. 2014. “Seasonal and Annual Precipitation Time Series Trend Analysis in North Carolina, United States.” Atmospheric Research 137:183–194. https://doi.org/10.1016/j.atmosres.2013.10.012.
  • Schlund, M., K. Scipal, and M. W. J. Davidson. 2017. “Forest Classification and Impact of BIOMASS Resolution on Forest Area and Aboveground Biomass Estimation.” International Journal of Applied Earth Observation and Geoinformation 56:65–76. https://doi.org/10.1016/j.jag.2016.12.001.
  • Shi, Y., L. Xu, Y. Zhou, B. Ji, G. Zhou, H. Fang, J. Yin, et al. 2018. “Quantifying Driving Factors of Vegetation Carbon Stocks of Moso Bamboo Forests Using Machine Learning Algorithm Combined with Structural Equation Model.” Forest Ecology and Management 429:406–413. https://doi.org/10.1016/j.foreco.2018.07.035.
  • Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv Preprint arXiv1409:1556. https://doi.org/10.48550/arXiv.1409.1556.
  • Song, Y., J. Wang, Y. Ge, and C. Xu. 2020. “An Optimal Parameters-Based Geographical Detector Model Enhances Geographic Characteristics of Explanatory Variables for Spatial Heterogeneity Analysis: Cases with Different Types of Spatial Data.” GIScience and Remote Sensing 57 (5): 593–610. https://doi.org/10.1080/15481603.2020.1760434.
  • Stuke, A., P. Rinke, and M. Todorović. 2021. “Efficient Hyperparameter Tuning for Kernel Ridge Regression with Bayesian Optimization.” Machine Learning: Science and Technology 2 (3): 035022. https://doi.org/10.1088/2632-2153/abee59.
  • Vaswani, A., N. Shazeer., N. Parmar., Uszkoreit., J., Jones., L, Gomez., A. N., Łukasz Kaiser, G., and Polosukhin, I. 2017. “Attention is All You Need.” arXiv. https://doi.org/10.48550/arXiv.1706.03762.
  • Wallis, C. I. B., G. Brehm, D. A. Donoso, K. Fiedler, J. Homeier, D. Paulsch, D. Süßenbach, et al. 2017. “Remote Sensing Improves Prediction of Tropical Montane Species Diversity but Performance Differs Among Taxa.” Ecological Indicators 83:538–549. https://doi.org/10.1016/j.ecolind.2017.01.022.
  • Wang, Q., and P. M. Atkinson. 2018. “Spatio-Temporal Fusion for Daily Sentinel-2 Images.” Remote Sensing of Environment 204:31–42. https://doi.org/10.1016/j.rse.2017.10.046.
  • Wang, G., M. Wu, X. Wei, and H. Song. 2020. “Water Identification from High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks.” Remote Sensing 12 (5): 795. https://doi.org/10.3390/rs12050795.
  • Wang, Y., H. Zhang, and G. Zhang. 2019. “CPSO-CNN: An Efficient PSO-Based Algorithm for Fine-Tuning Hyper-Parameters of Convolutional Neural Networks.” Swarm and Evolutionary Computation 49:114–123. https://doi.org/10.1016/j.swevo.2019.06.002.
  • Wensel, L. C., J. Levitan, and K. Barber. 1980. “Selection of Basal Area Factor in Point Sampling.” Journal of Forestry78(2): 83–84. https://doi.org/10.1093/jof/78.2.83.
  • Whang, S. E., and J. G. Lee. 2020. “Data Collection and Quality Challenges for Deep Learning.” Proceedings of the VLDB Endowment 13 (12): 3429–3432. https://doi.org/10.14778/3415478.3415562.
  • Wickham, J., S. V. Stehman, D. G. Sorenson, L. Gass, and J. A. Dewitz. 2023. “Thematic Accuracy Assessment of the NLCD 2019 Land Cover for the Conterminous United States.” GIScience & Remote Sensing 60 (1): 2181143. https://doi.org/10.1080/15481603.2023.2181143.
  • Wu, Y., K. Shi, Z. Chen, S. Liu, and Z. Chang. 2022. “Developing Improved Time-Series DMSP-OLS-Like Data (1992–2019) in China by Integrating DMSP-OLS and SNPP-VIIRS.” IEEE Transactions on Geoscience & Remote Sensing60:1–14. https://doi.org/10.1109/TGRS.2021.3135333.
  • Xi, Z., C. Hopkinson, S. B. Rood, and D. R. Peddle. 2020. “See the Forest and the Trees: Effective Machine and Deep Learning Algorithms for Wood Filtering and Tree Species Classification from Terrestrial Laser Scanning.” ISPRS Journal of Photogrammetry and Remote Sensing 168:1–16. https://doi.org/10.1016/j.isprsjprs.2020.08.001.
  • Xi, Y., C. Ren, Q. Tian, Y. Ren, X. Dong, and Z. Zhang. 2021. “Exploitation of Time Series Sentinel-2 Data and Different Machine Learning Algorithms for Detailed Tree Species Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:7589–7603. https://doi.org/10.1109/JSTARS.2021.3098817.
  • Xi, Y., C. Ren, Z. Wang, S. Wei, J. Bai, B. Zhang, H. Xiang, et al. 2019. “Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China.” Forests 10 (9): 818. https://doi.org/10.3390/f10090818.
  • Xue, J., and B. Su. 2017. “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications.” Journal of Sensors 2017:1–17. https://doi.org/10.1155/2017/1353691.
  • Yanchen, B. O., and W. Jinfeng. 2004. “Exploring the Scale Effect in Thematic Classification of RemotelySensed Data: The Statistical Separability-Based Method.” Remote Sensing Technology and Application 19 (6): 443–449. https://doi.org/10.11873/j.issn.1004-0323.2004.6.443.
  • Yoo, C., D. Han, J. Im, and B. Bechtel. 2019. “Comparison Between Convolutional Neural Networks and Random Forest for Local Climate Zone Classification in Mega Urban Areas Using Landsat Images.” ISPRS Journal of Photogrammetry and Remote Sensing 157:155–170. https://doi.org/10.1016/j.isprsjprs.2019.09.009.
  • Yuan, K., and P. M. Bentler. 2006. “Structural Equation Modeling.” Handbook of Statistics26:297–358. https://doi.org/10.1002/9781118133880.hop202023.