Advancements in remote sensing tools for forestry analysis
Vol 6, Issue 1, 2023
VIEWS - 277 (Abstract) 254 (PDF)
Abstract
Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.
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1. Wang K, Franklin SE, Guo X, Cattet M. Remote sensing of ecology, biodiversity and conservation: A review from the perspective of remote sensing specialists. Sensors 2010; 10(11): 9647–9667. doi: 10.3390/s101109647.
2. Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 2016; 114: 24–31. doi: 10.1016/j.isprsjprs.2016.01.011.
3. Lahon D, Sahariah D, Debnath J, et al. Growth of water hyacinth biomass and its impact on the floristic composition of aquatic plants in a wetland ecosystem of the Brahmaputra floodplain of Assam, India. PeerJ 2023; 11: e14811. doi: 10.7717/peerj.14811.
4. Pu R. Mapping tree species using advanced remote sensing technologies: A state-of-the-art review and perspective. Journal of Remote Sensing 2021; 2021. doi: 10.34133/2021/9812624.
5. Chen W, Zheng Q, Xiang H, et al. Forest canopy height estimation using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technology based on full-polarized ALOS/PALSAR data. Remote Sensing 2021; 13(2): 174. doi: 10.3390/rs13020174.
6. Xiang TZ, Xia GS, Zhang L. Mini-unmanned aerial vehicle-based remote sensing: Techniques, applications, and prospects. Geoscience and Remote Sensing Magazine 2019; 7(3): 29–63. doi: 10.1109/MGRS.2019.2918840.
7. Gulati B, Sharma R, Kanga S, et al. Unraveling the relationship between stubble burning and air quality degradation in Punjab: A temporal and spatial analysis (2019–2022). Journal of Climate Change 2023; 9(2): 43–53. doi: 10.3233/JCC230014.
8. Gleason CJ, Im J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment 2012; 125: 80–91. doi: 10.1016/j.rse.2012.07.006.
9. Lauscha A, Bannehr L, Beckmanna M, et al. Linking earth observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecological Indicators 2016; 70: 317–339. doi: 10.1016/j.ecolind.2016.06.022.
10. Wang Y, Fang H. Estimation of LAI with the LiDAR technology: A review. Remote Sensing 2020; 12(20): 3457. doi: 10.3390/rs12203457.
11. Zhou D, Xiao J, Bonafoni S, et al. Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing 2019; 11(1): 48. doi: 10.3390/rs11010048.
12. Zhang K, Kimball JS, Running SW. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdisciplinary Reviews: Water 2016; 3: 834–853. doi: 10.1002/wat2.1168.
13. Mohanty SP, Hughes DP, Salathe M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 2016; 7: 1419. doi: 10.3389/fpls.2016.01419.
14. Zhu XX, Tuia D, Mou L, et al. Deep learning in remote sensing: A review. IEEE Geoscience and Remote Sensing Magazine 2017; 5(4): 8–36. doi: 10.1109/MGRS.2017.2762307.
15. Kuras A, Brell M, Rizzi J, Burud I. Hyperspectral and lidar data applied to the urban land cover machine learning and neural-network-based classification: A review. Remote Sensing 2021; 13(17): 3393. doi: 10.3390/rs13173393.
16. Tomar JS, Kranjčić N, Đurin B, et al. Forest fire hazards vulnerability and risk assessment in Sirmaur District Forest of Himachal Pradesh (India): A geospatial approach. ISPRS International Journal of Geo-Information 2021; 10(7): 447. doi: 10.3390/ijgi10070447.
17. Lausch A, Erasmi S, King DJ, et al. Understanding forest health with remote sensing-Part I—A review of spectral traits, processes and remote-sensing characteristics. Remote Sensing 2016; 8(12): 1029. doi: 10.3390/rs8121029.
18. Fundisi E, Tesfamichael SG, Ahmed F. Remote sensing of savanna woody species diversity: A systematic review of data types and assessment methods. PLoS One 2022; 1–29. doi: 10.1371/journal.pone.0278529.
19. Blaga L, Ilieș DC, Wendt JA, et al. Monitoring forest cover dynamics using orthophotos and satellite imagery. Remote Sensing 2023; 15(12): 3168. doi: 10.3390/rs15123168.
20. Xiao X, Zhang Q, Braswell B, et al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 2004; 91(2): 256–270. doi: 10.1016/j.rse.2004.03.010.
21. Gupta SK, Kanga S, Meraj G, et al. Uncovering the hydro-meteorological drivers responsible for forest fires utilizing geospatial techniques. Theoretical and Applied Climatology 2023; 153: 675–695. doi: 10.1007/s00704-023-04497-y.
22. Huang Y, Zhang Q, Ferro-Famil L. Forest height estimation using a single-pass airborne L-Band Polarimetric and Interferometric SAR system and tomographic techniques. Remote Sensing 2021; 13(3): 487. doi: 10.3390/rs13030487.
23. Hu T, Su Y, Xue B, et al. Mapping global forest aboveground biomass with spaceborne LiDAR, optical imagery, and forest inventory data. Remote Sensing 2016; 8(7): 565. doi: 10.3390/rs8070565.
24. Kumar L, Mutanga O. Remote sensing of above-ground biomass. Remote Sensing 2017; 9(9): 935. doi: 10.3390/rs9090935.
25. Hyde P, Dubayah R, Walker W, et al. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment 2006; 102(1–2): 63–73. doi: 10.1016/j.rse.2006.01.021.
26. Shi W, Zhang M, Zhang R, et al. Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sensing 2020; 12(10): 1688. doi: 10.3390/rs12101688.
27. Kanga S, Singh SK, Meraj G, et al. Assessment of the impact of urbanization on geoenvironmental settings using geospatial techniques: A study of Panchkula district, Haryana. Geographies 2022; 2(1): 1–10. doi: 10.3390/geographies2010001.
28. Barreras A, Alanís de la Rosa JA, Mayorga R, et al. Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data. Ecology and Evolution 2023; 13(5): e10090. doi: 10.1002/ece3.10090.
29. Dash JP, Watt MS, Pearse GD, et al. Dungey. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing 2017; 131: 1–14. doi: 10.1016/j.isprsjprs.2017.07.007.
30. Yao H, Qin R, Chen X. Unmanned aerial vehicle for remote sensing applications—A review. Remote Sensing 2019; 11(12): 1443. doi: 10.3390/rs11121443.
31. Liu J, Wu J, Sun L, Zhu H. Image data model optimization method based on cloud computing. Journal of Cloud Computing 2020; 9(1). doi: 10.1186/s13677-020-00178-7.
32. Tian L, Wu X, Tao Y, et al. Review of remote sensing-based methods for forest aboveground biomass estimation: Progress, challenges, and prospects. Forest 2023; 14(6): 1086. doi: 10.3390/f14061086.
DOI: https://doi.org/10.24294/sf.v6i1.2269
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