Characterizing and estimating forest structure using active remote sensing: An overview

Main Article Content

Elaheh Zadbagher
Aycan Murat Marangoz
Kazimierz Becek

Abstract

Vegetation plays an important role in supporting our lives by maintaining many environmental and ecological services. Forests, as part of the vegetation cover, are the most critical components of the Earth's carbon cycle. The information about the forest structure is vital for ecosystem health, carbon cycle assessment, and a better understanding of the forest resources. Forest structural parameters estimation by field-based methods has limitations, such as being expensive, impractical, labor-intensive, and time-consuming at a large scale. Remote sensing has proven to be a more competent and low-cost tool for monitoring and measuring forest parameters compared to field surveys. Active remote sensing systems i.e., Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR) provide horizontal and vertical forest structure information. In addition, these systems are susceptible to the forest components arrangement, given their ability to penetrate the different depths of the canopy. Therefore, there are many types of research focusing on the estimating of the forest aboveground biomass (AGB) which is one of the critical measures of forest resources, using active remote sensing. This research investigates the potential of active remotely sensed data to estimate forest structural parameters and extract data information. Furthermore, this research focused on various methods used for AGB estimation with active remote sensing.

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How to Cite
Zadbagher, E., Marangoz, A. M., & Becek, K. (2023). Characterizing and estimating forest structure using active remote sensing: An overview. Advanced Remote Sensing, 3(1), 38–46. Retrieved from https://publish.mersin.edu.tr/index.php/arsej/article/view/836
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References

Latifi, H. (2012). Characterizing forest structure by means of remote sensing: a review. Remote Sensing-Advanced Techniques and Platforms, 1-26.

García, M., Saatchi, S., Ustin, S., & Balzter, H. (2018). Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. International journal of applied earth observation and geoinformation, 66, 159-173.

Bispo, P. D. C., Dos Santos, J. R., Valeriano, M. D. M., Graça, P. M. L. D. A., Balzter, H., França, H., & Bispo, P. D. C. (2016). Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajós region, Brazilian Amazon. PloS one, 11(4), e0152009.

Haidari, M., Namiranian, M., Gahramani, L., Zobeiri, M., & Shabanian, N. (2013). Study of vertical and horizontal forest structure in Northern Zagros Forest (Case study: West of Iran, Oak forest). Eurepean Journal of Experimental Biology, 3(1), 268-278.

Zhao, J., Li, J., & Liu, Q. (2013). Review of forest vertical structure parameter inversion based on remote sensing technology. Journal of Remote Sensing, 17, 697-716.

Neumann, M., Saatchi, S. S., Ulander, L. M., & Fransson, J. E. (2012). Assessing performance of L-and P-band polarimetric interferometric SAR data in estimating boreal forest above-ground biomass. IEEE Transactions on Geoscience and Remote Sensing, 50(3), 714-726.

Mangla, R., Kumar, S., & Nandy, S. (2016, May). Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data. In Lidar Remote Sensing for Environmental Monitoring XV (Vol. 9879, pp. 109-119). SPIE.

Marchesan, J., Alba, E., Schuh, M. S., Favarin, J. A. S., & Pereira, R. S. (2020). Aboveground biomass estimation in a tropical forest with selective logging using random forest and Lidar data. Floresta, 50(4), 1873-1882.

Sadeghi, Y., St-Onge, B., Leblon, B., & Simard, M. (2016). Canopy height model (CHM) derived from a TanDEM-X InSAR DSM and an airborne lidar DTM in boreal forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 381-397.

Berninger, A., Lohberger, S., Zhang, D., & Siegert, F. (2019). Canopy height and above-ground biomass retrieval in tropical forests using multi-pass X-and C-band Pol-InSAR data. Remote Sensing, 11(18), 2105.

Persson, H. (2014). Estimation of forest parameters using 3D satellite data (Vol. 2014, No. 2014: 84).

Teubner, I. E., Forkel, M., Jung, M., Liu, Y. Y., Miralles, D. G., Parinussa, R., ... & Dorigo, W. A. (2018). Assessing the relationship between microwave vegetation optical depth and gross primary production. International journal of applied earth observation and geoinformation, 65, 79-91.

Schlund, M., & Davidson, M. W. (2018). Aboveground forest biomass estimation combining L-and P-band SAR acquisitions. Remote Sensing, 10(7), 1151.

Becek, K. (2010). Biomass representation in synthetic aperture radar interferometry data sets. Saechsische Landesbibliothek-Staats-und Universitaetsbibliothek Dresden

Zhu, L., Suomalainen, J., Liu, J., Hyyppä, J., Kaartinen, H., & Haggren, H. (2018). A review: Remote sensing sensors. Multi-purposeful application of geospatial data, 19-42.

Zou, W., Li, Y., Li, Z., & Ding, X. (2009). Improvement of the accuracy of InSAR image co-registration based on tie points–a review. Sensors, 9(02), 1259-1281.

Nie, S., Wang, C., Xi, X., Luo, S., Li, G., Tian, J., & Wang, H. (2018). Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data. Optics express, 26(10), A520-A540.

Huang, S., Hager, S. A., Halligan, K. Q., Fairweather, I. S., Swanson, A. K., & Crabtree, R. L. (2009). A comparison of individual tree and forest plot height derived from lidar and InSAR. Photogrammetric Engineering & Remote Sensing, 75(2), 159-167.

Lim, K., Treitz, P., Wulder, M., St-Onge, B., & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in physical geography, 27(1), 88-106.

Kaasalainen, S., Holopainen, M., Karjalainen, M., Vastaranta, M., Kankare, V., Karila, K., & Osmanoglu, B. (2015). Combining lidar and synthetic aperture radar data to estimate forest biomass: status and prospects. Forests, 6(1), 252-270.

Akay, A. E., Wing, M. G., & Sessions, J. (2012). Estimating structural properties of riparian forests with airborne lidar data. International Journal of Remote Sensing, 33(22), 7010-7023.

Brigot, G., Simard, M., Colin-Koeniguer, E., & Boulch, A. (2019). Retrieval of forest vertical structure from PolInSAR data by machine learning using LIDAR-derived features. Remote Sensing, 11(4), 381.

Jin, S., Su, Y., Gao, S., Hu, T., Liu, J., & Guo, Q. (2018). The transferability of Random Forest in canopy height estimation from multi-source remote sensing data. Remote Sensing, 10(8), 1183.

Fagua, J. C., Jantz, P., Rodriguez-Buritica, S., Duncanson, L., & Goetz, S. J. (2019). Integrating LiDAR, multispectral and SAR data to estimate and map canopy height in tropical forests. Remote Sensing, 11(22), 2697.

Pourshamsi, M., Xia, J., Yokoya, N., Garcia, M., Lavalle, M., Pottier, E., & Balzter, H. (2021). Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing, 172, 79-94.

Nguyen, T. D., & Kappas, M. (2020). Estimating the aboveground biomass of an evergreen broadleaf forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, using SPOT-6 data and the random forest algorithm. International Journal of Forestry Research, 2020, 1-13.

Hunter, M. O., Keller, M., Victoria, D., & Morton, D. C. (2013). Tree height and tropical forest biomass estimation. Biogeosciences, 10(12), 8385-8399.

Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T., Salas, W., ... & Morel, A. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the national academy of sciences, 108(24), 9899-9904.

St‐Onge, B., Hu, Y., & Vega, C. (2008). Mapping the height and above‐ground biomass of a mixed forest using lidar and stereo Ikonos images. International Journal of Remote Sensing, 29(5), 1277-1294.

Solberg, S., Næsset, E., Gobakken, T., & Bollandsås, O. M. (2014). Forest biomass change estimated from height change in interferometric SAR height models. Carbon Balance and Management, 9, 1-12.

Dai, S., Zheng, X., Gao, L., Xu, C., Zuo, S., Chen, Q., ... & Ren, Y. (2021). Improving plot-level model of forest biomass: A combined approach using machine learning with spatial statistics. Forests, 12(12), 1663.

Ghosh, S. M., & Behera, M. D. (2018). Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, 96, 29-40.

Zhang, Y., Ma, J., Liang, S., Li, X., & Li, M. (2020). An evaluation of eight machine learning regression algorithms for forest aboveground biomass estimation from multiple satellite data products. Remote Sensing, 12(24), 4015.

Chen, L., Ren, C., Zhang, B., Wang, Z., & Xi, Y. (2018). Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests, 9(10), 582.

Santi, E., Paloscia, S., Pettinato, S., Cuozzo, G., Padovano, A., Notarnicola, C., & Albinet, C. (2020). Machine-learning applications for the retrieval of forest biomass from airborne P-band SAR data. Remote Sensing, 12(5), 804.

Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T. D., & Tien Bui, D. (2018). Improving accuracy estimation of Forest Aboveground Biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: A case study of the Hyrcanian forest area (Iran). Remote Sensing, 10(2), 172.

Baloloy, A. B., Blanco, A. C., Candido, C. G., Argamosa, R. J. L., Dumalag, J. B. L. C., Dimapilis, L. L. C., & Paringit, E. C. (2018). Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: Rapideye, planetscope and sentinel-2. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 4, 29-36.

Rex, F. E., Silva, C. A., Dalla Corte, A. P., Klauberg, C., Mohan, M., Cardil, A., ... & Hudak, A. T. (2020). Comparison of statistical modelling approaches for estimating tropical forest aboveground biomass stock and reporting their changes in low-intensity logging areas using multi-temporal LiDAR data. Remote Sensing, 12(9), 1498.

Zhang, W., Zhao, L., Li, Y., Shi, J., Yan, M., & Ji, Y. (2022). Forest Above-Ground Biomass Inversion Using Optical and SAR Images Based on a Multi-Step Feature Optimized Inversion Model. Remote Sensing, 14(7), 1608.

Gleason, C. J., & Im, J. (2012). Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sensing of Environment, 125, 80-91.

Dang, A. T. N., Nandy, S., Srinet, R., Luong, N. V., Ghosh, S., & Kumar, A. S. (2019). Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecological Informatics, 50, 24-32.

Chen, L., Wang, Y., Ren, C., Zhang, B., & Wang, Z. (2019). Optimal combination of predictors and algorithms for forest above-ground biomass mapping from Sentinel and SRTM data. Remote Sensing, 11(4), 414.

Jiang, F., Zhao, F., Ma, K., Li, D., & Sun, H. (2021). Mapping the forest canopy height in Northern China by synergizing ICESat-2 with Sentinel-2 using a stacking algorithm. Remote Sensing, 13(8), 1535.

López-Serrano, P. M., Cárdenas Domínguez, J. L., Corral-Rivas, J. J., Jiménez, E., López-Sánchez, C. A., & Vega-Nieva, D. J. (2019). Modeling of aboveground biomass with Landsat 8 OLI and machine learning in temperate forests. Forests, 11(1), 11.

Su, H., Shen, W., Wang, J., Ali, A., & Li, M. (2020). Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests. Forest Ecosystems, 7, 1-20.