In a study published in Remote Sensing, researchers used polarimetric decomposition techniques on synthetic aperture radar (SAR) satellite remote sensing data to support the management and planning activities of poplar plantations in Italy.
Why is it Crucial to Monitor Poplar Plantations?
Poplar plantations are the most significant source of industrial timber production for the plywood, pulp and paper, packaging, and wood-based panel sectors in Italy. More than 50% of the domestic industrial hardwood supply comes from poplar plantations.
Poplars require less energy to grow and have a higher capacity to absorb and store CO2 than most other trees, making them an essential crop for climate change mitigation and adaptation. In addition, poplars are beneficial for both flood mitigation and phytoremediation due to their ability to stabilize riparian soils.
Poplar is one of the most widely distributed and rapidly growing tree species in forest plantations. However, poplar plantation areas are subject to significant inter-annual variations because of their short rotation and high demand in the timber market.
Therefore, monitoring poplar plantations’ area and age classes necessitate regular updating of information, which is unfeasible for the National Forest Inventories because of their low periodicity.
This regular monitoring is possible using remote sensing methods because of the growing availability of satellite imaging that provides frequent data covering vast territories.
Synthetic Aperture Radar Satellite Data (SAR) for Poplar Plantation Monitoring
The primary data source for monitoring poplar cultivation with remote sensing data is multispectral imaging gathered from satellites or airborne platforms.
SAR data is utilized widely in vegetation categorization, including in natural regions, forest plantations, and agricultural areas. However, it has not been widely used for monitoring poplar cultivation.
The primary benefit of SAR is its capacity to collect data in any weather, even cloudy conditions when most optical satellite imaging systems become ineffective. In addition, in forests, SAR systems collect data in various polarimetric modes, providing information on the geometric features of the visible surface.
SAR on the ALOS2 Japanese satellite generates data images regardless of night or day. It can reach the ground and partially penetrate plants to collect data about the ground surface and vegetation.
The research area is located in the lower Po River valley, between the Emilia-Romagna and Veneto regions of northern Italy. The average number of trees planted per hectare is 200-300 ha−1, and the average time between harvests is 10-12 years. In this area, frequent thunderstorms and abrupt hailstorms cause severe damage to crops.
Using Polarimetric Decomposition Techniques on ALOS2 Data to Detect Clear-Cut Plantations
The first objective was to identify clear-cut patches of poplar. In Italy, the plantation cut often occurs during the winter/autumn months at the end of the cultivation season and before the next.
Thus, two ALOS2 SAR Fine Beam dual-polarization pictures were taken on dates before and after the cutoff; dual-pol images were chosen since they are readily available in the archives.
The second objective was to categorize the age of the poplar plantings. A quad-polarization ALOS2 SAR picture was used. This data format contained more information but was harder to access.
Achieving these objectives can give strategic information for forestry planning and management activities, such as assessing timber harvesting and productivity time, new plantation planning, carbon accounting data, and post-disaster evaluations considering frequent destructive thunderstorms in Northern Italy.
Significant Findings of the Study
This study proves that it is possible to monitor and categorize poplar plantations using SAR polarimetric data, focusing on identifying inter-annual cuts and classifying stands of various ages, giving useful data for forest planning and management operations.
The significance of this study is emphasized by the fact that in previous studies, the detection of mature stands or the categorization of only two age groups yielded lower or comparable values.
These favorable outcomes are unquestionably attributable to the significance of the quad-pol ALOS2 data. In addition, minimal discrepancies were seen across the various machine learning algorithms, demonstrating their equality in classification activities.
There were minor variations when decompositions were used instead of bands. Additional testing is recommended because the decompositions’ computation can be accomplished simply with open-source software.
The best results were attained on the prior four-class categorization test. In addition, full-pol data should be preferred over dual-pol data because they contain more information and have a greater spatial resolution.
The availability of SAR data will undoubtedly improve in the future, improving the monitoring of plantations and forest resources.
Vaglio Laurin, G., Mattioli, W., Innocenti, S., Lombardo, E., Valentini, R., & Puletti, N. (2022) Potential of ALOS2 Polarimetric Imagery to Support Management of Poplar Plantations in Northern Italy. Remote Sensing. https://www.mdpi.com/2072-4292/14/20/5202/htm
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