BUSCADOR RECOLECTA

Forest monitoring plays a critical role in achieving sustainable forest management practices. The ability to identify ongoing harvesting activities is crucial for developing targeted strategies to maintain forest health. Traditional monitoring methods, which rely on field inventories, are often expensive and time-consuming. Remote sensing offers an interesting alternative, leveraging dense time series of satellite imagery and various algorithms for disturbance detection. This study presents and assesses a novel methodology for identifying forest harvesting practices (clear-cutting and thinning) using Continuous Change Detection and Classification (CCDC) algorithm, available in Google Earth Engine. The methodology comprises two steps. In the first step, performed at the pixel level, the CCDC algorithm was used to detect changes in the vegetation cover by considering Landsat 8 spectral bands, vegetation indices, and different combinations thereof. In the second step, two optimal thresholds were determined to identify forest harvesting practices based on the proportion of pixels flagged as change. This study was conducted in forest stands consisting of different conifer and broadleaf species. Accuracy was assessed using an independent set of photo-interpreted samples. The results indicated that the short-wave infrared 2 was the best individual band for forest harvesting practices identification, with an average F-score of 0.77 ± 0.06, overperforming vegetation indices. The combination of all spectral bands was the most effective to identify both clear-cuts and thinning (F-score = 0.85 ± 0.05). This combination was used to evaluate the accuracy of this approach for identifying harvesting practices over different tree species. Poplar (Populus sp.) had the highest identification rate (F-score = 0.99 ± 0.02), while black pine (Pinus nigra J.F. Arnold) stands had the lowest F-score (0.74 ± 0.05). These results highlight the ability to accurately identify forest harvesting practices even in heterogeneous forests with a high diversity of tree species using dense time series of Landsat imagery., Predoctoral scholarship funded by the Government of Navarre, and grants ReSAg (PID2019-107386RB-I00) and DAMAGE (PID2023-152885OB-I00) funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.

The design of effective forest management strategies requires the precise characterization of forested areas. Currently, different remote sensing technologies can be used for forest mapping, with optical sensors being the most common. The objective of this study was to evaluate the synergistic use of Sentinel-1, Sentinel-2, and LiDAR data for classifying forest types and species. With this aim, a case study was conducted using random forest, considering three classification levels of increasing complexity. The classifications incorporated Sentinel-1 and Sentinel-2 monthly composites, along with LiDAR metrics and topographic variables. The results showed that the combination of Sentinel-2 monthly composites, LiDAR, and topographic variables obtained the highest overall accuracies (0.90 for level 1, 0.80 for level 2, and 0.79 for level 3). The most important variables were identified as Sentinel-2 red-edge and NIR bands from June, July, and August, along with height-related LiDAR and topographic variables. Although not as precise as Sentinel-2 at the species level, Sentinel-1 enabled the classification of broad forest types with remarkable accuracy (0.80), especially when combined with LiDAR data (0.83). Altogether, the results of this study demonstrate the potential of combining data from different Earth observation technologies to enhance the mapping of forest types and species., This research was co-funded by the project forestOBS (project code 0011-1365-2021-000072), granted in the 2021 call for R&D projects by the Government of Navarre, and the projects ReSAg (PID2019-107386RB-I00) and DAMAGE (PID2023-0152885OB-I00), funded by the Spanish State Research Agency (Agencia Estatal de Investigación, AEI), Ministry of Science, Innovation and Universities. Projects included funds from the European Regional Development Fund (FEDER-UE).