Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

The pan-European and the local CLMS components are coordinated by the European Environment Agency (EEA). They offer information services based on satellite Earth observation and in situ (non-space) data. These information services are freely and openly accessible to users through six thematic Copernicus services: atmosphere monitoring, marine environment monitoring, land monitoring, climate change, emergency management and security. Broader information on about the thematic scope of CLMS products can be found in the [CLMS portfolio].

Furthermore, several key European Union (EU) policies and regulations are aimed at protecting forests (e.g., Forest Strategy, Timber Regulation, Nature Directives) and the High-Resolution Layer Forest Type (HRL FTY) product could be considered as a key tool in the completing of these missions. This product uses the Food and Agriculture Organization’s definition of forests to filter out non-forested but tree-covered areas from the High-resolution layer dominant leaf type (HRL DLT) product. Due to the fact that the HRL FTY assumes participation in a number of tasks, the quality of data contained therein is considered vital.

This section captures an overview in terms of definitions and product specifications for the high-resolution layer (HRL) Forest products as part of the CLMS, coordinated by the EEA. The HRLs are currently produced in regular 3-year intervals at a 10–20 meter spatial resolution for 39 European countries (EEA 39). Evolving scientific developments and user requirements are continuously analysed in a close stakeholder interaction process with the European Entrusted Entities (EEE), targeting a future pan-European roll-out of new/improved CLMS products and assessing transferability to global applications [Sevillano M.E. et al. 2019].

For example, according to product documentation [CLMS 2021], the forest related HRLs with the reference year 2018 have been produced in the European Terrestrial Reference System 1989 (ETRS89) and in Lambert Azimuthal Equal Area (LAEA) projection by a consortium of European service providers. The forest related HRL portfolio for the year 2018 covers two primary status layers: Dominant leaf type (DLT) and tree cover density (TCD) at a 10 m spatial resolution, both derived from multi-temporal imagery of the Sentinel-2 satellite, operated by the European Space Agency (ESA). The above mentioned products provide information about DLT (broad-leaved/coniferous) and the TCD at a pixel level (in %) that allows users to choose a (user defined) forest definition best-matching for a canopy crown cover threshold. So far, this information is available for the EEA members, covering 39 countries and providing details on the TCD of those areas and the distribution of coniferous and broad-leaved trees. What is valuable is that not only is the current status of maps available, but it is also possible to monitor changes due to the 3 year update cycle of the HRLs, delivering information on both the loss of the tree cover and the gains. All the provided forest-related products could serve as a supporting source of information for the member states in their reporting obligations to the United Nations Economic Commission for Europe (UNECE) and the Food and Agriculture Organization (FAO).

Of note, the HRLs were designed for use by a broad user community as a basis for environmental analyses and for supporting political decision-making. It is noted that they can serve as a support in the reporting on LULUCF from 2021 onwards for more frequent monitoring of land areas and land cover changes. Nevertheless, LULUCF reporting requires the use of IPCC compliant LC/LU class definitions, which at the moment, are not fully met through the HRLs [CLMS 2021]. Relevant data covered by the HRLs are created by semi-automatic procedures over satellite imagery every 3 years [CLMS 2021]. According to general product specifications of forest-related HRLs, reference forest stratum of forest-related HRLs for the year 2018 has been defined at 10 m spatial resolution, with no Minimum Mapping Unit (MMU), since the products are pixel-based, and with a Minimum Mapping Width (MMW) of 10m with the consistent multi-temporal coverage: 01.03.2018 – 31.10.2018.

Within a wide group of a forest-related HRL portfolio for the year 2018, a number of secondary layers have also been prepared. One of them, namely the HRL FTY product (in 10 m and aggregated 100 m resolution), was prepared taking into account elements of the FAO’s forest definition. It should be underlined that TCD and DLT do not consider any of the forest definitions, so the application of filtering enables users to adapt the existing TCD and DLT products to their forest definition (if different from the FAO definition). Furthermore, based on the TCD and DLT, two change products exist, providing information on the gain and loss of tree cover and its associated leaf type between the reference years of HRL 2015 (2016) and HRL 2018 in 20 m resolution. Those are the Copernicus High-Resolution Forest Layer Tree Cover Change Mask (TCCM20m) 2015–2018 and DLTC (DLTC20m). The TCCM 20m raster product provides information on the change between the reference years 2015 and 2018 and consists of four thematic classes (unchanged areas with no tree cover; new tree cover; loss of tree cover; unchanged areas with tree cover) at a 20 m spatial resolution. DLTC20m raster product provides information on the change between the reference years 2015 and 2018 and consists of 9 thematic classes, of which there are 5 change classes (new broadleaved cover; new coniferous cover; loss of broadleaved cover; loss of coniferous cover; and potential change among DLTs). Taking into account all of the above, the portfolio of commonly recognised primary and secondary status layers and change forests related CLMS products consists of the following: –

Dominant Leaf Type 10 m (DLT 10m);

In regard to HRL FTY product data application, its use allows one to get as close as possible to the FAO forest definition. However, in many cases (when MMU differs from the minimum area of forest definition) potential application will result in a significant underestimation in terms of the domestic area representativeness of the forest class within the area from 0.1 to 0.5 ha. HRL FTY with its original (10 m (2018) / 20 m (2012, 2015)) resolution is only including two products: 1) a product that has a MMU of 0.5 ha, as well as has a 10% TCD threshold applied, and 2) a support layer that maps, based on the DLT product, trees under agricultural use and in an urban context (derived from Corine Land Cover and imperviousness 2009 data). For the final 100 m, product trees under agricultural use and urban context from the support layer were removed. A recently available 10 m 2018 reference year FTY product has the agricultural/urban trees removed. The Forest Type product is also available as an aggregated version in 100 m spatial resolution, fully aligned to the EEA 100 m reference grid.

Contrary to the TCD and DLT products non-forest trees are excluded in HRL FTY following the forest definition of the FAO [FAO 2000]. The forest definition of the FAO covers the following features/elements:

Includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, cleared tracts, firebreaks and other small open areas < 0.5 ha and/or < 20 m width. Forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and a width of more or equal than 20 m; plantations primarily used for forestry purposes, including cork oak stands.

The 10 m Forest Type product has been produced externally by applying a “forest” definition, mostly following the FAO definition, whereas tree cover in traditional agroforestry systems such as Dehesa/Montado is explicitly included for EEA purposes. The product is derived through a spatial intersection of the two primary status layers TCD and DLT and excludes areas under agricultural use and in urban contexts as provided by the FADSL (Forest Additional Support Layer). The Mediterranean wooded pasturelands known as “DEHESA” in Spain and “MONTADO” in Portugal are agroforestry systems of high natural and cultural value (HNCV) that cover around 3.5 million hectares of the south-western Iberian Peninsula, where they are the main land use systems [Moreno et al. 2013] and form one of the largest agroforestry systems in Europe [Der Herder et al. 2017]. Lastly, it should be mentioned that the HRL FTY has the following main specifications:

10 m spatial resolution.

Study area for the thematic accuracy assessment encompasses of land classified in Poland as a “forest” according to Art. 3 of the [Act on forests 1991]. This assessment adheres to internationally accepted standards and takes into consideration forest land associated with forest management. Forest land area in Poland, as of 1 January 2022, was equal to 9,450.1 thousand ha. However, the assessment was carried out taking into account historical data in the respectable context of which the forest area was identified at the level of 9,434.1 thousand ha. According to the standard adopted for international assessments, taking into account land related to forest management, the share of forest land in the country’s area assessed for the year 2022 was 30.9%. According to that of the year 2022, the forests themselves covered an area of 9,264.7 thousand ha, accounting for 29.6% of the country’s total area. The forested area in the country has exhibited a consistent upward trend over the years, with an annual increase of 4.4 thousand ha [Statistics Poland 2022].

The spatial arrangement of forest habitats is largely mirrored in the spatial composition of prevailing tree species. With the exception of mountainous regions, where stand composition is characterized by either spruce in the west or a mix of spruce and beech in the east, and a handful of other areas featuring diversified species structures, the predominant species in most of the country’s stands is pine. The diversity of growing conditions for forests in Poland is associated with the allocation of natural-forest habitats, as illustrated in Figure 1.

Figure 1.

In terms of forest area, coniferous species dominate Polish forests, encompassing 68.6% of the total forested area. Poland’s favourable climatic and site conditions within the Euro-Asiatic natural range have led to the development of several important ecotypes of pine. Pine forests account for 58.1% of the total forest area across all ownership categories, with 60.8% in State Forests and 55.3% in privately-owned forests. It’s worth noting that since 1945, there have been significant changes in the species composition of forests, including a notable increase in the proportion of broad-leaved tree stands. When considering state forests, where this trend can be tracked through annual updates of forest land area and timber resources, the total area covered by broad-leaved stands has risen from 13% to 31.4% [Statistics Poland 2022]. Despite this increase in broad-leaved forest area, their share still falls below their potential, which is influenced by the distribution of forest habitats in Figure 2.

Figure 2.

Recent observations (Table 1; Table 2) indicate a decrease in the area of the youngest stands (age classes I and II) noticed for several decades, which may raise concerns about the desired structure of age classes [Zajączkowski et al 2020]. The reasons for this trend include a significant reduction in afforestation, limiting felling (decreasing area of regenerated forests) in favour of pre-final cutting forced by the condition of the forest, and reducing the area of clear-cuts (recommended, among others, for ecological reasons).

The consequence of the reduction in the level of felling is an increase in the area of older stands (Table 2). Keeping mature stands for felling for too long may cause depreciation of the wood raw material and increase the risk of damage caused by abiotic factors. Observed changes in the age structure of forests, beyond their quantitative implications in terms of volume changes, can significantly impact the accuracy of remote assessments of forest resources, particularly in the qualitative dimension.

The imperative for large-scale forest inventories is notably derived from [Act on forests 1991]. This legal provision mandates state forests, among other responsibilities, to periodically conduct extensive inventories of forest conditions. The statutory requirements pertaining to the assessment and monitoring of forest conditions, utilizing the results of large-scale inventories, are also embedded in the Act on the Environmental Protection Inspection [Act on environmental… 1991]. Furthermore, the execution of a comprehensive inventory is a prerequisite for Poland’s active participation in international processes related to forests and forestry.

The National Forest Inventories (NFIs) are conducted by The Bureau of Forest Management and Forest Geodesy based on a contract with the General Directorate of State Forests, marking the 4th cycle of the State Forest Inventory, covering the period from 2016 to 2020. This initiative builds upon the efforts of previous cycles spanning from 2005 to 2019.

The main objective of the historical NFI (carried out in forests of all forms of ownership) was to assess the overall forest condition and its evolution on a large scale. The inventory was designed to provide reliable information on the forest, in particular on species’ structure, age, health status and the presence of damage. Measurements and observations were made on permanent sample plots in a grid pattern. The basis for determining the gridding pattern of these plots was the system of the ICP Forest utilised for the assessment of damage in forests, consistent with the system in force in the European Union [Commission Regulation]. General information collected for each sample plot consists of, among other aspects: geographic coordinates, ownership type, land use category, topographic plot location, forest stand origin, stand dominant species and age, quality class, forest cover and density index, stand vertical structure, forest site type, felling and silviculture activities, general health condition, damage types and intensity. Additionally, the following information is also recorded: forest function, nature protection form, protection category and forest management type [Talarczyk 2014].

Since nature protection forms have been considered an element in the data collection process, an assessment conducted considered an accuracy metrics calculation for particular forms of nature protection, including Natura 200 sites, national parks and nature reserves. Within the Natura 2000 sites, the following areas were considered collectively in line with the list of protection sites as stipulated by updated LULUCF Regulation [Regulation (EU) 2023/839]:

Sites of Community importance adopted and special areas of conservation designated in accordance with Article 4 of Council Directive 92/43/EEC and land units outside of those which are subject to protection and conservation measures under Article 6(1) and (2) of that Directive in order to meet site conservation objectives;

A distinct assessment was carried out specifically for national parks and nature reserves. The potential overlap of various forms of nature protection could introduce a bias in the evaluation of remote sensing data. Nevertheless, considering the diverse functions of national parks and nature reserves, along with the various protective activities associated with them that result in variability in the species and age structure of protected forests, an analysis of the accuracy of the associated imagery in these areas was conducted. The objective was to potentially identify the impact of activities that differ between these forms of nature protection.

National parks were established with the primary goal of preserving biodiversity, resources, inanimate nature and landscape values. Their aim is to restore the proper condition of natural resources and components, and to recreate distorted natural habitats, plant habitats, animal habitats or fungi’s habitats, in accordance with the Act on nature protection [Act on nature… 2004].

In contrast, nature reserves encompass areas preserved in their natural or slightly changed state. They include ecosystems, refuges and natural habitats, along with plant habitats, animal habitats and fungi habitats. Nature reserves also cover creations and components of inanimate nature distinguished by special natural, scientific, cultural or landscape values, as stipulated in the Act on nature protection [Act on nature… 2004].

The reference data applied in the accuracy assessment could not have been considered comprehensively. Although the HRL Forest with the Forest Type product already provides one type of forest product following a forest definition that could be applied in this case, the MMU (minimum number of pixels to form a patch) for the HRL FTY of 0.5 ha (50 pixels) derived through a spatial intersection of the two primary status layers TCD and DLT, as already mentioned in the assessment, is resulting in a significant underestimation in terms of the area representativeness of the forest class within the area from 0.1 to 0.5 ha. To fully represent the forest class in Poland, taking into account that the HRL FTY does not provide relevant forest products strictly following Polish domestic forest definition, supplementation by the information as contained in the HRL SWF should be further explored. The issue of domestic forest class area representativeness could be fixed by consideration of harmonized information on linear structures such as hedgerows, as well as patches (100 m² ≤ area ≤ 5000 m²) of woody features—the HRL SWF. While working with the 2018 input data it has been noted that not all Polish landscapes have specific SWFs corresponding with geometric rules. Here, woody vegetation can appear not as distinct patches or linear features. That is why this concept and product use is of great importance in agricultural and managed landscapes with distinct hedgerows and/or patches of woody vegetation embedded in the agricultural matrix.

The accuracy requirements of each HRL product were subject to an internal validation. The accuracies achieved were 90% for PA (commission) and 90% for UA (omission). We have noticed significant differences in overall areas addressing particular classes within HRL Forest of the Forest Type. The OA assessed for the HRL FTY with the NFI data is equal to 69.22%. There are no significant differences between the OA statistics associated to analysed nature protection sites. Within the following nature protection forms of Natura 2000, nature reserves and national parks’ OA were calculated to 70.45, 70.38 and 66.59%, respectively.

By applying simple Pearson’s correlation [Cohen 1988], as provided in the Figure 11, investigating the correlation among a number of NFI plots in particular age classes (taking into account increasing age of the forests class) and OA of HRL FTY in those classes, it has been noted that a statistically insignificant linear relationship exists between those two variables with the correlation coefficient applied at the aggregated level amounted to −0.27. The negative correlation suggests that, as the age of the assessed class increases (and the number of NFI plots decreases), there is a trend toward an increase in the OA of HRL FTY. The fact that the decrease in the number of NFI sample plots in older age classes does not lead to a significant decrease in OA is interesting. This could imply that the assessment of remote data remains relatively accurate even with fewer NFI plots in older age classes. The obtained correlation coefficient indicates a discernible but not a strong linear relationship. It is important to note that correlation does not imply causation. Other factors may influence the observed relationship. Additional statistical tests or analyses to validate the findings and explore potential confounding variables are needed. In summary, based on the provided information, there seems to be a statistically significant negative correlation between the number of NFI plots in age classes and the OA of HRL FTY. However, further analyses and considerations are needed to draw robust conclusions and implications from these results.

Figure 11.

In terms of the classification system, particularly in the context of estimating forest types, some discrepancies have been identified between remotely sensed data and ground observations. The OA of the HRL classification ranges from 17.69% (for non-tree covered areas at the general level) to 84.09% (for the age class as of the ages between 71–80) as indicated in Table 7. This suggests variability in the accuracy of the system with the age dependency appearing to be influenced by the age of the forest. Older forests (especially at the age of over 40 years), which are typically more widely represented (Table 1; Table 2), tend to have a higher OA compared to younger forests. This metric is slightly reduced at the age of over 100 years (Table 7). Furthermore, Table 7 contains detailed numerical data supporting the observed tendencies for different forest age classes and nature protection forms where similar observation has been noticed.

Of note, the precision standards for each HRL product underwent internal validation conducted by the data providers, aiming for 90% accuracy in PA (commission) and 90% in UA (omission). An observation revealed substantial discrepancies in specific class areas within the HRL FTY. The OA appraised for the HRL FTY using the NFI data stands at 69.22%.

An in-depth analysis of the accuracy of the national HRL FTY layer indicated that on a national scale, broad-leaved stands exhibit higher commission and omission errors than expected within internal validation (10% in both cases), reaching 29.9% and 30.6%, respectively. Similar patterns emerge when considering Natura 2000 sites and National Parks only. For Natura 2000 sites, broad-leaved stands are linked to larger commission and omission errors, ranging from 28.9% to 32.4% and 28.4% to 30.1%, respectively. Notably, while nature reserve sites exhibit a higher EO (20.3%), the EC for broad-leaved stands is lower than for coniferous stands (23.0% and 34.6%, respectively).

The elevated omission errors concerning broad-leaved stands primarily stem from underestimating forest plantations and nurseries (correctly reflected in NFI measurements). Conversely, the relatively higher error in overestimating broad-leaved stands is attributed to an inclination to overrate broad-leaved forests at the expense of coniferous forests. These observations align with similar findings presented by [Mirończuk A et al. 2020] while assessing the primary layer of HRL DLT 2018. The EC related to broad-leaved forests predominantly results from misclassifying small coniferous forest patches as broad-leaved. Generally, ECs occur more frequently in broad-leaved forests than in coniferous ones, where in many instances, coniferous forest patches were misidentified as broad-leaved forests.

In general, the area covered by broad-leaved forests is exaggerated, and in the author’s opinion, is affected by the visibility and composition of the forest underneath layers (second floor of forests and understory affecting general species composition of tree stands) on remote observations. Considering the expected significant changes in species structure between coniferous and deciduous species (as indicated by the stable trend in Fig. 2) and acknowledging the substantial predominance of coniferous species in Poland’s total forest area (as depicted in Fig. 1), investigation of the issue of overestimating deciduous species requires further scrutiny to enhance accuracies in subsequent iterations of CLMS products.