Figure 3 - uploaded by Leónia Nunes
Content may be subject to copyright.
Source publication
National Forest Inventories (NFIs) collect and provide a large amount of information regarding the forest volume, carbon stocks, vitality, biodiversity, non-wood forest products and their changes. Forest stands variables data are paramount to understanding their composition, especially on those related with understory characteristics and the covera...
Contexts in source publication
Context 1
... forest polygons were identified in both land use maps according to the FAO definition of forest area. Figure 3 presents the workflow of the different steps to establish the forest types. Specifically, we used as features for the K-means clustering the coverage of each species for each of the seven height layers per plot (pseudo-species) and also the percentage of litter. ...
Context 2
... aspect and slope position, in raster format, was calculated using GDAL tools implemented in software QGIS. In order to have for each inventory plot the needed data, we used the plugin of QGIS named Point Sampling Tool, and added the values from the three rasters (DEM, slope aspect and slope position) to the points layer containing information of forest types (see the workflow at Figure 3). Latitude data was provided by the y-coordinate of each plot. ...
Context 3
... sensitive trees prefer slopes facing north, where sun insolation is lesser, like forest types dominated by F. sylvatica, C. sativa, P. sylvestris and other conifers. Forest types dominated by shrubs formations are located mainly in steeper areas ( Figure S3) with southwest and west facing slopes ( Figure S4). Forest types dominated by evergreen oak species (e.g., T.Az, o.M.Az and M.Sb) prefer flatter slopes with south and southwest facing slopes. ...
Similar publications
Cut-to-length harvesters collect detailed information on the dimensions and characteristics of individual harvested trees. When equipped with global navigation satellite system (GNSS) receivers and motion sensors, the obtained measurements can be linked to locations of single harvested trees, benefitting a range of forest inventory applications. We...
Citations
... Convolutional Neural Networks (CNNs) utilized in semantic segmentation have revolutionized computer vision and object detection in recent years, including for remote sensing data analysis [21][22][23][24][25]. Past studies have demonstrated the potential of CNNs in forestry applications, achieving high accuracy in classifying tree species from hyperspectral imagery [26][27][28] and grouping forest types from other remote sensing images such as Sentinel-2 imagery [29,30]. These studies, along with others [31,32], have shown that diverse machine learning approaches, particularly those incorporating CNNs, possess the capability to discern pertinent patterns within these data recordings. ...
Forests are essential for maintaining the ecological balance of the planet and providing critical ecosystem services. Amidst an increasing rate of global forest loss due to various natural and anthropogenic factors, many countries are committed to battling forest loss by planting new forests. Despite the reported national statistics on the land area in plantations, accurately delineating boundaries of planted forests with remotely sensed data remains a great challenge. In this study, we explored several deep learning approaches based on Convolutional Neural Networks (CNNs) for mapping the extent of planted forests in the Korean Peninsula. Our methodology involved data preprocessing, the application of data augmentation techniques, and rigorous model training, with performance assessed using various evaluation metrics. To ensure robust performance and accuracy, we validated the model’s predictions across the Korean Peninsula. Our analysis showed that the integration of the Near Infrared band from 10 m Sentinel-2 remote sensing images with the UNet deep learning model, incorporated with unfrozen ResNet-34 backbone architecture, produced the best model performance. With a recall of 64% and precision of 76.8%, the UNet model surpassed the other pixel-based deep learning models, including DeepLab and Pyramid Sense Parsing, in terms of classification accuracy. When compared to the ensemble-based Random Forest (RF) machine learning model, the RF approach demonstrates a significantly lower recall rate of 55.2% and greater precision of 92%. These findings highlight the unique strength of deep learning and machine learning approaches for mapping planted forests in diverse geographical regions on Earth.
... The geographic distribution of the most fragmented forests (FL 4, Fig. 6b) exhibited a remarkable concurrence with the forest types characteristic of the northern half of Portugal ("Open forests of pines, eucalypts, or other species", and others dominated by Pinus pinaster and Eucalyptus sp. (Nunes et al., (2020)), which are closely linked to the occurrence of forest fires (Nunes et al., 2019a). Notably, these forest types are also characteristic to Spain in ER 2, which showed the most comparable rho values between the two countries. ...
... Intellectual capital is more important as a source of competitive advantage in the case of small and medium enterprises than large companies because the tangible resources are often lower, and SMEs should compete through intangible resources (Jardón & Martos, 2012). Forests in Portugal and Spain even if sharing similar environmental conditions, fire propensity, stand structure characteristics and common species, have always had been influenced by different historical, cultural, political and economic contexts (Nunes et al., 2020) and as such have to be studies separately from each other. Similarly, relational capital is important for the extraction sector, as most of the sector is unorganized and depends on the relationship with the customers in order to trade its products. ...
This paper examines the importance of marketing and relational capital in value addition. We studied the effect that relational capital has in the timber and related industries of Galicia (Spain) and Portugal for 2002–2018. The industry is characterized by a large number of SMEs, many of them operating in the subsistence and unorganized sectors. Direct marketing and personal contacts with clients are forms of enhancing the relational capital and to add value. The wood sector was divided into three major groups, namely: extraction, conversion and finished products, and using a panel data model, the value addition created by relational capital and the effect that direct marketing can have on the business were measured. While relational capital helps in creating value, its effect is not the same in every sector, implying that much has to be done by managers and CEOs to improve company relationships with stakeholders to develop business alliances.
... Mediterranean forests are among the biomes considered highly vulnerable to drought-induced processes of decline and mortality (Carnicer et al., 2011;Spathelf et al., 2014). Maritime pine (Pinus pinaster Ait.) is a species characteristic of the Western Mediterranean forests, being the most widespread conifer in Spain and Portugal (Nunes et al., 2020). The subspecies mesogeensis Fieschi & Gaussen mainly occupies inland areas and the mountains surrounding the Spanish Northern and Southern plateaus. ...
Recent forest decline and amplified mortality have been documented around the world, mainly triggered by the rising water stress associated with more frequent extreme weather events. However, other abiotic and biotic factors may predispose and contribute to these processes. Mediterranean forests are among the biomes considered highly vulnerable to drought-induced decline and mortality. Pinus pinaster ssp. mesogeensis, is a typical western Mediterranean Forest species widely distributed in Spain, where traditional management has focused on a mixed timber-resin production. In the last decades, the species has experienced a severe and generalized process of decline and mortality, especially on inland areas in the Spanish Northern Plateau. The main objectives of this study were to (1) provide an accurate assessment of P. pinaster mortality in resin-tapped forests within the region, (2) identify the main predisposing and inciting abiotic factors controlling the process of dieback and mortality of the species and (3) develop a model for forecasting the annual rate of mortality at landscape scale. We used operational data collected by the Forest Service during 2012–2019, including annual censuses of tree mortality carried out in lots under resin-tapping and periodical forest management inventories. Analysis using spatiotemporal generalized linear mixed models indicated widespread mortality of the species in the territory, which in some areas reached 20 per cent of the trees over an 8-year period. Tree mortality is triggered in warm and dry years and was accelerated following the extreme droughts of 2017 and 2019. High stand stocking and tree aging have been identified as predisposing factors increasing susceptibility to forest decline and mortality. In addition, stands where the species grows mixed with Pinus pinea are more vulnerable, pointing to a possible displacement of P. pinaster in the territory. The developed spatiotemporal generalized linear mixed model allows unbiased estimates of the annual rate of mortality to be calculated through the territory. The model may be used by forest managers in order to identify the most vulnerable areas where the application of adaption strategies should be prioritized, in order to preserve these forests and their associated provision of ecosystem services.
... Rights reserved. oceanic region is mainly composed of beech, pine, and eucalyptus forests (Navascués et al. 2006;Barbati et al. 2007;Uva et al. 2015;Nunes et al. 2020). The derivation of forest vulnerability to drought implies the use of several datasets in the development of its components (exposure, sensitivity, and adaptive capacity). ...
... In the case of Region 1 (central Portugal), the dominant needle-leaved species are pines (Pinus pinaster, Pinus pinea), eucalyptus, and the dominant broad-leaved deciduous is cork oak (Quercus suber) (Nunes et al. 2020). As previously mentioned, cork oak trees are characterized by a protective mechanism to buffer the potential unavailability of water, being nevertheless sensitive to late spring precipitation, which is decreasing and is expected to further decrease in the next decades (Pessoa et al. 2014;Kurz-Besson et al. 2014), resulting in the exacerbation of the occurrence of summer droughts that may result in the decline of these species. ...
... Region 3, located in the Spanish autonomous region of Catalonia, is characterized by several different tree species, namely beech (Fagus sylvatica), holm oak (Quercus ilex), Scots pine (Pinus sylvestris), and Aleppo pine (Pinus halepensis) forests (Nunes et al. 2020). This area has experienced a gradual increase in temperature and evapotranspiration, and a decrease in precipitation in the last decades (Rubio-Cuadrado et al. 2018). ...
The increase in frequency, severity, and duration of droughts poses as a serious issue to the management of forests in the Iberian Peninsula, with particular emphasis on the decline of forest growth and forest dieback. Hence, the adoption of adaptation and mitigation measures in forest ecosystems that are more vulnerable to drought is a pressing matter that needs to be addressed in the near future.
This work aims at identifying the regions in the Iberian Peninsula where forest exhibit high vulnerability to drought conditions. To accomplish that, a vulnerability map is produced by considering three pillar components: exposure, sensitivity, and adaptive capacity to drought. Exposure is estimated based on the multi-scalar drought index Standardized Precipitation-Evapotranspiration Index (SPEI) and aridity, while the remotely sensed Vegetation Health Index (VHI) and mean forested cover are used to assess the regions’ sensitivity to drought. Finally, elevation, water table depth, fire radiative energy, and annual solar irradiation are compiled as indicators to assess adaptive capacity. Principal component analysis was then applied to the three pillar components to identify the areas more vulnerable to drought. This approach allows for the identification of forested areas vulnerable to drought in terms of vulnerability classes automatically determined.
Forests presented very high vulnerability in eastern Spain, and central Portugal. Within the most vulnerable vegetation communities, mosaic tree and shrub types revealed to be extremely vulnerable to droughts in the Iberian Peninsula, followed by needle-leaved forests (in Central Portugal, and Northeast Iberia). This work highlights the regions and primary vegetation communities to which the effort of adapting and mitigating drought consequences should be utterly enforced by the responsible authorities.
... This information is key for characterizing fuel in each vegetation type and for predicting the behavior of a wildfire. Vertical structure is also essential for classifying and identifying forest types [2], evaluating ecosystem services and assisting on the estimation of the chemical components of the system, including volatile organic compounds. It is therefore important to have information on forest vertical structure for specific variables such as species biomass, leaf area index and bulk density. ...
... The differences found between CBD and CLBD are in accordance with previous results on percentage cover by each vertical layer for the tree species/groups in the Iberian Peninsula [2]. The three most important tree species in Portugal, by area of occupation, are Pinus pinaster, widely spread in the north of the Tagus River, Eucalyptus globulus, distributed in the coastal part and Quercus suber mostly located in the south. ...
... In this study, we found higher values of CBD in trees species such as Pinus pinaster and Eucalyptus globulus. These are species with high probability to burn [2], and have been the most affected by wildfires in the Iberian Peninsula [82,83]. ...
Bulk density for shrubs and tree crowns is an important variable, useful for many purposes, namely estimations for biomass and carbon sequestration and potential fire behavior prediction. In the latter case, bulk density is required to predict the rate of spread and intensity of crown fires. However, bulk density information is scarce. The estimation of bulk density is crucial to help choosing proper pyrosilviculture options to decrease fire susceptibility. Due to the similar environmental conditions and fuel characteristics in Portugal and Spain, we modelled bulk density for the most common woody species in all the Iberian Peninsula. We used 10 different shrub type formations and a set of tree species or groups common to both countries. Equations for bulk density, in both forest canopy and understory layers, were fitted as a function of biometric variables commonly used in forest inventories for the selected species. Standardized estimates of bulk density can be associated with data from the National Forest Inventories from Portugal and Spain, to estimate biomass of the forest ecosystems and to evaluate potential fire behavior involving tree canopies and shrubs.
... Historical PNOA orthophotography (dates between 1998-2003) and the imagery spectral bands supported phenological interpretation during the analysis. Furthermore, the National Forest Inventory (NFI) (version 2 (1986-1995) and version 3 (1997-2007)) provided robust information about forest species composition in the inventory plot locations over time [97]. SIOSE datasets provide particularly valuable and useful cartographic information for the Spanish territory; however, the complexity of the territory and land cover dynamics makes it necessary to have an appropriate set of filtering measures to avoid inconsistencies in the TTA. ...
Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.
... To obtain consistent, reliable results for forest ecosystems at international level, it is vital that the data are standardized or harmonized in order to upscale the information. Nunes et al. [6] develop a homogeneous characterization of the forests of the Iberian Peninsula using data from the NFIs of Portugal and Spain to classify and identify forest types. This harmonized information allows cross-border analysis of various aspects, such as hazards and wildfires, as well as facilitating management and conservation of forest biodiversity. ...
There is much demand for forest information at the regional, national, and international level, covering aspects as varied as growing stock, carbon pools, and non-wood forest products, as well as information on forest biodiversity, risks, and disturbances, or social indicators [...]
Vegetation is a key biosphere component to supporting biodiversity on Earth, and its maintenance and proper functioning are essential to guarantee the well-being of humankind. From a broad perspective, a fundamental goal of vegetation ecology is to understand the roles of abiotic and biotic factors that affect vegetation structure, distribution, diversity, and functioning, considering the relevant spatial and temporal scales. In this contribution, we reflect on the difficulties and opportunities to accomplish this grand objective by reviewing recent advances in the main areas of vegetation ecology. We highlight theoretical and methodological challenges and point to alternatives to overcome them. Our hope is that this contribution will motivate the development of future research efforts that will strengthen the field of vegetation ecology. Ultimately, vegetation science will continue to provide a strong knowledge basis and multiple theoretical and technological tools to better face the current global environmental crisis and to address the urgent need to sustainably conserve the vegetation cover of our planet in the Anthropocene.