Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to ...compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring in burned forests of the central Apennines (Italy) at different development stages. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2) and a novel index called Forest Recovery Index 2 (FRI2) were used to compute post-fire recovery metrics throughout 11 years (2008–2018). FRI2 achieved the highest significant correlation (Pearson’s r = 0.72) with tree canopy cover estimated by field sampling (year 2017). The Theil–Sen slope estimator of linear regression was employed to assess the rate of change and the direction of SVIs recovery metrics over time (2010–2018) and the Mann–Kendall test was used to evaluate the significance of the spectral trends. NDVI displayed the highest amount of recovered pixels (38%) after 11 years since fire occurrence, whereas the mean value of NDMI, NBR, NBR2, and FRI2 was about 27%. NDVI was more suitable for tracking early stages of the secondary succession, suggesting greater sensitivity toward non-arboreal vegetation development. Predicted spectral recovery timespans based on pixels with a statistically significant monotonic trend did not highlight noticeable differences among normalized SVIs, suggesting similar suitability for monitoring early to mid-stages of post-fire forest succession. FRI2 achieved reliable results in mid- to long-term forest recovery as it produced up to 50% longer periods of spectral recovery compared to normalized SVIs. Further research is needed to understand this modeling approach at advanced stages of post-fire forest recovery.
Where are Europe's last primary forests? Sabatini, Francesco Maria; Burrascano, Sabina; Keeton, William S. ...
Diversity & distributions,
October 2018, Volume:
24, Issue:
9/10
Journal Article
Peer reviewed
Open access
Aim: Primary forests have high conservation value but are rare in Europe due to historic land use. Yet many primary forest patches remain unmapped, and it is unclear to what extent they are ...effectively protected. Our aim was to (1) compile the most comprehensive European-scale map of currently known primary forests, (2) analyse the spatial determinants characterizing their location and (3) locate areas where so far unmapped primary forests likely occur. Location: Europe. Methods: We aggregated data from a literature review, online questionnaires and 32 datasets of primary forests. We used boosted regression trees to explore which biophysical, socio-economic and forest-related variables explain the current distribution of primary forests. Finally, we predicted and mapped the relative likelihood of primary forest occurrence at a 1-km resolution across Europe. Results: Data on primary forests were frequently incomplete or inconsistent among countries. Known primary forests covered 1.4 Mha in 32 countries (0.7% of Europe's forest area). Most of these forests were protected (89%), but only 46% of them strictly. Primary forests mostly occurred in mountain and boreal areas and were unevenly distributed across countries, biogeographical regions and forest types. Unmapped primary forests likely occur in the least accessible and populated areas, where forests cover a greater share of land, but wood demand historically has been low. Main conclusions: Despite their outstanding conservation value, primary forests are rare and their current distribution is the result of centuries of land use and forest management. The conservation outlook for primary forests is uncertain as many are not strictly protected and most are small and fragmented, making them prone to extinction debt and human disturbance. Predicting where unmapped primary forests likely occur could guide conservation efforts, especially in Eastern Europe where large areas of primary forest still exist but are being lost at an alarming pace.
Bois noir (BN) is the most important phytoplasma disease of Euro-Mediterranean area and induces severe loss of production and even the death of vines. Understanding the delicate balance between ...disease progression and recovery of BN infected plants over space and time is crucial to set up management tools. The data collected and analysed allowed to provide insights into dispersal pattern of the disease, caused by'Candidatus Phytoplasma solani'. Point pattern analysis (PPA) was applied to assess the spatial arrangement of symptomatic plants and the spatial correlation of disease levels in four vineyards. For spatio-temporal patterns of BN, a mark-correlation function was applied. Space-time PPA over multiple years (2011-2015) provided graphical visualisation of grapevines more severely affected by BN along the borders of the vineyards, mainly in 2011 when disease incidence was high. PPA across the symptomatic plants in the four vineyards confirmed this visual trend: an overall aggregated pattern at small (<10 m) spatial scales (2013) that were more evident later at all spatial scales (0-15 m). Application of this innovative spatial approach based on point and surface pattern analyses allowed the spread and severity of BN to be monitored, to define the dispersal routes of the pathogen. Such data will contribute to better understand the distribution of symptomatic plants over space and time and to define a model for preventive strategies to reduce future infections.
Land use science usually adopts a case study approach to investigate landscape change processes, so we considered a meta-analysis an appropriate tool for summarizing general patterns and ...heterogeneous findings across multiple case studies over a large geographic area. Mountain landscapes in the Apennines (Italy) have undergone significant variations in the last century due to regional and national socio-economic changes. In this work, we reviewed 51 manuscripts from different databases and examined 57 case studies. We explored heterogeneous data sets, adopting a stepwise approach to select the case studies: Step 1, a general overview of the main studies; Step 2, an analysis of the features of the study sites and of land-use/cover transitions; Step 3, a landscape pattern analysis. We standardized the processing methods to obtain a new set of homogeneous data suitable for comparative analysis. After some pre-processing of the selected paper due to the broad heterogeneity of the data, we calculated common landscape metrics ex novo. We obtained digital images used to perform automatic segmentation with eCognition Developer 64 software. Our review indicated that most case studies were in Central and Southern Italy, 83% were examined at local scale, 77% carried out change detection, but only 38% included both change detection and landscape spatial pattern analysis. The results revealed a clear trend of forest expansion (+78%) and the reduction of croplands (−49%) and grasslands (−19%). We did not find significant changes in the landscape spatial patterns.
Chilean territory is recurrently affected by severe wildfires, which drastically reduce the forest cover and promote runoff, soil erosion and slope instabilities. To understand how the geomorphic ...system responds to wildfires in terms of sediment dynamics, the assessment of sediment connectivity, i.e. the property describing the relationships between compartments of a geomorphic system, is crucial. This study aims to quantify the spatial linkages between fire severity and sediment connectivity to identify common patterns and driving factors. The compound use of field data and open-source satellite imagery helped to apply the Relative differenced Normalized Burn Ratio (RdNBR) and the Index of Connectivity (IC) in the context of two consecutive wildfires, occurred in 2002 and 2015, in the Rio Toro catchment (Chile). The fire severity assessment showed that the 2002 event affected 90% of the catchment, with high severity areas representing around 70%. The 2015 wildfire instead, affected 76% of the catchment with moderate severity around 42%. Accordingly, as result of the sudden reduction in forest cover in severely affected areas, the IC changed after both wildfires with an overall increase of 1.07 and 0.54, respectively. However, only for the second disturbance, it was possible to observe a clear relationship between the RdNBR and the IC variations. The different degree of vegetation cover heterogeneity between the two pre-wildfire scenarios contributed to different fire severity and IC variability between the two disturbances. The use of open-source satellite data and the development of a weighting factor (W), to be used in IC and able to capture the land cover change driven by the wildfires, could make the application of this approach straightforward, promoting its reproducibility in other catchments for land management and risk mitigation purposes.
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•Burned catchments show sudden ecological and hydro-geomorphic alterations.•Fire severity and sediment connectivity interaction is assessed using RdNBR and IC.•An approach to involve post-wildfire vegetation changes in the IC is presented.•Fire severity and sediment connectivity maps share similar patterns.
Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and ...radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions.
Questions: How do climate, topography and human impact affect land-cover changes, elevation of treelines and dominant tree species composition at multiple spatial scales? Location: Apennine ...Mountains, Italy. Methods: At the regional scale (n = 776 municipalities covering 43,000 km2), we assessed the relationship between human demographic processes and forest cover dynamics for the 1990–2012 period using Corine Land Cover maps and a national census data set. At the landscape scale (n = 18 landscape units of 16 km2 each), we tested the effects of site topography on forest cover changes between 1954 and 2012. At the local scale (n = 5,484 sampling points), we extracted the location and species composition of the current treeline (year 2012) using semi-automatic segmentation methods. We quantified the association of climatic, topographic and anthropogenic variables with the position of upper treelines in the Apennines. Results: Regional scale: human population in the Apennines decreased by 3% between 1991 and 2011. During the same time period, there was an increase in the extent of shrublands (+7%) and forests (mixed +4%, conifers +2%, broad-leaf +1%) and a decrease in the extent of pastures (-9%). Landscape scale: forests expanded more on southwest (+109%) than on northeast (+19%) slopes. Local scale: the mean treeline altitude was 1,755 m a.s.l. Fagus sylvatica L. was the most widespread species (94%), but we also found Pinus nigra Arn. plantations and Pinus mugo Turra shrublands in the central Apennines, and Pinus heldreichii H.Christ in the southern Apennines. Overall, the elevations of the current treelines are negatively related to population density, road proximity and southwest exposures, especially among P. nigra stands. Conclusions: At the regional scale, demographic and land-cover changes provide evidence of widespread land abandonment and forest expansion. At the landscape scale, secondary succession occurred particularly at sites with more solar radiation (SW slopes) and a previous heavier human footprint, followed by a widespread abandonment. Treelines of the dominant tree species (F. sylvatica) were found at elevations lower than would be predicted based on climate conditions alone, suggesting a widespread and strong role of past human influence on the location of treelines. The altitudinal transition from broad-leaf to conifer species does not generally occur here, as would be expected from a global ecological model. Anthropogenic treelines of the Apennines will react differently than natural climatic treelines to global environmental changes. Models of treeline response to global change in the Mediterranean area should account for land-use history.
Although wood anatomical features can provide yearly resolved climatic information at sub-seasonal resolution, the occurrence of intra-annual density fluctuations (IADFs) might be triggered by ...several abiotic factors under different ecological settings. Here, we use information on cambial age and tree-ring width to standardize the frequency of IADFs in European black pines from three different mountain slopes in the central Apennines (Italy). At each site, we sampled isolated 15–30-year pioneer pines above the forest limit, as well as close-grown 40–60-year planted pines at the forest limit. Mainly restricted to the latewood of both pioneer and planted trees, the occurrence of IADFs reveals a significant positive relationship with cambial age and ring width. Although the standardized IADFs are well synchronized between the planted and pioneer pines, the frequency of IADFs in narrow rings was higher in the pioneer pines. Drought conditions in July and August are responsible for the highest IADFs frequency in planted and pioneer pines, respectively. Our study underlines the value of IADFs to obtain a more nuanced understanding of the climatic drivers of wood formation at the intra-annual scale.
Recent studies have highlighted the role of lianas in shaping stand dynamics both in tropical and temperate forests. However, English ivy (Hedera helix L.), one of the most widespread lianas in ...Europe, has received little attention. We conducted a study in the Siro Negri alluvial forest (NW Italy) to determine what factors most affected ivy distribution and investigate its interactions with the trees in the stand. We evaluated the influence of tree size, age, species, and neighborhood crowding on ivy occurrence. In addition, growth ring widths were used to explore the development pattern of climbing stems. Fifty-two percent of trees in our study plots carried ivy, a value comparable to liana incidence found in mature tropical forests. Tree characteristics and their spatial pattern significantly influenced ivy distribution. Preferred hosts were large, isolated trees, while the effect of tree age and species on ivy occurrence was marginal. Growth pattern analysis revealed that radial growth was positively related to the available space on the tree trunk for each ivy stem. We conclude that neighborhood crowding around trees and competition among climbing stems relying on the same trunk may reduce the colonization rate of ivy.
In this study, airborne laser scanning-based and traditional field-based survey methods for tree heights estimation are assessed by using one hundred felled trees as a reference dataset. Comparisons ...between remote sensing and field-based methods were applied to four circular permanent plots located in the western Italian Alps and established within the Alpine Space project NewFor. Remote sensing (Airborne Laser Scanning, ALS), traditional field-based (indirect measurement, IND), and direct measurement of felled trees (DIR) methods were compared by using summary statistics, linear regression models, and variation partitioning. Our results show that tree height estimates by Airborne Laser Scanning (ALS) approximated to real heights (DIR) of felled trees. Considering the species separately, Larix decidua was the species that showed the smaller mean absolute difference (0.95 m) between remote sensing (ALS) and direct field (DIR) data, followed by Picea abies and Pinus sylvestris (1.13 m and 1.04 m, respectively). Our results cannot be generalized to ALS surveys with low pulses density (<5/m2) and with view angles far from zero (nadir). We observed that the tree heights estimation by laser scanner is closer to actual tree heights (DIR) than traditional field-based survey, and this was particularly valid for tall trees with conical shape crowns.
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