•The study aims at constructing wall-to-wall estimates of forest growing stock (GSV).•We combine NFI plot data, remotely sensed and auxiliary variables.•We applied the methodology in Mediterranean ...Forest.•We create a wall-to-wall GSV forest map in a large test area.•The GSV map was used to produce model-based estimates of GSV at small scale.
Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.
For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.
Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Key message
This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid ...rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests.
Context
The rate of post-disturbance vegetation recovery is an important component of forest dynamics.
Aims
In this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied.
Methods
We used LTS surface reflectance data (1999–2015).We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data.
Results
The coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems.
Conclusion
The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system.
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Purpose of Review
Forests provide multiple ecosystem services (ES) to society, and the demand for ES is growing at the global level. However, how to manage forests for the provision of multiple and ...sometimes conflicting services is a complex and still unresolved issue. In this study, we reviewed the scientific literature for the period 2010–2020 dealing with forest management and multiple ES in Mediterranean forests, with the aim of (1) outlining the progress in research, (2) identifying knowledge gaps and research needs, and (3) discussing management approaches considering multiple ES. The selected literature was analyzed considering different aspects of multiple ES (e.g., drivers of changes, modeling approaches, trade-offs, and synergies).
Recent Findings
Our results show that wood production is still one of the main management objectives, with an increasing attention toward non wood forest products. Carbon sequestration and biodiversity were the most investigated regulating functions, but also specific aspects are gaining attention (e.g., lichens for microclimate regulation). Changes in stand structure and density, the impact of coppice vs. high forest, and the effect of management practices vs. abandonment were considered as drivers of change at the stand/management unit scale, while the impact of climate changes and disturbances were considered at the landscape/regional scale using modeling.
Summary
Despite the progress made in the last decade, our review highlights that further research is needed to fill the gaps in the scientific literature regarding how forest management influences the provision of multiple ES in the Mediterranean region. From a conceptual point of view, there is the need for a shift to a new paradigm based on an adaptable, flexible management, and planning approach to sustain self-organization, adaptive capacity, and overall resilience of Mediterranean forests, overcoming the ecosystem “service” approach; operatively, research should move toward a transdisciplinary approach, which considers problems from a diversity of points of view and involves extended peer communities not only in the dissemination of research results, but also in the research process itself.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The development of laser scanning technologies has gradually modified methods for forest mensuration and inventory. The main objective of this study is to assess the potential of integrating ALS and ...TLS data in a complex mixed Mediterranean forest for assessing a set of five single-tree attributes: tree position (TP), stem diameter at breast height (DBH), tree height (TH), crown base height (CBH) and crown projection area radii (CPAR). Four different point clouds were used: from ZEB1, a hand-held mobile laser scanner (HMLS), and from FARO® FOCUS 3D, a static terrestrial laser scanner (TLS), both alone or in combination with ALS. The precision of single-tree predictions, in terms of bias and root mean square error, was evaluated against data recorded manually in the field with traditional instruments. We found that: (i) TLS and HMLS have excellent comparable performances for the estimation of TP, DBH and CPAR; (ii) TH was correctly assessed by TLS, while the accuracy by HMLS was lower; (iii) CBH was the most difficult attribute to be reliably assessed and (iv) the integration with ALS increased the performance of the assessment of TH and CPAR with both HMLS and TLS.
Nowadays, forest inventories are frequently carried out using a combination of field measurements and remote sensing data, often acquired with light detection and ranging (LiDAR) sensors. Several ...studies have investigated how three-dimensional laser scanning point clouds from different platforms can be used to acquire information traditionally collected with forest instruments, such as hypsometers and callipers to detect single-tree attributes like tree height and diameter at the breast height. The present study has tested the performances of the ZEB1 instrument, a type of hand-held mobile laser scanner, for single-tree attributes estimation in pure Castanea sativa Mill. stands cultivated for fruit production in Central Italy. In particular, the influence of walking scan path density on single-tree attributes estimation (number of trees, tree position, diameter at breast height, tree height, and crown base height) was investigated to test the efficiency of field measures. The point clouds were acquired by walking along straight lines drawn with different spacing: 10 and 15 m apart. A single-tree scan approach, which included walking with the instrument around each tree, was used as reference data. In order to evaluate the efficiency of the survey, the influence of the walking scan path was discussed in relation to the accuracy of single-tree attributes estimation, as well as the time and cost needed for data acquisition, pre-processing, and analysis. Our results show that the 10 m scan path provided the best results, with an omission error of 6%; the assessment of single-tree attributes was successful, with values of the coefficient of determination and the relative root mean square error similar to other studies. The 10 m scan path has also proved to decrease the costs by about €14 for data pre-processing, and a saving of time for data acquisition and data analysis of about 37 min compared to the reference data.
A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on ...developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.
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Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm ...hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019–October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE < 3725.3 ha, SE% < 9.69, OA > 89.7, PA and UA > 0.87, gmean > 0.83).
In the context of the potential future use of unmanned ground vehicles for forest inventories, we present the first experiences with SPOT, a legged robot equipped with a LiDAR instrument and several ...cameras that have been used with a teleoperation approach for single-tree detection and measurements. This first test was carried out using the default LiDAR system (the so-called enhanced autonomy payload-EAP, installed on the board of SPOT to guide autonomous movements) to understand advantages and limitations of this platform to support forest inventory activities. The test was carried out in the Vallombrosa forest (Italy) by assessing different data acquisition methods. The first results showed that EAP LiDAR generated noisy point clouds where only large trees (DBH ≥ 20 cm) could be identified. The results showed that the accuracy in tree identification and DBH measurements were strongly influenced by the path used for data acquisition, with average errors in tree positioning no less than 1.9 m. Despite this, the best methods allowed the correct identification of 97% of large trees.
The aim of this study was to investigate the potential of the Biological Soil Quality index (QBS-ar index) for monitoring the impact of wildfires on Mediterranean pine forests soils. The QBS-ar is a ...simplified eco-morphological index, based on edaphic microarthropods. We analyzed the QBS-ar values of pine stands burned one or two times and at different time spans from fire (from 10 days to 11 years after fire), in comparison to the unburned control area. Effect of fire on the biological soil quality evaluated with QBS-ar index was analyzed using univariate and multivariate techniques. Our results showed a significant variation of QBS-ar values between burned areas and those never burned. The impact of fire on the QBS-ar index and the microarthropod community was higher in the areas burned twice. A relevant average dissimilarity of taxonomic groups was found between the control area and the area burned twice, but also between the control area and the one observed 3 months after the fire. The taxa that contributed more to the dissimilarity were Pseudoscorpiones and Diplopoda, which reduced their presence after fire. Our study shows that the QBS-ar index is a dependable method for monitoring the impact of fire on soil quality in forest ecosystems and indicates that edaphic microarthropods communities in these Mediterranean pine forests return to the before fire condition 11 years after fire. The QBS-ar index could therefore be a useful tool for monitoring forest ecosystem rehabilitation after fire.
Rapid climate changes lead to an increase in forest disturbance, which in turn lead to growing concerns for biodiversity. While saproxylic beetles are relevant indicators for studying different ...aspects of biodiversity, most are smaller than 2 mm and difficult to sample. This, together with a high number of species and trophic roles, make their study remarkably challenging, time-consuming, and expensive.
The Landsat mission provides data since 1984 and represents a powerful tool in this scenario. While we believe that remote sensing data cannot replace on-site sampling and analysis, in this study we aim to prove that the Landsat Time Series (TS) may support the identification of insects’ hotspots and consequently guide the selection of areas where to concentrate field analysis.
With this aim, we constructed a Landsat-derived NDVI TS (1984–2020) and we summarised the NDVI trend over time by calculating eight Temporal Metrics (TMs) among which four resulted particularly successful in predicting the amount of saproxylic insects: (i) the slope of the regression line obtained by linear interpolating the NDVI values over time; (ii) the Root Mean Square Error (RMSE) between the regression line and the NDVI TS; (iii) the median, and the (iv) minimum values of the NDVI TS.
The study area consists of four monitoring sectors in a Mediterranean-managed beech forest located in the Apennines (Molise, Italy), where 60 window flight traps for flying beetles were installed.
First, the saproxylic beetle's biodiversities of monitoring sectors were quantified in terms of species richness and alpha-diversity. Second, the capability of TMs in predicting the richness of saproxylic beetles family and trophic categories was assessed in terms of Pearson's product-moment correlation.
The alpha diversity and species richness analysis indicate dissimilarities across the four monitored sectors (Shannon and Simpson's index ranging between 0.67 to 2.31 and 0.69 to 0.88, respectively), with Landsat TS resulting in effective predictors for estimating saproxylic beetle richness. The strongest correlation was reached between the Monotomidae family and the RMSE temporal metric (R = 0.66). The mean absolute correlation (r) between the NDVI TMs and the saproxylic community was 0.46 for Monotomidae, 0.31 for Cerambycidae, and 0.25 for Curculionidae.
Our results suggest that Landsat TS has important implications for studying saproxylic beetle distribution and, by helping the selection of monitoring areas, increasing the amount of information acquired while decreasing the effort required for field analysis.
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