Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

Peer reviewe

Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy)

In some cases a canopy height model (CHM) is the only available source of forest height information. For these cases it is important to understand the predictive power of CHM data for forest attributes. In this study we examined the use of lidar-derived CHM metrics to predict forest structure classes according to the amount of basal area present in understory, midstory, and overstory trees. We evaluated two approaches to predict sizebased forest classifications: in the first, we attempted supervised classification with both linear discriminant analysis (LDA) and random forest (RF); in the second, we predicted basal areas of lower, mid, and upper canopy trees from CHM-derived variables by k-nearest neighbour imputation (k-NN) and parametric regression, and then classified observations based on their predicted basal areas. We used leave-one-out cross-validation to evaluate our ability to predict forest structure classes from CHM data and in the case of prediction-based classification approach we look at the performances in predicting basal area. The strategies proved moderately successful with a best overall classification accuracy of 41% in the case of LDA. In general, we were most successful in predicting the basal areas of small and large trees (R 2 respectively of 71% and 69% in the case of k-NN imputation)

Airborne Laser Scanning to support forest resource management under alpine, temperate and Mediterranean environments in Italy

Abstract This paper aims to provide general considerations, in the form of a scientific review, with reference to selected experiences of ALS applications under alpine, temperate and Mediterranean environments in Italy as case studies. In Italy, the use of ALS data have been mainly focused on the stratification of forest stands and the estimation of their timber volume and biomass at local scale. Potential for ALS data exploitation concerns their integration in forest inventories on large territories, their usage for silvicultural systems detection and their use for the estimation of fuel load in forest and pre-forest stands. Multitemporal ALS may even be suitable to support the assessment of current annual volume increment and the harvesting rates. Keywords: Airborne laser scanning, area-based approaches, individual tree crown approaches, forest management, timber volume estimation, multitemporal ALS surveys. Introduction Information about the state and changes to forest stands is important for environmental and timber assessment on various levels of forest ecosystem planning and management and for the global change science community [Corona and Marchetti, 2007]. Standing volume and above-ground tree biomass are key parameters in this respect. Actually, fine-scale studies have demonstrated the influence of structural characteristics on ecosystem functioning: characterization of forest attributes at fine scales is necessary to manage resources in a manner that replicates, as closely as possible, natural ecological conditions. To apply this knowledge at broad scales is problematical because information on broad-scale patterns of vertical canopy structure has been very difficult to be obtained. Passive remote sensing tools cannot help for detailed height, total biomass, or leaf biomass estimates beyond early stages of succession in forests with high leaf area or biomass [Means et al., 1999]. Over the last decades, survey methods and techniques for assessing such biophysical attributes have greatly advanced [Corona, 2010]. Among others, laser scanning techniques from space o

Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy)

In some cases a canopy height model (CHM) is the only available source of forest height information. For these cases it is important to understand the predictive power of CHM data for forest attributes. In this study we examined the use of lidar-derived CHM metrics to predict forest structure classes according to the amount of basal area present in understory, midstory, and overstory trees. We evaluated two approaches to predict size-based forest classifications: in the first, we attempted supervised classification with both linear discriminant analysis (LDA) and random forest (RF); in the second, we predicted basal areas of lower, mid, and upper canopy trees from CHM-derived variables by k-nearest neighbour imputation (k-NN) and parametric regression, and then classified observations based on their predicted basal areas. We used leave-one-out cross-validation to evaluate our ability to predict forest structure classes from CHM data and in the case of prediction-based classification approach we look at the performances in predicting basal area. The strategies proved moderately successful with a best overall classification accuracy of 41% in the case of LDA. In general, we were most successful in predicting the basal areas of small and large trees (R² respectively of 71% and 69% in the case of k-NN imputation).Keywords: lidar, linear discriminant analysis, forest structure, parametric regression, random forests, k-nearest neighbour imputatio

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.Peer reviewe

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.Peer reviewe

Importance of tree species size dominance and heterogeneity on the productivity of spruce-fir-beech mountain forest stands in Europe

12 Pág.There is concern in the scientific community and among forest managers about potential reductions in the provisioning of forest ecosystem services due to the loss of tree species diversity. Many studies have shown how species diversity influences forest functioning, especially productivity, but the influence of structural diversity, such as tree size heterogeneity, has received much less attention. This study focused on understanding the relationship between stand productivity and several structural characteristics of spruce-fir-beech mountain forest stands in Europe. We used a dataset of 89 long-term plots in spruce-fir-beech forests distributed along the European mountains where the three species, Norway spruce (Picea abies (L.) Karst.), silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.), represent at least 75% of the basal area. Site-dependent conditions were accounted for in a linear mixed-effect basic model, which related the stand productivity with the morphological, climatic and pedological characteristics. The influence of tree species diversity, tree size heterogeneity, species size dominance, and species overlapping in the size distribution on stand productivity was analysed by adding variables to the basic model one by one and evaluating the change in the Akaike's Information Criterion (AIC). The variables that resulted in significant reductions in the AIC, and that were not correlated with each other, were used to build a model to estimate stand productivity. The model showed that in spruce-fir-beech mixed mountain forests (i) when Norway spruce, silver fir and European beech are evenly present within the size distribution (high evenness) the productivity decreases, (ii) the stand productivity increases when the diameter distribution is skewed to the right (higher numbers of smaller individuals), (iii) the stand productivity increases as the proportion of basal area that is spruce increases, and (iv) stand productivity increases with the variability in diameter. We discuss the implications of our results for the management of spruce-fir-beech mountain forest in Europe and for preserving and increasing the stand productivity of these mixed forests.This study was finalized in the frame of the COST (European Cooperation in Science and Technology) Action CLIMO (Climate-Smart Forestry in Mountain Regions - CA15226) financially supported by the EU Framework Programme for Research and Innovation HORIZON 2020. Additionally, Michal Bosela was supported by the Slovak Research and Development Agency (Slovakia) via the project No. APVV-15-0265. Thomas A. Nagel received support from the Slovenian Research Agency (Slovenia) via the project No. J4-1765. Sitkova Zuzana received support by the Slovak Research and Development Agency (Slovakia) via the project No. APVV-16-0325.Peer reviewe

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available