Marco Ciolli

A GIS decision support system for regional forest management to assess biomass availability for renewable energy production

Currently, the use of a mix of renewable and traditional energy sources is deemed to help in solving increasing energy demands and environmental issues, thus making it particularly important to assess the availability of renewable energy sources. In a heavily forested region, such as the Italian Alps, one of the main renewable energy sources is woody biomass. A reliable evaluation of biomass availability must take into account the local management of forest resources and the ability to reach forest areas, which is related to existing road networks, and the characteristics and morphology of the terrain. We have developed a new methodology to estimate forest biomass availability for energy production in the Alpine area and to support management decisions, combining the morphological features of the mountain landscape with the current capabilities of forest technology. The approach has been implemented in a tool for forest biomass evaluation based on the Free and Open Source Software for Geospatial (FOSS4G) framework and to refine the current estimates made by the local government. The methodology was tested on the forests of Trentino province (Italy), providing an accurate evaluation of biomass availability, which can be effectively used to identify possible locations for biomass power plants and to suggest new forest management guidelines. The methodology, combining GRASS, PostgreSQL and PostGIS, can be applied to a wide area and can also be executed as a new GRASS module. Being open source it is already available for testing and development.

Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS)

PyGRASS is an object-oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS), a powerful open source GIS widely used in academia, commercial settings and governmental agencies. We present the architecture of the PyGRASS library, covering interfaces to GRASS modules, vector and raster data, with a focus on the new capabilities that it provides to GRASS users and developers. Our design concept of the module interface allows the direct linking of inputs and outputs of GRASS modules to create process chains, including compatibility checks, process control and error handling. The module interface was designed to be easily extended to work with remote processing services (Web Processing Service (WPS), Web Service Definition Language (WSDL)/Simple Object Access Protocol (SOAP)). The new object-oriented Python programming API introduces an abstract layer that opens the possibility to use and access transparently the efficient raster and vector functions of GRASS that are implemented in C. The design goal was to provide an easy to use, but powerful, Python interface for users and developers who are not familiar with the programming language C and with the GRASS C-API. We demonstrate the capabilities, scalability and performance of PyGRASS with several dedicated tests and benchmarks. We compare and discuss the results of the benchmarks with dedicated C implementations.

Development and Application of 2D and 3D GRASS Modules for Simulation of Thermally Driven Slope Winds

The ability to manage and process fully three-dimensional information has only recently been made available for a few Geographical Information Systems (GIS). An example of integrated and complementary use of 2D and 3D GRASS modules for the evaluation and representation of thermally induced slope winds over complex terrain is presented. The analytic solution provided by Prandtl (1942) to evaluate wind velocity and (potential) temperature anomaly induced by either diurnal heating or nocturnal cooling on a constant angle slope is adopted to evaluate wind and temperature profiles at any point over both idealised and real complex terrain. As these quantities depend on the slope angle of the ground and on the distance from the slope surface suitable procedures are introduced to determine the coordinate n of a point in the 3D volume measured along the direction locally normal to the terrain surface. A new GRASS module has been developed to evaluate this quantity and to generate a 3D raster file where each cell is assigned the value of the cell on the surface belonging to the normal vector. The application of the algorithm implemented in

Landscape changes, traditional ecological knowledge and future scenarios in the Alps: A holistic ecological approach.

In recent decades, a dramatic landscape change has occurred in the European alpine region: open areas have been naturally recolonized by forests as traditional agricultural and forest activities were reduced and reorganized. Land use changes (LUC) are generally measured through GIS and photo interpretation techniques, but despite many studies focused on this phenomenon and its effects on biodiversity and on the environment in general, there is a lack of information about the transformation of the human-environment connection. The study of Traditional Ecological Knowledge (TEK), such as the ability to recognize wild plants used as medicine or food, can suggest how this connection evolved through time and generations. This work investigates the relationship between the natural forest cover expansion that influences the loss of open areas and the loss of TEK. Different data sources and approaches were used to address the topic in all its complexity: a mix of questionnaire investigations, historical maps, GIS techniques and modelling were used to analyse past land use changes and predict future scenarios. The study area, Trentino, Italy, is paradigmatic of the alpine situation, and the land use change in the region is well documented by different studies, which were reviewed and compared in this paper. Our findings suggest that open area loss can be used as a good proxy to highlight the present state and to produce future scenarios of Traditional Ecological Knowledge. This could increase awareness of the loss of TEK in other Alpine regions, where data on TEK are lacking, but where environmental trends are comparable.

Using camera trap data to assess the impact of bushmeat hunting on forest mammals in Tanzania

Bushmeat hunting is a pantropical threat to rainforest mammals. Understanding its effects on species richness, community composition and population abundance is of critical conservation relevance. As data on the pre-hunting state of mammal populations in Africa are not generally available, we evaluated the impacts of illegal bushmeat hunting on the mammal community of two ecologically similar forests in the Udzungwa Mountains of Tanzania. The forests differ only in their protection status: one is a National Park and the other a Forest Reserve. We deployed systematic camera trap surveys in these forests, amounting to 850 and 917 camera days in the Forest Reserve and the National Park, respectively, and investigated differences between the two areas in estimated species-specific occupancies, detectabilities and species richness. We show that the mammal community in the Forest Reserve is degraded in all aspects relative to the National Park. Species richness was almost 40% lower in the Forest Reserve (median 18 vs 29 species, highest posterior density intervals 15–30 and 23–47, respectively). Occupancy of most species was also reduced significantly and the functional community appeared significantly altered, with an increase in rodents, and loss of large carnivores and omnivores. Overall, our results show how ineffective reserve management, with almost absent law enforcement, leads to uncontrolled illegal hunting, which in turn has a significant impact on the mammal fauna of globally important sites for conservation.

Co-benefits of Smart and Sustainable Energy District Projects: An Overview of Economic Assessment Methodologies

The concept of “co-benefit” is commonly adopted to define any additional positive impact of a policy, program, or project, arising alongside the desired primary goal. Co-benefits relate to human health and well-being, as well as environmental, economic, and social aspects. The concept, investigated beginning in the 1990s, is recognized today, as supported worldwide by several notable organizations, to provide a better grasp of the economic value of foreseen or applied measures. Nevertheless, given the complexity of achieving complete pictures and understanding many interrelations or cascade effects, co-benefits are often only analyzed locally or measured qualitatively. Therefore, the aim of this paper is to provide an overview of the methodologies for economic assessment that are applicable to the monetization of co-benefits related to Smart and Sustainable Energy District Projects. Starting from a previously defined framework of expected co-benefits, we analyzed the various techniques, identifying the most appropriate with respect to target stakeholders and expected outcomes. As a result, we obtained a clear and comprehensive assessment model, tailored to a specific project type, and operationally applicable. This model would sustain the funding, public acceptance, and political commitment of Smart and Sustainable Energy District Projects, enabling the various stakeholders to better understand the entire economic value of a project, in addition to energy saving and greenhouse gasses reduction.

21 – Understanding Forest Changes to Support Planning: A Fine-Scale Markov Chain Approach

Abstract The abandonment of farming and agriculture is leading to an increase in forest coverage in most European mountain areas. Based on a long series of data (1859–2006), this study presents the development of a spatially explicit fine-scale Markov chain model to predict future changes in forests over a broad area and to assist the management of Paneveggio Nature Park, Italy. The results predict an increase in forest coverage and a reduction in the extent of open habitats, which are a priority for conservation. This protocol can be used for designing long-term management measures focusing on endangered habitats.

Primates in Human-Modified and Fragmented Landscapes: The Conservation Relevance of Modelling Habitat and Disturbance Factors in Density Estimation

Accurate density estimations of threatened animal populations is essential for management and conservation. This is particularly critical for species living in patchy and altered landscapes, as is the case for most tropical forest primates. In this study, we used a hierarchical modelling approach that incorporates the effect of environmental covariates on both the detection (i.e. observation) and the state (i.e. abundance) processes of distance sampling. We applied this method to already published data on three arboreal primates of the Udzungwa Mountains of Tanzania, including the endangered and endemic Udzungwa red colobus (Procolobus gordonorum). The area is a primate hotspot at continental level. Compared to previous, ‘canonical’ density estimates, we found that the inclusion of covariates in the modelling makes the inference process more informative, as it takes in full account the contrasting habitat and protection levels among forest blocks. The correction of density estimates for imperfect detection was especially critical where animal detectability was low. Relative to our approach, density was underestimated by the canonical distance sampling, particularly in the less protected forest. Group size had an effect on detectability, determining how the observation process varies depending on the socio-ecology of the target species. Lastly, as the inference on density is spatially-explicit to the scale of the covariates used in the modelling, we could confirm that primate densities are highest in low-to-mid elevations, where human disturbance tend to be greater, indicating a considerable resilience by target monkeys in disturbed habitats. However, the marked trend of lower densities in unprotected forests urgently calls for effective forest protection.

Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates

Spatially explicit models of animal abundance are a critical tool to inform conservation planning and management. However, they require the availability of spatially diffuse environmental predictors of abundance, which may be challenging, especially in complex and heterogeneous habitats. This is particularly the case for tropical mammals, such as nonhuman primates, that depend on multi-layered and species-rich tree canopy coverage, which is usually measured through a limited sample of ground plots. We developed an approach that calibrates remote-sensing imagery to ground measurements of tree density to derive basal area, in turn used as a predictor of primate density based on published models. We applied generalized linear models (GLM) to relate 9.8-ha ground samples of tree basal area to various metrics extracted from Landsat 8 imagery. We tested the potential of this approach for spatial inference of animal density by comparing the density predictions for an endangered colobus monkey, to previous estimates from field transect counts, measured basal area, and other predictors of abundance. The best GLM had high accuracy and showed no significant difference between predicted and observed values of basal area. Our species distribution model yielded predicted primate densities that matched those based on field measurements. Results show the potential of using open-access and global remote-sensing data to derive an important predictor of animal abundance in tropical forests and in turn to make spatially explicit inference on animal density. This approach has important, inherent applications as it greatly magnifies the relevance of abundance modeling for informing conservation. This is especially true for threatened species living in heterogeneous habitats where spatial patterns of abundance, in relation to habitat and/or human disturbance factors, are often complex and, management decisions, such as improving forest protection, may need to be focused on priority areas.

Advertising value of the brown bear in the Italian Alps

Abstract In Italy, the reintroduction of the brown bear (Ursus arctos) has created conflicts with people because bears may damage livestock, crops, or honey farms. These damages—and their financial costs—are sometimes used as an argument by those who oppose the presence of this carnivore. Despite such damages, bears can provide economic benefits, such as attracting eco-tourists for bear-watching. The Advertising Value Equivalent was used to assess the value of the bears’ appearances in newscasts and documentaries from 2011 to 2015. The marketing value of the bear as a promoter largely exceeds the amount of reimbursements for damages. This method can be used to highlight the economic benefit that the bear can produce for a destination and contribute to complex discussions with managers and stakeholders.