The engagement of citizen in the management of urban forests: the LIFE URBANGREEN experience in Krakow

How do we make the ecosystem services of urban green areas and their benefits against climate change visible in real-time and even more effective? The cities of Krakow (Poland) and Rimini (Italy), in conjunction with their technological and scientific partners, addressed these topics in the EU-project LIFE URBANGREEN (LIFE17 CCA/IT/000079). This project developed innovative tools to assist the green area managers in their daily work and tested the effectiveness of these tools in the parks and streets of Rimini and Kraków.To ensure a broad understanding of the LIFE URBANGREEN project, along with the promotion of the importance and values of urban greenery, communication activities played a crucial role in engaging citizens to these topics. The most relevant communication activities the project carried out were: -creating awareness on urban forests among the target audience; -calculating and understanding the value of ecosystem services provided by trees and green areas for the two analysed cities;-communicating the environmental benefits and the overall importance of trees to the citizen.An important communication channel developed within the project is the web portal “Trees of Krakow and their benefits” (https://krakow.lifeurbangreen.eu). Here citizens can consult the main green areas of the city and retrieve information including the benefits of these areas in terms of Carbon assimilation, sequestration and storage, air amelioration and ambient air cooling. The data displayed on the portal are calculated in real-time, based on the spatial database of trees of Krakow trees, daily updated during maintenance activities.The data on ecosystem services are based on new algorithms developed during the LIFE URBANGREEEN Project, which consider the tree species, their age and size, the current weather condition and transpiration coefficients developed by the scientific component of the project with measurements on hundreds of trees over a period of three years.Citizens showed great interest in the project activities. The Public Portals of Krakow and Rimini are registering high consultation numbers, also one year after the end of the LIFE URBANGREEN project. Since the environmental benefits served by urban trees are calculated dynamically and updated every night, the values represented reflect the actual situation of the two cities

Smart water management in urban forests: the approach of life urbangreen

Urban trees are exposed to increasing levels of stress due to extended droughts. This affects not only the health of the trees, but also the quantity of benefits which trees serve to the city environment. A tool to monitor the tree vigor, based on their characteristics, but also on meteorological data and on scientific parameters to estimate the transpiration rate, can be very effective to detect trees under stress and help maintainers with instructions on irrigation needs

The use of remotely sensed data and polish NFI plots for prediction of growing stock volume using different predictive methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons

The use of remotely sensed data and polish NFI plots for prediction of growing stock volume using different predictive methods

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS-and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons