Sentiment analysis on social media such as Twitter is a challenging task given the data characteristics such as the length, spelling errors, abbreviations, and special characters. Social media sentiment analysis is also a fundamental issue with many applications. With particular regard of the tourism sector, where the characterization of fluxes is a vital issue, the sources of geotagged information have already proven to be promising for tourism-related geographic research. The paper introduces an approach to estimate the sentiment related to Cilento’s, a well known tourism venue in Southern Italy. A newly collected dataset of tweets related to tourism is at the base of our method. We aim at demonstrating and testing a deep learning social geodata framework to characterize spatial, temporal and demographic tourist flows across the vast of territory this rural touristic region and along its coasts. We have applied four specially trained Deep Neural Networks to identify and assess the sentiment, two word-level and two character-based, respectively. In contrast to many existing datasets, the actual sentiment carried by texts or hashtags is not automatically assessed in our approach. We manually annotated the whole set to get to a higher dataset quality in terms of accuracy, proving the effectiveness of our method. Moreover, the geographical coding labelling each information, allow for fitting the inferred sentiments with their geographical location, obtaining an even more nuanced content analysis of the semantic meaning.

The need for energy and the increasing importance of climate change mitigation are leading to a conversion from conventional to renewable energy sources. Solar photovoltaic (PV) power has seen the most significant increase among all renewable energy sources. However, most of these installations are land-based, significantly changing global land use (LU). The real impacts, whether positive or negative, are poorly understood. This study was undertaken to have a better understanding of the impacts of solar parks on the microclimate and vegetation dynamics. First, different solar parks were visited to take measurements of the surface temperature (T_surf), photosynthetic active radiation (PAR), air temperature (T_air), and humidity (RH) to quantify the microclimate and perform a vegetation relevé. The measurements were taken at different positions: underneath, in between, and outside solar panels. For vegetation, the data were first converted to diversity indices, which in turn contributed to a multi-indicator land use impact assessment that evaluated effects on vegetation, biodiversity, soil and water. Solar parks had clear effects on microclimate: if the panels were high enough from the ground, they could lower the T_surf by providing shade and enough airflow. Additionally, the multidimensional functional diversity (FD) analysis of the vegetation indicated that there was less light at a higher humidity and lower temperature underneath the panels. Interestingly, the species underneath the panels also preferred a lower pH and a higher nitrogen level. Finally, the land use impact assessment found that the total land use impact for a wheat field was higher than that of the solar park, which suggests that the conversion of conventional intensive agriculture to a solar park would be beneficial.

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system.

Currently, the worsening impacts of urbanizations have been impelled to the importance of monitoring and management of existing urban trees, securing sustainable use of the available green spaces. Urban tree species identification and evaluation of their roles in atmospheric Carbon Stock (CS) are still among the prime concerns for city planners regarding initiating a convenient and easily adaptive urban green planning and management system. A detailed methodology on the urban tree carbon stock calibration and mapping was conducted in the urban area of Brussels, Belgium. A comparative analysis of the mapping outcomes was assessed to define the convenience and efficiency of two different remote sensing data sources, Light Detection and Ranging (LiDAR) and WorldView-3 (WV-3), in a unique urban area. The mapping results were validated against field estimated carbon stocks. At the initial stage, dominant tree species were identified and classified using the high-resolution WorldView3 image, leading to the final carbon stock mapping based on the dominant species. An object-based image analysis approach was employed to attain an overall accuracy (OA) of 71% during the classification of the dominant species. The field estimations of carbon stock for each plot were done utilizing an allometric model based on the field tree dendrometric data. Later based on the correlation among the field data and the variables (i.e., Normalized Difference Vegetation Index, NDVI and Crown Height Model, CHM) extracted from the available remote sensing data, the carbon stock mapping and validation had been done in a GIS environment. The calibrated NDVI and CHM had been used to compute possible carbon stock in either case of the WV-3 image and LiDAR data, respectively. A comparative discussion has been introduced to bring out the issues, especially for the developing countries, where WV-3 data could be a better solution over the hardly available LiDAR data. This study could assist city planners in understanding and deciding the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system.