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.
The acceleration of Digital Agriculture is evident through the increased adoption of digital technologies on farms including smart machines, sensors and cloud computing. In this paper we present the preliminary results of the research project funded by Università Politecnica delle Marche in 2018 “PFRLab: Setting of a precision farming robotic laboratory for cropping system sustainability and food safety and security”, which is still underway. In this context, as first result, an interdepartmental Research and Services Center called “Smart Farming” has been set up with the aim to strengthen multidisciplinary collaborations in the fields of Agriculture and Forestry, Geomatics, ICT and Robotics. Regarding field activities the SPAD 502 as well as Normalized Difference Vegetation Index (NDVI) provide a good estimate of the Chlorophylla+b content in durum wheat leaves so can be used to predict in a quickly and non-destructively way, the crop greenness status and to identify any nutritional deficiencies in real time. Future research activities are certainly needed to fully explore the potentialities of conservation agriculture and precision farming, and to drive the transition process from conventional agriculture to modern conservation agriculture and precision farming techniques. In-depth studies are planned on the combined effect of nitrogen fertilization and soil management on the main production variables of durum wheat in order to evaluate whether specific tools for precision agriculture applications can find significant diffusion even in Mediterranean cereal based cropping systems.
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