123 research outputs found
Remote sensing approaches in carbon stock (CS) mapping considering the dominant tree species in urban areas.
No full textRecentemente, Negli ultimi anni, il preoccupante incremento del contributo del carbonio atmosferico al cambio climatico ha sollevato con forza il dibattito. In area urbana, le misure di mitigazione si stanno concentrando, tra le altre, sul ruolo delle infrastrutture verdi. In particolare, gli approcci improntati a sistemi di pianificazione e gestione del verde urbano sostenibili sembrano essere promettenti. Informazioni esplicite e tempestive sulla composizione strutturale e funzionale delle infrastrutture verdi e sulle singole strutture o aggregati di alberi urbani, in merito al loro ruolo nella cattura del carbonio atmosferico
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(CS), sono essenziali affinché i responsabili delle amministrazioni locali adottino azioni immediate per mitigare il peggioramento dell’impatto delle attività antropiche.
In questo studio, è stata adottata una metodologia dettagliata per la calibrazione e mappatura del CS delle alberature in due aree urbane. Uno studio è stato condotto a Sassuolo (MO), una città italiana di dimensioni medie. L'altro, è stato condotto nella regione della capitale del Belgio (Bruxelles), dove sono state anche valutate ed analizzate in modo comparativo le due diverse fonti di dati di telerilevamento (LiDAR e WorldView 3 (WV3)) ed i rispettivi risultati di mappatura. A Sassuolo, la mappatura del CS è stata eseguita utilizzando solo dati da immagini WV3 per un'area di studio alla scala locale di dettaglio. In particolare, alla scala di parco urbano, sono state selezionate 22 parcelle (10 m × 10 m ciascuna) di cui 7 per la validazione dei risultati della classificazione delle specie arboree e della calibrazione e mappatura del CS. In un secondo esperimento, a Bruxelles, l'approccio è stato replicato per un'area di studio più ampia, alla scala di città metropolitana. In questo caso, 75 parcelle (10 m × 10 m ciascuna) sono state utilizzate, di cui 20 per la convalida della calibrazione dei risultati della mappatura del CS. In entrambi i casi, sia a Sassuolo sia a Bruxelles, le specie arboree dominanti sono state identificate e classificate utilizzando immagini WV3 ad alta risoluzione. L'approccio di classificazione OBIA (Object-Based Image Analysis) è stato impiegato con successo ottenendo una precisione complessiva (Overall Accuracy) del 78% e del 71%, rispettivamente, per le relative specie arboree. Le stime del CS per ciascun caso sono state computate, a livello del singolo plot, utilizzando un modello allometrico basato su dati dendrometrici rilevati in campo, ad esempio altezza della pianta (H) e diametro del fusto all'altezza del petto (DBH). Successivamente il CS calcolato in base ai dati dicampo, insieme alle variabili derivate dall’elaborazione dei dati WV3 (NDVI) e LiDAR (CHM), sono stati mappati usando il sistema informativo geografico QGIS. I risultati ottenuti per entrambe le città hanno permesso di validare l'approccio, quale metodo efficiente e conveniente per mappare alla scala urbana, sia media che metropolitana, il contributo delle specie arboree dominanti nella cattura del carbonio atmosferico (CS). Questo studio aiuterà sicuramente urbanisti e pianificatori a meglio comprendere e meglio progettare la pianificazione delle infrastrutture verdi urbane, basandosi su dati provenienti da fonti telerilevate da remoto, e ove possibile prossimali, in base alla loro disponibilità ed al livello di opportunità, al fine di implementare un sistema sostenibile di gestione del verde urbano.Recently, severe intensification of atmospheric carbon recognizes the importance of urban tree contributions in atmospheric carbon mitigation in city areas considering a sustainable urban green planning and management system. Explicit and timely information on urban trees and their roles in atmospheric Carbon Stock (CS) are essential for the policymakers to take immediate actions to recover the effects of deforestation and their worsening outcomes. This doctoral study will be a way out for the policymakers in CS mapping for the dominant tree species in their cities based on Remote Sensing (RS) data sources. The mapping approach could be a useful tool especially for developing countries, where hyperspectral data could be a better solution over the hardly available LiDAR data. In this study, a detailed methodology on the urban tree CS calibration and mapping was done for two urban areas one of which was in Sassuolo (MO), a smaller city in Italy. The other one was conducted in the capital region of Belgium (Brussels), where also the comparative analysis of the two different remote sensing data sources (LiDAR and WorldView 3 (WV3)) and their mapping outcomes were assessed to define the convenience and applicability of the data sources. In Sassuolo, CS mapping was done utilizing only the WV3 image data for a smaller study area of 22 plots (10m×10m each) where the 7 plots were utilized to validate the results of tree species classification and the CS calibration and mapping. Later in Brussels, the approach was implied for a larger study area of 75 plots (10m×10m each) where 20 plots were utilized for the validation of CS calibration and mapping outcomes. In all cases, either in Sassuolo or in Brussels, dominant tree species were identified and classified utilizing the high-resolution WV3 image. The Object-Based Image Analysis (OBIA) classification approach was successfully employed to attain the overall accuracy of 78% and 71% for the tree species in Sassuolo and Brussels respectively. The field estimations of CS for each plot were done utilizing an allometric model based on the field data on tree dendrometry i.e. Height (H) and Diameter at Breast Height (DBH). Later the computed CS based on the field data along with the WV3 (NDVI) and LiDAR (CHM) data derived variables, had been mapped in QGIS. The results were found quite evident for both cities which did approve the approach as an efficient and convenient way of mapping, certainly recognizing the dominant tree species contributions in atmospheric CS. No doubt, this study will assist the city planners to understand and decide the applicability of remote sensing data sources based on their availability and the level of expediency, ensuring a sustainable urban green management system
Semi-Automatic Extraction of Hedgerows from High-Resolution Satellite Imagery
No full textSmall landscape elements are critical in ecological systems, encompassing vegetated and non-vegetated features. As vegetated elements, hedgerows contribute significantly to biodiversity conservation, erosion protection, and wind speed reduction within agroecosystems. This study focuses on the semi-automatic extraction of hedgerows by applying the Object-Based Image Analysis (OBIA) approach to two multispectral satellite datasets. Multitemporal image data from PlanetScope and Copernicus Sentinel-2 have been used to test the applicability of the proposed approach for detailed land cover mapping, with an emphasis on extracting Small Woody Elements. This study demonstrates significant results in classifying and extracting hedgerows, a smaller landscape element, from both Sentinel-2 and PlanetScope images. A good overall accuracy (OA) was obtained using PlanetScope data (OA = 95%) and Sentinel-2 data (OA = 85%), despite the coarser resolution of the latter. This will undoubtedly demonstrate the effectiveness of the OBIA approach in leveraging freely available image data for detailed land cover mapping, particularly in identifying and classifying hedgerows, thus supporting biodiversity conservation and ecological infrastructure enhancement
UAV-Spherical Data Fusion Approach to Estimate Individual Tree Carbon Stock for Urban Green Planning and Management
No full textDue to ever-accelerating urbanization in recent decades, exploring the contributions of trees in mitigating atmospheric carbon in urban areas has become one of the paramount concerns. Remote sensing-based approaches have been primarily implemented to estimate the tree-stand atmospheric carbon stock (CS) for the trees in parks and streets. However, a convenient yet high-accuracy computation methodology is hardly available. This study introduces an approach that has been tested for a small urban area. A data fusion approach based on a three-dimensional (3D) computation methodology was applied to calibrate the individual tree CS. This photogrammetry-based technique
employed an unmanned aerial vehicle (UAV) and spherical image data to compute the total height (H) and diameter at breast height (DBH) for each tree, consequently estimating the tree-stand CS. A regression analysis was conducted to compare the results with the ones obtained with high-cost laser scanner data. Our study demonstrates the applicability of this method, highlighting its advantages even for large city areas in contrast to other approaches that are often more expensive. This approach could serve as an efficient tool for assisting urban planners in ensuring the proper utilization of the available green space, especially in a complex urban environment
Innovation in olive-growing. Proximal sensing LiDAR for tree volume estimation
No full textThe olive tree is a significant part of the Mediterranean landscape and traditions, providing high-quality olive oil. Pruning techniques are vital for optimal harvesting conditions and tree health. Innovations such as LiDAR technology are being used to assess tree metrics and support pruning activities. These advancements can be integrated into a decision support system to assist farmers in the future
Pattern analysis of olive grove distribution in the time series. A case study if Cartoceto (Italy)
No full textThe municipality of Cartoceto in Italy and its surrounding areas have a long-standing tradition of olive orchards. A recent study analyzed the transformation of the landscape in Cartoceto from 1820 to the present, focusing on olive groves. The study identified six different classes of olive-growing typologies and assessed changes and transitions among them. Despite urban expansion and natural reforestation, Cartoceto's territory maintains distinct landscape typologies, with some olive farmers establishing new orchards in suitable arable lands. This comprehensive study provided valuable information about land use changes over time and can contribute to the development of a geo-database for managing olive growing in the area
Urban Land-Cover Classification Utilizing Sentinel 2 Data for Landscape Planning and Management in Vietnam
No full textThe Copernicus Sentinel 2 data could be essential for an effective land-cover classification for urban policymakers. While huge expenses and complicated post-processing issues are well-recognized by the city authorities, a convenient approach is hardly available to apply with other commercially available data sources. Freely available Sentinel 2 data, even with 10–60 m resolution, could be an essential addition for policymakers, especially for classifying urban landscapes. Here, the Geographic Object-based Image Analysis (GEOBIA) approach has been applied in the eCognition platform utilizing a Sentinel 2 image for the urban land-cover classification in Hue, Vietnam. Currently, four different classes, i.e., streets, buildings, vegetation, and water resources, have been applying the GEOBIA approach. Concerning the data availability and post-processing approach, this study could be a way out for urban policymakers due to the convenience and simplicity of the applied algorithms. So, this study will certainly assist city planners either in the case of developed or third-world developing ones, where it is much more necessary for efficient urban space management
Toward carbon neutral cities: A comparative analysis between Sentinel 2 and WorldView 3 satellite image processing for tree carbon stock mapping in Brussels
No full textBecause of the high costs associated with data sources, urban policymakers struggle to employ cost-effective remote sensing methods for evaluating trees and their potential contributions to atmospheric Carbon Stock (CS). While free data sources like Copernicus Sentinel satellite data could be explored, there are a few studies illustrating its potential for mapping urban tree C. Here, the Sentinel 2 (S2)-derived Normalized Difference Vegetation Index (NDVI) was used to model CS for street trees in Brussels. In parallel, the WorldView 3 (WV3)-derived NDVI layer was also used for a similar study area to compare the CS mapping outcomes regarding dominant tree species. The accuracy level was around 90 % (R2=0.89, r=0.94, and RMSE= 97 kg) in the case of WV3 data, whereas it was about 60 % (R2=0.60, r=0.79, and RMSE = 189.6 kg), even with a coarse resolution regarding the S2 data. This study also shows the strength and scope of using S2 data over WV3 data, illustrating the convenience in terms of accuracy and cost-effectiveness compared to existing methods. The applied methodology could be utilized to monitor urban trees and predict the level of possible carbon sequestration, even considering a larger city like Brussels with a complex agglomeration. It could be a solid additional support for the authorities of European towns and developing countries, especially in terms of being cost-efficient and readily embraced by users
Designing and Implementing a Multifunctional Network of Urban Green Infrastructures
No full textThis paper introduces an affordable, userfriendly and time-efficient approach for improving public greenspace networks using geospatial data and land surveying techniques. Our study focuses on various green areas in Ancona, Italy and builds upon previous initiatives such as the CyberParks project and the Network of Botanical Gardens. Our research aims to boost public engagement, optimise management, and promote a sense of connection between urban green spaces and the citizens, focusing, among others, on gamificatio
Olive grove landscape change: A spatial analysis using multitemporal geospatial datasets
No full textThis research examines the evolution of olive landscapes in Cartoceto, Italy, post-World War II. This study investigates the structural and functional attributes of two distinct olive cultivation models, mixed olive groves with a low tree density and high-density olive groves. The research employs remote sensing aerial imagery and land use cartography to reconstruct the past terrain and classify distinct categories of olive orchards. Landscape metrics are utilised to evaluate the impact of human and natural factors on the temporal development of olive landscapes across multiple spatial configurations, defined as Landscape-scale and sub-units or Landscape Units. The results indicate that olive landscapes have experienced noteworthy alterations, characterised by a reduction in the conventional large-scale system and an increase in specialised planting models of smaller sizes, classified as “high intensity”. The region surrounding Cartoceto's historic centre has conserved a greater variety of mixed olive heritage and has witnessed the emergence of novel high densities of trees compared to other areas. Obtaining these results has been facilitated solely by utilising diverse spatial configurations of smaller sub-units than the entire landscape. The outcomes of this investigation provide a foundation for forthcoming enquiries into olive terrains and the development of sustainable rural landscape strategies
Photogrammetry and Remote Sensing for the identification and characterization of trees in urban areas
No full textFor the last few decades, there have been a lot of studies recognising the significant roles of the urban trees as a high-quality carbon sink. This work is a preliminary study about how remote sensing and photogrammetry could be useful tools to identify urban trees for the purpose of Carbon Storage (CS) computation in urban areas. Our first study area is a typical urban park located in Sassuolo, a municipality in the northern part of Italy in the so-called "Pianura Padana". We measured the tree Height (H) and the Diameter at Breast Height (DBH), required for the calibration of the CS, based on the tree allometry during the field data collection along with the constructing a 3D model through the photogrammetric approach. A high-resolution WorldView (WV) 3 satellite image of the same area, was classified using an object-oriented approach to count the number of trees varied with different species. This preliminary study will enhance the possibilities of the application of these approaches in case of the larger urban areas to ascertain the accuracy of the tree CS calibration
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