High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments

Abstract

BackgroundUrban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany.Main textRecent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified, especially due to increasing climate change effects. This is important for calibrating and validating recent prognoses of urban forest carbon offset, which have so far scarcely addressed longer timeframes. Additionally, higher resolution remote sensing of urban forest carbon estimates can improve upscaling approaches, which should be extended to reach a more precise global estimate for the first time.ConclusionsUrban forest carbon offset can be made more relevant by making more standardized assessments available for science and professional practitioners, and the increasing availability of high resolution remote sensing data and the progress in data processing allows for precisely that.

Remote Sensing in Urban Forestry: Recent Applications and Future Directions

“…In tandem with the ever-growing availability of high-resolution remotely sensed imagery from diverse airborne and spaceborne platforms, research continues to broaden the application and utilisation of remote sensing techniques to support and advise urban forest studies, such as the accurate modelling and quantification of urban ecosystem services [27,35]. To effectively and adequately realise the potential of remote sensing, a strong connection and collaboration between urban foresters and remote sensing experts is highly advised.…”

“…First, although many remote sensing data and products are available (Table 3), there are technical issues with regard to data fusion and synergy (such as fusing optical, hyperspectral imagery, synthetic aperture radar remote sensing data that operate at disparate resolutions and use diverse acquisition approaches) as well as data matching to meet urban foresters' practical needs [35]. Second, a direct link is still lacking between ecological and geographical features, which can be extracted from remote sensing, and the adequate monitoring and modelling of ecosystem services provision, which usually requires diverse data inputs and sophisticated data processing [27]. Third, although several urban forest management software programmes (e.g., i-Tree developed by the USDA Forest Service) have standardised remote sensing data inputs, they are far from being developed as internationally transferable tools due to insufficient field calibration and validation [83].…”

“…Ultimately, temporal analysis would become a precursor to predicting the future and informing long-term urban forest strategy based on trends derived from past observations [84]. 3Owing to the complexity of natural and anthropogenic factors in urban areas, urban forest studies relying on remote sensing data at a larger scale are still at the experimental stage [27,50]. Multi-scale analyses would help to access spatial information over a wide range of scales from local to regional and even global.…”

“…They provide a full spectrum of ecosystem services, referring to the benefits that human populations obtain directly or indirectly [7,12,13] from natural ecosystems [14,15], which serve as the basis of economic development and social wellbeing [16][17][18]. Some well-recognised and well-studied examples of urban forest ecosystem services include alleviating summer heat through evaporation and shading [19,20], reducing stormwater runoff by intercepting and absorbing water and improving infiltration [21,22], enhancing air quality by removing gaseous pollutants and particulate matter [23], reducing greenhouse gas emissions by storing and sequestering a considerable amount of carbon [24][25][26][27]; addressing food security by supplying food, medicine and materials [28], pollination [29][30][31], outdoor recreation opportunities [1,32], as well as serving as important places for aesthetics and spirituality [33,34].…”

“…These traditional approaches have curtailed the accurate, consistent, replicable, and spatially explicit measurement of spatiotemporal changes of urban forests and the associated evaluation of ecosystem service provision [43,44]. Consequently, research faces challenges due to non-uniform and multifunctional approaches, different methodologies, and a lack of relevant information [27].…”