Understanding the biomass, characteristics, and carbon sequestration of urban forests is crucial for maintaining and improving the quality of life and ensuring sustainable urban planning. Approaches to urban forest management have been incorporated into interdisciplinary, multifunctional, and technical efforts. In this review, we evaluate recent developments in urban forest research methods, compare the accuracy and efficiency of different methods, and identify emerging themes in urban forest assessment. This review focuses on urban forest biomass estimation and individual tree feature detection, showing that the rapid development of remote sensing technology and applications in recent years has greatly benefited the study of forest dynamics. Included in the review are light detection and ranging-based techniques for estimating urban forest biomass, deep learning algorithms that can extract tree crowns and identify tree species, methods for measuring large canopies using unmanned aerial vehicles to estimate forest structure, and approaches for capturing street tree information using street view images. Conventional methods based on field measurements are highly beneficial for accurately recording species-specific characteristics. There is an urgent need to combine multi-scale and spatiotemporal methods to improve urban forest detection at different scales.

Urban ecosystems are complex systems with anthropogenic features that generate considerable CO₂ emissions, which contributes to global climate change. Quantitative estimates of the carbon footprint of urban ecosystems are crucial for developing low-carbon development policies to mitigate climate change. Herein, we reviewed more than 195 urban carbon footprint and carbon footprint related studies, collated the recent progress in carbon footprint calculation methods and research applications of the urban ecosystem carbon footprint, analyzed the research applications of the carbon footprint of different cities, and focused on the need to study the urban ecosystem carbon footprint from a holistic perspective. Specifically, we aimed to: (i) compare the strengths and weaknesses of five existing carbon footprint calculation methods [life cycle assessment, input–output analysis, hybrid life cycle assessment, carbon footprint calculator, and Intergovernmental Panel on Climate Change (IPCC)]; (ii) analyze the status of current research on the carbon footprint of different urban subregions based on different features; and (iii) highlight new methods and areas of research on the carbon footprint of future urban ecosystems. Not all carbon footprint accounting methods are applicable to the carbon footprint determination of urban ecosystems; although the IPCC method is more widely used than the others, the hybrid life cycle assessment method is more accurate. With the emergence of new science and technology, quantitative methods to calculate the carbon footprint of urban ecosystems have evolved, becoming more accurate. Further development of new technologies, such as big data and artificial intelligence, to assess the carbon footprint of urban ecosystems is anticipated to help address the emerging challenges in urban ecosystem research effectively to achieve carbon neutrality and urban sustainability under global change.

Rapid urbanization has led to the continuous deterioration of the surrounding natural ecosystem. It is important to identify the key urbanization factors that affect ecosystem services and analyze the potential effects of these factors on the ecosystem. We selected the Beijing, Tianjin, and Hebei (BTH) urban agglomeration to investigate these effects, and designed three indicators to map the urbanization level: Population density, gross domestic product (GDP) density, and the construction land proportion. Four indicators were chosen to quantify ecosystem services: Food production, carbon sequestration and oxygen production, water conservation, and soil conservation. To handle the nonlinear interactions, we used a random forest (RF) method to assess the effect of urbanization on ecosystem services in the BTH area from 2000 to 2014. Our study demonstrated that population density and economic growth were the internal driving forces affecting ecosystem services. We observed changing trends in the effect of urbanization: The effect of population density on ecosystem services increased, the effect of the proportion of construction land was consistent with population density, and the effect of GDP density on ecosystem services decreased. Our results suggest that controlling the population and GDP would significantly influence the sustainable development in large urban areas.