The retrieval of tree height percentiles with satellite lidar waveforms over the mountainous ar-eas is greatly challenged due to slanted and rugged topographic relief of underlying ground. A novel algorithm is proposed to extract the height percentiles based on the target response waveform (TRW) that is resolved by using a Richardson–Lucy deconvolution algorithm with an adaptive iterative time. The height metrics of the energy percentiles of 25%, 50%, 75% and 95% for the cumulative TRW distribution are determined by their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs resemble closely to the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias and root mean square error (RMSE) taking val-ue of 0.92, 0.0813 and 0.0016. In addition, the accuracies of the derived height percentiles from the proposed algorithm have been greatly improved compared with the conventional Gaussian de-composition method and the slope-adaptive waveform metrics method. The RMSE values de-crease by the mean values of 1.96 m and 2.72 m, respectively. It demonstrates that the proposed algorithm has good potential in the extraction of forest structure parameter over rugged moun-tainous areas.

Social sustainability is the major concern of planners and local officials when urban renewal projects are being conducted. Extreme individualism can potentially cause conflicts of interest, making urban renewal in Western cities fraught with various types of social risks. As a country with deep-rooted socialist tradition, urban renewal projects in China are influenced by collectivist culture and show different features from those of the West. The objective of this research is to investigate how different stakeholders in urban redevelopment projects, including local residents, social organizations, the local state, and developers, interact with each other and how the associated social risks are hedged against. Using a recent well-known project in the city of Guangzhou, the authors attempt to present the latest progress in social risk management in China. With the support from a government-sponsored project, the authors have conducted a questionnaire-based survey and year-long follow-up fieldwork. Using ATLAS.ti software, we found that that “residents’ demand”, “status of collaboration”, and “degree of trust” are the keys to risk management. The results of an ordered probit model show that residents are worried about the overall planning, the relocation timetable, and whether their personal needs are taken into account. It is also indicated that the timely disclosure of project information, high-quality public participation, and a reasonable compensation plan can possibly boost the support rate. The authors suggest that utilizing China’s collectivist culture could be an effective way to mitigate social risks, and residents’ personal interests should also be respected.