Remote sensing-based methods of aboveground biomass(AGB)estimation in forest ecosystems have gained increased attention,and substantial research has been conducted in the past three decades.This paper provides a surve...Remote sensing-based methods of aboveground biomass(AGB)estimation in forest ecosystems have gained increased attention,and substantial research has been conducted in the past three decades.This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues–collection of field-based biomass reference data,extraction and selection of suitable variables from remote sensing data,identification of proper algorithms to develop biomass estimation models,and uncertainty analysis to refine the estimation procedure.Additionally,we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure.Although optical sensor and radar data have been primary sources for AGB estimation,data saturation is an important factor resulting in estimation uncertainty.LIght Detection and Ranging(lidar)can remove data saturation,but limited availability of lidar data prevents its extensive application.This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas.More research is needed to extract a vertical vegetation structure(e.g.canopy height)from interferometry synthetic aperture radar(InSAR)or optical stereo images to incorporate it into horizontal structures(e.g.canopy cover)in biomass estimation modeling.展开更多
With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a c...With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management.It combines a high-performance computing environment(cyberGIS-Jupyter)and multi-criteria decision analysis models(Weighted Sum Model(WSM)and Technique for Order Preference by Similarity to Ideal Solution Model(TOPSIS))with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation.Social media data(e.g.Twitter data)was used as an additional tool to support the decision-making process.Our case study involves two decision goals generated based on a past flood event in the city of Austin,Texas,U.S.A.As our result shows,WSM produces more diverse values and higher output category estimations than the TOPSIS model.Finally,the model was validated using an innovative questionnaire.This cyberGIS-enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers,where different emergency responders can formulate their decision objectives,select relevant evaluation criteria,and perform interactive weighting and sensitivity analyses.展开更多
基金a grant from Research Center of Agricultural and Forestry Carbon Sinks and Ecological Environmental Remediation,Zhejiang A&F University.
文摘Remote sensing-based methods of aboveground biomass(AGB)estimation in forest ecosystems have gained increased attention,and substantial research has been conducted in the past three decades.This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues–collection of field-based biomass reference data,extraction and selection of suitable variables from remote sensing data,identification of proper algorithms to develop biomass estimation models,and uncertainty analysis to refine the estimation procedure.Additionally,we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure.Although optical sensor and radar data have been primary sources for AGB estimation,data saturation is an important factor resulting in estimation uncertainty.LIght Detection and Ranging(lidar)can remove data saturation,but limited availability of lidar data prevents its extensive application.This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas.More research is needed to extract a vertical vegetation structure(e.g.canopy height)from interferometry synthetic aperture radar(InSAR)or optical stereo images to incorporate it into horizontal structures(e.g.canopy cover)in biomass estimation modeling.
基金supported by the U.S.National Science Foundation under[grant numbers:1047916,1429699,1443080,1551492,and 1664119].
文摘With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management.It combines a high-performance computing environment(cyberGIS-Jupyter)and multi-criteria decision analysis models(Weighted Sum Model(WSM)and Technique for Order Preference by Similarity to Ideal Solution Model(TOPSIS))with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation.Social media data(e.g.Twitter data)was used as an additional tool to support the decision-making process.Our case study involves two decision goals generated based on a past flood event in the city of Austin,Texas,U.S.A.As our result shows,WSM produces more diverse values and higher output category estimations than the TOPSIS model.Finally,the model was validated using an innovative questionnaire.This cyberGIS-enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers,where different emergency responders can formulate their decision objectives,select relevant evaluation criteria,and perform interactive weighting and sensitivity analyses.
