Integration of Landsat time-series vegetation indices improves consistency of change detection

Vegetation indices(VIs)were used to detect when and where vegetation changes occurred.However,different VIs have different or even diametrically opposite results,which obstructed the in-depth understanding of vegetation change.Therefore,this study examined the spatial and temporal consistency offive VIs(EVI;NBR;NDMI;NDVI;and NIRv)in detecting abrupt and gradual vegetation changes,and provided an ensemble algorithm which integrated the change detection results of thefive indices to reduce the uncertainty of change detection using a single index.The spatial consistency of thefive indices in abrupt change detection accounted for 50.6%of the study area,but the temporal consistency was low(21.6%).Wetness indices(NBR,NDMI)were more sensitive to negative abrupt changes,greenness indices(EVI,NDVI,NIRv)were more sensitive to positive abrupt changes;and both types of indices were similar in detecting gradual and total changes.The overall accuracy of the ensemble method was 81.60%and higher than that of any single index in abrupt change detection.This study provides a comprehensive evaluation of the spatial and temporal inconsistencies of change detection in model-fitting errors and various types of vegetation changes.The proposed ensemble method can support robust change-detection.

A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems

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.

Global observation of urban expansion and land-cover dynamics usingsatellite big-data

Humans deeply influence the urbanizing of earth’s surface system in an exacerbating manner across space and time[1].Around the globe,urban land-use/cover changes reflect the intensities of human activities and land shifts from nature or semi-nature lands to man-made–dominated surfaces[2].

Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon

Previous research has explored the potential to integrate lidar and optical data in aboveground biomass(AGB)estimation,but how different data sources,vegetation types,and modeling algorithms influence AGB estimation is poorly understood.This research conducts a comparative analysis of different data sources and modeling approaches in improving AGB estimation.RapidEye-based spectral responses and textures,lidar-derived metrics,and their combination were used to develop AGB estimation models.The results indicated that(1)overall,RapidEye data are not suitable for AGB estimation,but when AGB falls within 50–150 Mg/ha,support vector regression based on stratification of vegetation types provided good AGB estimation;(2)Lidar data provided stable and better estimations than RapidEye data;and stratification of vegetation types cannot improve estimation;(3)The combination of lidar and RapidEye data cannot provide better performance than lidar data alone;(4)AGB ranges affect the selection of the best AGB models,and a combination of different estimation results from the best model for each AGB range can improve AGB estimation;(5)This research implies that an optimal procedure for AGB estimation for a specific study exists,depending on the careful selection of data sources,modeling algorithms,forest types,and AGB ranges.