|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis显示文摘 | Yu Liu Qinghua Guo Maggi Kelly | 2008 | ISPRS Journal of Photogrammetry and Remote Sensing2008，，4： | 1 |
| 2 | Bartonella quintana endocarditis in dogs显示文摘 | Kelly P RolainJM Maggi R | 2006 | Emerging Infectious Diseases2006，12，12： | 1 |
| 3 | A spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery显示文摘 | Desheng Liu Maggi Kelly Peng Gong | 2005 | Remote Sensing of Environment2005，，2： | 1 |
| 4 | Understanding large-scale deforestation in southern Jinotega, Nicaragua from 1978 to 1999 through the examination of changes in land use and land cover显示文摘 | ESTHER B ZELEDON N MAGGI KELLY | 2009 | Journal of Environmental Management2009，90，： | 1 |
| 5 | Classification of the wildland-urban interface: A comparison of pixeland objectbased classifications using high-resolution aerial photography显示文摘 | Casey Cleve Maggi Kelly Kearns F R | 2008 | Computers Environment and Urban Systems2008，32，4： | 1 |
| 6 | Obesity and the Food Environment: Income and Ethnicity Differences Among People With Diabetes显示文摘 | Jessica C. Jones-Smith Andrew J. Karter E. Margaret Warton Maggi Kelly Ellen Kersten Howard H. Moffet Nancy Adler Dean Schillinger Barbara A. Laraia | 2013 | Diabetes Care2013，，9： | 1 |
| 7 | Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes显示文摘Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy. | Qiuli Yang Yanjun Su Tianyu Hu Shichao Jin Xiaoqiang Liu Chunyue Niu Zhonghua Liu Maggi Kelly Jianxin Wei Qinghua Guo | 2022 | Forest Ecosystems2022，9，5： | 0 |
| 8 | Canopy structure:An intermediate factor regulating grassland diversity-function relationships under human disturbances显示文摘Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth,and severe anthropogenic-induced grassland ecosystem function declines have been reported recently.Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies.Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate(e.g.,light,water,and wind),and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions.However,the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated.Here,we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China.Aboveground net primary productivity(ANPP)and resilience,two indicators of grassland ecosystem functions,were measured from field data and satellite remote sensing data.Terrestrial laser scanning data were collected to measure canopy structure(represented by mean height and canopy cover).Our results showed that plant species diversity was positively correlated to canopy structural traits,and negatively correlated to human activity intensity.Canopy structure was a significant indicator for ANPP and resilience,but their correlations were inconsistent under different human activity intensity levels.Compared to plant species diversity,canopy structural traits were better indicators for grassland ecosystem functions,especially for ANPP.Through structure equation modeling analyses,we found that plant species diversity did not have a direct influence on ANPP under human disturbances.Instead,it had a strong indirect effect on ANPP by altering canopy structural traits.As to resilience,plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover.This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances,which should be included in future grassland monitoring and management. | Xiaoxia Zhao Yuhao Feng Kexin Xu Mengqi Cao Shuya Hu Qiuli Yang Xiaoqiang Liu Qin Ma Tianyu Hu Maggi Kelly Qinghua Guo Yanjun Su | 2023 | Fundamental Research2023，3，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员