检索结果-南通市图书馆

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

引用

forest Ecosystems 2022年第5期9卷 617-629页

作者： Qiuli Yang Yanjun Su Tianyu Hu Shichao Jin Xiaoqiang Liu Chunyue Niu Zhonghua Liu Maggi Kelly Jianxin Wei Qinghua Guo State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing100093China University of Chinese Academy of Sciences Beijing100049China Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesCollaborative Innovation Centre for Modern Crop Production co-sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China Department of Environmental Sciences Policy and ManagementUniversity of CaliforniaBerkeleyCA94720-3114USA Division of Agriculture and Natural Resources University of CaliforniaBerkeleyCA94720-3114USA College of Geography and Remote Sensing Sciences Xinjiang UniversityUrumqiXinjiang830017China Xinjiang Lidar Applied Engineering Technology Research Center UrumqiXinjiang830002China Xinjiang Land and Resources Information Center UrumqiXinjiang830002China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing100871China

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 ***,combining the advantages of active and passive data sources to improve estimation accuracy remains ***,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 *** 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 *** 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 *** 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 *** proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf ***,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 *** proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

关键词： forest aboveground biomass Drone LiDAR Allometric relationship Power law Tree height Vegetation index

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Modelling the integrated effects of land use and climate change scenarios on forest ecosystem aboveground biomass, a case study in Taihe County of China

引用

Journal of Geographical Sciences 2017年第2期27卷 205-222页

作者： WU Zhuo DAI Erfu GE Quansheng XI Weimin WANG Xiaofan Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and NaturalResources Research CAS Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Department of Biological and Health Sciences Texas A&M University Kingsville Texas 78363 USA Key Laboratory of Land Use Ministry of Land and Resources China Land-Surveying and Planning InstituteBeijing 100035 China

Global and regional environmental changes such as land use and climate change have significantly integrated and interactive effects on forest. These integrated effects will undoubtedly alter the distribution, function and succession processes of forest ecosystems. In order to adapt to these changes, it is necessary to understand their individual and integrated effects. In this study, we proposed a framework by using coupling models to gain a better understanding of the complex ecological processes. We combined an agent-based model for land use and land cover change（ABM/LUCC）, an ecosystem process model（PnET-Ⅱ）, and a forest dynamic landscape model（LANDIS-Ⅱ） to simulate the change of forest aboveground biomass（AGB） which was driven by land use and climate change factors for the period of 2010–2050 in Taihe County of southern China, where subtropical coniferous plantations dominate. We conducted a series of land use and climate change scenarios to compare the differences in forest AGB. The results show that：（1） land use, including town expansion, deforestation and forest conversion and climate change are likely to influence forest AGB in the near future in Taihe County.（2） Though climate change will make a good contribution to an increase in forest AGB, land use change can result in a rapid decrease in the forest AGB and play a vital role in the integrated simulation. The forest AGB under the integrated scenario decreased by 53.7%（RCP2.6 ＋ land use）, 57.2%（RCP4.5 ＋ land use）, and 56.9%（RCP8.5 ＋ land use） by 2050, which is in comparison to the results under separate RCPs without land use disturbance.（3） The framework can offer a coupled method to better understand the complex and interactive ecological processes, which may provide some supports for adapting to land use and climate change, improving and optimizing plantation structure and function,and developing measures for sustainable forest management.

关键词： RCPs plantation forest aboveground biomass ABM LANDIS-Ⅱ Taihe County

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests

引用

forest Ecosystems 2020年第4期7卷 851-870页

作者： Huiyi Su Wenjuan Shen Jingrui Wang Arshad Ali Mingshi Li College of forestry Nanjing Forestry UniversityNanjing 210037China. Co-Innovation Center for Sustainable forestry in Southern China Nanjing Forestry UniversityNanjing 210037China. Shenzhen Academy of Environmental Sciences Shenzhen 518000China.

Background:aboveground biomass(AGB)is a fundamental indicator of forest ecosystem productivity and health and hence plays an essential role in evaluating forest carbon reserves and supporting the development of targeted forest management ***:Here,we proposed a random forest/co-kriging framework that integrates the strengths of machine learning and geostatistical approaches to improve the mapping accuracies of AGB in northern Guangdong Province of *** used Landsat time-series observations,Advanced Land Observing Satellite(ALOS)Phased Array L-band Synthetic Aperture Radar(PALSAR)data,and National forest Inventory(NFI)plot measurements,to generate the forest AGB maps at three time points(1992,2002 and 2010)showing the spatio-temporal dynamics of AGB in the subtropical forests in Guangdong,***:The proposed model was capable of mapping forest AGB using spectral,textural,topographical variables and the radar backscatter coefficients in an effective and reliable *** root mean square error of the plotlevel AGB validation was between 15.62 and 53.78 t∙ha^(−1),the mean absolute error ranged from 6.54 to 32.32 t∙ha^(−1),the bias ranged from−2.14 to 1.07 t∙ha^(−1),and the relative improvement over the random forest algorithm was between 3.8%and 17.7%.The largest coefficient of determination(0.81)and the smallest mean absolute error(6.54 t∙ha^(−1)were observed in the 1992 AGB *** spectral saturation effect was minimized by adding the PALSAR data to the modeling variable set in *** adding elevation as a covariable,the co-kriging outperformed the ordinary kriging method for the prediction of the AGB residuals,because co-kriging resulted in better interpolation results in the valleys and plains of the study ***:Validation of the three AGB maps with an independent dataset indicated that the random forest/cokriging performed best for AGB prediction,followed by random forest coupled with ordinary kriging(random forest/ordinary krigi

关键词： forest aboveground biomass Random forest co-kriging ALOS PALSAR Landsat TM National forest inventory Digital elevation model

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：