将基于个体的森林生长模型与遥感数据相结合的自举法

IF 3 2区 农林科学 Q1 FORESTRY
Forestry Pub Date : 2024-02-16 DOI:10.1093/forestry/cpae003
Mathieu Fortin, Olivier van Lier, Jean-François Côté, Heidi Erdle, Joanne White
{"title":"将基于个体的森林生长模型与遥感数据相结合的自举法","authors":"Mathieu Fortin, Olivier van Lier, Jean-François Côté, Heidi Erdle, Joanne White","doi":"10.1093/forestry/cpae003","DOIUrl":null,"url":null,"abstract":"Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.","PeriodicalId":12342,"journal":{"name":"Forestry","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A bootstrap-based approach to combine individual-based forest growth models and remotely sensed data\",\"authors\":\"Mathieu Fortin, Olivier van Lier, Jean-François Côté, Heidi Erdle, Joanne White\",\"doi\":\"10.1093/forestry/cpae003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\\\\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\\\\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\\\\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\\\\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\\\\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\\\\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.\",\"PeriodicalId\":12342,\"journal\":{\"name\":\"Forestry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-02-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Forestry\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1093/forestry/cpae003\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forestry","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1093/forestry/cpae003","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0

摘要

在基于广义层次模型(GHMB)的推论框架下,可以将森林生长模型与遥感数据相结合。这意味着存在两个子模型:生长模型本身($\mathcal{M}_{1}$)和将生长预测与某些遥感变量联系起来的第二个子模型($\mathcal{M}_{2}$)。分析 GHMB 估计器可用于拟合子模型 $\mathcal{M}_{2}$,并考虑子模型 $\mathcal{M}_{1}$(即生长模型)产生的不确定性。然而,当生长模型以个体为基础时,通常过于复杂,无法对其参数进行区分。因此,无法使用分析型 GHMB 估计器。在本研究中,我们为 GHMB 推断框架开发了一种引导方法,以便将基于个体的森林生长模型与遥感数据相结合。通过模拟研究,我们发现当两个子模型都是线性的时候,自举估计器几乎是无偏的。当子模型 $mathcal{M}_{1}$ 变得复杂,即无差别,且子模型 $mathcal{M}_{2}$ 是非线性的,具有异质方差和相关误差项时,参数估计值的估计值仍几乎无偏。方差估计器显示出一些偏差,但这些偏差相对较小。我们通过实际案例研究进一步证明,基于个体的复杂模型的预测结果可以与加拿大魁北克北方森林区的陆地卫星-8近红外光谱波段联系起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A bootstrap-based approach to combine individual-based forest growth models and remotely sensed data
Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Forestry
Forestry 农林科学-林学
CiteScore
6.70
自引率
7.10%
发文量
47
审稿时长
12-24 weeks
期刊介绍: The journal is inclusive of all subjects, geographical zones and study locations, including trees in urban environments, plantations and natural forests. We welcome papers that consider economic, environmental and social factors and, in particular, studies that take an integrated approach to sustainable management. In considering suitability for publication, attention is given to the originality of contributions and their likely impact on policy and practice, as well as their contribution to the development of knowledge. Special Issues - each year one edition of Forestry will be a Special Issue and will focus on one subject in detail; this will usually be by publication of the proceedings of an international meeting.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信