Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables

Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years....

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Main Authors: Dailiang Peng, Helin Zhang, Liangyun Liu, Wenjiang Huang, Alfredo R. Huete, Xiaoyang Zhang, Fumin Wang, Le Yu, Qiaoyun Xie, Cheng Wang, Shezhou Luo, Cunjun Li, Bing Zhang
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/19/2270
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language English
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author Dailiang Peng
Helin Zhang
Liangyun Liu
Wenjiang Huang
Alfredo R. Huete
Xiaoyang Zhang
Fumin Wang
Le Yu
Qiaoyun Xie
Cheng Wang
Shezhou Luo
Cunjun Li
Bing Zhang
spellingShingle Dailiang Peng
Helin Zhang
Liangyun Liu
Wenjiang Huang
Alfredo R. Huete
Xiaoyang Zhang
Fumin Wang
Le Yu
Qiaoyun Xie
Cheng Wang
Shezhou Luo
Cunjun Li
Bing Zhang
Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
Remote Sensing
three-north shelterbelt forest program (tnsfp)
aboveground biomass (agb)
planted forest
stand age
time-series landsat images
author_facet Dailiang Peng
Helin Zhang
Liangyun Liu
Wenjiang Huang
Alfredo R. Huete
Xiaoyang Zhang
Fumin Wang
Le Yu
Qiaoyun Xie
Cheng Wang
Shezhou Luo
Cunjun Li
Bing Zhang
author_sort Dailiang Peng
title Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
title_short Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
title_full Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
title_fullStr Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
title_full_unstemmed Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
title_sort estimating the aboveground biomass for planted forests based on stand age and environmental variables
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-09-01
description Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (<i>PF_AGB</i>) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate <i>PF_AGB</i>. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of <i>PF_AGB</i> estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the <i>PF_AGB</i> in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The <i>PF_AGB</i> in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated <i>PF_AGB</i> had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, <i>p</i> &lt; 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series <i>PF_AGB</i> using satellite repetitive measures, particularly for arid or semi-arid areas.
topic three-north shelterbelt forest program (tnsfp)
aboveground biomass (agb)
planted forest
stand age
time-series landsat images
url https://www.mdpi.com/2072-4292/11/19/2270
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spelling doaj-5cfda9746f094082bb8fbbd70af644642020-11-25T02:36:19ZengMDPI AGRemote Sensing2072-42922019-09-011119227010.3390/rs11192270rs11192270Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental VariablesDailiang Peng0Helin Zhang1Liangyun Liu2Wenjiang Huang3Alfredo R. Huete4Xiaoyang Zhang5Fumin Wang6Le Yu7Qiaoyun Xie8Cheng Wang9Shezhou Luo10Cunjun Li11Bing Zhang12Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaBeijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USAInstitute of Agricultural Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, ChinaMinistry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaSchool of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia.Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaBeijing Research Center for Information Technology in Agriculture, Beijing 100097, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaMeasuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (<i>PF_AGB</i>) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate <i>PF_AGB</i>. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of <i>PF_AGB</i> estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the <i>PF_AGB</i> in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The <i>PF_AGB</i> in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated <i>PF_AGB</i> had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, <i>p</i> &lt; 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series <i>PF_AGB</i> using satellite repetitive measures, particularly for arid or semi-arid areas.https://www.mdpi.com/2072-4292/11/19/2270three-north shelterbelt forest program (tnsfp)aboveground biomass (agb)planted foreststand agetime-series landsat images