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Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples.

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Title: Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples.
Authors: Cheng, Kai1,2 (AUTHOR), Su, Yanjun3,4 (AUTHOR), Guan, Hongcan1,2 (AUTHOR), Tao, Shengli2 (AUTHOR), Ren, Yu1,2 (AUTHOR), Hu, Tianyu3,4 (AUTHOR), Ma, Keping3,4 (AUTHOR), Tang, Yanhong2 (AUTHOR), Guo, Qinghua1,5 (AUTHOR) guo.qinghua@pku.edu.cn
Source: ISPRS Journal of Photogrammetry & Remote Sensing. Feb2023, Vol. 196, p356-371. 16p.
Subjects: Forest mapping, Wooden beams, Remote-sensing images, Tree planting, Digital elevation models, Forest canopies, Landsat satellites
Geographic Terms: China
Abstract: Tree planting has been suggested as a potentially effective solution for mitigating climate change. China has implemented the world's largest afforestation and reforestation project since the 1970s, but high-resolution maps of China's planted forests remain unavailable. In this study, we explored the use of multi-source remote sensing images and crowdsourced samples to produce the first high-resolution (30-m) map of China's planted forests. We constructed a Google Earth Engine (GEE)-based mapping framework using spectral, temporal, structural, textural and topographic features derived from Landsat and Sentinel-1 time series imagery, Digital Elevation Model (DEM) and Chinese Forest Canopy Height (CFCH) data. Over 300,000 high-quality crowdsourced samples were collected for training the mapping pipeline. Validation against independent field samples indicated an accuracy of 84.93 % and an F1 score of 0.85. The uncertainty map of each pixel was also constructed and showed that the areas of low and medium uncertainties accounted for 38.27 % and 50.98 % of the total area, respectively, indicating the high estimation reliabilities of the planted forest map. We show that China's planted forests in the year of 2020 had a total area of 769853.01 km2, accounting for 31.30 % of the world's total planted forests. The majority (77.45 %) of China's planted forests were located in the Eastern, Center-South, and Southwestern regions. By further assessing the performance of the image features used to map the planted forests, we found that temporal features are key to identifying the planted forests in East and Center-South of China, where they are mainly timber plantations. However, structural and textural features were more useful for locating the planted forests in North and Northeast of China, where are dominated by planted shelterbelts. Our study demonstrated that combining crowdsourced samples with high-resolution satellite images allows mapping planted forests with unprecedented resolution (30-m) across large areas. Our map could contribute to the sustainable management of China's forests and a more accurate quantification of the carbon balance of China's natural ecosystems. [ABSTRACT FROM AUTHOR]
Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 161791160
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples.
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  Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Kai%22">Cheng, Kai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Su%2C+Yanjun%22">Su, Yanjun</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guan%2C+Hongcan%22">Guan, Hongcan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tao%2C+Shengli%22">Tao, Shengli</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Yu%22">Ren, Yu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Tianyu%22">Hu, Tianyu</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Keping%22">Ma, Keping</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Yanhong%22">Tang, Yanhong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Qinghua%22">Guo, Qinghua</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<i> guo.qinghua@pku.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ISPRS+Journal+of+Photogrammetry+%26+Remote+Sensing%22">ISPRS Journal of Photogrammetry & Remote Sensing</searchLink>. Feb2023, Vol. 196, p356-371. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Wooden+beams%22">Wooden beams</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Tree+planting%22">Tree planting</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+elevation+models%22">Digital elevation models</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+canopies%22">Forest canopies</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink>
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– Name: Abstract
  Label: Abstract
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  Data: Tree planting has been suggested as a potentially effective solution for mitigating climate change. China has implemented the world's largest afforestation and reforestation project since the 1970s, but high-resolution maps of China's planted forests remain unavailable. In this study, we explored the use of multi-source remote sensing images and crowdsourced samples to produce the first high-resolution (30-m) map of China's planted forests. We constructed a Google Earth Engine (GEE)-based mapping framework using spectral, temporal, structural, textural and topographic features derived from Landsat and Sentinel-1 time series imagery, Digital Elevation Model (DEM) and Chinese Forest Canopy Height (CFCH) data. Over 300,000 high-quality crowdsourced samples were collected for training the mapping pipeline. Validation against independent field samples indicated an accuracy of 84.93 % and an F1 score of 0.85. The uncertainty map of each pixel was also constructed and showed that the areas of low and medium uncertainties accounted for 38.27 % and 50.98 % of the total area, respectively, indicating the high estimation reliabilities of the planted forest map. We show that China's planted forests in the year of 2020 had a total area of 769853.01 km2, accounting for 31.30 % of the world's total planted forests. The majority (77.45 %) of China's planted forests were located in the Eastern, Center-South, and Southwestern regions. By further assessing the performance of the image features used to map the planted forests, we found that temporal features are key to identifying the planted forests in East and Center-South of China, where they are mainly timber plantations. However, structural and textural features were more useful for locating the planted forests in North and Northeast of China, where are dominated by planted shelterbelts. Our study demonstrated that combining crowdsourced samples with high-resolution satellite images allows mapping planted forests with unprecedented resolution (30-m) across large areas. Our map could contribute to the sustainable management of China's forests and a more accurate quantification of the carbon balance of China's natural ecosystems. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=161791160
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.isprsjprs.2023.01.005
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 356
    Subjects:
      – SubjectFull: Forest mapping
        Type: general
      – SubjectFull: Wooden beams
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: Tree planting
        Type: general
      – SubjectFull: Digital elevation models
        Type: general
      – SubjectFull: Forest canopies
        Type: general
      – SubjectFull: Landsat satellites
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Mapping China's planted forests using high resolution imagery and massive amounts of crowdsourced samples.
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            NameFull: Cheng, Kai
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            NameFull: Su, Yanjun
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            NameFull: Guan, Hongcan
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            NameFull: Tao, Shengli
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            NameFull: Ren, Yu
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            NameFull: Hu, Tianyu
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            NameFull: Ma, Keping
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            NameFull: Tang, Yanhong
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            NameFull: Guo, Qinghua
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            – D: 01
              M: 02
              Text: Feb2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 09242716
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              Value: 196
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            – TitleFull: ISPRS Journal of Photogrammetry & Remote Sensing
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