Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images
Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using rem...
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| フォーマット: | Online |
| 言語: | 英語 |
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InTechOpen
2021
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| 主題: | |
| オンライン･アクセス: | ONIX_20210602_10.5772/65665_328 |
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| _version_ | 1869529256345731072 |
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| author | Cristina Gonçalves, Ana Sousa, Adélia Marques daSilva, José R. |
| author_browse | Cristina Gonçalves, Ana Marques daSilva, José R. Sousa, Adélia |
| author_facet | Cristina Gonçalves, Ana Sousa, Adélia Marques daSilva, José R. |
| author_sort | Cristina Gonçalves, Ana |
| collection | Directory of Open Access Books |
| description | Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment. |
| format | Online |
| id | doab-20.500.12854ir-70541 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | InTechOpen |
| publisherStr | InTechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-705412024-04-11T20:35:24Z Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images Cristina Gonçalves, Ana Sousa, Adélia Marques daSilva, José R. QuickBird, multi‐resolution segmentation, crown horizontal projection, forest inventory, regressions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology Assessment and monitoring of forest biomass are frequently done with allometric functions per species for inventory plots. The estimation per area unit is carried out with an extrapolation method. In this chapter, a review of the recent methods to estimate forest above‐ground biomass (AGB) using remote sensing data is presented. A case study is given with an innovative methodology to estimate above‐ground biomass based on crown horizontal projection obtained with high spatial resolution satellite images for two evergreen oak species. The linear functions fitted for pure, mixed and both compositions showed a good performance. Also, the functions with dummy variables to distinguish species and compositions adjusted had the best performance. An error threshold of 5% corresponds to stand areas of 8.7 and 5.5 ha for the functions of all species and compositions without and with dummy variables. This method enables the overall area evaluation, and it is easily implemented in a geographic information system environment. 2021-02-10T12:58:18Z 2021-06-02T10:09:37Z 2017 chapter ONIX_20210602_10.5772/65665_328 https://library.oapen.org/handle/20.500.12657/49214 https://directory.doabooks.org/handle/20.500.12854/70541 eng open access image/jpeg image/jpeg n/a n/a https://library.oapen.org/bitstream/20.500.12657/49214/1/52664.pdf https://library.oapen.org/bitstream/20.500.12657/49214/1/52664.pdf InTechOpen 10.5772/65665 10.5772/65665 035ecc65-6737-43cf-a13a-6bdf67ce01f4 open access |
| spellingShingle | QuickBird, multi‐resolution segmentation, crown horizontal projection, forest inventory, regressions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology Cristina Gonçalves, Ana Sousa, Adélia Marques daSilva, José R. Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images |
| title | Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images |
| title_full | Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images |
| title_fullStr | Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images |
| title_full_unstemmed | Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images |
| title_short | Chapter Above‐Ground Biomass Estimation with High Spatial Resolution Satellite Images |
| title_sort | chapter above ground biomass estimation with high spatial resolution satellite images |
| topic | QuickBird, multi‐resolution segmentation, crown horizontal projection, forest inventory, regressions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology |
| topic_facet | QuickBird, multi‐resolution segmentation, crown horizontal projection, forest inventory, regressions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology |
| url | ONIX_20210602_10.5772/65665_328 |
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