Modeling Aboveground Forest Biomass
Forest biomass modelling is essential for monitoring and storage. However, biomass in stands and forests varies according to the species, stand structure, and site. Biomass models can be developed using data obtained from destructive sampling, forest inventory, remote sensing, and ancillary. There i...
I tiakina i:
| Hōputu: | Online |
|---|---|
| Reo: | Ingarihi |
| I whakaputaina: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20260416T142754_9783725861378_41 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869523091752747008 |
|---|---|
| collection | Directory of Open Access Books |
| description | Forest biomass modelling is essential for monitoring and storage. However, biomass in stands and forests varies according to the species, stand structure, and site. Biomass models can be developed using data obtained from destructive sampling, forest inventory, remote sensing, and ancillary. There is a wide range of data science methods and techniques that are currently applied in order to fit the models and evaluate their uncertainties. Biomass models are utilized in order to produce management alternatives. This reprint offers an overview of the various datasets and modelling methods employed to develop biomass functions, as well as their applicability at both the tree and area levels. The topics covered include the following: Biomass models at the tree level; Biomass models at the stand level; Datasets used in biomass modelling; Data science methods and techniques used in biomass modelling; Model performances and uncertainties; Development of management alternatives with biomass models. |
| format | Online |
| id | doab-20.500.12854ir-175186 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1751862026-04-16T19:23:36Z Modeling Aboveground Forest Biomass Gonçalves, Ana Cristina Fonseca, Teresa Fidalgo Biomass models Forest carbon stock Data Science methods Remote sensing thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNA Agribusiness and primary industries::KNAL Forestry industry Forest biomass modelling is essential for monitoring and storage. However, biomass in stands and forests varies according to the species, stand structure, and site. Biomass models can be developed using data obtained from destructive sampling, forest inventory, remote sensing, and ancillary. There is a wide range of data science methods and techniques that are currently applied in order to fit the models and evaluate their uncertainties. Biomass models are utilized in order to produce management alternatives. This reprint offers an overview of the various datasets and modelling methods employed to develop biomass functions, as well as their applicability at both the tree and area levels. The topics covered include the following: Biomass models at the tree level; Biomass models at the stand level; Datasets used in biomass modelling; Data science methods and techniques used in biomass modelling; Model performances and uncertainties; Development of management alternatives with biomass models. 2026-04-16T19:23:28Z 2026-04-16T19:23:28Z 2026 book ONIX_20260416T142754_9783725861378_41 9783725861378 9783725861385 https://directory.doabooks.org/handle/20.500.12854/175186 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/12098 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-6138-5 10.3390/books978-3-7258-6138-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725861378 9783725861385 286 CH open access |
| spellingShingle | Biomass models Forest carbon stock Data Science methods Remote sensing thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNA Agribusiness and primary industries::KNAL Forestry industry Modeling Aboveground Forest Biomass |
| title | Modeling Aboveground Forest Biomass |
| title_full | Modeling Aboveground Forest Biomass |
| title_fullStr | Modeling Aboveground Forest Biomass |
| title_full_unstemmed | Modeling Aboveground Forest Biomass |
| title_short | Modeling Aboveground Forest Biomass |
| title_sort | modeling aboveground forest biomass |
| topic | Biomass models Forest carbon stock Data Science methods Remote sensing thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNA Agribusiness and primary industries::KNAL Forestry industry |
| topic_facet | Biomass models Forest carbon stock Data Science methods Remote sensing thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNA Agribusiness and primary industries::KNAL Forestry industry |
| url | ONIX_20260416T142754_9783725861378_41 |