Forest Type Differentiation Using GLAD Phenology Metrics, Land Surface Parameters, and Machine Learning
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
- Nicolas Gruber & James N. Galloway, 2008. "An Earth-system perspective of the global nitrogen cycle," Nature, Nature, vol. 451(7176), pages 293-296, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
- Auguères, Anne-Sophie & Loreau, Michel, 2016. "Biotic regulation of non-limiting nutrient pools and coupling of biogeochemical cycles," Ecological Modelling, Elsevier, vol. 334(C), pages 1-7.
- Xiaochen Lu & Binjie Li & Guangsheng Chen, 2023. "Responses of Soil CO 2 Emission and Tree Productivity to Nitrogen and Phosphorus Additions in a Nitrogen-Rich Subtropical Chinese Fir Plantation," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
- Le, Hong Hanh & Viviani, Jean-Laurent, 2018.
"Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios,"
Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
- Hong Hanh Le & Jean-Laurent Viviani, 2018. "Predicting bank failure: An improvement by implementing machine learning approach on classical financial ratios," Post-Print halshs-01615106, HAL.
- João Chang Junior & Fábio Binuesa & Luiz Fernando Caneo & Aida Luiza Ribeiro Turquetto & Elisandra Cristina Trevisan Calvo Arita & Aline Cristina Barbosa & Alfredo Manoel da Silva Fernandes & Evelinda, 2020. "Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
- Arthur De Sá Ferreira & Ney Meziat-Filho & Ana Paula Antunes Ferreira, 2021. "Double threshold receiver operating characteristic plot for three-modal continuous predictors," Computational Statistics, Springer, vol. 36(3), pages 2231-2245, September.
- Masabho P Milali & Samson S Kiware & Nicodem J Govella & Fredros Okumu & Naveen Bansal & Serdar Bozdag & Jacques D Charlwood & Marta F Maia & Sheila B Ogoma & Floyd E Dowell & George F Corliss & Maggy, 2020. "An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
- Kajal Lahiri & Cheng Yang, 2023.
"ROC and PRC Approaches to Evaluate Recession Forecasts,"
Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
- Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," CESifo Working Paper Series 10449, CESifo.
- Tzu-Hsuan Lin & Jehn-Ruey Jiang, 2021. "Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
- Jie Zhang & Jia Liu & Guilong Li & Meng Wu, 2024. "Screening Potential Nitrification Inhibitors through a Structure–Activity Relationship Study—The Case of Cinnamic Acid Derivatives," Sustainability, MDPI, vol. 16(13), pages 1-10, July.
- Huang, Suo & Bartlett, Paul & Arain, M. Altaf, 2016. "An analysis of global terrestrial carbon, water and energy dynamics using the carbon–nitrogen coupled CLASS-CTEMN+ model," Ecological Modelling, Elsevier, vol. 336(C), pages 36-56.
- L.J. Li & D.H. Zeng & R. Mao & Z.Y. Yu, 2012. "Nitrogen and phosphorus resorption of Artemisia scoparia, Chenopodium acuminatum, Cannabis sativa, and Phragmites communis under nitrogen and phosphorus additions in a semiarid grassland, China," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 58(10), pages 446-451.
- Alfred Krzywicki & David Muchlinski & Benjamin E. Goldsmith & Arcot Sowmya, 2022. "From academia to policy makers: a methodology for real-time forecasting of infrequent events," Journal of Computational Social Science, Springer, vol. 5(2), pages 1489-1510, November.
- Dueñas, Marco & Ortiz, Víctor & Riccaboni, Massimo & Serti, Francesco, 2021. "Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis," Working papers 79, Red Investigadores de Economía.
- Zhen-Zhen Zheng & Li-Wei Zheng & Min Nina Xu & Ehui Tan & David A. Hutchins & Wenchao Deng & Yao Zhang & Dalin Shi & Minhan Dai & Shuh-Ji Kao, 2020. "Substrate regulation leads to differential responses of microbial ammonia-oxidizing communities to ocean warming," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
- Qingsong Guan & Yiqiao Zhou & Shuo Li & Fan Yang & Rentao Liu, 2024. "Denitrification and Anammox and Feammox in the Yinchuan Yellow River wetland," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 70(11), pages 731-738.
- Wei-Hsuan Lo-Ciganic & Julie M Donohue & Eric G Hulsey & Susan Barnes & Yuan Li & Courtney C Kuza & Qingnan Yang & Jeanine Buchanich & James L Huang & Christina Mair & Debbie L Wilson & Walid F Gellad, 2021. "Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
- Nica-Avram, Georgiana & Harvey, John & Smith, Gavin & Smith, Andrew & Goulding, James, 2021. "Identifying food insecurity in food sharing networks via machine learning," Journal of Business Research, Elsevier, vol. 131(C), pages 469-484.
- Ali J. Ghandour & Huda Hammoud & Samar Al-Hajj, 2020. "Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach," IJERPH, MDPI, vol. 17(11), pages 1-13, June.
- Douglas, Niall Edward, 2008. "Modelling the Costs of Climate Change and its Costs of Mitigation: A Scientific Approach," MPRA Paper 13650, University Library of Munich, Germany.
More about this item
Keywords
forest type mapping; forests; phenology; machine learning; digital terrain analysis; Landsat;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jgeogr:v:2:y:2022:i:3:p:30-515:d:888486. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.