Cargando…

Forestry Digital Twin With Machine Learning in Landsat 7 Data

Forest succession analysis can predict forest change trends in the study area, which provides an important basis for other studies. Remote sensing is a recognized and effective tool in forestry succession analysis. Many forest modeling studies use statistic values, but only a few uses remote sensing...

Descripción completa

Detalles Bibliográficos
Autores principales:	Jiang, Xuetao, Jiang, Meiyu, Gou, YuChun, Li, Qian, Zhou, Qingguo
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2022
Materias:	Plant Science
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234487/ https://www.ncbi.nlm.nih.gov/pubmed/35769290 http://dx.doi.org/10.3389/fpls.2022.916900

Ejemplares similares

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review
por: Estrada, Juan Sebastián, et al.
Publicado: (2023)

Improve the Deep Learning Models in Forestry Based on Explanations and Expertise
por: Cheng, Ximeng, et al.
Publicado: (2022)

A Dataset for Forestry Pest Identification
por: Liu, Bing, et al.
Publicado: (2022)

Editorial: Big data and artificial intelligence technologies for smart forestry
por: Jing, Weipeng, et al.
Publicado: (2023)

Editorial: Ecological Consequences of Biodiversity and Biotechnology in Agriculture and Forestry
por: Weih, Martin, et al.
Publicado: (2016)

Ecology and Prediction of Compensatory Growth: From Theory to Application in Forestry
por: Li, Chao, et al.
Publicado: (2021)

Editorial: Digital twins of plant and forest
por: Lv, Zhihan, et al.
Publicado: (2022)

Molecular Approaches for Low-Cost Point-of-Care Pathogen Detection in Agriculture and Forestry
por: Baldi, Paolo, et al.
Publicado: (2020)

Coarse spatial resolution remote sensing data with AVHRR and MODIS miss the greening area compared with the Landsat data in Chinese drylands
por: Zhang, Jianshuang, et al.
Publicado: (2023)

The Threat of the Combined Effect of Biotic and Abiotic Stress Factors in Forestry Under a Changing Climate
por: Teshome, Demissew Tesfaye, et al.
Publicado: (2020)

Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset
por: Liu, Bing, et al.
Publicado: (2023)

Severity assessment of wheat stripe rust based on machine learning
por: Jiang, Qian, et al.
Publicado: (2023)

Characterization and attribution of vegetation dynamics in the ecologically fragile South China Karst: Evidence from three decadal Landsat observations
por: Pei, Jie, et al.
Publicado: (2022)

Morphological and Genetic Variation in Monocultures, Forestry Systems and Wild Populations of Agave maximiliana of Western Mexico: Implications for Its Conservation
por: Cabrera-Toledo, Dánae, et al.
Publicado: (2020)

Mapping Aboveground Biomass of Four Typical Vegetation Types in the Poyang Lake Wetlands Based on Random Forest Modelling and Landsat Images
por: Wan, Rongrong, et al.
Publicado: (2019)

Editorial: Digital imaging of plants
por: Pisante, Michele, et al.
Publicado: (2022)

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data
por: Wu, Mingquan, et al.
Publicado: (2015)

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve
por: Zhao, Dongmiao, et al.
Publicado: (2022)

Accurate prediction of huanglongbing occurrence in citrus plants by machine learning-based analysis of symbiotic bacteria
por: Liu, Hao-Qiang, et al.
Publicado: (2023)

A Case Study of the Relationship Between Vegetation Coverage and Urban Heat Island in a Coastal City by Applying Digital Twins
por: Qi, Yansu, et al.
Publicado: (2022)

Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming
por: Farber, Charles, et al.
Publicado: (2022)

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
por: Moztarzadeh, Omid, et al.
Publicado: (2023)

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery
por: Wu, Haoran, et al.
Publicado: (2022)

Delivering forestry courses online: experiences, lessons learned, and future of forestry online education in the Asia Pacific
por: Shrestha, Anil, et al.
Publicado: (2022)

The dictionary of forestry /
Publicado: (1998)

Urban forestry
por: Grey, Gene W. 1931-
Publicado: (1992)

The data not collected on community forestry
por: Hajjar, Reem, et al.
Publicado: (2016)

Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal †
por: Acharya, Tri Dev, et al.
Publicado: (2019)

Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms
por: Li, Yingchang, et al.
Publicado: (2020)

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning
por: Jiang, Zewei, et al.
Publicado: (2023)

Hyperspectral machine-learning model for screening tea germplasm resources with drought tolerance
por: Chen, Sizhou, et al.
Publicado: (2022)

Forecasting Corn Yield With Machine Learning Ensembles
por: Shahhosseini, Mohsen, et al.
Publicado: (2020)

Sequential power analysis framework in assessing social forestry outcomes
por: Sahide, Muhammad Alif K., et al.
Publicado: (2020)

Wheat yield estimation using remote sensing data based on machine learning approaches
por: Cheng, Enhui, et al.
Publicado: (2022)

Estimation of wheat tiller density using remote sensing data and machine learning methods
por: Hu, Jinkang, et al.
Publicado: (2022)

Classification models for Tobacco Mosaic Virus and Potato Virus Y using hyperspectral and machine learning techniques
por: Chen, Haitao, et al.
Publicado: (2023)

Estimation of Botanical Composition in Mixed Clover–Grass Fields Using Machine Learning-Based Image Analysis
por: Sun, Sashuang, et al.
Publicado: (2021)

A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images
por: Petrovskaia, Anna, et al.
Publicado: (2022)

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning
por: Li, Yanjie, et al.
Publicado: (2021)

Creeping Bentgrass Yield Prediction With Machine Learning Models
por: Zhou, Qiyu, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_eer8ek15dgntlc6ni69a0silok