{"help":"Return the metadata of a dataset (package) and its resources. :param id: the id or name of the dataset :type id: string","success":true,"result":[{"id":"cb0d0132-ee71-45b1-83d3-a47e2abfce32","name":"data-modeling-tree-canopy-height-using-machine-learning-over-mixed-vegetation-landscapes","title":"Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes","author":"Hui Wang, Travis Seaborn, Zhe Wang","author_email":"huiwang@uidaho.edu","maintainer":"RCDS Data Repository","maintainer_email":"rcds-web@uidaho.edu","license_title":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/","notes":"\u003Cp\u003EAlthough the random forest algorithm has been widely applied to remotely sensed data to predict characteristics of forests, such as tree canopy height, the effect of spatial non-stationarity in the modeling process is oftentimes neglected. Previous studies have proposed methods to address the spatial variance at local scales, but few have explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance. By combining Light Detection and Ranging (LiDAR) and Landsat datasets, we used spatially-weighted geographical random forest (GRF) and traditional random forest (TRF) methods to predict tree canopy height in a mixed dry forest woodland in complex mountainous terrain. Comparisons between TRF and GRF models show that the latter can lower predefined extreme residuals, and thus make the model performance relatively stronger. Moreover, the relationship between model performance and degree of variation of true canopy height can vary considerably within different height quantiles. Both models are likely to present underestimates and overestimates when the corresponding tree canopy heights are high (\u0026gt;95% quantile) and low (\u0026lt;median), respectively. This study provides a critical insight into the relationship between tree canopy height and predictive abilities of random forest models when taking account of spatial non-stationarity. Conclusions indicate that a trade-off approach based on the actual need of project should be taken when selecting an optimal model integrating both local and global effects in modeling attributes such as canopy height from remotely sensed data.\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EData Use\u003C\/strong\u003E\u003Cbr \/\u003E\n\u003Cem\u003ELicense\u003C\/em\u003E: \u003Ca href=\u0022https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/\u0022\u003ECC-BY-NC-SA 4.0\u003C\/a\u003E\u003Cbr \/\u003E\n\u003Cem\u003ERecommended Citation\u003C\/em\u003E: Wang, H., Seaborn, T., \u0026amp; Wang, Z. (2021). Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes [Data set]. University of Idaho. \u003Ca href=\u0022https:\/\/doi.org\/10.7923\/VJ7D-KS92\u0022\u003Ehttps:\/\/doi.org\/10.7923\/VJ7D-KS92\u003C\/a\u003E\u003C\/p\u003E\n","url":"https:\/\/data.nkn.uidaho.edu\/dataset\/data-modeling-tree-canopy-height-using-machine-learning-over-mixed-vegetation-landscapes","state":"Active","log_message":"Update to resource Data Access | Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes (Compressed directory)","private":true,"revision_timestamp":"Thu, 10\/06\/2022 - 10:11","metadata_created":"Wed, 10\/13\/2021 - 09:59","metadata_modified":"Thu, 10\/06\/2022 - 10:11","creator_user_id":"6cc16f2c-77c2-4d12-ac06-56bbb86b535b","type":"Dataset","resources":[{"id":"f77daa66-11c8-4555-8613-45ecbf561171","revision_id":"","url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/Wangetal2021_Data_Code.zip","description":"\n\t","format":"zip","state":"Active","revision_timestamp":"Thu, 10\/06\/2022 - 10:11","name":"Data Access | Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes (Compressed directory)","mimetype":"application\/zip","size":"4.13 MB","created":"Wed, 10\/13\/2021 - 10:50","resource_group_id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","last_modified":"Date changed Thu, 10\/06\/2022 - 10:11"},{"id":"e273fa9b-f9a9-496b-a4ba-df2f88e3cc94","revision_id":"","url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/vj7d-ks92.xml","description":"","format":"xml","state":"Active","revision_timestamp":"Thu, 10\/06\/2022 - 10:12","name":"Metadata Access | Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes (xml)","mimetype":"text\/xml","size":"5.46 KB","created":"Thu, 10\/06\/2022 - 10:11","resource_group_id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","last_modified":"Date changed Thu, 10\/06\/2022 - 10:12"}],"tags":[{"id":"e4ce4a6f-2c54-4f22-9735-2de3fd3e6955","vocabulary_id":"2","name":"tree canopy height"},{"id":"7312fb89-d0e1-4b0a-83cd-fc98c287cf3d","vocabulary_id":"2","name":"lidar"},{"id":"45f2553a-e4f8-478e-8107-207f1a075228","vocabulary_id":"2","name":"random forest"},{"id":"2ed5ce41-f46b-4064-9ab1-75c317097f1e","vocabulary_id":"2","name":"spatial non-stationary"},{"id":"bea52d56-0d1b-4747-bb9f-1a31dc9f6b6d","vocabulary_id":"2","name":"Landsat"}],"groups":[{"description":"\u003Cp\u003ESince 1889, the University of Idaho has provided motivated students with a transformative higher education experience that prepares them to solve real-world problems and achieve success in their lives and careers.\u003C\/p\u003E\n\u003Cp\u003ELearn more at \u003Ca href=\u0022https:\/\/www.uidaho.edu\u0022\u003Ehttps:\/\/www.uidaho.edu\u003C\/a\u003E\u003C\/p\u003E\n","id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/UI_Main_horizontal_4c.jpg","title":"University of Idaho","name":"group\/university-idaho"},{"description":"\u003Cp\u003EGEM3 is an NSF EPSCoR research program seeking to understand how genetic diversity and phenotypic plasticity affect species response to environmental change, shaping both population response and adaptive capacity.\u003C\/p\u003E\n\u003Cp\u003EVisit them at: \u003Ca href=\u0022https:\/\/www.idahogem3.org\u0022\u003Ehttps:\/\/www.idahogem3.org\u003C\/a\u003E\u003C\/p\u003E\n","id":"1812a312-11a4-492d-960b-9a78b4abfe0c","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/GEM3_nov5_logo.png","title":"EPSCoR GEM3","name":"group\/epscor-gem3"},{"description":"\u003Cp\u003EThe primary objective of Idaho EPSCoR is to stimulate research in niche areas that can become fully competitive in the disciplinary and multidisciplinary research programs of the National Science Foundation and other relevant agencies. Idaho EPSCoR provides support for sustainable increases in Research and Development capacity and advances science and engineering capabilities within the state. \u003C\/p\u003E\n\u003Cp\u003EVisit them at \u003Ca href=\u0022https:\/\/www.idahoepscor.org\u0022\u003Ehttps:\/\/www.idahoepscor.org\u003C\/a\u003E\u003C\/p\u003E\n","id":"e696b239-9ecb-412e-b032-03a75b2b9fd6","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/Idaho_epscor_logo_no_white_background.png","title":"Idaho EPSCoR","name":"group\/idaho-epscor"}]}]} Error | RCDS Data Repository

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