{"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":"6f127c50-f826-492c-9e5e-13172d9e07f5","name":"agricultural-land-use-maps-rapideye-5m-scale-vea-watershed-ghana-2012","title":"Agricultural Land Use Maps at RapidEye (5m) Scale for Vea Watershed (Ghana) 2012 ","author":"Gerald Forkuor","author_email":"\tgforkuor@yahoo.com","maintainer":"WASCAL Scientific Research Data Catalog ","maintainer_email":"sysadmin@wascal.org","license_title":"https:\/\/opendatacommons.org\/licenses\/odbl\/1.0\/","notes":"\u003Cp\u003EThese LULC maps were created through automatic digital classification of RapidEye imagery acquired during the cropping season of 2012. Two monthly time-steps (June and October) were analyzed.Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from May to October 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. Two levels of classification were conducted: (1) a level 1 classification which includes four broad LULC classes and (2) a level 2 classification which comprises of nine LULC classes. The poor temporal coverage of the RapidEye imagery made the accurate delineation of certain crop classes (e.g. groundnuts) very challenging. Nonetheless, an overall accuracy of 79% was obtained\u003C\/p\u003E\n","url":"https:\/\/www.wascal-dataportal.org\/?q=dataset\/agricultural-land-use-maps-rapideye-5m-scale-vea-watershed-ghana-2012","state":"Active","log_message":"Update to resource \u0027Agricultural Land Use Maps at RapidEye (5m) Scale for Vea Watershed (Ghana) 2012 and 2013\u0027","private":true,"revision_timestamp":"Wed, 03\/06\/2024 - 08:54","metadata_created":"Fri, 10\/25\/2019 - 11:37","metadata_modified":"Wed, 03\/06\/2024 - 08:54","creator_user_id":"194c0007-bfe7-4a34-8899-abd1126a8cbd","type":"Dataset","resources":[{"id":"8b5b8139-78b4-4c1f-a995-e4859182a956","revision_id":"","url":"","description":"\u003Cp\u003EThese LULC maps were created through automatic digital classification of RapidEye imagery acquired during the cropping season of 2012. Two monthly time-steps (June and October) were analyzed.Reference (or field) data on which the classification was based were acquired through a field campaign that lasted from May to October 2012. Standard image pre-processing techniques such as geometric and radiometric correction were conducted on the data prior to classification. The Random Forest classification algorithm was used for classification. Two levels of classification were conducted: (1) a level 1 classification which includes four broad LULC classes and (2) a level 2 classification which comprises of nine LULC classes. The poor temporal coverage of the RapidEye imagery made the accurate delineation of certain crop classes (e.g. groundnuts) very challenging. Nonetheless, an overall accuracy of 79% was obtained\u003C\/p\u003E\n","format":"csv","state":"Active","revision_timestamp":"Wed, 03\/06\/2024 - 08:54","name":"Agricultural Land Use Maps at RapidEye (5m) Scale for Vea Watershed (Ghana) 2012 and 2013","mimetype":"csv","size":"","created":"Fri, 10\/25\/2019 - 11:38","resource_group_id":"","last_modified":"Date changed  Wed, 03\/06\/2024 - 08:54"}],"tags":[{"id":"73ea7870-c30e-42e5-a5f9-a91215691486","vocabulary_id":"2","name":"Agriculture"}]}]}