Assessing remote sensing application on rangeland insurance in Canadian prairies
| dc.contributor.advisor | Guo, Xulin | en_US |
| dc.contributor.committeeMember | Kong, Xianhua | en_US |
| dc.contributor.committeeMember | Gebremeskel, Seifu | en_US |
| dc.contributor.committeeMember | Akkerman, Abraham | en_US |
| dc.creator | Zhou, Weidong | en_US |
| dc.date.accessioned | 2007-07-03T15:38:56Z | en_US |
| dc.date.accessioned | 2013-01-04T04:41:30Z | |
| dc.date.available | 2008-07-04T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T04:41:30Z | |
| dc.date.created | 2007 | en_US |
| dc.date.issued | 2007 | en_US |
| dc.date.submitted | 2007 | en_US |
| dc.description.abstract | Part of the problem with implementing a rangeland insurance program is that the acreage of different pasture types, which is required in order to determine an indemnity payment, is difficult to measure on the ground over large areas. Remote sensing techniques provide a potential solution to this problem. This study applied single-date SPOT (Satellite Pour I’Observation de la Terre) imagery, field collected data, and geographic information system (GIS) data to study the classification of land cover and vegetation at species level. Two topographic correction models, Minnaert model and C-correction, and two classifying algorithms, maximum likelihood classifier (MLC) and artificial neural network (ANN), were evaluated. The feasibility of discriminating invasive crested wheatgrass from natives was investigated, and an exponential normalized difference vegetation index (ExpNDMI) was developed to increase the separability between crested wheatgrass and natives. Spectral separability index (SSI) was used to select proper bands and vegetation indices for classification. The results show that topographic corrections can be effective to reduce intra-class rediometric variation caused by topographic effect in the study area and improve the classification. An overall accuracy of 90.5% was obtained by MLC using Minnaert model corrected reflectance, and MLC obtained higher classification accuracy (~5%) than back-propagation based ANN. Topographic correction can reduce intra-class variation and improve classification accuracy at about 4% comparing to the original reflectance. The crested wheatgrass was over-estimated in this study, and the result indicated that single-date SPOT 5 image could not classify crested wheatgrass with satisfactory accuracy. However, the proposed ExpNDMI can reduce intra-class variation and enlarge inter-class variation, further, improve the ability to discriminate invasive crested wheatgrass from natives at 4% of overall accuracy. This study revealed that single-date SPOT image may perform an effective classification on land cover, and will provide a useful tool to update the land cover information in order to implement a rangeland insurance program. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-07032007-153856 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Remote Sensing | en_US |
| dc.subject | Rangeland insurance | en_US |
| dc.subject | Land cover | en_US |
| dc.subject | Classification | en_US |
| dc.title | Assessing remote sensing application on rangeland insurance in Canadian prairies | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Geography | en_US |
| thesis.degree.discipline | Geography | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |