Sponsor
Portland State University. Department of Geography
First Advisor
Jiunn-Der Duh
Date of Publication
Fall 11-21-2014
Document Type
Thesis
Degree Name
Master of Arts (M.A.) in Geography
Department
Geography
Language
English
Subjects
Artificial satellites in agriculture, Crops -- Remote sensing, Field crops -- Argentina -- Santiago del Estero -- Phenology -- Case studies, Field crops -- Kansas -- Phenology -- Case studies, Soybean
DOI
10.15760/etd.2100
Physical Description
1 online resource (xi, 159 pages)
Abstract
Subtropical deforestation in Latin America is thought to be driven by demand for agricultural land, particularly to grow soybeans. However, existing remote sensing methods that can differentiate crop types to verify this hypothesis require high spatial or spectral resolution data, or extensive ground truth information to develop training sites, none of which are freely available for much of the world. I developed a new method of crop classification based on the phenological signatures of crops extracted from multi-temporal MODIS vegetation indices. I tested and refined this method using the USDA Cropland Data Layer from Kansas, USA as a reference. I then applied the method to classify crop types for a study site in Pellegrini, Santiago Del Estero, Argentina. The results show that this method is unable to effectively separate summer crops in Pellegrini, but can differentiate summer crops and non-summer crops. Unmet assumptions about agricultural practices are primarily responsible for the ineffective summer crop classification, underlining the need for researchers to have a complete understanding of ground conditions when designing a remote sensing analysis.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Persistent Identifier
http://archives.pdx.edu/ds/psu/15332
Recommended Citation
Keifer, Jarrett Alexander, "Agricultural Classification of Multi-Temporal MODIS Imagery in Northwest Argentina Using Kansas Crop Phenologies" (2014). Dissertations and Theses. Paper 2102.
https://doi.org/10.15760/etd.2100
Python source code for pyhytemporal tools