Agricultural Crop Change in the Willamette Valley, Oregon, from 2004 to 2017

Bogdan M Strimbu,Kristin Trippe,George Mueller-Warrant

doi:10.3390/data6020017

Bogdan M Strimbu, Kristin Trippe + Show 1 more

Open Access

https://doi.org/10.3390/data6020017

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Abstract

The Willamette Valley, bounded to the west by the Coast Range and to the east by the Cascade Mountains, is the largest river valley completely confined to Oregon. The fertile valley soils combined with a temperate, marine climate create ideal agronomic conditions for seed production. Historically, seed cropping systems in the Willamette Valley have focused on the production of grass and forage seeds. In addition to growing over two-thirds of the nation’s cool-season grass seed, cropping systems in the Willamette Valley include a diverse rotation of over 250 commodities for forage, seed, food, and cover cropping applications. Tracking the sequence of crop rotations that are grown in the Willamette Valley is paramount to answering a broad spectrum of agronomic, environmental, and economical questions. Landsat imagery covering approximately 25,303 km2 were used to identify agricultural crops in production from 2004 to 2017. The agricultural crops were distinguished by classifying images primarily acquired by three platforms: Landsat 5 (2003–2013), Landsat 7 (2003–2017), and Landsat 8 (2013–2017). Before conducting maximum likelihood remote sensing classification, the images acquired by the Landsat 7 were pre-processed to reduce the impact of the scan line corrector failure. The corrected images were subsequently used to classify 35 different land-use classes and 137 unique two-year-long sequences of 57 classes of non-urban and non-forested land-use categories from 2004 through 2014. Our final data product uses new and previously published results to classify the western Oregon landscape into 61 different land use classes, including four majority-rule-over-time super-classes and 57 regular classes of annually disturbed agricultural crops (19 classes), perennial crops (20 classes), forests (13 classes), and urban developments (5 classes). These publicly available data can be used to inform and support environmental and agricultural land-use studies.

Highlights

Remote sensing of the Earth provides an unimaginable wealth of data about our planet
Because 82% of the Landsat imagery for western Oregon over the 14-year period we investigated was too cloudy to use in any manner, it was not feasible to gap fill missing data using normalized differential vegetation index (NDVI) or EVI-based approaches
We found that the three to four highest significance principal component analysis (PCA) levels were equivalent to the full set of all raw imagery bands in the performance of the maximum likelihood (ML) remote sensing classifier, while reducing operational running time and minimizing the occurrence of 0-variance errors present when two ground-truth training classes have identical signatures over one or more of image bands, whether in the raw data format or PCA

Summary

Introduction

Remote sensing of the Earth provides an unimaginable wealth of data about our planet. Landsat is the satellite program that earned a preeminent place in Earth surveying. The Landsat program (i.e., first launched in 1972) has provided uninterrupted coverage of the Earth; an area is observed approximately every 16 days with a current spatial resolution of 15 or 30 m. Even when the Landsat missions exhibited difficulties, the images supplied by the satellite were so valuable that they continued to be acquired even after the failures were noticed. To correct the recording failures, various procedures were developed to fit specific objectives, such as the one for agricultural crops and other landscape features of MuellerWarrant [1]. Once the corrections were applied, the imagines were post-processed [2], interpreted using parametric or non-parametric procedures

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