Land-use in the Willamette Valley from 2004-2017

The Willamette Valley, bounded on the west by the Coast Range and on 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 seed commodities for forage, 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 images covering an area of 25,3032 km were used to identify agricultural crops in production from 2004 to 2017. The agricultural crops were distinguished by classifying images acquired by three platforms: Landsat 5 for the 2003-2017 period, Landsat 7 for the 2003-2017 period, and Landsat 8 for the 2013-2017 period. Before classification, the images acquired by the Landsat 7 were pre-processed by using a procedure to reduce the impact of the scan line corrector failure (Mueller Warrant 2019). The corrected images were subsequently used in classification of 61 different land use classes, namely 4 super-classes and 57 regular classes. The land was classified into annual crops (19 classes), perennial crops (20 classes), forests (13 classes), and urban developments (5 classes). Annual drive-by ground truth efforts validated the classifications and were used to develop object-based procedures to improve accuracy (Meuller-Warrant et al. 2017).

威拉米特河谷（Willamette Valley）西以海岸山脉为界，东以喀斯喀特山脉为界，是完全隶属于俄勒冈州的最大河谷。肥沃的河谷土壤搭配温和的海洋性气候，为种子生产打造了理想的农艺条件。长期以来，威拉米特河谷的种子种植体系以禾本科与饲用种子生产为核心方向。该区域不仅供应了全美三分之二以上的冷季型禾本科种子，其种植体系还涵盖了超过250种可应用于饲用、食用及覆盖作物种植的多样化种子作物轮作模式。明确威拉米特河谷的作物轮作序列，对解答广泛覆盖农艺、环境与经济领域的各类问题至关重要。本研究使用覆盖面积达253032平方千米的陆地卫星（Landsat）影像，对2004年至2017年的在产农作物开展识别。农作物的区分通过三类平台获取的影像分类实现：2003-2017年时段采用陆地卫星5号（Landsat 5）与陆地卫星7号（Landsat 7）影像，2013-2017年时段额外采用陆地卫星8号（Landsat 8）影像。在分类处理前，研究人员通过特定流程对陆地卫星7号获取的影像进行预处理，以降低扫描线校正器故障（scan line corrector failure）带来的影响（Mueller Warrant 2019）。经校正的影像随后被用于61种土地利用类别的分类，其中包含4个超级类别与57个常规类别。土地被划分为一年生作物（19个类别）、多年生作物（20个类别）、林地（13个类别）与城市建设用地（5个类别）。研究通过年度驾车巡测地面实况（ground truth）调查对分类结果进行验证，并以此开发面向对象的处理流程以提升分类精度（Meuller-Warrant et al. 2017）。