کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8866410 1621184 2018 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Multi-decade, multi-sensor time-series modelling-based on geostatistical concepts-to predict broad groups of crops
ترجمه فارسی عنوان
چند دهه، چند حسگر سری زمانی مدل سازی بر اساس زمین آماری مفاهیم برای پیش بینی گروه گسترده ای از محصولات زراعی
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
چکیده انگلیسی
We have mapped the broad groups of crops grown each summer and winter, from 1987 to 2017, for a 300,000-km2 region of Queensland, Australia. These maps are part of a legislated decision-making process for the protection of prime agricultural land. For summer, the two groups of crops are 'Coarse-grain & Pulse' and 'Cotton'. For winter, the two groups of crops are 'Cereal' and 'Pulse'. Non-crop groups, present in both summer and winter, are 'Bare soil' and 'Other' (comprising pastures, woody vegetation, and crop residues). The foundation of the maps is time-series modelling-specifically, applying the concepts of geostatistics in the temporal domain-to model the variation in land-surface phenology within a growing season. The time-series model is flexible, robust, parsimonious, parallelisable, and able to deal with irregular observations. We combined satellite imagery from the Landsat sensors, as well as, when available, Sentinel-2A and MODIS (with the last two reprojected to the 30-m grid of Landsat). We applied the time-series model pixel-wise across the study region, to three variables derived from satellite imagery gathered for an individual growing season: enhanced vegetation index, and the sub-pixel proportions of bare-ground and non-photosynthetic vegetation. Weekly-averaged predicted phenological metrics then served as explanatory variables in a tiered, tree-based classification model, for the prediction of the groups. The classification model comprised two expert rules and two random forests. Prior to fitting the classification model, geospatial object-based image analysis was used to change the scale of analysis from individual pixels to (approximately) field-based segments. From the perspective of a map-user, in any given growing season we predicted 'Coarse-grain & Pulse' correctly in 79% of cases; the values for 'Cotton', 'Cereal', and 'Pulse' were 90%, 84%, and 73%, respectively; 'Bare soil' was 72% in summer, and 88% in winter. 'Other' was the most accurately mapped group (98% correct in summer, and 99% correct in winter).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 216, October 2018, Pages 183-200
نویسندگان
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