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Remote Sens. 2016, 8(4), 312; doi:10.3390/rs8040312

A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

1
Queensland Department of Science, Information Technology and Innovation, GPO BOX 5078, Brisbane, QLD 4102, Australia
2
Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Sydney, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Petri Pellikka, Lars Eklundh, Clement Atzberger and Prasad S. Thenkabail
Received: 14 December 2015 / Revised: 30 March 2016 / Accepted: 31 March 2016 / Published: 8 April 2016
(This article belongs to the Special Issue Monitoring of Land Changes)
View Full-Text   |   Download PDF [9197 KB, uploaded 21 April 2016]   |  

Abstract

Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity. View Full-Text
Keywords: Crop-mapping; agriculture; land-use; time series; synthetic image generation; classification; land management; GEOBIA; Landsat Time Series (LTS); data mining Crop-mapping; agriculture; land-use; time series; synthetic image generation; classification; land management; GEOBIA; Landsat Time Series (LTS); data mining
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Schmidt, M.; Pringle, M.; Devadas, R.; Denham, R.; Tindall, D. A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics. Remote Sens. 2016, 8, 312.

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