Remote sensing applications for precision agriculture: A learning community approach

Seelan, Santhosh; Laguette, Soizik; Casady, Grant M.; Seielstad, G. A.

doi:10.1016/j.rse.2003.04.007

Cited by 332 publications

(173 citation statements)

References 9 publications

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“…This is the beginning of further parallel processing routines that yield numerous qualitative and quantitative results and offers use as value-added product in applications such as precision agriculture (e.g., [4]). Here, we present three post-processing branches for utilizing the application-ready dataset of the basic workflow.…”

Section: Resultsmentioning

confidence: 99%

“…This work has demonstrated how an automated workflow, once set up, can deliver, in near-real-time, information to end-users and decision makers, who need no remote sensing knowledge to apprehend the results. However, despite the wealth of information produced, the value of the product can be appreciated only if the information aids a decision resulting in higher profitability [4].…”

Section: Resultsmentioning

confidence: 99%

“…Consequently, global demand for food by that year is predicted to double at least that of 2005, not only because of population growth, but also because of a shift to nutrient-richer diets in especially developing nations [3]. The latter scenario calls for technical solutions that help to improve crop yield, provide accurate information to aid in-field management decisions, increase the efficiency of applications of farm inputs, and boost profit margins in the agricultural sector [4]. In some high-income countries, the technology to aid this decision-making may have been established; however, in low-income countries, which are the most populous parts of our planet and which depend strongly on agriculture, information on crops and farm management practices is still primitive.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Stratoulias

Tolpekin

et al. 2017

Remote Sensing

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Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Stratoulias

Tolpekin

et al. 2017

Remote Sensing

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“…Remotely sensed satellite imagery can be used for a wide variety of applications within precision farming (Seelan et al 2003;Mulla 2013). The soil brightness image in Fig.…”

Section: Remote Sensingmentioning

confidence: 99%

Precision farming and archaeology

Webber

Heyd

Horton

et al. 2017

Archaeol Anthropol Sci

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With a significant growth in the agricultural technology industry, a vast amount of agricultural data is now being collected on farms throughout the world. Farmers aim to utilise these technologies to regularly record and manage the variation of crops and soils within their fields, to reduce inputs, increase yields and enhance environmental sustainability. In this paper, we aim to highlight the variety of different data types and methodological processes involved in modern precision farming systems and explore how potentially interconnected these systems are with the archaeological community. At present, no research has studied the effects of archaeological sites on soils in the context of precision farming practices. Yet from modern geophysical, geochemical and remote sensing techniques, a much greater volume of soil-and croprelated mapping is being undertaken, with huge potential for all kinds of archaeological study. From heritage management to archaeological prospection, how will the future of archaeological studies fit into this changing agricultural landscape?

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“…Such datasets provide monitoring of vegetation characteristics such as gross primary production (GPP) [2][6] across large geographical extents. There has been other work on the remote sensing of pastures [7], but this has tended to focus on crops [8], or on rangeland remote sensing [9].…”

Section: Introductionmentioning

confidence: 99%

Three regionalised analyses of a time-series of annual pasture production for southwest Western Australia

Handcock

Donald

Gherardi

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-A thirteen year time-series (1994 to 2006) of gross annual pasture production (GAPP; representing both pasture and crop) was created for the Mediterranean-climate area in the southwest of Western Australia (SWWA) using a light-use efficiency model, incorporating NOAA-AVHRR and NASA-MODIS images in combination with climate data. Trends across the GAPP time-series were quantified by aggregating pixels to spatial regions (called a partition, unit, or spatial support) so that the effects of local spatial noise were minimized. We compared the GAPP analysis using the three spatial partitioning schemes (precipitation zones, Interim Biogeographic Regionalisation for Australia (IBRA) eco-regions, and Statistical Local Areas), and showed that the aggregation unit's size & shape impacted on the analysis. Our results demonstrate trends in GAPP that may be indicative of broader trends in climate change for the SWWA.

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Remote sensing applications for precision agriculture: A learning community approach

Cited by 332 publications

References 9 publications

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Precision farming and archaeology

Three regionalised analyses of a time-series of annual pasture production for southwest Western Australia

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