Optimal classification methods for mapping agricultural tillage practices

South, Stephen R.; Qi, Jiaguo; Lusch, David P.

doi:10.1016/j.rse.2004.03.001

Cited by 95 publications

(49 citation statements)

References 13 publications

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“…Current methods to map agricultural practices consist of driveby, or what is commonly referred to as windshield surveys, to sample fields on a county-by-county basis. The drive-by method consists of designing transects, from which the results are used to estimate or extrapolate the agricultural system used in the entire county (South et al, 2004). In southern Europe, the follow-up of cropping systems by the European Union administrations has been achieved by sampling and ground visits to the selected farms.…”

Section: Introductionmentioning

confidence: 99%

“…Remotely-sensed data can offer the ability to efficiently identify agricultural cropping practices over large areas. Synoptic remotely-sensed imagery allows the classification of agricultural systems without any need for spatial averaging or the extrapolation of results to completely assess an area (South et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Several classification methods have been used for agricultural land cover classification studies (Oetter et al, 2000;South et al, 2004). Generally, the digital classification process can be unsupervised or supervised.…”

Section: Introductionmentioning

confidence: 99%

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Discriminating cropping systems and agro-environmental measures by remote sensing

Peña-Barragãn¹,

López‐Granados²,

García‐Torres³

et al. 2008

Agron. Sustain. Dev.

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-The agrarian policy of the European Union tends to support sustainable agriculture, subsidising only cropping systems that are implemented with specific agro-environmental measures. These actions require a precise follow-up of the crops and of the agricultural practices over a large surface. To that end, remote-sensing techniques are unique and cost-effective. We developed here a digital land cover classification in the Mediterranean dryland, mapping and assessing the main cropping systems and some agro-environmental measures such as cover crops in olive orchards and crop stubble for reducing soil erosion. We analysed a high spatial resolution satellite image (QuickBird) taken in early summer around Montilla, southern Spain. Images of the four broad wavebands, six band ratios and three vegetation indices were extracted from the satellite image and studied for the discrimination of nine land covers. The classified regions were determined by applying adequate boundary digital values to the selected images. Our results show that the land covers were discriminated with an overall accuracy of about 90%. Images of the normalised difference vegetation index and the ratio vegetation index discriminated between vegetation and non-vegetation zones. The visible wavebands discriminated roadside trees and herbaceous crops, and the near-infrared waveband highways and urban soil plus bare soil. The ratios blue/green and red/green were useful for distinguishing non-burnt stubble. The burnt stubble area was discriminated through the adapted burnt area index. Olive orchards were classified once the regions of vegetation, non-vegetation and non-burnt stubble were extracted. This technology will be a useful tool of agroecology control for the administration and will be a substitute for the current follow-up of cropping systems by ground visits. It can also be used on a farm level in order to help farmers and technicians to make decisions about the management of sustainable agricultural practices. burnt stubble / bare soil / cover crops / crop stubble / land cover classification / no-tillage / olive orchards / QuickBird

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminating cropping systems and agro-environmental measures by remote sensing

Peña-Barragãn¹,

López‐Granados²,

García‐Torres³

et al. 2008

Agron. Sustain. Dev.

View full text Add to dashboard Cite

show abstract

“…Distance-based classifiers (EMD and SAM) confirmed their underperformance compared to statistical and non-parametric algorithms (MLC and NN). This is probably due to EMD and SAM lacking capabilities in handling intra-class variance into the classification decision rules [South et al, 2004], and in their original design being based on spectral rather than multi-temporal information [Kruse et al, 1993;South et al, 2004]. Moreover, SAM invariance to relative magnitude of input features is possibly adding some confusion when inputs are multi-temporal VIs profiles [Kruse et al, 1993].…”

Section: Discussionmentioning

confidence: 99%

Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

Azar

Villa

Stroppiana

et al. 2016

European Journal of Remote Sensing

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This study investigated the feasibility of delivering a crop type map early during the growing season. Landsat 8 OLI multi-temporal data acquired in 2013 season were used to classify seven crop types in Northern Italy. The accuracy achieved with four supervised algorithms, fed with multi-temporal spectral indices (EVI, NDFI, RGRI), was assessed as a function of the crop map delivery time during the season. Overall accuracy (Kappa) exceeds 85% (0.83) starting from mid-July, five months before the end of the season, when maximum accuracy is reached (OA=92%, Kappa=0.91). Among crop types, rice is the most accurately classified, followed by forages, maize and arboriculture, while soybean or double crops can be confused with other classes.

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“…Optical remote sensing exploits the spectral differences between crop residues and soil using the shortwave infrared region of the electromagnetic spectrum and, particularly, with an absorption feature from cellulose and lignin at 2100 nm [127]. For example, South et al [128] tested five supervised classification methods to map no-till and conventional tillage using a single Landsat 7 ETM scene. They found that the spectral angle methods outperformed the conventional methods, with an overall accuracy above 96% and a Kappa above 0.92.…”

Section: Soil Tillagementioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Optimal classification methods for mapping agricultural tillage practices

Cited by 95 publications

References 13 publications

Discriminating cropping systems and agro-environmental measures by remote sensing

Discriminating cropping systems and agro-environmental measures by remote sensing

Assessing in-season crop classification performance using satellite data: a test case in Northern Italy

Remote Sensing and Cropping Practices: A Review

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