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Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery

Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery
Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery
Conservation tillage management has been advocated for carbon sequestration and soil quality preservation purposes. Past satellite image analyses have had difficulty in differentiating between no-till (NT) and minimal tillage (MT) conservation classes due to similarities in surface residues, and may have been restricted by the availability of cloud-free satellite imagery. This study hypothesized that the inclusion of high temporal data into the classification process would increase conservation tillage accuracy due to the added likelihood of capturing spectral changes in MT fields following a tillage disturbance. Classification accuracies were evaluated for Random Forest models based on 250-m and 500-m MODIS, 30-m Landsat, and 30-m synthetic reflectance values. Synthetic (30-m) data derived from the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) were evaluated because high frequency Landsat image sets are often unavailable within a cropping season due to cloud issues. Classification results from a five-date Landsat model were substantially better than those reported by previous classification tillage studies, with 94% total and ? 88% class producer's accuracies. Landsat-derived models based on individual image scenes (May through August) yielded poor MT classifications, but a monthly increase in accuracy illustrated the importance of temporal sampling for capturing regional tillage disturbance signatures. MODIS-based model accuracies (90% total; ? 82% class) were lower than in the five-date Landsat model, but were higher than previous image-based and survey-based tillage classification results. Almost all the STARFM prediction-based models had classification accuracies higher than, or comparable to, the MODIS-based results (> 90% total; ? 84% class) but the resulting model accuracies were dependent on the MODIS/Landsat base pairs used to generate the STARFM predictions. Also evident within the STARFM prediction-based models was the ability for high frequency data series to compensate for degraded synthetic spectral values when classifying field-based tillage. The decision to use MODIS or STARFM-based data within conservation tillage analysis is likely situation dependent. A MODIS-based approach requires little data processing and could be more efficient for large-area mapping; however a STARFM-based analysis might be more appropriate in mixed-pixel situations that could potentially compromise classification accuracy.
landsat, modis, random forest, starfm, tillage
0034-4257
66-75
Watts, Jennifer D.
09a58646-529f-468f-997a-fc660a2c07bf
Powell, Scott L.
bf9a5ead-05e5-4584-a8b9-e2892e2564da
Lawrence, Rick L.
f1aa2532-831b-4daf-a34e-af75fd3d05f5
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40
Watts, Jennifer D.
09a58646-529f-468f-997a-fc660a2c07bf
Powell, Scott L.
bf9a5ead-05e5-4584-a8b9-e2892e2564da
Lawrence, Rick L.
f1aa2532-831b-4daf-a34e-af75fd3d05f5
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40

Watts, Jennifer D., Powell, Scott L., Lawrence, Rick L. and Hilker, Thomas (2011) Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sensing of Environment, 115 (1), 66-75. (doi:10.1016/j.rse.2010.08.005).

Record type: Article

Abstract

Conservation tillage management has been advocated for carbon sequestration and soil quality preservation purposes. Past satellite image analyses have had difficulty in differentiating between no-till (NT) and minimal tillage (MT) conservation classes due to similarities in surface residues, and may have been restricted by the availability of cloud-free satellite imagery. This study hypothesized that the inclusion of high temporal data into the classification process would increase conservation tillage accuracy due to the added likelihood of capturing spectral changes in MT fields following a tillage disturbance. Classification accuracies were evaluated for Random Forest models based on 250-m and 500-m MODIS, 30-m Landsat, and 30-m synthetic reflectance values. Synthetic (30-m) data derived from the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) were evaluated because high frequency Landsat image sets are often unavailable within a cropping season due to cloud issues. Classification results from a five-date Landsat model were substantially better than those reported by previous classification tillage studies, with 94% total and ? 88% class producer's accuracies. Landsat-derived models based on individual image scenes (May through August) yielded poor MT classifications, but a monthly increase in accuracy illustrated the importance of temporal sampling for capturing regional tillage disturbance signatures. MODIS-based model accuracies (90% total; ? 82% class) were lower than in the five-date Landsat model, but were higher than previous image-based and survey-based tillage classification results. Almost all the STARFM prediction-based models had classification accuracies higher than, or comparable to, the MODIS-based results (> 90% total; ? 84% class) but the resulting model accuracies were dependent on the MODIS/Landsat base pairs used to generate the STARFM predictions. Also evident within the STARFM prediction-based models was the ability for high frequency data series to compensate for degraded synthetic spectral values when classifying field-based tillage. The decision to use MODIS or STARFM-based data within conservation tillage analysis is likely situation dependent. A MODIS-based approach requires little data processing and could be more efficient for large-area mapping; however a STARFM-based analysis might be more appropriate in mixed-pixel situations that could potentially compromise classification accuracy.

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More information

Accepted/In Press date: 14 April 2010
e-pub ahead of print date: 10 September 2010
Published date: 17 January 2011
Keywords: landsat, modis, random forest, starfm, tillage
Organisations: Earth Surface Dynamics

Identifiers

Local EPrints ID: 384707
URI: http://eprints.soton.ac.uk/id/eprint/384707
ISSN: 0034-4257
PURE UUID: 9a14e60e-f98e-4a6f-a78f-b019065d0a74

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Date deposited: 18 Apr 2016 14:19
Last modified: 14 Mar 2024 22:03

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Contributors

Author: Jennifer D. Watts
Author: Scott L. Powell
Author: Rick L. Lawrence
Author: Thomas Hilker

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