Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments
An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-03-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2017.1290913 |
| _version_ | 1797679067503263744 |
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| author | Cynthia S.A. Wallace Prasad Thenkabail Jesus R. Rodriguez Melinda K. Brown |
| author_facet | Cynthia S.A. Wallace Prasad Thenkabail Jesus R. Rodriguez Melinda K. Brown |
| author_sort | Cynthia S.A. Wallace |
| collection | DOAJ |
| description | An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user’s and producer’s accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America’s Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use. |
| first_indexed | 2024-03-11T23:09:02Z |
| format | Article |
| id | doaj.art-07dbb9b40cd440b491f61492fea44b73 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:02Z |
| publishDate | 2017-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-07dbb9b40cd440b491f61492fea44b732023-09-21T12:34:13ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262017-03-0154225828210.1080/15481603.2017.12909131290913Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessmentsCynthia S.A. Wallace0Prasad Thenkabail1Jesus R. Rodriguez2Melinda K. Brown3Western Geographic Science Center, U.S. Geological SurveyWestern Geographic Science Center, U.S. Geological SurveyThe University of ArizonaESRIAn important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user’s and producer’s accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America’s Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.http://dx.doi.org/10.1080/15481603.2017.1290913modis-ndvifallowed croplandsfallow-land algorithmwater and food securityndvi greenness anomalies |
| spellingShingle | Cynthia S.A. Wallace Prasad Thenkabail Jesus R. Rodriguez Melinda K. Brown Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments GIScience & Remote Sensing modis-ndvi fallowed croplands fallow-land algorithm water and food security ndvi greenness anomalies |
| title | Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments |
| title_full | Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments |
| title_fullStr | Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments |
| title_full_unstemmed | Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments |
| title_short | Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments |
| title_sort | fallow land algorithm based on neighborhood and temporal anomalies fanta to map planted versus fallowed croplands using modis data to assist in drought studies leading to water and food security assessments |
| topic | modis-ndvi fallowed croplands fallow-land algorithm water and food security ndvi greenness anomalies |
| url | http://dx.doi.org/10.1080/15481603.2017.1290913 |
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