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Part 1: Document Description
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Citation |
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Title: |
Large-Scale Mechanized Agricultural Crop Types across Mato Grosso, Goias, and Matopiba, Brazil |
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Identification Number: |
doi:10.7910/DVN/ZFHCTI |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2019-10-02 |
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Version: |
1 |
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Bibliographic Citation: |
Spera, Stephanie, 2019, "Large-Scale Mechanized Agricultural Crop Types across Mato Grosso, Goias, and Matopiba, Brazil", https://doi.org/10.7910/DVN/ZFHCTI, Harvard Dataverse, V1 |
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Citation |
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Title: |
Large-Scale Mechanized Agricultural Crop Types across Mato Grosso, Goias, and Matopiba, Brazil |
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Identification Number: |
doi:10.7910/DVN/ZFHCTI |
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Authoring Entity: |
Spera, Stephanie (The University of Richmond) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Spera, Stephanie |
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Depositor: |
Spera, Stephanie |
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Date of Deposit: |
2019-10-02 |
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Holdings Information: |
https://doi.org/10.7910/DVN/ZFHCTI |
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Study Scope |
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Keywords: |
Agricultural Sciences, Earth and Environmental Sciences, remote sensing, land cover, agriculture, crop type, Cerrado, Brazil |
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Abstract: |
Moderate Resolution Imaging Spectroradiometer (MODIS) was used to map annual large-scale mechanized agriculture across Mato Grosso, Tocantins, Goiás, Maranhão, western Bahia, and southern Piauí. All MODIS (MOD13Q1) Enhanced Vegetation Index (EVI), day of year (DOY), and VI Quality 16‐day, 250 m resolution data composited between August 2002 and July 2017 were used. A growing season is defined as beginning in August and ending in July: the data used to analyze the 2003 growing season span August 1, 2002–July 31, 2003. A published decision‐tree algorithm (Spera et al., 2014, Environmental Research Letters) was modified to incorporate crop cycles and calendars for the states of Tocantins, Goiás, Maranhão, western Bahia, and southern Piauí. The modified Spera et al. algorithm adds single‐cropped corn as a crop class, as it is common in Matopiba. Crop rotations identified include: (i) single cropping (a single rotation of soy, corn, or cotton within one growing season); (ii) double cropping (a soy‐corn or soy‐cotton rotation within one growing season); and (iii) irrigated agriculture. This algorithm uses metrics that include growing season EVI standard deviation, minima and maxima; it derives green‐up and harvest dates to differentiate natural vegetation from croplands. Due to the 250 m spatial resolution of the data, we mapped only agricultural fields greater than 25 ha. Thus, these cropland estimates are likely relatively conservative. The results of our algorithm were validated using 883 validation points collected across the Cerrado over the study period using the Landsat data repository within Google Earth Engine. Compositing false‐color Landsat 5 and 7 images (NIR Band 4 as red, SWIR1 Band 5 as green, red Band 3 as blue) from the growing season permitted visual identification and separation of corn, soy, cotton, pasture, and natural vegetation land covers. Across the study region, annual agriculture was distinguished from natural vegetation and pasture with 95% accuracy, and crop types were separated with 87% accuracy. Because pasture and sugarcane are difficult to separate using the phenological signal from a single growing season, we integrated previously published (spatially explicit) sugarcane data (Canasat: Rudorff et al., 2010) into our land‐cover maps. A data key is published with the files. |
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Time Period: |
2002-08-01-2017-07-31 |
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Kind of Data: |
GeoTiffs |
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Notes: |
Feel free to reach out with questions! |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Spera SA, Galford GL, Coe MT, Macedo MN, Mustard JF (2016) Land-Use Change Affects Water Recycling in Brazil’s Last Agricultural Frontier. Global Change Biology, 22, 3405-3413. https://doi.org/10.1111/gcb.13298 |
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Bibliographic Citation: |
Spera SA, Galford GL, Coe MT, Macedo MN, Mustard JF (2016) Land-Use Change Affects Water Recycling in Brazil’s Last Agricultural Frontier. Global Change Biology, 22, 3405-3413. https://doi.org/10.1111/gcb.13298 |
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