Published October 28, 2024 | Version v2024
Software Open

Crop Sequence Boundaries GitHub Repository

Creators

  • 1. United States Department of Agriculture National Agricultural Statistics Service
  • 2. United States Department of Agriculture Economic Research Service

Description

This is a GitHub repository containing code to create Crop Sequence Boundaries using United States Department of Agriculture National Agricultural Statistics Service historic Cropland Data Layers.

Files

Files (152 Bytes)

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Additional details

Identifiers

URL
https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/metadata_Crop-Sequence-Boundaries-2023.htm

Related works

Describes
Journal article: 10.3233/SJI-230078 (DOI)
Is supplemented by
Dataset: https://www.nass.usda.gov/Research_and_Science/Crop-Sequence-Boundaries/index.php (URL)

Dates

Available
2024-10-28

Software

Repository URL
https://github.com/USDA-REE-NASS/crop-sequence-boundaries
Programming language
Python
Development Status
Active

References

  • [1] National Academies of Sciences, Engineering, and Medicine. Principles and Practices for a Federal Statistical Agency: Sixth Edition. Washington, DC: The National Academies Press. (2017) ; doi: 10.17226/24810. [2] Boryan C, Yang Z, Mueller R, Craig M. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International. (2011) ; 26: (5): 341-58. [3] Boryan CG, Yang Z, Willis P. US Geospatial Crop Frequency Data Layers, Proc. of the Third International Conference on Agro-geoinformatics. August 11–14, (2014) , Beijing, China, doi: 10.1109/Agro-Geoinformatics.2014.6910657. [4] Boryan, CG, Yang, Z, Di L. Deriving 2011 Cultivated Land Cover Data Sets Using USDA National Agricultural Statistics Service Historic Cropland Data Layers. Proc. of IEEE International Geoscience and Remote Sensing Symposium, July 22–27, (2012) , Munich, Germany. [5] Yan, L and Roy DP. Automated crop field extraction from multi-temporal Web Enabled Landsat Data. Remote Sensing of Environment. (2014) ; 144: : 42-64. [6] Beeson, PC, Daughtry, CS, Wallander, SA. Estimates of conservation tillage practices using landsat archive. Remote Sensing. (2020) ; 12: (16): 2665. [7] Rahman MS, Di L, Yu Z, Eugene GY, Tang J, Lin L, Zhang C, Gaigalas J. Crop Field Boundary Delineation using Historical Crop Rotation Pattern. In 2019 8th International Conference on Agro-Geoinformatics. IEEE Agro-Geoinformatics. 2019: : 1-5. [8] Jong M, Guan K, Wang S, Huang Y, Peng B. Improving field boundary delineation in ResUNets via adversarial deep learning. International Journal of Applied Earth Observation and Geoinformation. (2022) ; 112: : 102877. [9] Yan L, Roy DP. Conterminous United States crop field size quantification from multi-temporal Landsat data. Remote Sensing of Environment. (2016) ; 172: : 67-86. [10] Ji CY. Delineating agricultural field boundaries from TM imagery using dyadic wavelet transforms. ISPRS Journal of Photogrammetry and Remote Sensing. (1996) ; 51: (6): 268-83. [11] Cheng T, Ji X, Yang G, Zheng H, Ma J, Yao X, Zhu Y, Cao W. DESTIN: A new method for delineating the boundaries of crop fields by fusing spatial and temporal information from WorldView and Planet satellite imagery. Computers and Electronics in Agriculture. (2020) ; 178: : 105787. [12] Xu L, Ming D, Du T, Chen Y, Dong D, Zhou C. Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images. Computers and Electronics in Agriculture. (2022) ; 192: : 106611. [13] Graesser J, Ramankutty N. Detection of cropland field parcels from Landsat imagery. Remote Sensing of Environment. (2017) ; 201: : 165-80. [14] Garcia-Pedrero A, Gonzalo-Martin C, Lillo-Saavedra M. A machine learning approach for agricultural parcel delineation through agglomerative segmentation. International Journal of Remote Sensing. (2017) ; 38: (7): 1809-19. [15] Masoud KM, Persello C, Tolpekin VA. Delineation of agricultural field boundaries from Sentinel-2 images using a novel super-resolution contour detector based on fully convolutional networks. Remote Sensing. (2019) ; 12: (1): 59. [16] Wagner MP, Oppelt N. Extracting agricultural fields from remote sensing imagery using graph-based growing contours. Remote Sensing. (2020) ; 12: (7): 1205. [17] Wagner MP, Oppelt N. Deep learning and adaptive graph-based growing contours for agricultural field extraction. Remote Sensing. (2020) ; 12: (12): 1990. [18] Taravat A, Wagner MP, Bonifacio R, Petit D. Advanced fully convolutional networks for agricultural field boundary detection. Remote Sensing. (2021) ; 13: (4): 722. [19] Persello C, Wegner JD, Hänsch R, Tuia D, Ghamisi P, Koeva M, Camps-Valls G. Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities. IEEE Geoscience and Remote Sensing Magazine. (2022) ; 10: (2): 172-200. [20] Waldner F, Diakogiannis FI, Batchelor K, Ciccotosto-Camp M, Cooper-Williams E, Herrmann C, Mata G, Toovey A. Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images. Remote Sensing. (2021) ; 13: (11): 2197. [21] Estes, LD, Ye S, Song L, Luo B, Eastman JR, Meng Z, Zhang Q, McRitchie D, Debats SR, Muhando J, Amukoa AH. High resolution, annual maps of field boundaries for smallholder-dominated croplands at national scales. Frontiers in Artificial Intelligence. (2022) ; 4: : 744863. [22] Hong R, Park J, Jang S, Shin H, Kim H, Song I. Development of a parcel-level land boundary extraction algorithm for aerial imagery of regularly arranged agricultural areas Remote. Sensing. (2021) ; 13: (6): 1167. [23] Kumar S, Jayagopal P. Delineation of field boundary from multispectral satellite images through U-Net segmentation and template matching. Ecological Informatics. (2021) ; 64: : 101370. [24] Turker M, Kok EH. Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping. ISPRS Journal of Photogrammetry and Remote Sensing. (2013) ; 79: : 106-21. [25] Li T, Johansen K, McCabe MF. A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data. ISPRS Journal of Photogrammetry and Remote Sensing. (2022) ; 186: : 83-101. [26] Waldner F, Diakogiannis FI, Batchelor K, Ciccotosto-Camp M, Cooper-Williams E, Herrmann C, Mata G, Toovey A. Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images Remote. Sensing. (2021) ; 13: (11): 2197. [27] Fritz S, See L, McCallum I, You L, Bun A, Moltchanova E, Duerauer M, Albrecht F, Schill C, Perger C, Havlik P. Mapping global cropland and field size. Global Change Biology. (2015) ; 21: (5): 1980-92. [28] USDA Farm Service Agency. Common Land Unit (CLU) Information Sheet April 12, (2012) . Available from: https//www.fsa.usda.gov/Internet/FSA_File/clu__infosheet_2012.pdf. [Accessed 28 March 2024]. [29] USDA National Agricultural Statistics Service. NASS – Quick Stats. USDA National Agricultural Statistics Service. Available from: https://data.nal.usda.gov/dataset/nass-quick-stats [Accessed: 29 March 2023]. [30] 2019 TIGER/Line Shapefiles (machine readable data files)/prepared by the US. Census Bureau, (2019) . [31] Lin L, Di L, Zhang C, Guo L, Di Y, Li H, Yang A. Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. Sci Data. (2022) ; 9: (1): 63. doi: 10.1038/s41597-022-01169-w. [32] Lark T, Mueller R, Johnson D, Gibbs H. Measuring land-use and land-cover change using the US. department of agriculture's cropland data layer: Cautions and recommendations. International Journal of Applied Earth Observation and Geoinformation. (2017) ; 62: : 224-35. doi: 10.1016/j.jag.2017.06.007. [33] Abernethy J, Beeson PC, Boryan CG, Hunt KA, Satore L. Preseason crop type prediction using crop sequence boundaries. Computers and Electronics in Agriculture. (2023) ; 208: : 107768. ISSN 0168-1699, doi: 10.1016/j.compag.2023.107768. [34] Jennings R. Georeferencing Process [WWW Document]. Esri Map Gall. Available from: https//www.esri.com/en-us/about/events/uc/agenda/virtual-map-gallery#/submission-detail/62cb72d2ff6b1a12e0ccfbf0. [Accessed 29 March 2023]. [35] Chen X, Chambers RG, Bandaru V, Jones C, Ochsner T, Witt T. Precision Nitrogen Management for Winter Wheat Under Price and Weather Uncertainty. (in review) Available at SSRN: https://ssrn.com/abstract=4435286; or doi: 10.2139/ssrn.4435286.
  • Wang, Z., & Müller, J.-C. (1998). Line Generalization Based on Analysis of Shape Characteristics. Cartography and Geographic Information Systems, 25(1), 3–15