TY - GEN
T1 - Crop field boundary delineation using historical crop rotation pattern
AU - Rahman, Md Shahinoor
AU - Di, Liping
AU - Yu, Zhiqi
AU - Yu, Eugene G.
AU - Tang, Junmei
AU - Lin, Li
AU - Zhang, Chen
AU - Gaigalas, Juozas
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - GIS data layer on crop field boundary has many applications in agricultural research, ecosystem study, crop monitoring, and land management. Crop field boundary mapping through field survey is not time and cost effective for vast agriculture areas. Onscreen digitization on fine-resolution satellite image is also labor-intensive and error-prone. The recent development in image segmentation based on their spectral characteristics is promising for cropland boundary detection. However, processing of large volume multi-band satellite images often required high-performance computation systems. This study utilized crop rotation information for the delineation of field boundaries. In this study, crop field boundaries of Iowa in the United States are extracted using multi-year (2007-2018) CDL data. The process is simple compared to boundary extraction from multi-date remote sensing data. Although this process was unable to distinguish some adjacent fields, the overall accuracy is promising. Utilization of advanced geoprocessing algorithms and tools on polygon correction may improve the result significantly. Extracted field boundaries are validated by superimposing on fine resolution Google Earth images. The result shows that crop field boundaries can easily be extracted with reasonable accuracy using crop rotation information.
AB - GIS data layer on crop field boundary has many applications in agricultural research, ecosystem study, crop monitoring, and land management. Crop field boundary mapping through field survey is not time and cost effective for vast agriculture areas. Onscreen digitization on fine-resolution satellite image is also labor-intensive and error-prone. The recent development in image segmentation based on their spectral characteristics is promising for cropland boundary detection. However, processing of large volume multi-band satellite images often required high-performance computation systems. This study utilized crop rotation information for the delineation of field boundaries. In this study, crop field boundaries of Iowa in the United States are extracted using multi-year (2007-2018) CDL data. The process is simple compared to boundary extraction from multi-date remote sensing data. Although this process was unable to distinguish some adjacent fields, the overall accuracy is promising. Utilization of advanced geoprocessing algorithms and tools on polygon correction may improve the result significantly. Extracted field boundaries are validated by superimposing on fine resolution Google Earth images. The result shows that crop field boundaries can easily be extracted with reasonable accuracy using crop rotation information.
KW - Agriculture
KW - CDL
KW - Crop Field
KW - Crop Rotation
KW - Field Boundary
UR - http://www.scopus.com/inward/record.url?scp=85072938383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072938383&partnerID=8YFLogxK
U2 - 10.1109/Agro-Geoinformatics.2019.8820240
DO - 10.1109/Agro-Geoinformatics.2019.8820240
M3 - Conference contribution
AN - SCOPUS:85072938383
T3 - 2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
BT - 2019 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2019
Y2 - 16 July 2019 through 19 July 2019
ER -
