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Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya

Please always quote using this URN: urn:nbn:de:bvb:20-opus-173285
  • Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a studyCropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer’s accuracy and UA: user’s accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10–20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.show moreshow less

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Metadaten
Author: Kyalo Richard, Elfatih M. Abdel-Rahman, Sevgan Subramanian, Johnson O. Nyasani, Michael Thiel, Hosein Jozani, Christian Borgemeister, Tobias Landmann
URN:urn:nbn:de:bvb:20-opus-173285
Document Type:Journal article
Faculties:Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie
Language:English
Parent Title (English):Sensors
Year of Completion:2017
Volume:17
Issue:11
Article Number:2537
Source:Sensors (2017) 17(11):2537. https://doi.org/10.3390/s17112537
DOI:https://doi.org/10.3390/s17112537
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie
Tag:Kenya; RapidEye; bi-temporal; cropping systems; random forest; remote sensing
Release Date:2021/06/24
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International