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Selected Publication:

Som-ard, J; Immitzer, M; Vuolo, F; Ninsawat, S; Atzberger, C.
(2022): Mapping of crop types in 1989, 1999, 2009 and 2019 to assess major land cover trends of the Udon Thani Province, Thailand
COMPUT ELECTRON AGR. 2022; 198, 107083 FullText FullText_BOKU

Crop production and productivity monitoring play a crucial role for food security and livelihoods, international trade and sustainable agricultural practices. Earth Observation (EO) data provides high spectral, spatial and temporal data for various agricultural applications. However, mapping and monitoring small crop fields and complex landscapes are still challenging, in particular when attempting to trace the historical evolution of land use changes. To address this issue, a study was set up in the Udon Thani Province of Thailand, with small agricultural parcels and highly fragmented landscapes, covering an area of approximately 11,000 km2. Three decades of crop type dynamics were monitored and assessed using different combinations of multi-temporal Sentinel-1, Sentinel-2 and Landsat data and the random forest (RF) classifier. The combined multi-temporal EO datasets proved the most efficient for mapping crop types. Classification results achieved overall accuracy (OA) of 87.9%, 88.1%, 84.8% and 92.6% for the four base-years 1989, 1999, 2009 and 2019, respectively. Thanks to the availability of high-quality reference labels, the crop type map of 2019 showed the highest overall and class-specific accuracies. The 2019 classification model separated many crop classes well, especially sugarcane, cassava, rice and para rubber. On the contrary, for 1989, 1999 and 2009 drops in accuracy had to be accepted, as direct field reference observations were unavailable and reference information had to be sourced through photo-interpretation or trimming approaches. Overall, however, the RF method together with multi temporal EO satellite data from multiple platforms showed high potential and excellent efficiency in crop type classification in complex landscapes. The most dominant classes of crop types for the four base-years were rice, sugarcane, and cassava, respectively. Land cover changes indicated that transitions of 1,529 km2 (14%) occurred between 1989 and 1999, mainly as increase in sugarcane and rice areas. From 1999 to 2009, significant land changes were observed covering 2,340 km2 (21%), primarily as increased cassava and para rubber cultivation. During the most recent period 2009 to 2019, an additional 3,414 km2 (31%) were transformed, mainly through the expansion of para rubber and sugarcane plantations. The main drivers for the observed land use changes in the three decades were commodity prices and agricultural policies. The cost-efficiently derived results provide valuable information to inform land use management decisions of policymakers and other stakeholders, including the consideration of environmental aspects.
Authors BOKU Wien:
Atzberger Clement
Immitzer Markus
Vuolo Francesco

Find related publications in this database (Keywords)
Crop type
Earth Observation
Multi-temporal imagery
Random Forest
Post-classification change detection

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