SPATIOTEMPORAL CHANGE OF URBAN AGRICULTURE USING GOOGLE EARTH IMAGERY: A CASE OF MUNICIPALITY OF NAKHONRATCHASIMA CITY, THAILAND
- 1Faculty of Sciences and Liberal Arts, Rajamangala University of Technology Isan; 30000 Nakhon Ratchasima, Thailand
- 2Academic Division of Chulachomklao Royal Military Academy; 26001 Nakhon Nayok, Thailand
- 3Faculty of Computer Science and Information Technology, Rambhaibarni Rajabhat University, 22110 Chantaburi, Thailand
- 4Vongchavalitkul University, 30000 Kakhon Ratchasima, Thailand
Keywords: Urban Agriculture, Spatiotemporal Analysis, Google Earth Imagery, Municipality of Nakhonratchasima City
Abstract. Presently, urban agriculture (UA) is an important part of the urban ecosystem and a key factor that can help in the urban environmental management. Therefore, this paper studies a spatial-temporal analysis of UA areas and types in Municipality of Nakhonratchasima City (MNC), Thailand. This UA types referred land use classification system of Land Development Department (LDD). Google Earth images acquired in the years of 2007, 2011, 2015 and 2018 were used to examine UA change with segmentation-based classification method in QGIS to classify Google Earth images into thematic maps. Moreover, this study showed different spatiotemporal change patterns, composition and rates in the study area and indicates the importance of analyzing UA change. Therefore, the results of this classification consisted of eleven classes – abandoned paddy field, rice paddy, abandoned field crop, mixed field crop, cassava, betel palm, mixed orchard, coconut, rose apple, truck crop, and fish farm. Truck crop had the greatest cover in study area while floricultural covered the minimal space over periods of study. The UA change analysis over time for entire study areas provides an overall picture of change trends. Furthermore, the UA change at census sector scale gives new insights on how human-induced activities (e.g., built-up areas and roads) affect UA change patterns and rates. This research indicates the necessity to implement change detection for better understanding the UA change patterns and rates.