Is remote sensing useful for finding and monitoring urban farms?. (March 2017)

• Record Type: Journal Article
• Authors: Brown, M.E.; McCarty, J.L.
• Is Part Of: Applied geography. Volume 80(2017:Jun.)
• Page Start: 23
• Page End: 33
• Publication Date: 2017-03
• Subjects: Urban and peri-urban agriculture -- Vegetation -- MODIS -- Landsat -- Dakar -- Harare -- Detroit -- Ho Chi Minh city; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2017.01.008
• Languages: English
• ISSNs: 0143-6228

Abstract

Abstract: As the world becomes increasingly urbanized, the need for fresh fruits and vegetables in urban areas grows while the difficulty of bringing these perishable products to these areas also increases. Small-scale agriculture located in urban areas is a highly effective and profitable way to provide these products to communities that are far from extensive commercial agricultural areas. Here we describe how remote sensing can be used with data mining approaches to monitor urban and peri-urban farms within cities in both developed and developing countries. Using very high resolution satellite imagery together with moderate and coarse resolution imagery and information from social media and the web, we analyze the usefulness of different methods to identify farms within urban boundaries in four countries. The analysis shows how a mixed-method approach is necessary in order to identify where urban farming is occurring and to monitor its change through time. Although remote sensing-based vegetation and water indices were useful, without ancillary data they are not effective at remotely mapping the locations of urban farms. However, remote sensing is a good way to monitor vegetation condition in locations where actively managed urban farms are known to exist. Highlights: Urban and peri-urban agriculture (UPA) was identified using secondary information and remote sensing data. Urban agriculture fields in Dakar, Harare, Detroit, Ho Chi Minh City were explored. Vegetation and water indices were extracted using Landsat and MODIS data. Indices were effective at monitoring vegetation condition but not remote identification of UPA.