digital earth: a use case in urban agriculture ...

XX CONVEGNO NAZIONALE DELL’ASSOCIAZIONE ITALIANA DI AGROMETEOROLOGIA (AIAM) XLVI CONVEGNO NAZIONALE DELLA SOCIETÀ ITALIANA DI AGRONOMIA (SIA)

_______________________________________ Strategie integrate per affrontare le sfide climatiche e agronomiche nella gestione dei sistemi agroalimentari

Integrated strategies for agro-ecosystem management to address climate change challenges Milano 12 - 14 settembre 2017 a cura di Francesca Ventura Giovanna Seddaiu Gabriele Cola

Dipartimento di Scienze Agrarie Università di Bologna

ISBN 9788898010707 DOI 10.6092/unibo/amsacta/5692 COMITATO SCIENTIFICO Francesca Ventura (Vicepresidente AIAM) Giovanna Seddaiu Gabriele Cola

COMITATO ORGANIZZATIVO Luca Bechini Stefano Bocchi Gabriele Cola Pietro Marino Alessia Perego

SEGRETERIA ORGANIZZATIVA Federico Spanna (Presidente AIAM) Simone Falzoi Tiziana La Iacona Irene Vercellino Grafica di copertina realizzata da Matteo Grandi

Bologna, 2017

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DIGITAL EARTH: A USE CASE IN URBAN AGRICULTURE GEOSPATIAL DATASET CREATION DIGITAL EARTH: UN CASO D’USO DI CREAZIONE DI DATASET GEOSPAZIALI SULL’AGRICOLTURA URBANA Flavio Lupia1*, Giuseppe Pulighe1, Francesca Giarè1 1

CREA Centro di ricerca Politiche e Bio-economia, Via Po, 14, 00198 Roma *[email protected]

Abstract Urban agriculture is being recognized as spreading activity in many metropolitan areas with positive impacts on the socioeconomic sphere, favouring forms of sustainable urban environments. A stepping stone for analysing potential benefits, sustainability aspects and to define planning strategies is the availability of high resolution land use datasets. Often, metropolitan areas have limited geospatial datasets and, if available, they lack of details especially when very small cultivated parcels are considered. In this work, Digital Earth tools (i.e. Google Earth) are exploited for a mapping exercise carried out for the city of Milan by using web-based freely available very high resolution imagery. We built a geodatabase of cultivated polygons in 2014 through photointerpretation by performing a classification based on different typologies of urban agriculture. A first assessment of the phenomenon is done by reporting statistics and a spatial representation. Keywords: urban agriculture; Google Earth, Milano, food garden, land use. Parole chiave: agricoltura urbana; Google Earth, orti urbani, uso del suolo.

Introduction Several studies during the last decades have highlighted the growing interest on urban agriculture (UA) and its aspects in many metropolitan areas with different facets in Global North and Global South. Sustaining the food production within cities can contribute to urban resilience (Barthel and Isendahl 2013) and to facilitate the relationships between agriculture, urban water and recycled nutrients to address future climate changes (Lovell 2010; Moglia 2014). As urbanization and population trends keep growing, UA contributes to reduce environmental degradation and enhance biodiversity, ecosystem services, quality of life, human health and wellbeing of citizens (Lin et al. 2015). UA responds also to different economic, social and environmental needs and is characterized by a high number of relationships with different actors. UA shows a high capacity to maintain and/or create social services and ecosystem services (García-Llorente et al. 2016) and contributes to create inclusive communities (Giarè, 2012). The basis of many UA experiences - also in the case of farms - is the multifunctional approach to agriculture and a model of agricultural development not based on the productive intensification and industrial modernization of the primary sector, but on the recognition of new and different functions. Although UA farms are not able to feed the whole population of a city, their activity provides a broad range of products and services that enhance the ecological, social, and even economic sustainability of metropolitan areas (Henke et al. 2015). Understanding the role of UA within the urban environment requires data on the spatial distribution and characteristics of cultivated sites. This information is a stepping stone for any research question and for defining strategies for the management of urban spaces and resources. However, many metropolitan areas, like in Italy, lack of detailed and updated land use map that can enable to explore the existing UA geographies as well as to trace temporal changes. Detailed mapping is imperative for capturing some of UA sites, such as hobby farming usually characterize by very small parcels. Several mapping approaches have been carried out in various metropolitan areas worldwide. For example, in the city of Philadelphia (USA) Kremer and DeLiberty (2011) used a pixel-based classification techniques in conjunction with a Geographic Information System (GIS) to estimate the available land for food production. Similarly, in the city of Dunedin (New Zealand) Mathieu et al. (2007) identified private gardens through object-oriented classification of very highresolution images. Pulighe and Lupia (2016), successfully implemented a detailed inventory of UA in Rome (Italy) through photointerpretation of Google Earth imagery. Similarly, manual photointerpretation of Google Earth imagery was conducted by Drechsel and Dongus (2010) in Dar er Saalam (Tanzania) to detect areas with crop production larger than 1000 m2. In addition, municipalities, non-governmental organizations and research institutes promote and realize UA geospatial inventories, sometimes with the support and partnerships of city gardeners, citizens, associations and students (Lupia, 2015). Today, in the era of Geospatial Big Data, vast amounts of data are generated and collected from several sources that can be mined to extract knowledge about several phenomena, particularly within urban spaces. Freely available maps and imagery


data, distributed by web mapping tools and virtual globes (i.e. Google Earth, NASA World Wind, Microsoft Bing Maps), (Goodchild et al. 2012) are opening new routes in the geospatial data production determining a paradigm shift from knowledge-driven to data-driven science (Kitchin, 2014). In this study we present the results of mapping exercise carried out to detect and inventory cultivated polygons located within the administrative area of Milan. We exploited the very high resolution imagery (year 2014) provided by Google Earth to build a comprehensive dataset on UA where cultivated parcels are recognized and classified into five distinct typologies by integrating additional information derived from other sources. We provide statistics on the UA sites and observation about their spatial distribution within the city. We argue that UA geospatial dataset is a starting point for strategic planning and further researches on the role of UA to creating a more sustainable food system and urban land use. The city of Milan has a long history of agricultural activities dating back to the inception of the industrial period when food gardens were the main economic subsidy for the new working class. Nowadays, UA is growing rapidly with different forms led by citizens initiatives and thanks to the support of the Municipality. The public support to UA has taken place with projects, assignments of vacant lands to citizens and regulations. These initiatives encourage alternative and sustainable urban land uses to counterbalance urbanization and soil sealing in a city where artificial areas cover more than 76% of the total surface (ISPRA, 2012). Materials and Methods The spatial inventory of UA in Milan was based on a photointerpretation approach centred on the use of the very high resolution imagery provided by Google Earth followed by an integration procedure with additional datasets and a final validation step. The total time spent for the entire process was about 200 hours. Photointerpretation was carried out on 2014 images. Nevertheless, Google Earth provides archived imagery (2001-2017 for the Milan area), with adequate spatial resolution and accuracy (Pulighe et al. 2016) to identify cultivated parcels enabling for temporal updated of the datasets and change detection analysis. The approach was based on the following phases: 1.! identification of cultivated parcels through photointerpretation of Google Earth 2014 very high resolution imagery; 2.! identification of the parcel within imagery of other web mapping services (i.e. Google Maps, Microsoft Bing Maps and Google Street View) in order to confirm the recognition thanks to different visual perspective (i.e. panoramic and street-level view); 3.! acquisition of data and documents to be used as support for the parcel identification and attribute assignment (e.g. reports, scientific publications, cartographic datasets, etc.); 4.! digitization of polygons representing the cultivated parcels with Google Earth tools; 5.! assignment of attributes to each polygon (i.e. typology of UA site and agricultural land use); 6.! dataset validation with in-field visits and street-level imagery from Google Street View; 7.! GIS dataset post-processing and geodatabase creation. Five types of UA sites were identified in the city (Tab. 1): residential gardens, community gardens, urban farms, institutional garden and “illegal” gardens. Each type was also classified according to the dominant agricultural land use with the following classes: horticulture, fruit trees, mixed crops, vineyards and olive trees. Fig. 1 depict an example of the five types of sites detected with the street-level view provided by Google Street View used to support both sites detection and validation phases. Type Residential garden

Community garden

Urban farm

Institutional garden

“Illegal” garden

Description Small-size parcel located nearby houses (e.g. backyard), villas, buildings, industrial and commercial activities. Large-size area subdivided into multiple plots managed individually (i.e. allotment) or collectively by citizens (i.e. community garden). Group of parcels managed by professional farmers with an intensive and an advanced cropping system. Areas with variable size managed by institutions or organizations (e.g. schools, religious center, prisons and non-profit organizations, etc.). Small-size isolated parcel or larger areas subdivided in small plots, cultivated probably without authorization in public or private spaces.

Tab. 1: The five types of urban agriculture sites identified through photointerpretation. Tab. 2: Le cinque tipologie di siti di agricoltura urbana identificati attraverso la fotointerpretazione.


In the following we focus on polygons belonging to non-professional farming (residential, community, institutional and “illegal” gardens). The inventory of the urban farms actually underestimate the total farmed area since several uncropped areas (e.g. set-aside) cannot be distinguished from other natural areas only through photointerpretation.

Fig. 1: Street-level recognition trough Google Street View for each urban agriculture type. Fig. 1: Riconoscimento mediante la visualizzazione a livello stradale di Google Street View per ogni tipologia di agricoltura urbana.

Results and Discussion The mapping approach allowed to build a comprehensive inventory of non-professional agricultural activities located in the administrative area of Milan that extends for more than 18000 hectares. As far as spatial distribution is concerned UA sited are distributed outside the city centre with a concentration in the southern and eastern borders where artificial areas have a sparse pattern (Fig. 2). This concentration is connected to the wide area of the Parco Agricolo Sud Milano that forms a half-circle around Milan. Fewer sites, mainly residential and community gardens, are found in the north side.

Fig. 2: Spatial distribution of urban agriculture sites classified into the five typologies inside the administrative boundary of Milan. Points represent the centroid of the polygons identified on 2014 Google Earth imagery. A single urban farm is generally made up of a set of cultivated polygons. Fig. 2: Distribuzione spaziale dei siti di agricoltura urbana classificati nelle cinque tipologie all’interno dell’area amministrativa di Milano. Ogni punto rappresenta il centroide del poligono identificato sulle immagini Google Earth del 2014. Una singola azienda Agricola urbana è generalmente costituita da un insieme di poligoni coltivati.


A total of 945 sites are located in the city with a total farmed area of 80 hectares. Despite the number of sites highlights an important diffusion of non-professional farming, in terms of farmed area the latter constitutes a small percentage compared to the area covered by urban farms (80 over 2730 ha, 3.4% ca.). In terms of number of polygons, residential gardens prevail over all categories (595; 63%), followed by community gardens (272; 29 %), institutional gardens (61; 6%) and “illegal” gardens (17; 2%). When cultivated area is considered, community gardens have the largest extension (about 57 ha ; 71%), followed by residential gardens (about 18 ha ; 23%), institutional gardens (about 4 ha.; 5%) and “illegal” gardens (0.7 ha; 1%).

Fig. 3: Number and area covered by urban agriculture sites for each non-professional category. Fig. 3: Numero e superficie coperta dei siti di agricoltura urbana per le categorie non professionali.

Conclusions The growing interest around urban food production by the so-called “citizen farmers” opens new questions about sustainable urban land use and resources that can be addressed if detailed and updated geospatial datasets are available. We demonstrated that the exploitation of the rising amount of very high resolution imagery provided through web mapping services and virtual globes (e.g. Google Earth) is a viable option for building geospatial datasets on cultivated sites in urban areas. Building geospatial datasets on UA is the first step toward the definition of planning strategies. Furthermore, these datasets would allow to explore several related issues on the sustainable use of urban resources (i.e. water and soil), the interaction with peri-urban areas and the expanding artificial surfaces, the ecosystem services provided and the socioeconomic impacts. References Barthel, S. & Isendahl, C., 2013. Urban gardens, Agriculture, And water management: Sources of resilience for long-term food security in cities. Ecological Economics, 86, pp.224–234. Available at: http://dx.doi.org/10.1016/j.ecolecon.2012.06.018. Drechsel, P. & Dongus, S., 2010. Dynamics and sustainability of urban agriculture: Examples from sub-Saharan Africa. Sustainability Science, 5(1), pp.69–78. García-Llorente, M. et al., 2016. Social Farming in the Promotion of Social-Ecological Sustainability in Rural and Periurban Areas. Sustainability, 8(12), p.1238. Available at: http://www.mdpi.com/2071-1050/8/12/1238. Giarè F., Henke R., Vanni F., 2015. Agriculture in urban poles: an empirical analysis of farm strategies in Italy, 2nd International Conference on Agriculture in an Urbanizing Society, Rome, 14-17 September 2015 Giarè, F., 2012. Forme e modi dell'agricoltura, Agriregionieuropa. Available at: https://agriregionieuropa.univpm.it/it/content/article/31/30/forme-e-modi-dellagricoltura


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