Using multispectral imagery to monitor urban food forests in a changing climatic environment: a case from Zimbabwe

Attaining the UN Sustainable Development Goal No. 2, which calls for “zero hunger” by 2030, remains a pipe dream unless challenges imposed on food production systems by climate change are addressed. Climate change poses a huge threat to urban food security in Africa. The objective of this paper is to investigate how climate change is affecting Urban Food Forests, using a case study of an orchard in Harare, by tracking environmental assessment indices between 2013 and 2023. Time series analyses of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), as well as climate variables, including Standardized Precipitation Evapotranspiration Index (SPEI), Precipitation, Maximum temperature and Minimum temperature, were undertaken in a Google Earth Engine environment. NDVI and NDWI were calculated from Sentinel 2 images, while SPEI data was extracted from the CSIC/SPEI/2_10″ Global SPEI database, and Precipitation, Maximum temperature and Minimum temperature data were derived from Climate Explorer. Data outputs from Google Earth Engine were analyzed further using correlation and multiple regression analyses in an Excel environment. The results indicate that plant health in Urban Food Forests is seriously impacted by the persistence of drought and dry spells arising from climate change. The results also depict shortage of water in the environment, signified by perpetually negative NDWI values, with potential to suppress vegetation health, as indicated by relatively low NDVI values that dominate the time series. These results have potential application for managing Urban Food Forests and transforming the current fragile urban food systems into more robust food systems. The paper concludes that such a transformation would enhance the resilience and adaptive capacity of urban households against climate change. The study recommends the development of mobile based applications that enable urban farmers to monitor NDVI, NDWI and productivity more easily and efficiently.