Leveraging on big data from earth observation to map landscape structure and composition in coffee agroecosystem
Creators: name: Mosomtai, Gladys
name: Odindi, John
name: Elfatih M.  Abdel-Rahman
name: Landmann, Tobias
name: David, Guillaume
name: Pinard, Fabrice
name: Babin, Régis
Publisher: name: Gates Open Research
Year: 2019

description: Mapping farming systems in an agricultural landscape is important for quantifying ecosystem services provided by landscapes such as management of pests and pathogens, conservation of beneficial arthropods, improving crop pollination and crop yields. Landscape structure is defined as the spatial arrangement (configuration) of land cover types (composition) that influence ecological processes and biodiversity distribution at varying scales. Human induced landscape modification of once-pristine natural environment has resulted to habitat loss and disturbance of species communities and their interaction, which has compromised on ecosystem functioning and service provision. Big data from earth observation and machine learning algorithms provide unprecedented opportunity for mapping these agricultural landscapes. However, continuous cloud cover and intercropping in smallholdings poses challenges in landscape characterisation especially in the tropics.<br><br>We conducted our study in Murang'a County, which is one of the five counties in central Kenya that grow coffee and tea. Coffee grows in four sub zones of the upper Midland (UM) agro-ecological zone that cuts across an elevation gradient of 1300 – 2000 masl. UM1 is the transition zone for growing tea and coffee. UM2 is the major coffee growing zone whereas UM3 is the marginal coffee zone. At UM4, coffee is grown under irrigation. We explored multi-source satellite images to evaluate the best dataset with least cost for mapping coffee farms and identified fragmentation levels of cover types in each agroecological subzone. Using reference data from Google Earth, commercially available PlanetScope (3m spatial resolution) and freely available Sentinel 2 (10m spatial resolution) and Landsat 8 (30m spatial resolution) images combined with vegetation indices (VI) were tested using a random forest classifier. The dataset with the highest accuracy for land use land cover classification was further analyzed using fragstats software to measure landscape fragmentation. <br><br>13-band Sentinel 2 had the highest accuracy for mapping coffee (kappa - 0.98) compared to 4-band PlanetScope (kappa – 0.85) and 11-band Landsat 8 (kappa – 0.88). Despite having the highest spatial resolution, PlanetScope had the lowest accuracy which only improved when combined with VI. The final land use land cover map from Sentinel 2 mapped the following land use land cover classes: annuals, bananas, bareland, coffee, agroforest, grassland, perennials/shrubs, settlements, tea and waterbody. Coffee covered over 50% of the total landscape in UM1 and UM2 while annual crops occupied 43% of the total landscape in UM3. Other perennials and annual crops occupied 29% and 22% of UM4 respectively. The results also showed that Coffee was highly fragmented in UM3 and UM4 with the largest patch occupying 3% and 1.4% of the total landscape compared to UM1 which occupied 62% of the total landscape. Additionally, Agroforest and bananas were more fragmented in UM2 and UM3 while annual crops, shrubs, grassland and settlements in UM4.<br><br>We show in this study that Sentinel 2 is the most robust satellite data to map land use and cover types with high level of accuracies even in challenging landscapes such as smallholder agroecosystems. This is due to high number of spectral bands that delineates vegetation type more accurately compared to other satellites. Furthermore, it has the added advantage of being freely available; this dataset can be taken up easily in resource-constrained projects. UM3 and UM4 are the marginal coffee growing zones. Within these landscapes, coffee is highly fragmented and interspersed with annual crops which are the dominant cover type. Naturally, coffee grows as an understorey crop. At UM3 and UM4, agroforest trees are also highly fragmented, hence, within these landscapes coffee is more vulnerable to pest and disease infestation, low crop yields and adverse effects of climate change compared to coffee in UM1 and UM2.