Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango ( Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe

Bester Tawona Mudereri, Elfatih M. Abdel-Rahman, Shepard Ndlela, Louisa Delfin Mutsa Makumbe, Christabel Chiedza Nyanga, Henri E. Z. Tonnang and Samira A. Mohamed
Additional contact information
Bester Tawona Mudereri: International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya
Elfatih M. Abdel-Rahman: International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya
Shepard Ndlela: International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya
Louisa Delfin Mutsa Makumbe: Plant Quarantine Services Institute, 33 km peg Mazowe Bindura Highway, P. Bag 2007, Mazowe, Zimbabwe
Christabel Chiedza Nyanga: Plant Quarantine Services Institute, 33 km peg Mazowe Bindura Highway, P. Bag 2007, Mazowe, Zimbabwe
Henri E. Z. Tonnang: International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya
Samira A. Mohamed: International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, 00100 Nairobi, Kenya

Sustainability, 2022, vol. 14, issue 10, 1-23

Abstract: Generating tree-specific crop maps within heterogeneous landscapes requires imagery of fine spatial and temporal resolutions to discriminate among the rapid transitions in tree phenological and spectral features. The availability of freely accessible satellite data of relatively high spatial and temporal resolutions offers an unprecedented opportunity for wide-area land use and land cover (LULC) mapping, including tree crop (e.g., mango; Mangifera indica L.) detection. We evaluated the utility of combining Sentinel-1 (S1) and Sentinel-2 (S2) derived variables ( n = 81) for mapping mango orchard occurrence in Zimbabwe using machine learning classifiers, i.e., support vector machine and random forest. Field data were collected on mango orchards and other LULC classes. Fewer variables were selected from ‘All’ combined S1 and S2 variables using three commonly utilized variable selection methods, i.e., relief filter, guided regularized random forest, and variance inflation factor. Several classification experiments ( n = 8) were conducted using 60% of field datasets and combinations of ‘All’ and fewer selected variables and were compared using the remaining 40% of the field dataset and the area underclass approach. The results showed that a combination of random forest and relief filter selected variables outperformed (F1 score > 70%) all other variable combination experiments. Notwithstanding, the differences among the mapping results were not significant ( p ≤ 0.05). Specifically, the mapping accuracy of the mango orchards was more than 80% for each of the eight classification experiments. Results revealed that mango orchards occupied approximately 18% of the spatial extent of the study area. The S1 variables were constantly selected compared with the S2-derived variables across the three variable selection approaches used in this study. It is concluded that the use of multi-modal satellite imagery and robust machine learning classifiers can accurately detect mango orchards and other LULC classes in semi-arid environments. The results can be used for guiding and upscaling biological control options for managing mango insect pests such as the devastating invasive fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae ).

Keywords: Google Earth Engine (GEE); land use and land cover (LULC); machine learning; multi-algorithm classification; pest control; variable selection; SAR (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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