Accounting for phenology in maize yield prediction using remotely sensed dry dekads

Kuri, Farai; Murwira, Amon; Murwira, Karin S.; Masocha, Mhosisi

doi:10.1080/10106049.2017.1299798

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Isolated woody vegetation, bushland, grasslands, and rocky hills dominate the ecosystems in the study area. Most of the communal farmers perform subsistence agriculture as the main economic activity [44]. Maize is the major farming system in both the communal and commercial farming systems in the three districts.…”

Section: Study Areamentioning

confidence: 99%

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

et al. 2022

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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).

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Section: Study Areamentioning

confidence: 99%

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

et al. 2022

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“…Within the Southern African region, most recent studies of drought impact on vegetation are localized and focus mainly on crops (Kuri et al 2018;Chikodzi and Mutowo 2014;Unganai and Kogan 1998). Consequently, the drought impact on natural vegetation in Southern African biomes is still a research challenge.…”

Section: Introductionmentioning

confidence: 99%

Geospatial analysis of meteorological drought impact on Southern Africa biomes

Marumbwa

Cho

Chirwa

2020

International Journal of Remote Sensing

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Within Southern African biomes, droughts are recurrent with devastating impacts on ecological, economic, and human wellbeing. In this context, understanding the drought impact on vegetation is of extreme importance. However, information on drought impact on natural vegetation at the biome level is scanty and remains poorly understood. Most studies of drought impact on vegetation have largely focussed on crops. The few existing studies on natural vegetation are based on experiments and field measurements at individual tree level which are not representative of biomes. In this study, we mapped the spatial extent and severity of drought using the Standardized Precipitation Evapotranspiration Index (SPEI) and then quantified the drought impact on Southern African biomes using the Vegetation Condition Index (VCI) for the period 1998 to 2017. To compare drought impact across the biomes, we computed the percentage area of the biome with seasonal VCI <30. The drought trend for each biome was computed for each pixel using a linear regression model in R software using the seasonal VCI images from 1998 to 2017. Our result showed that extreme drought impact on vegetation was mainly confined to the southwestern biomes (i.e. the Nama karoo and desert biomes) with most drought occurring during the first half of the season. We also observed an increasing trend of VCI (1998 to 2017) across all biomes and this increasing VCI trend might be explained by woody encroachment which is prevalent in the Savannah and Grassland biomes. The results of this study provide baseline information on drought hotspots.

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