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ISPRS Int. J. Geo-Inf. 2018, 7(11), 429; https://doi.org/10.3390/ijgi7110429

Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya

1
International Center of Insect Physiology and Ecology (ICIPE), P.O. Box 30772, Nairobi 00100, Kenya
2
Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan
3
Center for Development Research (ZEF), Department of Ecology and Natural Resources Management, University of Bonn, Genscherallee 3, 53113 Bonn, Germany
4
RSS-Remote Sensing Solutions Gmbh, Isarstr. 3, 82065 Baierbrunn, Germany
*
Author to whom correspondence should be addressed.
Received: 1 January 1970 / Revised: 24 October 2018 / Accepted: 27 October 2018 / Published: 3 November 2018
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Abstract

Citrus is considered one of the most important fruit crops globally due to its contribution to food and nutritional security. However, the production of citrus has recently been in decline due to many biological, environmental, and socio-economic constraints. Amongst the biological ones, pests and diseases play a major role in threatening citrus quantity and quality. The most damaging disease in Kenya, is the African citrus greening disease (ACGD) or Huanglongbing (HLB) which is transmitted by the African citrus triozid (ACT), Trioza erytreae. HLB in Kenya is reported to have had the greatest impact on citrus production in the highlands, causing yield losses of 25% to 100%. This study aimed at predicting the occurrence of ACT using an ecological habitat suitability modeling approach. Specifically, we tested the contribution of vegetation phenological variables derived from remotely-sensed (RS) data combined with bio-climatic and topographical variables (BCL) to accurately predict the distribution of ACT in citrus-growing areas in Kenya. A MaxEnt (maximum entropy) suitability modeling approach was used on ACT presence-only data. Forty-seven (47) ACT observations were collected while 23 BCL and 12 RS covariates were used as predictor variables in the MaxEnt modeling. The BCL variables were extracted from the WorldClim data set, while the RS variables were predicted from vegetation phenological time-series data (spanning the years 2014–2016) and annually-summed land surface temperature (LST) metrics (2014–2016). We developed two MaxEnt models; one including both the BCL and the RS variables (BCL-RS) and another with only the BCL variables. Further, we tested the relationship between ACT habitat suitability and the surrounding land use/land cover (LULC) proportions using a random forest regression model. The results showed that the combined BCL-RS model predicted the distribution and habitat suitability for ACT better than the BCL-only model. The overall accuracy for the BCL-RS model result was 92% (true skills statistic: TSS = 0.83), whereas the BCL-only model had an accuracy of 85% (TSS = 0.57). Also, the results revealed that the proportion of shrub cover surrounding citrus orchards positively influenced the suitability probability of the ACT. These results provide a resourceful tool for precise, timely, and site-specific implementation of ACGD control strategies. View Full-Text
Keywords: African citrus triozid; citrus greening disease; MaxEnt; phenological metrics; land use/cover African citrus triozid; citrus greening disease; MaxEnt; phenological metrics; land use/cover
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MDPI and ACS Style

Richard, K.; Abdel-Rahman, E.M.; Mohamed, S.A.; Ekesi, S.; Christian, B.; Landmann, T. Importance of Remotely-Sensed Vegetation Variables for Predicting the Spatial Distribution of African Citrus Triozid (Trioza erytreae) in Kenya. ISPRS Int. J. Geo-Inf. 2018, 7, 429.

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