Using Sentinel-2 Multispectral Images to Map the Occurrence of the Cossid Moth (Coryphodema tristis) in Eucalyptus Nitens Plantations of Mpumalanga, South Africa

Abstract

Coryphodema tristis is a wood-boring insect, indigenous to South Africa, that has recently been identified as an emerging pest feeding on Eucalyptus nitens, resulting in extensive damage and economic loss. Eucalyptus plantations contributes over 9% to the total exported manufactured goods of South Africa which contributes significantly to the gross domestic product. Currently, the distribution extent of the Coryphodema tristis is unknown and estimated to infest Eucalyptus nitens compartments from less than 1% to nearly 80%, which is certainly a concern for the forestry sector related to the quantity and quality of yield produced. Therefore, the study sought to model the probability of occurrence of Coryphodema tristis on Eucalyptus nitens plantations in Mpumalanga, South Africa, using data from the Sentinel-2 multispectral instrument (MSI). Traditional field surveys were carried out through mass trapping in all compartments (n = 878) of Eucalyptus nitens plantations. Only 371 Eucalyptus nitens compartments were positively identified as infested and were used to generate the Coryphodema tristis presence data. Presence data and spectral features from the area were analysed using the Maxent algorithm. Model performance was evaluated using the receiver operating characteristics (ROC) curve showing the area under the curve (AUC) and True Skill Statistic (TSS) while the performance of predictors was analysed with the jack-knife. Validation of results were conducted using the test data. Using only the occurrence data and Sentinel-2 bands and derived vegetation indices, the Maxent model provided successful results, exhibiting an area under the curve (AUC) of 0.890. The Photosynthetic vigour ratio, Band 5 (Red edge 1), Band 4 (Red), Green NDVI hyper, Band 3 (Green) and Band 12 (SWIR 2) were identified as the most influential predictor variables. Results of this study suggest that remotely sensed derived vegetation indices from cost-effective platforms could play a crucial role in supporting forest pest management strategies and infestation control.

Monitoramento Fitossanitário Utilizando Sensoriamento Remoto: Avanços e Desafios

“…Por exemplo, o estudo potencial do uso de imagens multiespectrais RapidEye na detecção e na discriminação de áreas infestadas pelo besouro-da-raiz, Migdolus fryanus (Westwood) (Coleoptera: Cerambycidae), assim como de nematoides na cultura da cana-de-açúcar (MARTINS; GALO, 2014). Por meio da plataforma Sentinel-2, banda 5 (vermelha de borda), banda 4 (vermelha), banda 3 (verde) e banda 12 (SWIR 2), Kumbula et al (2019) identificaram áreas sob infestação de Coryphodema tristes (Drury) (Lepidoptera: Cossidae) em áreas de Eucalyptus nitens, na África do Sul, utilizando o modelo Mexent com variáveis preditoras de bandas espectrais, índices de vegetação e combinações.…”

Pemanfaatan Citra Sentinel 2 untuk Memprediksi Sebaran Hama Wereng Cokelat di Sebagian Wilayah Kabupaten Pangandaran

“…Salah satu satelit penginderaan jauh yang ada merupakan satelit sentinel 2. Satelit ini memiliki 13 saluran spectral yang memiliki panjang gelombang 443,9 nm sampai 2202,4 nm, saluran spectral yang paling efektif digunakan untuk monitoring status vegetasi pada citra sentinel 2 ini merupakan saluran spectral pada red edge (saluran 5, 6 dan 7) dengan panjang gelombang 703,9 nm, 740,2 nm dan 782,5 nm (Kumbula et al, 2019).…”

“…Kinerja model ditentukan berdasarkan luasan yang berada di bawah kurva Receiver Operating Characteristics (ROC) luas area ini disebut dengan Area Under The Curve AUC yang merupakan salah satu output yang dihasilkan model (Kumbula et al, 2019).…”

Eucalyptus Productivity Increase in China Comes From Newly Afforested Plantations

“…The basic goal of using geostatistics is to achieve local optimal estimation of geological variables by linear weighting. The Kriging method, also known as spatial local estimation or spatial local interpolation, is one of the two main topics in the field of geostatistics [19][20][21][22][23] . The Kriging method is essentially an approach for linear optimal estimation of regionalized variables at unknown sampling points using the original data of regionalized variables and structural characteristics of a variogram.…”