Ismail, R

Onisimo, M

Kumar, Lalit

Bob, U

2006

Sirex noctilio is causing considerable mortality in commercial pine plantations in KwaZulu-Natal, South Africa. The ability to remotely detect variable (e.g. low, medium and high) S.noctilio infestation levels remains crucial for the monitoring of the actual spread of the disease and for the effective deployment of suppression activities. Although high resolution image data can detect and monitor S.noctilio infestations there are no guidelines to the appropriate spatial resolutions that are suitable for monitoring and detection purposes. This study examines the use of variance to analyze Sirex noctilio infestations in an effort to determine an optimal spatial resolution of remotely sensed data for forest health monitoring purposes. High resolution (0.5 m) image data was collected using a four band airborne sensor and infestation levels were derived using the normalized difference vegetation index (NDVI) and Gaussian maximum likelihood classifier. It was determined that the appropriate spatial resolution for the detection and monitoring of Sirex noctilio infestations as estimated by the minimum variance of sub samples narrowly differed based on the level of localized infestations present in the study area. Pixel sizes larger than 2.3 m will not provide adequate information for high infestation levels, while using pixel sizes smaller than the 1.75 m for detecting low to medium infestation levels will yield inappropriate results. The results of this study contribute to establishing the necessary spatial resolution guidelines needed for the operational monitoring and detection of Sirex noctilio.

Proceedings of the 6th International Conference on Earth Observation & Geoinformation Sciences in Support of Africa’s Development p.104

1920017100

https://hdl.handle.net/1959.11/4697

National Authority for Remote Sensing and Space Sciences (NARSS) & African Association of Remote Sensing of the Environment (AARSE)

Examining the effects of spatial resolution on 'Sirex Noctilio' infestations using NDVI data

Conference Publication

Publication