Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data
NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
World Bank Group, Global Facility for Disaster Reduction and Recovery, Washington, DC 20006, USA
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3113; https://doi.org/10.3390/rs12193113
Received: 28 July 2020 / Revised: 11 September 2020 / Accepted: 20 September 2020 / Published: 23 September 2020
(This article belongs to the Special Issue Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data)
This paper presents a simple and efficient image processing method for estimating the number of coconut trees in the Tonga region using very high spatial resolution data (30 cm) in the blue, green, red and near infrared spectral bands acquired by the WorldView-3 sensor. The method is based on the detection of tree shadows and the further analysis to reject false detection using geometrical properties of the derived segments. The algorithm is evaluated by comparing coconut tree counts derived by an expert through photo-interpretation over 57 randomly distributed (4% sampling rate) segments of 200 m × 200 m over the Vaini region of the Tongatapu island. The number of detected trees agreed within 5% versus validation data. The proposed method was also evaluated over the whole Tonga archipelago by comparing satellite-derived estimates to the 2015 agricultural census data—the total tree counts for both Tonga and Tongatapu agreed within 3%.