GEOG researchers (S.Skakun, I.Becker-Reshef) in collaboration with NASA GSFC (E.Vermote) and World Bank (K.Saito) published a paper on "Remote Sensing of Coconut Trees in Tonga Using Very High Spatial Resolution WorldView-3 Data" in journal Remote Sensing (https://doi.org/10.3390/rs12193113). This study used very high spatial resolution satellite imagery acquired by WordView-3 (at 30 cm resolution) to count coconut trees in the Tonga archipelago. The proposed algorithm can be used for estimating the impact of natural disasters (e.g. cyclone Gita) on the coconut plantations.
This paper is the first one published in the Special Issue "Remote Sensing of Land Use/Cover Changes Using Very High Resolution Satellite Data" (https://www.mdpi.com/journal/remotesensing/special_issues/LULC_VHR) that aims at NASA-funded researchers who have been using VHR data in LU/CC research and applications.
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%.