Investigation method for shaded coffee plantation detection using aerial photography

Abstract

Abstract Coffee plantation is one of the main agricultural sectors in the world, especially in Indonesia. For sustainable coffee management, it is important to obtain an assessment of the spatial distribution of coffee plantation. In fact, most of the coffee trees planted under the forest canopy as jungle coffee. However, remote sensing studies devoted to coffee in Indonesia have been limited due to spectral similarity with forest. Hence, the shaded coffee plantation has been difficult to be mapped. This condition takes aerial photography as an alternative solution to detect the distribution of shaded coffee plantation with high spatial resolution. This paper presents an assessment of the classification method of aerial photography for detecting shaded coffee plantation. The data used in this study is aerial photography captured in a shaded coffee field located in Gunung Puntang. Some classification methods namely Pixel-Based and Object-Based Image Analysis (OBIA) were used. Those method performances were evaluated by comparing the classification result as overall accuracy. The result shows that OBIA is the best method for detecting shaded coffee plantation. It produces an overall accuracy of 73.01%.