Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey

Hazelnuts and tea are two major agricultural crops grown in the eastern Black Sea region in Turkey. Since this part of Turkey is not industrialized, most of the local people work in agriculture, making hazelnuts and tea a part of their lives. For the government side, it is crucial to keep records of the amount of harvested croplands to implement agricultural policies. In fact, the harvested area and crop type of each cadastral parcel are collected either during cadastral surveys or with the declaration of individual farmers, yet this information is mostly not up-to-date and does not reflect the current land-use status. This study aims to determine the extent and distribution of hazelnuts and tea grown areas using the Random Forest (RF) classification algorithm. Tea and hazelnuts give similar spectral reflectance values to surrounding vegetation, which makes it difficult to distinguish them using only their spectral properties. To tackle this problem, the normalized difference vegetation index (NDVI) and texture extraction methods such as the Grey Level Co-occurrence Matrix (GLCM) and Gabor filter were integrated with the RF algorithm, and their contributions to the success of the RF classification method were examined. WorldView-2 satellite images, which have eight multispectral bands (MS: 2 m) and one higher spatial resolution panchromatic band (PAN: 0.5 m), were used. Since the study area contains agricultural products grown in different seasons, satellite images belonging to both summer and winter periods were used. Preliminary results acquired using only spectral values indicated that the RF method gives 79.05% and 71.84% overall accuracies for summer and winter periods, respectively. Integrating texture information improves the performance of the RF algorithm such that the overall classification accuracies are increased to 83.54% and 87.89% when texture information extracted with GLCM and the Gabor filter is added. The classification performance of the winter image is also boosted to be 77.41% and 79.73% with the contribution of texture information obtained with GLCM and the Gabor filter, respectively. Finally, produced thematic maps were compared with the latest cadastral maps to validate classification results with ground truth data. The obtained results reveal the success of integrating texture features in classification since the created thematic maps are consistent with actual land use. The results also show that the crops grown on some cadastral parcels are not coherent with the most current cadastral database, which implies that the cadastral maps need to be updated. © 2015, Taylor & Francis.