NATIONAL LEVEL INVENTORY OF COFFEE PLANTATIONS USING HIGH RESOLUTION SATELLITE DATA

• Main Authors: Hebbar, R., Ravishankar, H. M., Trivedi, S., Manjula, V. B., Kumar, N. M., Mukharib, D. S., Mote, J. K., Sudeesh, S., Raj, U., Raghuramulu, Y., Ganesha Raj, K.
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Agriculture; Chronology; Classification; Coffee; Cultivation; Datasets; High resolution; Image classification; Mapping; Optimization; Parameters; Pests; Plantations; Planting density; Regions; Resolution; Satellites; Spectra; Spectral signatures; Support vector machines; Terrain; Texture
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLII-3-W6-293-2019

Coffee is the second most traded commodity in the world and its production has implications in both international and domestic economy. It is an important commercial crop of India and hence, reliable acreage and production estimation is most essential for taking up policy decisions. The coffee growing regions in India are mainly confined to the traditional South Indian states (Karnataka, Kerala and Tamil Nadu) and partly in non-traditional regions (Andhra Pradesh and Odisha) while to a smaller extent in North-Eastern states. Interpretation and mapping of coffee plantations using satellite data is quite challenging due to the diverse and complex cultivation practices. In the present study, multi-resolution and multi-source data was utilized for mapping of coffee plantations in the country. Temporal LISS-III (24.0 m) data was used for characterizing the phenology of coffee and other competing plantation crops for selection of optimal high resolution satellite (HRS) datasets. Accordingly, Cartosat-1 (2.5 m) and Resourcesat LISS-IV multispectral (5.0 m) datasets corresponding to February-April months were utilized. The spectral signature of coffee plantations is determined by the age category of coffee plantations, varietal difference, density & composition of shade trees along with terrain features like slope and aspect. The plantations manifested in different tones of red and mottled texture on the multispectral image. Object oriented classification approach showed encouraging results in homogenous & contiguous areas but showed poor mapping accuracy in heterogeneous regions due to complex spectral signature and varying texture. Thus, a combination of digital and visual interpretation techniques were used for mapping of coffee plantations depending on the suitability. Feature space optimization function was used for selection of object parameters and 14 image features consisting of mean spectral values, standard deviation, NDVI, geometry and contextual parameters were used for classification of coffee plantations using Support Vector Machine (SVM). In case of small holdings and heterogeneous areas, interactive visual interpretation of HRS data at 1 : 5,000 scale using tone, texture, shape and terrain characteristics was carried out for mapping of coffee plantations with the help of ground truth and field experience of Liaison Officials of Coffee Board. Post-interpretation field verification/validation of the interpreted maps was carried out for the accuracy a