Identifying valid training sites for supervised image classification can be difficult without access to a large number of pre-identified training sites. One way to identify training sites is to surv

If you're not provided with sample training sites for supervised image classification, you can use the following techniques to identify correct land cover types for training sites: 1. Visual interpretation: Examine the satellite or remote sensing imagery visually, and identify areas that represent distinct land cover types based on your familiarity with the region or domain knowledge. Pay attention to the colors, patterns, and textures of the different land cover types. 2. Ground truth data: Use available ground truth data, such as existing land cover maps, field surveys, GPS points, or geotagged photos, as reference information for identifying training sites. This can improve the accuracy of your training samples. 3. High-resolution imagery: Use high-resolution imagery, such as Google Earth or Bing Maps, to help you distinguish between different land cover types in your study area more accurately. High-resolution imagery can provide additional information about the landscape not available in the low-resolution data used for classification. 4. Ancillary data: Incorporate ancillary data sources, such as topography, land use, or soil maps, to help identify land cover types that correspond with particular environmental conditions or characteristics. Integrating these complementary data layers can help you better understand the spatial distribution of different land cover types in your study area. 5. Expert consultation: Consult with domain experts, such as ecologists, geographers, or land managers knowledgeable about the study area, to help identify land cover types and their locations accurately. This can be particularly valuable in complex ecosystems with subtle distinctions between land cover types. 6. Iterative approach: As you progress through the classification process, you may need to iteratively refine your training sites by visually inspecting the classified imagery and comparing it to the reference data. Remember that selecting accurate and representative training sites is crucial for the success of a supervised image classification. It's essential to ensure adequate coverage of all the land cover types in your study area and strive for a balanced distribution of sample sites to minimize biases and improve classification accuracy.