NASA ARSET_ Training Data and Land Cover Classification Example, Part 2_3

In this segment of the NASA Applied Remote Sensing Training (ARSET) series, participants are guided through the intricate process of Land Cover Classification using remote sensing data. Building upon the foundational concepts introduced in Part 1, this installment delves deeper into the methodology by focusing on the acquisition and preparation of training data – a crucial step in achieving accurate land cover classification results.

The training commences with an overview of the diverse sources of remote sensing data, encompassing satellite imagery, aerial photographs, and other geospatial datasets. Participants gain insights into the significance of selecting suitable data sources that align with the desired classification objectives and the spatial and spectral characteristics of the study area. This section also emphasizes the significance of data preprocessing techniques, such as atmospheric correction and radiometric calibration, to enhance the quality and reliability of the training data.

The subsequent segments of the training focus on the selection and delineation of training samples, which constitute representative areas of various land cover classes within the study area. Through a blend of theoretical guidance and practical exercises, participants learn the art of systematically identifying sample locations, ensuring their spatial distribution accurately captures the diversity of the landscape. Techniques for avoiding sampling bias and adequately representing rare or underrepresented land cover types are also addressed.

With the training samples in place, the module delves into the construction of spectral signatures – unique spectral profiles that characterize each land cover class. Participants are introduced to spectral reflectance curves and are shown how to extract spectral information from the training samples. The importance of selecting an appropriate number of training samples per class to maintain a balance between computational efficiency and classification accuracy is highlighted.

As the training progresses, participants transition from training data preparation to a comprehensive exploration of Land Cover Classification techniques. This module covers both supervised and unsupervised classification methods, including maximum likelihood classification, support vector machines, and clustering algorithms. Through hands-on examples and case studies, participants gain proficiency in utilizing these techniques to transform raw remote sensing data into meaningful land cover maps.

By the end of Part 2, participants have acquired a profound understanding of the critical role that well-prepared training data plays in the success of land cover classification endeavors. Armed with the knowledge of selecting appropriate data sources, preprocessing techniques, and representative training samples, participants are primed to apply sophisticated classification algorithms covered in Part 3. This module equips learners with a comprehensive skill set that empowers them to harness the potential of remote sensing data for accurate and insightful land cover classification, contributing to a deeper comprehension of our dynamic Earth's surface.