While most of the 10 criteria withstood the test of time, the first two criteria are not always attainable when mapping land use and land cover large, complex geographic areas. Did you ever imagine how the various processes occur so smoothly inside your body There are loads of activities that are performed in your body in a systematic manner. Note: These criteria were setup prior to the widespread use of satellite imagery and computer-aided classification.

Multiple uses of land should be recognized when possible. Comparison with future land use and land cover data should be possible 10. Categories can be divided into more detailed subcagories that can be obtained from large scale imagery or ground surveys. Suitable for use with remotely sensed data obtained at different times of the year.

Categorization permit land use be inferred from the land cover types 6. Repeatable results from one interpreter to another and from one time of sensing to another. The minimum interpretation accuracy with remotely sensed data is >=85% Accuracy of interpretation for several categories should be equal. Regroup the clusters into original classesĬriteria for USGS Landuse/landcover Classification System 1. Unsupervised classification to identify spectral clusters within the training sets. Hybrid Classification: It takes the advantage of both the supervised classification and unsupervised classification. It is the users’ responsibility to assign a class label to each of the clusters. Unsupervised classification: Instead of providing the computer with examples of features in multi-dimensional feature space, the users let the computer to identify pre-specified number of spectral clusters among which the difference between clusters are maximized and within clusters are minimized. The computer will first analyze the statistical parameters for the training data and then assign all other pixels to one of the classes in the examples based on statistical similarity. Computer-Aided Classification Parametric: assumes normal distribution of the data Supervised classification: provide the computer with some examples of known features in multi-dimensional feature space.

Human brain mapping manuscript central