Landuse and Landcover (LULC) Mapping using Conventional and Machine Learning approaches and its Prediction by Cellular Automata Markov Chain model
Prepare LULC maps using, Supervised, Unsupervised, Combined classification methods and ML Approaches and future prediction by CA Markov Chain model
In this online workshop and live practice, you can learn the complete process (A-Z) from scratch to production. Step by step guide of downloading satellite images, its processing, classification, change detection and future prediction using CA-Markov chain of Land Use Land Cover (LULC) in Erdas Imagine, Arc GIS and Terrset Environment. How to select parameters and why, download raster and vector data and process using different software. You will also learn the accuracy assessment of the LULC map using Kappa Statistics in Erdas Imagine software.
Land Use Land Cover (LULC)
Land Use Land Cover (LULC) applications involve both baseline mapping and subsequent monitoring, since timely information is required to know what current quantity of land is in what type of use and to identify the LULC changes from year to year. This knowledge will help develop strategies to balance conservation, conflicting uses, and developmental pressures. Issues driving land use studies include the removal or disturbance of productive land, urban encroachment, and depletion of forests. Also, it plays a significant and prime role in planning, management, and monitoring programs at local, regional, and national levels. This type of information, on one hand, provides a better understanding of land utilization aspects and on the other hand, it plays an important role in the formation of policies and programs required for development planning. For ensuring sustainable development, it is necessary to monitor the ongoing process of land use/land cover pattern over a period of time. In order to achieve sustainable urban development and to check the haphazard development of towns and cities, it is necessary that authorities associated with urban development generate such planning models so that every bit of available land can be used in the most rational and optimal way. This requires the present and past LULC information of the area. LULC maps also help us to study the changes that are happening in our ecosystem and environment. If we have an inch-by-inch information about LULC of the study unit we can make policies and launch programs to save our environment.
Keywords: LULC; Supervised; Unsupervised; Combined Classification; RF; SVM; LULC Change detection; CA Markov Chain
During this workshop, participants can prepare, process, predict and validate LULC map, change detection and prediction
Publication support from SCOPUS Index Journal
Provide supporting resources like ppt, code, research articles
Access recorded class videos of any time
Instructor: Mr. T. Mandal, WhatsApp- +91 94740 86902
Over two decades of experience and expertise in teaching Tech and coding helping students develop their tech skills for higher performance, better careers and growth in companies.
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