Satpalda Geospatial Services - Ghaziabad

Land cover maps are essential for making wise decisions about policy, development, planning, and resource management. The production of accurate and appropriate land cover maps for use in developing regions, however, is hampered by technological, capacity, and institutional obstacles. Many developing regions lack the integrated capacity, infrastructure, and technologies needed to create a reliable land cover monitoring system that satisfies land management requirements. External consultants or approaches that are not long-term sustainable may be used to replace local capabilities. Planners may try to suggest modifications to land-use patterns to accomplish a few social or economic effects, as part of an environmental preservation and durability mission, or to avoid a few predicted long-term negative impacts. Planners and the enterprises involved with planning can benefit from access to particular land-use maps.

As satellite imagery reflectance data in different seasons have been widely understood through classification methods, fully automated land cover maps have become widely produced. Classification and land cover creation have gotten much easier because of learning-based algorithms that are constantly refining their knowledge of signal data.

Normalized Difference Vegetation Index (NDVI)

To distinguish between land cover classes, the normalized differenced indices were used. By examining the land cover categories, the normalized differenced indices were employed for validation. The normalized difference vegetation index (NDVI) is a commonly used vegetation measure that is based on the reflectance characteristics of the vegetation-covered areas. It demonstrates the distinction between different land cover types by taking into consideration variations in green biomass, chlorophyll content, and canopy water stress.

Water Surface Mapping

It's also critical to differentiate areas from other types of land cover by identifying water surfaces. Water, for example, looks darker in the visible spectrum in Landsat imagery. Land cover may be confused or ambiguous for locations where water is present since signatures could be misinterpreted for other darker regions. The Automated Water Extraction Index (AWEI) was established to facilitate potential water signatures by using known ranges of water within an index and consistently learning and improving findings based on false-positive results. The index has been found to perform better than other methods such as Maximum Likelihood (ML) in detecting the variety and range of water that may appear on imaging data.

The Use of Semi-Automated Land Cover Mapping Techniques

Although methods for collecting reflectance data, applying atmospheric corrections, and performing classification are more automated in recent years, semi-automated approaches are still frequently required, especially in higher resolution imagery (e.g., 30-meter resolution) such as Landsat 7 data, where error and fluctuation from expected results are more likely. Random sampling and application of sampled data are used to not only assess the classification's accuracy but also to boost findings by informing on where the location is known to be.

Sophisticated computerized algorithms that apply categorization techniques, frequently employing machine learning methods such as random forest, have now been developed as knowledge about the Earth's land cover has grown with the capture of higher resolution data and with significant seasonal changes. The next objective will be to construct better regional and global maps utilizing even higher resolution images (e.g., 1-meter resolution satellite data), where automated methods are more likely to fail. This should necessitate more experts having access to high-performance computing resources so that different types of data can be processed more quickly.

Summary

Land cover maps show the actual coverage of the Earth's surface by different types (categories), such as forests, grasslands, croplands, lakes, and wetlands. Land cover changes are captured through dynamic land cover maps, which feature transitions between land cover classes across time. Land use maps show the spatial structure, actions, and inputs that people utilize to create, change, or sustain a specific land cover type. The map includes vegetation continuous field layers, which provide proportionate measurements of vegetation cover for a variety of land cover types. Without the assistance of other geospatial datasets, LULC mapping is impossible.