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Significance of Land Use / Land Cover (LULC) Maps

by Yashwant Singh
Introduction
 
The terms land use and land cover are often used interchangeably, but each term has its own unique meaning. Land cover refers to the surface cover on the ground like vegetation, urban infrastructure, water, bare soil etc. Identification of land cover establishes the baseline information for activities like thematic mapping and change detection analysis. Land use refers to the purpose the land serves, for example, recreation, wildlife habitat, or agriculture. 
 
When used together with the phrase Land Use / Land Cover (LULC) generally refers to the categorization or classification of human activities and natural elements on the landscape within a specific time frame based on established scientific and statistical methods of analysis of appropriate source materials.
                                      Digital mapping of soil organic and inorganic carbon status in India
 
Land cover is the physical material at the surface of the earth. Land use is the description of how people utilize the land for the socio-economic activities.
 
Why do we need LULC Maps?
 
The growth of a society totally depends on its social and economical development. This is the basic reason why socio-economic surveys are carried out. This type of survey includes both spatial and non-spatial datasets. LULC maps play a significant and prime role in planning, management and monitoring programmes 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 programme required for development planning. For ensuring sustainable development, it is necessary to monitor the on going process on 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 the urban development generate such planning models so that every bit of available land can be used in most rational and optimal way. This requires the present and past land use/land cover 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 Land Use/Land Cover of the study unit we can make policies and launch programmes to save our environment.
 
Change detection is shown at the same region at two different time periods using LULC map
 
LULC classification
 
LULC classification is one of the most widely used applications in remote sensing. The most commonly used approaches include :
 
->Unsupervised classification (calculated by software)
 
This type of classification is based on the software analysis of an image without the user provided sample classes. This involves grouping of pixels with common characteristics. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorithm the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified (such as wetlands, developed areas, coniferous forests, etc.).
 
Steps involved :
 
->Supervised classification (human guided)
 
 This is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into.
 
Steps involved :
 
->Image segmentation
 
Image segmentation is the partition and pick-up of the homogeneous regions of the image. In the results of segmentation, the consistency of gray the smoothing of boundary and the connectivity are fulfilled. The classical method of segmentation is the spatial cleaning based on the measurement space. Image segmentation is a crucial processing procedure for the classifications and feature extraction of high-resolution remote sensing image.
 
main image segmentation methods are :
 
  • Threshold based 
 
Threshold segmentation is the simplest method of image segmentation and also one of the most common parallel segmentation methods. It is a common segmentation algorithm which directly divides the image gray scale information processing based on the gray value of different targets.
 
  • Edge Detection Segmentation
 
The edge of the object is in the form of discontinuous local features of the image, that is, the most significant part of the image changes in local brightness, such as the gray value of the mutation, color mutation, texture changes and so on. The use of discontinuities is to detect the edges and to achieve the purpose of image segmentation.
 
  • Regional Growth Segmentation
 
The regional growth method is a typical serial region segmentation algorithm, and its basic idea is to have similar properties of the pixels together to form a region. The method requires first selecting a seed pixel and then merging the similar pixels around the seed pixel into the region where the seed pixel is located.
 
 
->NDVI
 
Normalized Difference Vegetation Index (NDVI) is simply a ratio of near infrared (NIR) reflectivity minus red reflectivity (VIS) over NIR plus VIS. It is the most common index that analysts use in remote sensing.
 
        NDVI=(NIR-VIS)/(NIR+VIS)
 
Specifically, NDVI was developed by a NASA scientist named Compton Tucker in  1977. The basic principle of NDVI relies on the fact that, due the spongy layers found on their backsides, leaves reflect a lot of light in the near infrared, in stark contrast with most non-plant objects. When the plant gets dehydrated or stressed, the spongy layer collapses and the leaves reflect less NIR light, but the same amount in the visible range. Thus, mathematically combining these two signals can help differentiate the plant from the non-plant and healthy plant from sickly plants.
 
NDVI always ranges from -1 to +1. For example, when you have negative values, Most likely it’s water. On the other hand, if you have a NDVI value close to +1, there’s a high possibility that there are dense green leaves but when NDVI is close to zero, it means there are no green leaves and it could even be an urbanized area.
 
 
 
Following are some of the LULC types and their respective classes
Urban or Built-up Land
  • Residential
  • Commercial and Services
  • Industrial
  • Communications and Utilities
  • Mixed Urban or Built-up Land
  • Other Urban or Built-up Land
Agricultural Land
  • Cropland and Pasture
  • Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas
  • Confined Feeding Operations
Rangeland
  • Herbaceous Rangeland
  • Shrub and Brush Rangeland
  • Mixed Rangeland
Forest Land
  • Deciduous Forest Land
  • Evergreen Forest Land
  • Mixed Forest Land
Water
  • Rivers
  • Streams and Canals
  • Lakes
  • Reservoirs
  • Bays and Estuaries
Wetland
  • Forested Wetland
  • No forested Wetland
Barren Land
  • Dry Salt Flats
  • Beaches
  • Sandy Areas Other than Beaches
  • Bare Exposed Rock
  • Strip Mines, Quarries, and Gravel Pits
  • Transitional Areas
  • Mixed Barren Land
Perennial Snow or Ice
  • Perennial Snowfields
  • Glaciers

 

 
 
                    Agricultural Land                                                          Wetland                                        Barren Land
 
 
Geospatial data and LULC
 
With the advancements in remote sensing, monitoring networks, and geographic information systems (GIS), the availability of spatial data is rapidly increasing. These geospatial data include not only maps and locations of land use and land cover (LULC), but also multiple attributes of data, such as socioeconomic data from the census. Improvements in the use and accessibility of multi-temporal, satellite-derived environmental data or other thematic raster data have contributed to the growing use in environmental modeling. Remote sensing provides synoptic information on vegetation growth conditions over a large geographic area in near real-time. The vegetation growth pattern is estimated using the normalized difference vegetation index (NDVI), which is based on visible (red) (VIS) and near-infrared (NIR) band reflectance derived from the most widely used global NDVI data sets.
 
Applications of LULC maps
  • Natural resource management
  • Wildlife habitat protection
  • Baseline mapping for GIS input
  • Urban expansion / encroachment
  • Routing and logistics planning for seismic / exploration/resource extraction activities
  • Damage delineation (tornadoes, flooding, volcanic, seismic, fire)
  • Legal boundaries for tax and property evaluation
  • Target detection - identification of landing strips, roads, clearings, bridges, land/water interface
 
Summary
 
Land Use / Land Cover (LULC) generally refers to the categorization or classification of human activities and natural elements on the landscape within a specific time frame based on established scientific and statistical methods of analysis of appropriate source materials. It has various methods of classifications. Various type of LULC elements is there like Urban or Built-up Land, Agricultural Land, Forest Land and many more. LULC maps has there wide applications like Natural resource management, Baseline mapping for GIS input, Legal boundaries for tax and property evaluation and many more. LULC mapping is not possible without the help of other geospatial datasets.

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