LULC types and applications

2. CONTENT
1
LAND USE AND LAND COVER
(LULC)
2
LULC TYPES AND THEIR
RESPECTIVE CLASSES
3
LAND USE MAPPING SYSTEM
4
LULC MAPPING APPLICATIONS
5
CONCLUSION

3. LAND USE AND LAND COVER (LULC)
1. LAND USE:
Land use includes a series of operations on land, carried out
by humans, with the intention to obtain products and benefits
through using land resources. It includes agricultural land,
urban area, wildlife management area, recreation area etc.
2. LAND COVER:
Land cover refers to observed physical and biological cover of
earth’s surface. It includes various types of vegetation, grass
land, forest, water bodies, barren land etc. (FAO, 2023).

4. At the local, regional, and national levels, LULC maps are
crucial for planning, management and monitoring programmes.
Provides a better understanding of land utilization
aspects.
It is crucial in the formation of the policies and
programmes essential for development planning.
To prevent the unplanned development of towns and cities
and to promote sustainable urban development.
IMPORTANCE OF LULC MAPS

5. LULC CLASSIFICATION
 One of the most widely used applications in remote sensing
is LULC classification. The most frequently used methods
are:
(Source: Paris et al., (2019), eo4society.esa.int)
Supervised
classification
Unsupervised
classification

6. Unsupervised classification and Supervised classification
Classification Advantages Disadvantages
Unsupervised
classification
No prior knowledge of the
region is required.
Allows for minimization of the
human error.
Spectrally distinct areas
presented which may not have
been obvious to the human eye.
Spectral grouping may not
correspond to information classes
of interest to the analyst.
Analyst has little control over the
classes.
Supervised
classification
Analyst have control.
Operator can often detect and
rectify images.
Collecting training data is time
consuming and costly.
There is no way to recognize and
represent categories which are not
represented in the training data.
(Source: slideshare.net, Dhanendra Bahekar)

7. Normalized Difference Vegetation Index (NDVI)
(Source: www.satpalda.com, 2018)
 The NDVI is calculated using near-infrared (NIR) and visible
red (R) light to look for a single band normalized vegetation
index in plants. Then, the NDVI is calculated using a digital
number (DN) and several band values (Özyavuz et al., 2015).
 For Landsat 8, the NDVI is derived using the NIR (Band 5),
and the Red (Band 4) band.
NDVI = (NIR – Red) / (NIR + Red)
or
NDVI = (Band 5 - Band 4) / (Band 5 + Band 4)

8. LULC METHODOLOGY
DATA
ACQUISITION
DIFFERENT TIME
PERIODS DATA
IMAGE PRE-
PROCESSING
 Geometric correction
 Radiometric correction
 Image enhancement
IMAGE CLASSIFICATION
 Unsupervised classification
 Supervised classification
LULC MAP
ACCURACY ASSESMENT
GPS & Google
Earth data
NDVI analysis
LULC CHANGE
DETECTION
LULC: Land use and land cover
GPS: Global Positioning System
NDVI: Normalized Difference
Vegetation Index

9. LULC TYPES AND THEIR RESPECTIVE CLASSES
TYPES CLASSES
 Residential
 Commercial and Services
 Industrial
 Communications and Utilities
 Mixed Urban or Built-up Land
 Other Urban or Built-up Land
 Cropland and Pasture
 Orchards, Nurseries, and Ornamental
Horticultural areas
 Confined Feeding operations
 Herbaceous Rangeland
 Shrub and Brush Rangeland
 Mixed Rangeland
 Deciduous Forest Land
 Evergreen Forest Land
 Mixed Forest Land
(Source: www.satpalda.com, 2018)

10. LULC TYPES AND THEIR RESPECTIVE CLASSES (cont.)
TYPES CLASSES
 Rivers
 Streams and Canals
 Lakes
 Reservoirs
 Bays and Estuaries
 Forested Wetland
 No forested Wetland
 Dry Salt Flats
 Beaches
 Sandy Areas other than Beaches
 Bare Exposed Rock
 Strip Mines, Quarries, and Gravel Pits
 Transitional Areas
 Mixed Barren Land
 Perennial Snowfields
 Glaciers
(Source: www.satpalda.com, 2018)

11. Figure 1: Land use and land cover map of India (Roy et al., 2015)

12. LAND USE MAPPING SYSTEM
LEVEL SCALE DATA SOURCE FREQUENCY
1.National 1:500,000
Medium Resolution
(56 m) Satellite data
Annually
2. State 1:250,000
Medium Resolution
(24 m) Satellite data
Once in 5 years
3. District 1:50,000
Medium Resolution
(24 m) Satellite data
Once in 5 years
4. Village 1:10,000
High resolution
satellite data (2.5 m)
Once in 8 years
5. Cadastral 1:5,000
Very High resolution
satellite data (<1 m)
Once in 3 years

13. Scale: 1:500,000 Scale: 1:250,000
Scale: 1:5,000
Scale: 1:50,000 Scale: 1:10,000

14. LULC MAPPING APPLICATIONS
Baseline mapping for GIS input
Urban expansion / encroachment
Natural resource management
Wildlife habitat protection
Routing and logistics planning for
exploration/resource extraction activities
Identification of roads, clearings, bridges,
land/water interface
Damage delineation (tornadoes, flooding,
volcanic, fire)
Legal boundaries for tax and property
evaluation

15. ⁂ Maps of an area’s land use and land cover (LULC) give
users a better understanding about the current landscape.
The annual monitoring of temporal dynamics of agricultural
ecosystems, forest conversions, surface water bodies, etc.
will be made possible by LULC information on national
spatial databases.
⁂ LULC maps play a significant and prime role in planning,
management and monitoring programmes at local, regional
and national levels.
⁂ For ensuring sustainable development, it is necessary to
monitor the on going process on land use/land cover pattern
over a period of time.
CONCLUSION

16. REFERENCES
1. https://www.satpalda.com/blogs/significance-of-land-use-land-cover-
lulc-maps
2. https://www.slideshare.net/DhanendraBahekar/land-cover-and-land-use
3. Roy, P.S., P. Meiyappan, P.K. Joshi, M.P. Kale, V.K. Srivastav, S.K.
Srivasatava, M.D. Behera, A. Roy, Y. Sharma, R.M.
Ramachandran, P. Bhavani, A.K. Jain, and Y.V.N. Krishnamurthy.
(2016). Decadal Land Use and Land Cover Classifications across India,
1985, 1995, 2005. ORNL DAAC, Oak Ridge, Tennessee, USA.
https://doi.org/10.3334/ORNLDAAC/1336
4. Özyavuz, Murat & Bilgili, Cemil & Salıcı, Aylin. (2015).
Determination of vegetation changes with NDVI method. Journal of
environmental protection and ecology. 16: 264-273.
5. Paris, Claudia & Bruzzone, Lorenzo & Fernandez-Prieto, Diego.
(2019). A Novel Approach to the Unsupervised Update of Land-Cover
Maps by Classification of Time Series of Multispectral Images. IEEE
Transactions on Geoscience and Remote Sensing. PP. 1-19.
10.1109/TGRS.2018.2890404.