This document discusses using remote sensing and GIS for land use/land cover mapping. It describes analyzing agricultural versus urban land to ensure development doesn't degrade farmland. Land cover refers to ground surface characteristics like vegetation or bare soil, while land use refers to how land is used, such as agriculture or recreation. The document outlines classification systems and criteria for remote sensing-based land use/land cover mapping. It also discusses digital classification techniques, global and national land use datasets, and applications of remote sensing for natural resource management and change detection analysis.

1. Remote Sensing and GIS in Land Use /
Land Cover Mapping
K.VENKATASALAM
I M.Sc., (Soil science & Agricultural chemistry)
ADAC&RI, Trichy
Tamil Nadu Agricultural University

2. Purpose
 Analyzing agricultural Vs. urban land use is important for ensuring
that development does not encroach on valuable agricultural land
 Ensure that agriculture is occurring on the most appropriate land and
will not degrade due to improper adjacent development or
infrastructure.

3. Land cover refers to the surface cover on the ground, whether
vegetation, urban infrastructure, water, bare soil or other.
 Identifying, delineating and mapping land cover is important for
global monitoring studies, resource management, and planning
activities.
 Land cover mapping serves as a basic inventory of land resources -
change detection studies

4. Land Use refers to the purpose the land serves, agriculture,
construction, recreation, wildlife habitat.
 Identify the land use changes from year to year.
 To conserve the resources (urban encroachment, and depletion of
forests) and make developmental plan

5. Land use / land cover classification system:
principles like:
 Level of intervention
 Generality
 Hierarchical structure
 Prime use / Ancillary use.

6. Anderson’s (1971) criteria of LU/LC classification system
(employing remote sensing data):
 The minimum level of interpretation accuracy in the identification of land
use and land cover categories should be at least 85 percent.
 The accuracy of interpretation for the several categories should be about
equal.
 Repeatable or repetitive results should be obtainable from one interpreter to
another and from one time of sensing to another.
 The classification system should be applicable over extensive areas.
 The categorization should permit vegetation and other types of land cover
to be used as surrogates for activity.

7.  The classification system should be suitable for use with remote sensor
data obtained at different times of the year.
 Effective use of subcategories that can be obtained from ground
surveys or from the use of larger scale or enhanced remote sensor data
should be possible.
 Aggregation of categories must be possible.
 Comparison with future land use data should be possible.
 Multiple uses of land should be recognized when possible.

8. Land Use / Cover Data Sets
Global data:
 International Geosphere Biosphere Project Discover at 1km resolution
 University of Maryland at 1km resolution
 Global Land Cover 2000 at 1km resolution
 MODIS Land cover product at 250m resolution.
National dataset:
 NRSC, ISRO with the collaboration of Ministry of Agriculture,
NBSS&LUP working on this system.

10. Digital classification
a. Supervised classification
 The information about spectral representation of a characteristic land cover
class has to be provided in the form of training sets. Several supervised
classification algorithms are employed for land use/cover information
extraction.
 However, supervised classification employing maximum likelihood
algorithm has been the most commonly used digital classification technique
on remotely sensed data (Richards, 1993).

11. b. Un-supervised classification
 No prior information about the land cover types or their distribution is required.
 Unsupervised classification methods divide the scene into more or less pure spectral
clusters, typically constrained by pre-defined parameters characterizing the
statistical properties of these clusters and the relationships among adjacent clusters.
 The assignment of land cover labels to individual spectral clusters is made
subsequently on the basis of ground information, obtained in the locations indicated
by the resulting clusters.
 when mapping a large area previously not well known, unsupervised classification
is a better strategy

12. Land use applications of remote sensing
 Natural resource management
 Wildlife habitat protection
 Baseline mapping for GIS input
 Urban expansion / encroachment
 Routing and logistics planning for exploration / resource extraction
activities
 Damage delineation (flooding, volcanic, seismic, tornadoes, fire)
 Legal boundaries for tax and property evaluation

13.  Target detection - identification of landing strips, roads, clearings,
bridges, land/water interface
 Remote sensing methods can be employed to classify types of land
use in a practical, economical and repetitive fashion, over large
areas.
 As it gives the synaptic view
 Multi-temporal data (various year / season)
 Any part of the word

14. Land cover /land use map

15. Methodology
 Data acquisition, loading (import), merging
 Ground data collection (collection of GCPs)
 Georeferencing and Image enhancement
 Training area definition, signature generation and classification
 Annotation, demarcation of administrative boundaries and cultural
features.
 Generation of statistics from the classified outputs

18. Change detection analysis:
 Land-use and land-cover change (LULCC) also known as land change
 Is a general term for the human modification of Earth's terrestrial
surface.
 These changes encompass the greatest environmental concerns of
human populations today, including climate change, biodiversity loss
and the pollution of water, soil and air.

19. Change detection analysis – A project report from University of Ibadan

20. Right solution at
right time