Forest change detection using RS and GIS

1. Forest Change Detection and Monitoring
Group-Shapefile
M.Sc. Forestry (2076-78)
AFU, FOF Hetauda
Submitted to: Assistant Professor Jeetendra Gautam

2. 3/21/2022 2
We all are involved in all aspect of this assessment, however
following were our major role.
 Anu Sharma - Moderator
 Kushal Shrestha – Power point preparation
 Padam Prakash Jaishi – Landsat 8 image download
 Rajkumari Rana – Zoom meeting host during group task
 Shrawan Giri – Data analysis and interpretation
 Sagar Budhathoki – Supervised classification
 Tekendra Mauni - Supervised classification
Team member and role

3. Outline of Presentation
1. Introduction
2. Objective
3. Methodology
4. Results
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4. Introduction
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 LULC is the change in the biophysical cover and use of land for different
purposes.
 The terrestrial or land covers of the earth and changes therein are central to a
large number of the biophysical processes of global environmental change
(Turner et al., 1995).
 Land uses and land covers change over time in response to evolving economic,
social, and biophysical conditions (Lebow et al., 2012).
 Land use and land cover changes may be grouped in two categories:
conversion and modification. (Baulies and Szejwach, 1998).
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5. 5
Objective
The general objective of this assignment is to detect the Forest Change in
Makwanpur District, Central Nepal.
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Study area:
We choose Makwanpur District
of central Nepal as our study
area.

6. 6
Methods and Methodology:
Image Download:
 We downloaded the Landsat 8 image from USGS website.
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7. 7
Methods and Methodology:
Composite Band Formation:
 We, added the first seven bands of Landsat 8 image in order to classify the land use
category and formed a composite band.
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8. 8
Methods and Methodology:
Band Composition:
For the ease of land use identification, we used 4,3,2 band (as a natural color band) and
6,5,2 band to distinguish between forest and agricultural land use.
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9. 9
Methods and Methodology:
 After adjusting the band combination we extracted the composite band by our study
area (Makwanpur District) using extract by mask tool.
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10. 10
Methods and Methodology:
 After extracting our image of interest we started to assign classes to the identified
land use classes in training sample manager. We classified into five land-use
category namely - forest, agriculture, buildup, water, and bare area.
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11. 11
Methods and Methodology:
 After assigning identified classes we ran interactive supervised classification under
classification panel. And we then assigned color to each land use class.
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12. 12
Methods and Methodology:
 After assigning color, we searched for raster to polygon tool in order to calculate
the area of respective land use category and merged the similar classes using
dissolve tool.
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13. 13
Methods and Methodology:
 After calculating the
area, we hence
classified the land use
of 2021. Then, we
added land use map of
2010 prepared by
ICIMOD in order to
detect the change.
 After adding both the
classified image of
2010 and 2021, we
intersected both
image through
Geoprocessing.
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Methods and Methodology:
 Attribute table of intersected image is opened and added the field named change
using text category. In this field, we ran field calculator and entered the following
formula;
[Class_2010] + “ – ” + [Class _2021]
 This helped us to determine the change class category,
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15. 15
Methods and Methodology:
 Again we added another field named area change using float category. Here we
ran calculate geometry in order to calculate the changed area to the respective
land use change category.
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16. 16
Results:
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17. 17
Results:
 We identified, positive grown in forest area by 5012 ha in between 2010 and 2021.
While agricultural land has decreased by 3121 ha. Similarly, barren land and water
bodies are in decreasing trend.
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0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
Agriculture Bare area Builtup area Forest Water
Sum of Area_2010
Sum of Area_2021

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Results:
 We also calculated
change in land use
category to forest and
forest to other
category, where we
detected there is huge
conversion of
agricultural land into
forest land use
category.
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0
2000
4000
6000
8000
10000
12000
14000
16000
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20000
Agriculture - Forest Bare area - Forest Builtup area -
Forest
Forest - Agriculture Forest - Bare area Forest - Builtup
area
Forest - water Water - Forest
Change into Forest and Forest to other land use in between 2010-2021

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Results:
 The following data shows the change from one class category to other.
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S.N. Land Use Change Area Change (ha) S.N. Land Use Change Area Change (ha)
1 Forest - Agriculture 13910.28936 14 Bare area - Forest 680.959292
2 Forest - Bare area 349.200093 15 Bare area - water 300.940669
3 Forest - Builtup area 278.842233 16 Builtup area - Agriculture 487.655108
4 Forest - Forest 163726.3334 17 Builtup area - Bare area 18.802076
5 Forest - water 21.13865 18 Builtup area - Builtup area 503.413076
6 Agriculture - Agriculture 29906.66642 19 Builtup area - Forest 13.050308
7 Agriculture - Bare area 1606.616395 20 Builtup area - water 2.072083
8 Agriculture - Builtup area 1380.002083 21 Water - Agriculture 2105.63637
9 Agriculture - Forest 18692.64299 22 Water - Bare area 773.228755
10 Agriculture - water 174.919282 23 Water - Builtup area 843.830896
11 Bare area - Agriculture 2278.713252 24 Water - Forest 249.630195
12 Bare area - Bare area 1905.569049 25 Water - water 351.441016
13 Bare area - Builtup area 492.652913

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