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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,
3/21/2022](https://image.slidesharecdn.com/forestchangedetectionusingrsandgis-220321164846/85/Forest-change-detection-using-RS-and-GIS-pptx-14-320.jpg)
Forest change detection using RS and GIS.pptx
- 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 3 3/21/2022
- 4. Introduction 4 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). 3/21/2022
- 5. 5 Objective The general objective of this assignment is to detect the Forest Change in Makwanpur District, Central Nepal. 3/21/2022 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. 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022
- 14. 14 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, 3/21/2022
- 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. 3/21/2022
- 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. 3/21/2022 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
- 18. 18 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. 3/21/2022 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 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
- 19. 19 Results: The following data shows the change from one class category to other. 3/21/2022 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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