TERM PROJECT

APPLICATION OF GIS TECHNIQUES TO STUDY GLACIAL RETREAT IN BUNI

ZUM, CHITRAL

• Group Memebers–SHAKEEL AHMED( ldr)–ANAS MEHMOOD–MEHREEN ALI–SHAHIDA ARIF KHAN–USAMA MAQSOOD

SEQUENCE

• Intro ,objectives and methodolgy adopted (Shakeel)• Pre processing (Anas)• Identification techniques– Threshold and classification (Usama)– Band ratios and indices i.e. NDSI, NDVI, NDWI (Shahida)

• Results (Mehreen)

INTRODUCTION

• Climate change is a grave issue and its effects are widespread, hence there is a gradual rise in temperature and its consequences are significant

• Same pattern can be observed in Pakistan specially in Northern Areas

• Global warming has led to a decrease in the ice mass of glaciers and an increase in the formation of glacial lakes

INTRODUCTION

• Siachen glacier has experienced rapid glacial retreat, losing 17% of its ice mass and receded by 2km in length since 1989

• The Gangorti glacier is retreating 98 feet per year• The spatial retreat of glaciers in district Ganche from

1978 to 2010 is about 380.5 sq km• The glaciers of Siachen, Baltoro and Biafo have

undergone a volume loss of 11.09, 6.14 and 3.79% respectively

INTRODUCTION

• Chitral is the largest district in the Khyber-Pakhtunkhwa province of Pakistan, covering an area of 14,850 km

• Shares a border with Gilgit-Baltistan to the east, with Afghanistans Kunar, Badakshan and Nuristan provinces to the north and west, and with Swat and Dir to the south

INTRODUCTION

• This area consists of many smaller glaciers including:– Phargram glacier– Gordoghan glacier– Golen glacier– Sohnyoan glacier– Rinzho glacier– Shiak glacier

• Buni Zum area of Chitral has been selected for study

OBJECTIVES

• To investigate the extent of glacial retreat in the study area within one decade

• To detect change in:-– Snow cover– Vegetation cover– Water

Methodology

Data Acquisition & Pre-Processing

• Dataset– Landsat Imagery • Landsat 7 ETM+ image • Landsat 8 OLI image

Stacking

Imagery Time Spatial Resolution Spectral Resolution

Landsat 7 July 2006 30m 8 Bands

Landsat 8 July 2013 30m 11 Bands

Stacking:• Multiple Bands in Image Scenes• Combined into a single image (.img) file using the

“Stacking” tool in ERDAS Imagine

DEM

• DEM was used to aid in the visual interpretation of the terrain– DEM Resolution 30 m

• A 3D model was created to enhance the visual understanding of the study area

WRS2 PATH & ROW

• Remotely sensed images provide a synoptic coverage of the entire Chitral region

• The glacial distribution and boundaries were identified from the satellite images– Google Earth

• WRS-2 – Path 151– Row 35

Mask

• AOI was extracted from larger tile of Raster Image• Shapefile was created of the Buni Zum area of

Chitral using Google earth– KML of shape using polygon tool– KML file converted to Shape File– Extract by Mask for AOI

Methodology

(Visual Analysis)

Thresholding (Visual Analysis)

• This procedure is carried out using visual analysis.• Features are identified by applying limits to their

Digital numbers/ Brightness values.• Inclusion and exclusion of features are derived

through the pixel values in different bands.• This method is used on hit and trail basis.

Original Image Processed Image Band 4 - 18000 & Band 6 - 8500

Classification

• Classification is the process of assigning pixels to the Land cover classes or themes in a remotely sensed data.

• An important part of image analysis is identifying groups of pixels that have similar spectral characteristics and to determine the various features or land covers represented by these groups. This form of analysis is known as classification.

• Spectral classes are groups of pixels that have nearly uniform spectral characteristics in all the spectral bands

• Information classes are various themes or object that represent the actual features on ground, Classes that human beings define

Types of Classification

• Un-Supervised Classification• Supervised Classification

• Both includes three stages but in different orders.1. Training Stage.2. Classification Stage.3. Output Stage.

Unsupervised Classification

• Unsupervised classification, commonly referred to as clustering

• This technique is primarily automated.• We give very limited input to the algorithm to

perform this task.

Essential Inputs

• There are three basic and necessary input parameters for this algorithm1. No. of Classes.2. No. of iterations.3. Convergence Threshold.

NOTE: Last two parameters are the basic controlling factors too.

2006 2013

Supervised Classification

• Supervised classification can be defined as the process of using samples of known classes to classify the remaining unknown pixels to these classes with in the image.

• Prior knowledge is needed for this type of classification.• We first make a signature file that includes all signatures

of the major land covers.• We go for maximum inter-class and minimum intra-class

variations (get pure signatures) so that to avoid confusion.

Essential Input

• For this process the essential input is just the signature file. This process will make one class against every one signature.

• Then there also comes the parametric rules– Maximum Likelihood. (Probability Function)– Minimum Distance to Mean. (Euclidean Distance)

2006 2013

Band Ratioing

• Band Rationing means dividing the pixels in one band by the corresponding pixels in a second band.

• The reason for this is twofold:• One is that differences between the spectral reflectance

curves of surface types can be brought out. • The second is that illumination, and consequently

radiance, may vary, the ratio between an illuminated and a not illuminated area of the same surface type will be the same.

• Band rationing enabled us to identify snow in various conditions

• A ratio of 0.3 was applied on the 2006 image and 0.65 was applied on the 2013 image.

Spectral Indices

• For decades, remote sensing scientists have used spectral indices to help them predict, model, or infer surface process.

• These indices have been developed to assess in the monitoring of several different land change processes including:

• Vegetation Health and Status, Snow Status, Water Status, Burned Area, Fire Severity etc.

NDSI• The NDSI is a spectral band

ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible spectral bands to identify snow versus other features in a scene.

• The same equation of NDWI is

used to calculate the

Normalized Difference Snow

Index (NDSI) with a different

threshold value. Bands 4 and 5

were used for 2006 image,

and bands 5 and 6 were used

for the 2013 image with a

threshold of 0.6

NDVI• The Normalized Difference

Vegetation Index (NDVI) gives a measure of the vegetative cover on the land surface over wide areas.

• NDVI= NIR-Red/ NIR + Red

• The ration is between -1 and

1.

• Closer to 1 shows healthy

vegetation. For the 2006

image bands 4 and 3 were

used and for 2013 image

bands 5 and 4 were used with

a threshold of 0.2

NDWI• The normalized difference water

index (NDWI) is derived using similar principles to the Normalised Difference Vegetation Index (NDVI). In an NDVI (the comparison of differences of two bands, red and near-infra-red (NIR)

• Using the equation for

an NDWI is

• NDWI= NIR-SWIR/ NIR

+ SWIR

• The values range from

-1 to 1.

• 2006: Band 4 & 5

• 2013: Band 5 &6

RESULTS AND DISCUSSIONS

PROCESS RESULTS CHANGE PERCENTAGE

2006 %age 2013 %age

Threshold 492045079.25 19.2963 419265093.910339 16.4421 72779985.339661 2.85 %

Supervised Classification

582776829.08 22.8545 405761200 15.9125 177015629.08 6.942 %

Unsupervised Classification

613910016.296 24.0754 590289695.090988 23.1491 23620321.205012 0.9263 %

Band Ratio 794306228.907 31.1499 373595395.376081 14.6511 420710833.530919 16.4988 %

NDSI 628167955.912 24.6345 539044046.7058 21.1394 89123909.2062 3.4951 %

NDVI 140115151.837 5.4948 244367227 9.5832 -104252075.163 4.0884 %

NDWI 35573836.746 1.3951 99299208.6212 3.8942 -63725371.8752 2.4991 %

• The average snow estimated in 2006 was 24.40% and in 2013 was 18.26%.

• On average the glacier area in the study site decreased about 6.14% from 622241221.889 m in 2006 to 465591086.217 sq. meters in 2013.

• A 6 percent decrease of the glacier area indicates that the glaciers are retreating rapidly.

• There is an acceleration of retreat in the recent decade.

Threshold • After assigning the threshold values we came to the conclusion that the

glacial retreat between the two images was about 2.85% which is equal to72779985.339 sq. meters.

Band Ratio• The percentage change in glaciated area using this technique

was found out to be 16.49%, equal to a decrease of 420710833.531 sq. meters.

Unsupervised Classification• After performing this method we found that the change in

glacial mass between the two years was about 0.93%. • This translates to 23620321.205 sq. meters and can be

observed in the figure.

Supervised Classification• The change detection we arrived at in this type of classification

was 2.85% that equates to a decline of 72779985.339 sq. meters.

NDSI • The change observed after applying NDSI was 89123909.206

sq. meters that makes it 3.495%.

NDVI• The percentage area change was 4.088% equal to

104252075.163 sq. meters.

NDWI• The increase in the water in the study area was found to be

2.499%, with an area of 63725371.8752 sq. meters.

RESULTS

• The results were overlayed with Contour map, created from DEM of the area, to analyze the distribution of glacial melting range.

• The usual distribution of glaciers ranges from about 3500 to 6200 meters.

• While glacier melting appears to be prominent at a range of about 4300- 6000 meters

Figure: Overlay of results with Contour map

Conclusions• The study was carried out to apply GIS techniques for identification

of snow and estimate changes in glacial cover. • On average the glacier area in the study site decreased about 6.14%

from 622241221.889 sq. meters in 2006 to 465591086.217 sq.meters in 2013.

• A 6 percent decrease of the glacier area over a period of only seven years indicates that the glaciers are depleting fast.

• Similarly other land cover features like vegetation and water were identified and their change mapped.

• As the glaciers were found to be receding, the extent of glacial lakes in the area increased because of increased melting.

• Another interesting result was that vegetation cover also increased over the years as the areas which were covered with snow earlier, now have vegetation growth.

THANKS