IMAGE PROCESSING AND CLUSTERING ALGORITHMS FOR FOREST COVER QUANTIFICATION

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International Journal of Advanced Research in Engineering and Technology (IJARET)
Volume 13, Issue 6, June 2022, pp.12-20 Article ID: IJARET_13_06_002
Available online at https://iaeme.com/Home/issue/IJARET?Volume=13&Issue=6
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
DOI: https://doi.org/10.17605/OSF.IO/NKS
© IAEME Publication Scopus Indexed
IMAGE PROCESSING AND CLUSTERING
ALGORITHMS FOR FOREST COVER
QUANTIFICATION
Pavan Mohan. N1 and Pranav. P1
1,2
Department of Electronics and Communication Engineering, SRM University AP,
Amaravati, Andhra Pradesh, India.
ABSTRACT
“Forest cover” refers to the relative land area covered by forests. Anthropological
interventions and the subsequent diminishing forest cover, result in environmental
degradation, impacting man-nature interactions. Hence, it became the need of the
moment to monitor the forest cover to minimize natural perils and promote sustainable
development. The present preliminary work focuses on implementing image processing
and k- means clustering techniques on satellite imagery to monitor and quantify the
forest cover of the Sundarbans delta, existing across India and Bangladesh. Image-
based algorithms relying on characteristic colouration were proposed for analysing the
percentage of forest cover in the predefined area. Among various methods of monitoring
and examining forest land, image-based algorithms can be of vital use due to the rise
in the accessibility of information and the potential of analysing large data sets with the
least processing time. The above-discussed techniques, along with the availability of
Machine Learning (ML) and spaceborne photography, will have a futuristic impact on
interpreting the variations in land cover and land utilization. Building upon the
following algorithm, it is now conceivable to conduct timely comprehensive analysis,
real-time evaluation, monitoring, and control on how events unfold. Similarly, data
collected from various geographical observation systems may provide several other
qualitative features that are more focused.
Key words: Geographical Observation Systems, Image Processing, k-means clustering,
Machine Learning, and Satellite Imagery.
Cite this Article: Pavan Mohan. N and Pranav. P, Image Processing and Clustering
Algorithms for Forest Cover Quantification, International Journal of Advanced
Research in Engineering and Technology (IJARET), 13(6), 2022, pp. 12-20.
https://iaeme.com/Home/issue/IJARET?Volume=13&Issue=6
1. INTRODUCTION
Several research studies and investigations have characterized forest cover as the amount of
land area covered by the forests. In India, the Ministry of Environment, Forest and Climate

Image Processing and Clustering Algorithms for Forest Cover Quantification
Change defines it as “an area of land greater than a hectare covered with tree canopy density
greater than ten percent” [1]. Forest cover, our primary objective, is studied with the help of
land cover imagery and Earth Observation systems.
Conversion of any forest land for urban or non-forest use involves clearing the forest land
or tree cover, called deforestation [2]. Consequences of tree cover loss are subjected to the
type/kind of forest [3] and its geographical location. For example, deforestation in montane
forests may lead to soil erosion and biodiversity loss. Similarly, such clearances in tropical
forests may lead to a decline in the absorbability of carbon dioxide in the atmosphere. However,
in general, the reduction of the forest area has many adverse effects impacting resource
availability, air quality, Environment, and local livelihoods.
Therefore, continual monitoring of forests helps us manage and protect these natural
habitats in an ecologically sustainable manner. Besides the benefits mentioned above, forest
cover can be an essential parameter and a good indicator of understanding global carbon
sequestration, rainfall patterns, and temperature fluctuations [4]. Continuous subsistence of
indigenous ecosystems can be made possible. Analysing forest cover and its relation with the
above-discussed parameters will help us have a refined view of environmental degradation and
its implications. It also brings the capabilities to measure and quantify afforestation measures.
In the present paper, emphasis is placed on understanding the forest cover in Sundarbans,
the world’s most extensive mangrove forests situated in the delta of the Ganges. This
ecologically active site spreads across India and Bangladesh over 10,000 square kilometers,
stretching from the Hooghly River in the Indian state of West Bengal to the Baleshwar River
in Khulna, Bangladesh. It is a habitat with vast biodiversity of various endemic faunal wildlife.
It currently provides sustainable livelihood to a population of 4.37 million [5], although most
people lack permanent habitation.
The Sundarbans are vulnerable, risking, along with them, their vast biodiversity. The loss
of Sundarbans mangrove cover is predicted to drastically increase the risk of coastal
communities to tsunamis and cyclones as the protective shield of the mangroves [6] will no
longer be effective. The major threats to this climatically vulnerable and ecologically fragile
region include seawater intrusion into the land due to global climate change and the increasing
intensity of cyclones. These extreme weather events reduce these mangrove forests’ resilience
and recovery potential.
2. BACKGROUND
Numerous studies were conducted to understand the chronological variability of land cover. An
object-oriented methodology was designed to handle the multiannual Satellite Image Time
Series, which was utilized to understand the evolution of land cover [7]. Vegetation Change
Tracker and Landsat Time Series Stacks were used to understand the disturbances in the
temperate forest located at the Sierra Madre Occidental, Durango, Mexico. This study
confirmed the changes in land cover with an overall accuracy of 97.6% [8]. Similar
chronological trend-based studies were done to monitor the change in forest cover in Maçka
State Forest Enterprise with the help of the Landsat Multispectral scanner in 1975 and 1987.
Using the compound interest formula, the annual deforestation rate was revealed to be 152
hectares per year, equivalent to 0.27% per year [9].
Similar other works [10]-[13] show Remote Sensing and Geographical Information systems
being used to emphasize forest mapping and land cover assessment. Identifying potential
environmental degradation and deforestation drivers is vital in preventing future damage and
enabling sustainable development. Furthermore, Land cover maps acquired from Japan
Aerospace Exploration Agency (JAXA) between 2008 and 2016 were examined to identify the
key drivers of deforestation in Myanmar [14].

Pavan Mohan. N and Pranav. P
A method was developed to map the boreal forest cover. It was tested in European Russia
between Circa 2000 and 2005 [15]. In the same way, a classification tree algorithm was
implemented to map the forest cover loss for the decade 2000- 2010 in the Democratic Republic
of Congo [16]. The potential effects of sea-level rise on the Sundarbans were investigated with
the help of remote and field measurements, simulation modeling, and geographic information
systems [17].
Processing of image-based data like image compression has gained momentum in recent
years—generally on greyscale or RGB (Red Green Blue) colour images. Methods like
maximum likelihood estimation classification, subpixel classification, and Normalized
Vegetation Index (NDVI) were compared, and it was revealed that traditional methods like
maximum likelihood classification did not reveal the exact nature of the mixed-water forest
[18].
A novel combination of manipulation, clustering, decoding, and encoding was presented,
along with some qualitative features for multispectral images [19]. A review of feature selection
and representation using computational intelligence in optical remote sensing was done to find
the capability of computational intelligence in optical remote sensing [20]. Implementation
[21]- [23] and possible modifications [24] to commonly used clustering algorithms and k-means
clustering were presented respectively to achieve better results and accuracy.
3. METHODOLOGY
3.1. Data Acquisition
The data for the following study was collected from Landsat 8, Operational Land Imager (OLI),
published by the United States Geological Survey (USGS). It contains information regarding
the three bands covering- Red, Green, and Blue.
The wavelengths of the following bands are:
• Red: 0.630 - 0.680 micrometres
(Band 4 in Landsat 8)
• Green: 0.525 - 0.600 micrometres
• Blue: 0.450 - 0.515 micrometres
All the bands mentioned above in the channel sequence red-green-blue form a combination
called the “natural colour composite.” The natural colour composite contains several colours
that are various combinations of these three primary colours- red, green, and blue. After reading
the image in Portable Network Graphics (PNG) format, the intensity of each band was found
to vary from 0 to 255 for each pixel, i.e. the image has an 8-bit radiometric resolution.
The following colour composite with natural colours resembles the natural human
perception of vision and can aid in a more straightforward interpretation of methods,
procedures, and results. Further, the image is stored in a three-dimensional array containing the
pixel location and the values corresponding to the magnitude of individual colour bands.
The current study aims to quantitatively assess the percentage of green colour resembling
the vegetation in the natural colour composite. Simultaneously, we make the necessary analysis
of the following image-based colour segmentation technique by integrating it with the k-means

clustering algorithm. This analysis helps differentiate the land usage and forest cover variations
based on the colour quantization.
3.2. Image Processing and Clustering
After understanding the organized data and its format, all outliers in the image were removed,
and the geographical area of interest (Sundarbans mangrove forest) was concentrated using geo-
referencing “Fig. 1”.
The coordinates of the study area are as follows: (22.1734 N, 88.6473 W), (22.1734 N,
89.9121 W), (22.1734 N, 88.6473 W), and (21.7799 N and 88.6473 W). The forest area studied
is 5,720 square kilometers.
Figure 1 Satellite Image of Sundarbans
The number of colours in the RGB image is substantially reduced in the processed image.
This reduction was performed for the sake of faster processing. This reduction in the number
of colours in an image is called colour quantization. Ideally, it allows for lower noise, smaller
data sizes, and minute variance.
Generally, the process of colour quantization is treated as a method of clustering in the
three-dimensional space. Where the axes in these three dimensions are the red, green, and blue
bands “Fig. 2”. Hence, any algorithm working in three dimensions capable of clustering can be
utilized for colour quantization.
After locating the clusters, every cluster is averaged to get the representative colours to
which different colours in the original image were mapped. In the following process, one might
choose the lab colour space instead of the above-discussed RGB bands by doing the necessary
transformations. The above is performed as the Euclidian distance is more persistent with a
perception difference.
Colour quantization was done using the nearest colour or straight-line distance. At this
stage, each colour in the original image was considered, and a close palette entry was
determined utilizing the corresponding distance between the two points in the three-
dimensional space.
If the colours in the image are the points represented by (𝑟1, 𝑔1, 𝑏1) and (𝑟2, 𝑔2, 𝑏2) the
distance to be minimized to find the closest colour in the palette is given by:
√(𝑟1 − 𝑟2)2 + (𝑔1 − 𝑔2)2 + (𝑏1 − 𝑏2)2 (1)

Through this process, it became clear that the colour cube was decomposed to a mapping
of a single-colour palette. Now colour quantization was combined with dithering to give the
appearance of the original image colour constancy “Fig. 3”.
Figure 2 RGB Colour Space Representation
Similarly, time complexity can also be reduced further by resizing the image, which lessens
the number of pixels. While applying the said processes, we ensure that the processed image or
the quantized version is visually similar to the original image.
Figure 3. Colour Quantized Image
Now K-means clustering technique was utilized to cluster all the pixels in the image into
two different clusters.
The advantages of utilizing k-means clustering are:
• Its capability to work on larger datasets
• Simplicity in implementation
• Adaptation to new data points
• Good generalization in clustering

One of the clusters represents the vegetation (green), and the other represents barren land
or water. Hence, the data make two relatively apparent clusters. As the number of clusters was
identified, the next two data points/ pixels were randomly selected. These selected data points
are the initial clusters. The distance between the first data point and the other initial cluster is
measured with reference to the coordinates of R-G-B in the colour space. The remaining data
points in the image were considered and assigned to the nearest cluster among the two. After
clustering all the data points/ pixels in the quantized image, the mean of each cluster is
calculated. This process was repeated using the mean values until the clusters no longer
changed.
Figure 4 Pipeline Diagram
These clusters can be used to classify vegetation from the rest. The corresponding values of
R-G-B representing each pixel were utilized to compute the centroids of both clusters. The
number of iterations can be increased so that the algorithm can provide more validated results.
4. RESULTS
In the following study, image processing and clustering algorithms were considered to obtain
helpful information from satellite imaging to conduct a real-time evaluation of variation in
forest cover. The whole methodology was summarized in the pipeline diagram “Fig. 4”.
It was observed that the variation within the clusters of the k-means algorithm (considered
a performance metric) varied along with the number of colours in the quantized image. This
metric describes the efficiency of the quantization technique and is illustrated in “Fig. 5”. The
same figure shows that the following algorithm performed well when the number of quantized
colours was two, as the variance reduced drastically with increasing colour quantization levels.
Similarly, it can be observed that the variance among the pixels in each cluster continuously
reduced when the number of quantized colours increased. However, the rate of change of
variation is almost meager when the clusters increase by more than four.
By utilizing the image processing and the clustering techniques, it was studied that the set
of pixels corresponding to the first cluster, representing the land covered by forest, was 67%,
and the total number of pixels forming the second cluster was 33%, which includes both barren
land and water. The following quantifications considered the total number of quantized colours
to be two. Depending upon these outcomes, in terms of the land area, the forest comprises
3832.4 Sq. Kms, whereas the non-vegetative land and water bodies were together found to

occupy 1887.6 Sq. Km. The results demonstrate the quantified forest cover by altering the
colour space of the image pixels.
Based upon the outcomes of the following study, the analysis of forest cover can be
replicated to several other land imaging sources like Unmanned Ariel Vehicle photographs and
several other means to understand its efficacy and dependency on resolution, image size, etc.
Figure 5 Performance of k-means Algorithm
5. DISCUSSION
Monitoring and assessing the forest cover is vital in effectively managing the risks caused by
deforestation and climate change. The latest innovation in Image Processing and computational
techniques can help us work with large datasets and use every data point in a pixel. These
advancements will go a long way in yielding useful and validated results with the least
processing time.
Some of the essential points of this study include extracting the information from an image
by effectively utilizing the R-G-B colour components and analysing the possible outcomes
using clustering techniques. The following algorithms can be directly implemented to
understand the working or combined with some other model to chronologically understand the
anthropological interactions on forest cover.
The outcomes of the following study show the implementation of image-based techniques
in providing the results in real-time. The following work showed that the forests covered 67%
of the land in Sundarbans while the other 33% was covered by barren land and water. However,
changing the image resolution and upgrading the computational technology may even provide
more validated and magnified details of forest cover.
6. CONCLUSION
The availability of vast amounts of data and increased open-source platforms significantly
improve the results through continuous experimentation. The operation of image processing
made the method of decision-making faster and more cost-effective.
When integrated with Machine Learning algorithms, these image processing methods will
play a pivotal role in understanding complex relations that exist in the Environment like drought
impacts, sea-level rise, increase in global temperature, and their effects on the natural shields
like Sundarbans.

This algorithm and other deterministic and fuzzy-logic algorithms will help iteratively
compare the obtained solutions with good generalization until an optimum and more
satisfactory solution is achieved. With the progress of computers, such algorithms have become
part and parcel of computer-aided design, which is widely used today. However, the operation
of the present model needs to be assessed on images with remote sensing data of high
resolutions, as the current model faces the problem of object ambiguity. On the other hand,
similar models could be developed to quantify parameters such as urban land cover, which can
be taken as a future scope.
It is also worth recollecting the importance of operationalizing the theory of sustainability
by understanding the purpose and value of the services provided by the Environment.
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