Mapping land cover types in Vientiane, Laos using multi-temporal composite Landsat 8 images Sanya Praseuth Economic, Technology and Environment Committee, Laos National Assembly Vie
Trang 1Mapping land cover types in Vientiane, Laos using multi-temporal composite Landsat 8 images
Sanya Praseuth
Economic, Technology and
Environment Committee,
Laos National Assembly
Vientiane, Laos
sanyapraseuth62@gmail.com
Hung Bui Quang
Center of Multidisciplinary Integrated
Technology for Field Monitoring,
University of Engineering and Technology
Hanoi, Vietnam
hungbq@fimo.edu.vn
Dung Pham Tuan
Center of Multidisciplinary Integrated Technology for Field Monitoring, University of Engineering and Technology
Hanoi, Vietnam dungpt@fimo.edu.vn
Thanh Nguyen Thi Nhat
Center of Multidisciplinary Integrated Technology for Field Monitoring, University of Engineering and Technology
Hanoi, Vietnam thanhntn@fimo.edu.vn
Chuc Man Duc
Center of Multidisciplinary Integrated Technology for Field Monitoring, University of Engineering and Technology
Hanoi, Vietnam chucmd@fimo.edu.vn
Abstract- Land-cover mapping is now effortless due to the
availability of multi-satellite imagery such as Landsat data
However, the global-scale land-cover classification method
performs a low accuracy at the local scale, especially in
developing countries, because of lacking ground truth data
This research tries to build land-cover maps in Vientiane
Capital, Lao PDR using multi-temporal composite Landsat 8
images In our study, the combination of multi-temporal
cloudless data with a strong classifier (XGBoost) gave an
overall accuracy of 75.13%
Keywords: land cover types, multi-temporal composite image,
Vientiane, Landsat 8
I INTRODUCTION Nowadays, Laos is in a stage of rapid economic
development Therefore, the urbanization process in Lao
PDR is increasing, although a traditionally low base Towns
and cities are becoming the engines of nationwide growth,
like other countries in the region [1] This process led to a
rapid change in land covers, i.e urban areas, agricultural
areas, particularly in the Vientiane capital As these trends
continue, urban planning and land management policies will
become ever more important mechanisms to guide
development and help protect communities, the environment
and cultural resources
Meanwhile, the latest land-cover maps for the country is
for 2014 and backward only With the current land-cover
mapping technology, the technician will determine land
covers on satellite imagery by using human eyes and prior
knowledge over the mapping regions The process is also
aided by using some GIS software support such as ENVI,
ArcGIS This makes the mapping time last long, up to 5-10
years depending on mapping areas, human resources and
budget There are few studies in researching land-cover
classification methodologies in Laos For example, the study
on Lao’ urban land management [1], using a time series of
Landsat and MODIS data and landscape metrics to delineate
the dynamics of shifting cultivation landscapes in Northern
Lao PDR Between 2000 and 2009 [2], investigating
urbanization in Vientiane Capital to address the relationship
between urbanization and land use [3], monitoring of land
use and land cover change Vientiane area using Landsat
images [4]
As a result, up-to-date land-cover information will not be available in time, causing difficulties in the management and planning needed by management agencies Therefore, it is necessary to develop a rapid, accurate method for updating the current status of the land covers to meet with the requirements
The objective of this research is to classify land cover types in Vientiane Capital, Lao PDR using multi-temporal composite Landsat 8 images, based on cloud-free composite images for classification year and a state-of-art classifier (XGBoost)
II STUDY AREA AND DATA
A Study area
In this study, Vientiane Capital was selected as the study area (Fig 1) Vientiane Capital, which was built in the 16th century, is located in the center of the country, now covers an area of 3,920 square kilometers The topography of Vientiane is mainly a mix of mountainous and small-scaled flatten areas Elevation ranges from 70 m to 950 m This area has dry and rainy seasons The dry season is from October to March, the rainy season is from April to September of the year afterward The average temperature is 25oC Rainfall is from 1,300mm – 2,100mm annual In terms of land covers,
in 2014, Vientiane Capital has eight main types: agriculture, forest, road, industrial, rice, bare land, urban, and water, as shown in Table I
From the actual demand and the present status of the land-cover information, in this study, eight land-cover types were identified: agriculture, forest, grass/shrub, industrial, rice, bare land/soil, urban, and water
Trang 2TABLE II SUMMARY OF LANDSAT 8SR IMAGES.
2015 71
2016 70
2017 61
Total 202
B Image data
Landsat 8 Surface Reflectance (Landsat 8 SR) data was
used as the main data in producing the up-to-date land-cover
map in this study Landsat 8 SR images are produced from
Landsat 8 OLI top of the atmosphere (TOA) images by using
LaSRC algorithm [5] The Landsat 8 SR images remove the
effect of the atmosphere on the reflection of the surface
object, thereby providing a more accurate signal Due to the
large area of the study site, there are four different Landsat 8
path/rows (images) (Fig 2) in the study area All of the
images should be collected before developing the algorithm
Therefore, the number of images that need to be processed is
large In addition, in order to ensure adequate data for
subsequent image synthesis, all Landsat 8 images taken in
the study area from 2015 to 2017 (2017 is the target year for
producing the up-to-date land-cover map) were collected
Images that are fully covered by clouds will be discarded
Table II provides information about the number of
images each year
TABLE III SUMMARY OF TRAINING AND TESTING DATA
Training data Testing data
C Training and testing data
To build and validate the land-covers classifier, training data and testing data are collected To collect training data, the Lao’s government 2014 land-cover data was used to generate randomized points using a stratified random sampling method The points are then collated with high-resolution imagery from Google Earth to give the corresponding label For testing data, this data was collected through a field trip in the study area (Fig 3)
Table III provides information on the number of training and testing points for each type of land cover
Figure 2 Ground truth points (yellow marker) displayed on Google Earth Figure 3 Landsat 8 footprints over the study area
Trang 3Figure 4 Official land-cover maps for 2014, from Lao government
D Ancillary data
In this study, the official land-cover map for 2014 (Fig
4) is used for two main purposes: (1) support for collection
of training data and (2) for validation of proposed land-cover
classifier.
III METHODOLOGY The proposed land-cover classification method is
presented in Fig 5 This method includes 4 parts: (1)
generation of cloud-free composite images for classification
year, (2) image stacking and feature extraction, (3)
classification using XGBoost classifier, (4) validation of the
classifier.
A Generation of composite images
The purpose of this step is to create a cloudless image
sequence of the classification year This is to capture the
major spectral variations of land-cover types in the study
area Composite images are essentially a raster image which
has the same specifications as original Landsat 8 SR images
But the point is that composite images have no or very little
cloudy pixels thus significantly improve classification
accuracy To create a composite image, the timing of the
image creation (target date) needs to be specified at the very
first step In this study, six target dates were selected as
15/01/2017 (Date of year - DOY 15), 16/03/2017 (DOY 75),
15/05/2017 (DOY 135), 14/07/2017 (DOY 195), 12/09/2017
(DOY 255), 11/11/2017 (DOY 315) Hereafter, composite
images are called target images Ancillary images, used to
create a composite image, are called candidate images
For each pixel of a target image, its values will be
selected among pixels of the same location from candidate
images The “best” pixel among candidate pixels is selected
by following a set of strict rules Here, the method for the
creation of composite images is followed by in the previous
study [6][7][8] There are 4 scores to consider: year, DOY,
opacity, distance to cloud/cloud shadow Those scores can
be categorized into image-level scores and pixel-level
scores Year and DOY are image-level scores This means
that all pixels of a particular candidate image has the same
value Opacity and distance to cloud/cloud shadow are
pixel-level scores In this case, each pixel has its own value
depending on its situation The year score is inversely
proportional to the distance from the target year (2017) to
the year of the candidate image (2016, 2015, 2014) Next,
the DOY score measures the proximity between the target
Figure 5 The proposed land-cover classification method image and the candidate image, which is modeled using the Gaussian function A DOY score is calculated as follows:
Where σ is the DOY standard deviation, calculated from the DOY value of all candidate images μ is DOY value of the target image (i.e 15, 75, 135, 195, 255, 315) xi is the DOY value of the ith candidate image DOYs are only concerned with the day-to-day difference between the target image and candidate images, regardless of the difference in years between the two For example, assuming two candidate images have the same DOY distance to the target image, then DOY scores of these two images will be the same, even
if the images are taken in different years The higher the DOY score, the more likely the pixel of the image is selected
The distance to the cloud/cloud shadow of a pixel is another score to consider Sigmoid function is used to calculate this score The cloud/cloud shadow map provided with the Landsat 8 SR image from the manufacturer (USGS)
is used to calculate this score [9] Specifically:
ScoreCloud/Shadow_Distance =
(2)
Where Di is the closest distance to the cloud/cloud shadow of the pixel in the candidate image under consideration Dreq is a predefined maximum distance of impact, here it is defined as 50 pixels The meaning is that the cloud/cloud shadow only has impacts on the surrounding area of within 50 pixels (~1500m) Dmin is a predefined minimum distance, here is 0 The closer the pixel to cloud /cloud shadow pixels, the lower the score
The opacity score needs an aerosol-level image as input [8] The Landsat 8 SR provides only discrete aerosol information which has four levels including high aerosol content, average aerosol content, low aerosol content, and climatology-level aerosol content Therefore, each aerosol level will be assigned a score by employing a sigmoid function The result is 4 points corresponding to 4 levels of aerosol content
Trang 4Scoring by
a Gaussian
function
pixel
content
Climatology-level aerosol content Finally, the total score of each pixel is the sum of year
score, DOY score, distance to cloud/cloud shadow score and
opacity score Candidate pixel owning the highest total score
will be selected as a replacement in the target image Table
IV summarizes the 4 scores mention above
B Feature extraction
Land-cover types have different spectral characteristics
Some land covers have more variable year-round
fluctuations than others For example, seasonal vegetation
(rice, short-term crops) is more volatile than evergreen
forests Specifically, rice is a plant with a special spectral
variation which is different from other land covers Spectral
signal of rice varies considerably throughout the growth
cycle, i.e from watering to ripening and harvesting Thus,
accurate mapping of such land covers requires a significant
amount of observations throughout the year In this study,
after creating five composite images of the classification
year, the images were stacked together into a single image
Features will be extracted from this stacked image In this
study, classification features are all of the spectral bands
Thus, there will eventually be 35 features corresponding to
35 spectral bands
C Classification method and evaluation
For classification, the XGBoost classifier was used in
this study [10] XGBoost is a new classifier and has been
proven to work well on a variety of applications However,
XGBoost has not yet been widely applied in land-cover
classification XGBoost is based on the principle of Gradient
Boosting Machines (GBM) with several improvements For
example, it can be trained in parallel mode, scalable, less
overfitted to data, and can work well on sparse data
The XGBoost model can be expressed as the sum of base
learners as follows:
In which, F is the function space of the base learners, xi is
the input data vector, Φ is model function In order to build
basic learners, an objective function is needed For XGBoost,
the objective function is defined by the following formula:
Overall accuracy (OA), precision, recall and F1 score (F1) are used as evaluation metrics in this study [11] [12]
OA and kappa coefficient are computed for the classification level PA, UA, and F1 are class-specific The formulas of the metrics are presented below
Precision = NT
correct / NT
Recall = NT_ref
In which:
Ncorrect: number of correct classified points
Ntotal: total number of points
NT correct : number of correctly classified points in a given class
NT classified: number of classified points in a given class
NT_ref correct: number of correctly classified in reference data of a given class NT_ref: number of points in reference data in a given class
Additionally, classification maps are validated against official land-cover data and visually examined
IV EXPERIMENT RESULTS This section presents the composition and classification evaluations It consists of three main parts: results of the image composite method, classification evaluation based on ground truth points, and classification evaluation based on the official land-cover map
A Result of image-composition method
The RGB-false color image of the composite images is shown in Fig 6 It can be observed that the images are not or little clouded Furthermore, each image shows surface changes at its scanned time in the study area Specifically, the composite image representing DOY 15 (15/01/2017) and DOY 195 (14/07/2017) showed that some rice-growing areas were in the watering stage The image of DOY 135 may represent the harvesting period For forest areas, urban areas, one could see some stability as compared to agricultural areas in the study area
Trang 5Figure 6 Composite images
Figure 7 Land-cover map for Vientiane capital in 2017
TABLE V CONFUSION MATRIX
Agriculture Forest Grass/Shrub Industrial Rice Bare land Urban Water Precision
(%)
Forest 8 120 19 0 2 1 1 0 88
(OA)
TABLE VI EVALUATION BASED ON OFFICIAL LAND-COVER MAP
Agriculture Forest Grass/Shrub Industrial Rice Bare land Urban Water
As land-cover map 241.28 1,774.38 624.40 44.87 539 41.53 243.93 160.07
As official land-cover map 335.95 2,034.25 120.905 16.650 670.1 23.56 276.4 204.71
B Validation against statistical data
Fig 7 shows the derived land-cover map for Vientiane
Capital in 2017 It can be seen that forest, rice, and urban
areas occupy most of the area
Table VI compares land covers areas derived from the
proposed method and the official land-cover map
It could be seen that areas of land-cover types have
certain differences Agricultural, forest, rice, and water
observed a decline in 2017 as compared to 2014 This is due
to: (1) for the agricultural area, especially rice, it is the
problem of abandoning paddy fields due to lack of human
resource and some other causes in Vientiane Capital; (2)
forest area is declining due to urbanization and
industrialization, and even agriculturalization under the
Lao’s government plan An increased area of grass/shrub
may be the result of the clearance of agricultural areas for urbanization and industrialization Comparative results have shown the effectiveness of the classification method and demonstrated the actual status and causes of variation of the major land covers in the study area
V CONCLUSION
In this study, an annual land-cover classification method for the Vientiane Capital, Lao PDR is proposed This method consists of four parts: (1) generation of cloud-free composite images for classification year, (2) image stacking and feature extraction, (3) classification using XGBoost classifier, (4) validation of the classifier The initial results show some potentials of the method as compared to the traditional mapping technique used in the region The image composition method can produce a cloudless image from a
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