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Article
A Monitoring System of Sand Mining in Large Rivers and Its
Application to the Ayeyarwady (Irrawaddy) River, Myanmar
Charles R. Gruel 1and Edgardo M. Latrubesse 2, *
Citation: Gruel, C.R.; Latrubesse,
E.M. A Monitoring System of Sand
Mining in Large Rivers and Its
Application to the Ayeyarwady
(Irrawaddy) River, Myanmar. Water
2021,13, 2331. https://doi.org/
10.3390/w13172331
Academic Editor: Achim A. Beylich
Received: 8 July 2021
Accepted: 19 August 2021
Published: 25 August 2021
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Attribution (CC BY) license (https://
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4.0/).
1Asian School of the Environment and Earth Observatory of Singapore, Nanyang Technological University,
Singapore 637551, Singapore; gruelrobin@gmail.com
2Environmental Sciences Program-CIAMB, Federal University of Goiás, Goiânia 74690-900, Brazil
*Correspondence: latrubesse23@gmail.com
Abstract:
Sand mining is one of the major sustainability challenges of the 21st century. Rates of
extraction are surpassing sand supply, and ensuing sand starvation is adversely impacting channel-
floodplains and deltas. Therefore, quantifying sand mining’s location and extent, through global
monitoring and detection, particularly in fluvial systems, is becoming a priority. Sand mining by
dredges and barges (extraction of sand and secondarily gravel) in South East Asian rivers, including
illegal sand mining, has become rampant, and a monitoring system is not yet in place. Here, we
present a high-resolution remote sensing-based mining monitoring system for sand mining in fluvial
systems. We used Sentinel-1 mission, a radar component of the Copernicus joint initiative of the
European Commission (EC) and the European Space Agency (ESA). The system, tested in the
Ayeyarwady, the second largest river in SE Asia, detects sand mining by barges almost in real-time
with a satisfactory accuracy level. An additional advantage of the monitoring system is that it does
not incur any costs, making it accessible to multiple users, decision-makers, and stakeholders.
Keywords: sand mining; monitoring system; Ayeyarwady River; Myanmar; Southeast Asia
1. Introduction
Sand mining has been defined as one of the major sustainability challenges of the 21st
century by the United Nations [
1
]. Fluvial sands, whose textural characteristics provide
the best quality source for various industries and construction, are highly sought after. The
current rate of sand and gravel mining in coastal areas and rivers (~40,000 Mt yr
−1
) [
2
]
is larger than the estimate of global fluvial sediment discharge (19,000 Mt yr
−1
) [
3
,
4
].
With extraction exceeding natural renewal [
2
], demand might outstrip supply by the mid-
century [
5
,
6
]. Additionally, excessive fluvial sand extraction and ensuing sand starvation
exacerbate rivers’ vulnerability and their channel-floodplain coupled system and deltas.
For example, the decrease in sediment fluxes to the coast alters coastal morphodynamics
and habitats [7,8].
The mining of fluvial aggregates (sand, gravel) in Southeast Asia (SE Asia) is exerting
enormous pressure on the regional rivers, given regional economies and populations’
growth. Research on mining of fluvial aggregates and sediment fluxes in SE Asia has mostly
focused on the Mekong River because of its strategic socio-economic and environmental
role. Sand flux entering the Mekong River delta has been estimated to be far smaller than
the current extraction rates (50 Mt yr−1) [4].
An additional problem is the proliferation of illegal mining of fluvial aggregates. By
2018, 70 countries had reported illegal fluvial sand extraction [
7
]. Cambodia, Vietnam, and
Indonesia are legal and illicit sand sources in the region [
7
]. Sand and gravel mining is also
increasing in Laos and skyrocketing in Myanmar.
For fluvial aggregates mining to be sustainable, we need to know where, for how long,
and what intensity fluvial aggregates have been extracted from Southeast Asian rivers.
Water 2021,13, 2331. https://doi.org/10.3390/w13172331 https://www.mdpi.com/journal/water
Water 2021,13, 2331 2 of 13
Nevertheless, to date, the areas of Southeast Asia impacted by sand mining have not even
been systematically identified, and no spatial-temporal analyses have been conducted.
Although sand mining is the most used terminology, the extraction of fluvial ag-
gregates from large rivers can include a dominant grain size population of sand and a
proportion of gravel. This article will generically refer to “sand mining”, although the
concept includes the dredging and extraction of sand but also secondarily gravel from
large river’s beds such as the Ayeyarwady River (also known as Irrawaddy River).
Remote sensing methods are useful tools because they allow monitoring sand extrac-
tion on large scales and in areas that are hard to access in time and space. To date, India
is the only country in Asia to have implemented a national remote sensing-based Mining
Surveillance System (MSS) to monitor illegal mining activities. However, the Indian MSS
system does not identify or monitor sand barges dredging fluvial beds, which are the
primary means of sand extraction and the cause of environmental concern in Southeast
Asian rivers. But sand extraction takes a large diversity of forms and occurs in a variety
of physical contexts. Thus, implementing a field-calibrated remote sensing monitoring
system capable of detecting, at a high resolution, mining activities in fluvial channels and
barges extracting sand from rivers and lakes is crucial for tackling this problem.
Herein, we present a methodology for a high-resolution regional multitemporal as-
sessment of sand mining activities in fluvial systems. We tested the Ayeyarwady River’s
surveillance system, the second largest river in Southeast Asia after the Mekong River but
though one of the unknown sand mining hotspots in the region, with very few mentions
of the sand/gravel extractions in the literature. The choice of the Ayeyarwady River was
not arbitrary, as multiple factors make the river a priority for environmental research. It is
the fluvial artery that concentrates the majority of the population and GDP of Myanmar,
and it is vulnerable to human-made environmental pressure such as mining, dredging,
deforestation, and damming. It is also a top priority among the world’s transboundary
river basins due to risks related to hydro-political tensions and the lack of water governance
at a national level [9].
2. Fluvial Aggregates Extraction in Myanmar and the Ayeyarwady River
The Ayeyarwady River stretches for about 2100 km from the Himalayan mountains to
the Andaman Sea. The basin drains an area of about 418,000 km
2
, with 91% of the basin
located in Myanmar, and 9% is shared with India (Manipur-Nagaland) and China (Yunnan-
Xizang) [
10
] (Figure 1). With a mean annual discharge of ~11,600 m
3
s
−1
, the Ayeyarwady
River is the second largest river in SE Asia after the Mekong River in water discharge,
and the second or third largest global contributor of wash load to the Oceans [
11
]. The
hydrological regime is monsoonal with floods during the northern hemisphere summer
(June–September) and a marked dry season and lower flows in winter (January–March).
The discharge reaches an average wet-season maximum of 3.9
×
10
4
m
3
s
−1
. Because of
the high seasonality, the mean monthly discharge in August is on average 10 times higher
than the discharge in February [11].
The Ayeyarwady River delta is still one of the most pristine in the world, but it is
experiencing increasing human-environmental pressure and degradation [
12
,
13
], and the
system is considered the last largest river of SE Asia to flow relatively freely [11].
Downstream Mandalay, the river alternates anabranching-multichannel alluvial reaches
with geologically constrained (nodal zones) segments that are dominated by single-channel
patterns. The anabranching reaches are characterized by large fluvial bars and islands. The
sediment load of the Ayeyarwady River is still not well quantified. While the suspended
load could reach 364 Mt y
−1
[
14
], and the wash load was estimated in
144 Mt y−1
[
11
],
the bedload transport of the Ayeyarwady River, the main source of the fluvial aggre-
gates extracted by dredging, has not been systematically assessed. High erosion rates
of
~1700 t km−2y−1
in the Chindwin River basin, draining the Indo-Burmese range, and
~1000 t km
−2
y
−1
mainly sourced by erosion of weather gneiss and plutonic rocks in the
Upper Ayeyarwady are the main sources of the sediment load (Figure 1) [11,15].
Water 2021,13, 2331 3 of 13
Figure 1.
The study area of the Ayeyarwady River between Sagaing (km 0) and Nyaungdon (km
703), with the main riparian cities. The red box indicates the reach where ~50% of the detected boats
are concentrated (WGS84/UTM zone 46 N).
Because of the size of the river, the monsoonal hydrological regime, and the geomor-
phologic style of the channel, which is characterized by large and dynamic fluvial bars, it is
assumed that the sandy load could represent a significant proportion of the total sediment
load. Still, it has yet to be thoroughly quantified.
Sand and gravel in Myanmar are considered natural resources owned by the state.
Under Article 37 of Myanmar’s 2008 constitution, the Union is the ultimate owner of all
lands and natural resources above and below the ground, above and beneath the water,
and in the atmosphere. The sand mining industry is regulated through licensing, which is
linked to specific tracts within the river. The location of tracks is delineated by geographic
coordinates, and the regulation could include limits on the volume of material that can
Water 2021,13, 2331 4 of 13
be extracted [
16
]. Part of the fluvial aggregates is used for local consumption or large
government infrastructure projects, and part is exported [16].
In Myanmar, sand mining management is regulated under the 2006 Conservation
of Water Resources and Rivers Law. Operations extracting above 50,000 m
3
have to be
assessed for their environmental impact [17].
However, before 2015 there was no limit on the number of suds (about 2.83 m
3
) that
could be extracted from the rivers. Since then, two factors have been considered for licenses
applications: first, the suitability of the area has to be assessed by the Directorate of Water
Resources and Improvement of Rivers (DWIR); second, the boat size each company can
operate cannot exceed two boats with a maximum capacity of 30 suds (85 m3) each [17].
Fluvial aggregates (sand and gravel) are mined in Myanmar both during the day and
at night [
17
] using various extraction techniques. For example, mixed grain size aggregates
are directly excavated from the sand bars during the dry season and stocked on temporary
storage-piles, nearby the banks. But the main technique of aggregates mining on the
Ayeyarwady River is managed by direct dredging of the river bed using mobile floating
structures organized in clusters of dredgers, pushers, and barges. The dredgers extract
the aggregates by suction throughout a pipe, sort them, and transfer them to the barges
using water as fluid. The barges have variable storage capacity and are moved by pusher
boats that can drive one or two attached barges simultaneously toward the main towns or
Yangon [16].
3. Data and Methods: Sand Mining Monitoring System
A challenge when monitoring fluvial sand mining is that because the extraction
concentrates underwater, the surface scars or other visible features used for remote sensing
mining-detection of surface mining are not present, boats and barges have to be targeted as
indicators of the activity. Furthermore, sand barges’ location shifts in time along and across
the rivers, once sand supply has been exhausted in a particular reach or once the river
shifts and generates new sandy areas. Additionally, adding more complexity, the dynamic
character of fluvial mining the diversity of these combined structures (clusterization of
barge, dredge, and pusher), make in some cases challenging to identify and characterize a
specific remote sensing signature. To tackle the problem, we have implemented a module
for sand mining that could efficiently monitor dredging barges along rivers. Similar
systems, using radars, have been used to detect ships in areas where fishing is illegal [18].
The methodological procedures are synthesized in Figure 2.
Figure 2. Methodological flowchart.
Water 2021,13, 2331 5 of 13
3.1. Imagery Selection
The primary objective was to identify barges effectively throughout the year, regardless
of weather constraints such as cloud cover, which substantially limits the use of optical
imagery in the tropics, and second, to devise a system that makes use of free imagery.
Costly imagery could make the system difficult or even impossible to use in developing
countries, where a substantial portion of sand mining from river-beds occurs. To set up
the surveillance system, we opted for the Sentinel-1 mission, a radar component of the
Copernicus joint initiative of the European Commission (EC) and the European Space
Agency (ESA).
We downloaded a total of 500 Ground Range Detected High Resolution (GRD-HR)
Sentinel 1 high-resolution products (five tiles by date) available every 12 days between
25 February 2017
, and 21 June 2020, from the Copernicus Open Access Hub (https://scihub.
copernicus.eu/dhus/#/home) (accessed on 19 August 2021). This dataset corresponds to a
total of 102 days i.e., 26 dates in 2017, 31 in 2018, 30 in 2019, and 15 in 2020.
Sentinel-1A satellite was launched on 3 April 2014 and the Synthetic Aperture Radar
(SAR) free-of-charge has the advantage to be suitable for ship detection, and their resolution
is constant, regardless of the distance from the observed targets [
19
,
20
]. Sentinel 1 provides
a 12-day (ground track) repeated cycle, acquired in Interferometric Wide swath (IW)
mode and a 6-day (ground track) repeat cycle for two satellites, with a 250-km swath, a
20 ×20 m
spatial resolution, and burst synchronization for interferometry. The incidence
angle variation across the swath is between 29.1
◦
and 46.0
◦
. We have chosen the ground
range detected (GRD) product processed at Level 1 at high resolution in the ascending
orbit because the time visit occurs more often yearlong after the sunset (explanation in
“Time Series”).
On the other side, Sentinel 2 optical imagery has a 10-m resolution. They provide
more details of objects and a large range of spectral bands than Sentinel 1, notably during
the detection and recognition of boats (the distinction between barges and dredgers and
the numbers of barges coupled). Unfortunately, the image quality is dependent on the sun
elevation angle, the atmospheric conditions, and cloud cover, which is significantly dense
in many places of Southeast Asia, where sand mining occurs.
The Sentinel-1 SAR is available in the C band (5.404 GHz central frequency, 5.6 cm
of wavelength), and is provided at a single or dual-polarization. Radars equipped with
C-band are generally not hindered by atmospheric effects and image through tropical
clouds and rain showers. The penetration capability concerning vegetation canopies or
soils is limited, restricted to the top layers (sources ESA). On the water bodies, the contrast
between the low backscatering of the water and the high signal from the complex and
angular structures of the boats, enables a high detection capability of vessels. Because of
our small object scale and the 20 m resolution of the radar, the goal was to maximize our
detection capabilities and avoid information loss (ex. small boats like dredges). Herein,
we pre-processed these images using the ESA Sentinel Applications Platform (SNAP)
with radiometric and geometric calibration using SRTM, removing the thermal noise, and
georeferenced them into WGS84. Because of the small ship size we targeted and their
proximity to each other, we did not apply a speckle filter and multilooking. Instead,
after visual inspection and simulations, we applied a minimal threshold of 0.15
γ
and
convertion into a binary raster. After testing different conditions, we selected the Vertical
transmission and reception (VV) co-polarization instead of a cross-polarization Vertical
transmission Horizontal reception (VH) because of the sharpest signal contrast between
the boat and the water. The calibrated backscatter coefficient varies with the incidence
angle and polarisation. The incidence angle mainly influences the geometry of vessels in
SAR images, and co-polarization generally has a higher value than cross-polarization [
20
].
Complementary, SRTM, and Landsat images collected from the US Geological Survey
(USGS) (https://earthexplorer.usgs.gov) (accessed on 19 August 2021) were used for
geomorphological mapping. Six Landsat tiles by year using green and infrared bands for
the NDWI Index were necessary to cover the studied area.
Water 2021,13, 2331 6 of 13
Water level datasets were sourced by the department of meteorology and hydrology
(DMH) of Myanmar (https://www.moezala.gov.mm/daily-water-level-forecast) (accessed
on 19 August 2021). They are water stage data measured in centimeters at 12:30 p.m.
local time.
3.2. Boat Types and Detection
The first objective of the system was to set up a method for ship detection. SAR images
are commonly used for ship detection on the ocean, and conventional methods generally
utilize a constant false alarm detection (CFAR) technique, such as CA-CFAR, OS-CFAR [
20
].
Ships are mostly made of metallic materials, and they usually have large hulls containing a
lot of dihedral and trihedral structures. When detected, ships appear as bright regions in
SAR images [
20
], mainly due to the double reflection of the radar pulses emitted by the
sensors [
20
,
21
]. The incidence angle is primarily influenced by the geometry of ships in
SAR-C images.
The main difficulty encountered on the ocean is related to the texture of the waves,
which decreases the contrast between the high backscattering of the ships and the specular
reflection of the water body. The high wave rugosity reduces the specular reflection
and increases the backscattering. Therefore, it is relatively simple to detect ships at high
incidence angles and low wind speeds. Both factors influence the sigma 0 value contrast
between the water and the ship [
20
]. These methods also give the possibility for automatic
classifications and measurements to know the size and the surface of the boats.
Applying the same method to detect vessels on rivers like the Ayeyarwady may
seem less effective because river barges are smaller, ranging from 40 to 70 m in length
and having 400–1200 m
2
of surface area (Figure 3). Also, the mean river channel width is
about 1 km during the dry season. However, the fluvial scale and dynamics reduce wave
roughness and size, and increase the specular reflection when the surface fluctuation is
less than the microwave wavelength (5.6 cm). Under these conditions, the river surface is
considered smooth, and radar backscatter appears as a homogeneous background in a SAR
image [
22
], which is particularly advantageous for boat detection on large rivers such as the
Ayeyarwady. Of course, limitations still exist. For example, the variety of sizes and types of
the river vessels mining the channel or harboring precludes distinguishes between one or
multiple vessels. It applies particularly to dredgers, which can be detected only in a cluster.
To tackle this limitation, we account for the boat’s backscattering surface as a measure itself,
understanding that this measure is also dependent on the position of the boat in relation to
the satellite angle or the material stored on the deck. However, our goal is not to calculate
the exact boat surface but to devise a standardized and reproducible method of detecting
vessels on a multi-temporal scale in large tracts of this complex multi-channel river. Since
the water body and sand-bars have flat and smooth surfaces and a high specular reflection
in band C, the probability of reliably detecting and distinguishing the studied objects
(boats) from other objects such as structures on the riverboats anchored to sand bars is
also low. Therefore, we have used a Sentinel-2 optical image for each year to ascertain the
central channel location during the dry season.
We focused our analysis by filtering the boats located in the mid-channel, as they
are detached from the bank and thus identifiable. Barges for transporting goods were
distinguished and filtered from sand-mining barges based on sand mining daily activity
and based on the mid-channel working or berthing location during the night. As mentioned
above, three kinds of vessels are used for sand mining: dredgers, barges, and pushers
(Figure 3). The dredgers typically range from 15 to 28 m in length (23 m on average).
According to a 2017 survey [
16
], there are three main classes of dredgers with different
storage capacities for sand and gravel extraction. Small dredgers can store from 0.5 to
18 suds, medium-sized dredgers from 20 to 29 suds, and the largest ones can reach up to
30 suds (1 sud = 2.83 m3).
Water 2021,13, 2331 7 of 13
Figure 3.
(
a
), Surface and length of individual dredgers, barges, and pushers on the Ayeyarwady River measured from
Pléiades satellites at 0.5-m resolution (Google Earth
®®®
). (
b
), Sentinel 1 SAR (at 6:15 p.m.) and (
c
), Pléiades (at 10:51 a.m.)
images taken in 1 October 2019 downstream of Thayet (km 410). (
d
), Sentinel 1 SAR (at 6:16 p.m.) and (
e
), Sentinel 2 (at
10:39 a.m.) images taken on 10 February 2020 downstream of Tonbo (km 502). In (
e
), the group of barges (north part) can be
distinguished from the clustered dredgers (black dots). (Copernicus Sentinel data 2020, processed by ESA). (
f
), A pusher,
barge, and dredger at Pyay, km 457 (2017).
The comparison between Sentinel-1 SAR (10-m resolution) and Pléiades images (0.5-m
resolution) taken on the same day and for boats at the same location shows that Sentinel-1
cannot detect solitary dredgers (which are the core of sand mining activity), and a minimum
of three dredgers together is required for the sensor to detect them. The barges can also
differ in backscattering power, which is influenced by their size, orientation concerning the
satellite swath axis, and the materials stocked on the platform, which either smoothen the
surface or increase rugosity. Barge-pusher couples seem to be frequently characterized by
two strong (peanut-shaped) backscatter signals (Figure 3b,d), with the first signal from the
pusher boat and the second for the barge’s bow. There is a weaker backscattering signal
from the flat or smooth central part between these extremities, where sand is stored on the
flatboat surface. Due to the sharper contrast in the signal between the boats and the water
background (Figures 3b,d and 4), we selected vertical polarisation (Gamma VV), rather
than vertical and horizontal (Gamma VH). After testing different situations to distinguish
the boats from the water without losing the low values (i.e., dredges), we defined the DN
value 0.15 as a threshold (Figures 2and 4).
Water 2021,13, 2331 8 of 13
Figure 4.
(
a
), Sentinel 1 SAR (Copernicus Sentinel data 2020), barges cluster (km 697, upstream of
Nyaungdon), the red lines correspond to the boat delineation according to the 0.15 digital number
detection threshold. (
b
), Signal intensity of the radar image after processing along the A-B cross-
section from (
a
). The 0.15 red line corresponds to the boat threshold used to distinguish pusher-barge
and dredge from the water body.
Compared to other monitoring systems on lakes or oceans, the remarkable variability
of the fluvial water extension (vertically and laterally) in a shifting river prevents using
a single automatically-derived water mask (for automated detection of boats). For that,
we delineated manually and extracted by mask each individual or clustered ships before
processing the monitoring system.
3.3. Time Series
Two satellites’ orbit swaths (ascending and descending) are available for the whole
length of 703 km, with a visit every 12 days for each orbit. The data allows for synchronic
results of the boat activity. However, we selected only the ascending orbit since this satellite
track enables imaging of the studied area around 6:15 p.m. (Myanmar time). On this orbit,
the satellite reaches the latitude of Mandalay (21
◦
58
0
N), the northernmost point of the
studied area, after the sunset during 192 days in a year (from 7 September to 18 March), and
the southernmost point of the studied area, Nyaugdone (17
◦
03
0
N), during 184 days (from
6 September to 9 March). In contrast, in the descending orbit, the satellite images the area
around 6.02 a.m., reaching Mandalay (21◦580N) before the sunrise during 163 days (from
16 October to 28 March), and Nyaugdone (17
◦
03
0
N) during 158 days (from 24 October to
31 March).
The selection of the ascending orbit is based on the following rationale. According
to the literature [
7
,
17
] and field observations, sand mining boats are active day and night
or barges storing the mined sand also use to stay anchored in the middle of the channel
during the night. In contrast, boats for goods transport are typically anchored in harbors
during the night to avoid hazardous navigation. Indeed, the later the image could be taken,
the better it could filter and discriminate the sand mining boats from vessels for transport.
Thus, following the sunset and hours of darkness, we selected the ascending satellite
orbits. Note that the images are taken from the ascending orbit in the daylight (before the
sunset) around 6:15 p.m. (between 9 March and 7 September), which corresponds partly to
the rainy season when the boat activity is supposed to be lower.
Our monitoring spans four full calendar years and three full hydrological-mining
years (HMY), from 25 February 2017 to 2020, with a complete and regular cover every
12 days. Beyond the selected time series used to set up and test the system, the surveillance
system has been implemented to monitor continuously.
Water 2021,13, 2331 9 of 13
3.4. Georeferencing of Detected Boats in a Temporally Mobile Alluvial Channel
Like many other large rivers in the world, the Ayeyarwaddy River is, morphogeneti-
cally, a mobile system. Thus, systematic multi-temporal monitoring of boats in a dynamic
fluvial system faces additional challenges because the river changes in planform through
lateral shifting and the generation of new fluvial landforms such as branches, islands, and
bars. For that reason, it was not possible to develop a simplified automatic detection system
by using a standardized and fixed water mask. The high rate of lateral mobility of the
Ayeyarwady River at a multi-year and even monthly scale, and the need to compare and
normalize the data of occurrence of boats and longitudinal locations, demanded the devel-
opment and implementation of a kilometer grid breakdown to locate boats as related to the
changing morphology of the river. Thus, we created a Thiessen polygons grid following
the axis (centreline) of the valley bottom (structural constraint and old terraces) delineation
to have a stable referenced geospatial base for studying the whole length and width river
changes over time. In our case, the period spans from 1988 (first Landsat images available)
to undetermined future years ahead.
The valley was delineated with the Valley bottom tool of the Fluvial corridors tool-
box [
23
] on ArcGIS
®®®
. It was applied to the Ayeyarwady River and its delta using both
the river stream and the SRTM DEM (30 m of spatial resolution). The geomorphological
characteristics of the valley and maximum flood extent were assessed from S1 C-SAR
images during the peak floods of 2015, 2018, and 2019. The river water body from 1988
to 2020 was used to control the lateral mobility, and manual adjustments were also made
where the active channel was close to steep slopes and on the narrow stream in rocky
controlled nodal reaches. Furthermore, an additional positive 50 m-wide buffer was created
around the floodplain boundaries to ensure that the water surface in the narrow zones
would be inside these limits.
Because the Thiessen polygon required a defined axis, a centreline was first designed
manually along the previously delineated valley bottom and extracted as a frame for
determining it. It provides coherence for the river stream multi-year channel delineation. A
smoothing tool was used for sharp angles among fluvial reaches, decreasing artifacts of the
Thiessen polygons angles to maintain better consistency for multi-temporal comparisons.
The grid extends from the Sagaing bridge and was numbered by kilometers, from upstream
(km 1) to downstream (Nyaungdon bridge, km 703).
Regarding the procedures described in Sections 3.1 and 3.2, Sentinel 1 Ground Range
Detected (level 1) data were first processed using the Sentinel Application Platform (SNAP).
The Sentinel 1 dual polarisation VV both Intensity and Amplitude in high resolution, IW
mode, were geometrically corrected using SRTM 30 m (1 Arc sec) Digital Elevation Model
and projected in the 46N UTM zone. The data were corrected for near-far range incidence
angle variations and are thus given as Gamma0 (γ) values.
Because the main objective is the surveillance of sand mining in almost real-time,
after obtaining the Gamma0_VV images corrected from the SNAP (Figure 2), the five tiles
covering the studied area were merged by date. Using the image’s displayed in standard
deviation, boats (single or clusters) detached from the bank were delineated with polygons
and the polygons combined by date. The polygon delineations were used as a mask to
extract the boats from the Gamma0_VV raster mosaic image to isolate the boat backscatter
from any other objects. Using the conditional tool, we excluded the values below the DN
0.15
γ
o threshold. The remaining cells corresponding to the boat backscattering were vec-
torized and then merged by date. To cross the boat entities with their kilometric locations,
we intersected the vectorized boats with the kilometric grid. The surface calculation in
square meters by kilometric polygon was calculated and exported as a table.
4. Discussion and Results
Sand Mining in the Ayeyarwady River
The Ayeyarwady River hydrological regime is monsoonal, with a flood season span-
ning from June to September and a low-flow season starting in November. Accordingly, we
Water 2021,13, 2331 10 of 13
use hydrological-mining years (HMY) in our analysis, which span from 1 July to 30 June of
the succeeding calendar year. Although sand businesses operate yearlong, extraction is
more intensive during the high-water season. The boats’ total detected surface displays
a correlation with the river’s water level, confirming the boats’ activity seasonality [
16
].
Hence, 82% of the boat activity occurs from November to June (Figure 5). Interestingly,
activity on 16 April 2019 was particularly low for the period, which could be related to
the Thingyan celebration and the day before the Burmese new year (17 April). The radar
images were not taken at this festive date but some days before or after for the other years.
Figure 5.
Total surface area of detected boats along the study area by date and water level (cm) at the
Pyay station Scheme 2017. (source DMH). Although sand businesses operate yearlong, extraction is
more intense during the high-water season, and 82% of the sand mining boat activity occurs from
November to June.
Boats are detected along the whole ~700-km studied reach, but about 50% of the
total concentrate in a short 140 km reach, starting 12 km upstream of Pyay and ending
18 km downstream of Monyo (Figures 1,6and 7). This reach, spanning between km
445 and km 586, represents 20% of the whole studied length. Several riparian towns
with 20,000 to 135,000 inhabitants are located along the study area. But except for Pyay
(
~135,000 inhabitants
), the six other towns along the reach that concentrates 50% of boats
have an average population of ~15,000 inhabitants. This intensively-mined reach is charac-
terized by a plethora of villages and roads leading to the river. Sand is accumulated for
distribution in sand piles close to the riverbanks. Observations on the field (2017) and from
Google Earth display that the sand piles and loading areas for transferring the sand to the
land are usually temporary, with their locations changing from one year to another. Along
the reach, we identified 32 sand piles throughout 2014 to 2020 with an average surface
area of ~1500 m
2
. About 72% of them (23) were located less than 1 km from the closest
main roads. Along this reach, distances by road to Yangon range from 175 to 367 km,
while distances to Yangon by boat along the river range from 217 km to 361 km. The most
significant concentration of boats is on the main channel. Clusters are also often detected
near towns and close to the distribution sites, shifting according to the mining location.
Water 2021,13, 2331 11 of 13
Figure 6.
Reach from km 1 to 703. The histogram corresponds to the total surface area of boats for all dates by km. The lines
indicate the cumulative surface area by hydrological year (HMY, from 1 July to 30 June). Data from 2016–2017 start only on
25 February 2017. The blue rectangle locates the 50% boat concentration between km 445 and km 586. The black bottom
line indicates the population’s density within a 5-km radius from the river centerline for the studied reach (source: Census
2014, DOP).
Figure 7.
(
a)
The location that concentrates 50% of the boat surface areas from km 445 to 586 and boat density per km (unit
of length) and per day within 1-km radius (red gradient) for the whole 2017–2020 period. Only the towns located in the
floodplain are displayed. (
b
–
d)
correspond to the total boat shape (black areas) location for each HMY, from upstream (km
512) to downstream (km 556) and rectangle location of (
b
–
d
), 20 February, 2018, 2019, and 2020 (WGS84/UTM zone 46N)
(Copernicus Sentinel data 2020, ESA).
Water 2021,13, 2331 12 of 13
To assess the fluvial reaches with the largest concentrations of boats and their geomor-
phologic variables and to minimize the noise in the data by removing the transportation
vessels, we used the 3rd percentile of boat-sum by date as a threshold filter.
The sand extraction is more intensive in river reaches that temporarily sustain one
channel and episodically two. Narrow nodal points, where the channel widths are less than
600 m, are not favorable to sand-bed extraction because of the structural-rocky control and
the hydrodynamic conditions that constrain the development of a well-structured mobile
sandy bed. The concentration of sand mining activities does not correlate with the increase
in migration rates or river width. River branching increase does not appear to be a factor
influencing sand mining activity because, in multibranched reaches, fluvial activity still
concentrates on the main channel and the head of some fluvial bars (Figure 7).
During the three HMYs, boats showed a similar longitudinal distribution pattern and
frequency. The long river reach from Mandalay (km 1) to around Pyay (km 458) had a
constant low boat density. A localized spot of higher boat activity was around Myinmu
(km 47). However, along the reach, the numbers of boats were closely linked to towns,
suggesting berthing or local transportation (Figures 6and 7a).
However, along the further 141 km river reach, from km 445 (upstream of Pyay)
to km 586 (downstream of Monyo), boat activity was massive during all the HMYs
(
Figures 6and 7a
). In the 2017–2018 HMY, the boat surface area in this section (141 km)
was 2.3 times larger than in the upstream reaches and was more than 2.5 times larger than
upstream during the 2018–2019 and 2019–2020 HMYs. Downstream, in Danubyu town
(km 675), local activity was also intensive in the three HMYs (Figure 6).
The general activity of boats and mining along the whole 703-km study area suggests
that mining activity in 2019–2020 was higher than the two previous HMYs.
5. Concluding Remarks
A global program for detection and monitoring of sand mining has been regarded
as crucial for quantifying the location and extent of sediment mining and the natural
variations in sand flux in the world’s rivers [1,6,7,24].
Thus, our outcomes are relevant for both efforts: (a) the design and implementation of
a field-calibrated remote sensing detection and monitoring system of sand mining that can
be applied worldwide; (b) specifically for Myanmar, where access to data and field works
is difficult, the monitoring and information we developed allow to understand the spatial-
temporal distribution and level of intensity of sand mining in the Ayeyarwady River, a
strategic fluvial basin in Southeast Asia. Sand mining in the Ayeyarwady River requires
close monitoring as excessive sand extraction can affect the morphodynamic balance, the
sediment budgets of the fluvial belt, and negatively impacts the supply of sediments to
the delta.
Our monitoring system demonstrates to be capable of detecting sand mining loca-
tions on large rivers and cloudy tropical areas in almost real-time, accurately, and free of
charge, making it accessible to a range of users, decision-makers, and stakeholders such as
government agencies, environmental organizations, NGOs and local communities.
Author Contributions:
E.M.L. and C.R.G. jointly conceived the study, designed the methodology,
and participated in field activities. C.R.G. processed the remote sensing products. C.R.G. and E.M.L.
analyzed the data and wrote the article in collaboration. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the Earth Observatory of Singapore and Asian School of the
Environment, Nanyang Technological University.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study is available on request from the
corresponding author.
Water 2021,13, 2331 13 of 13
Conflicts of Interest:
The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported in this paper.
References
1.
UNEP. Sand and Sustainability: Finding New Solutions for Environmental Governance of Global Sand Resources; United Nations
Environment Programme: Geneva, Switzerland, 2019.
2. Peduzzi, P. Sand, rarer than one thinks. Environ. Dev. 2014,11, 208–218.
3.
Milliman, J.D.; Farnsworth, K.L. River Discharge to the Coastal Ocean: A Global Synthesis; Cambridge University Press: Cambridge,
UK, 2011.
4.
Hackney, C.R.; Darby, S.E.; Parsons, D.R.; Leyland, J.; Best, J.L.; Aalto, R.; Nicholas, A.P.; Houseago, R.C. River bank instability
from unsustainable sand mining in the lower Mekong River. Nat. Sustain. 2020,3, 217–225. [CrossRef]
5.
Sverdrup, H.U.; Koca, D.; Schlyter, P.A. Simple system dynamics model for the global production rate of sand, gravel, crushed
rock and stone, market prices and long-term supply embedded into the WORLD6 model. Biophys. Econ. Resour. Qual.
2017
,2, 8.
[CrossRef]
6. Bendixen, M.; Best, J.; Hackney, C.; Iversen, L.L. Time is running out for sand. Nature 2019,571, 29–31. [CrossRef] [PubMed]
7. WWF. Uncovering Sand Mining’s Impacts on the World’s Rivers; World Wildlife Fund for Nature: Morges, Switzerland, 2018.
8. Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 2019,12, 7–21. [CrossRef]
9.
TWAP. United Nations Environment Programme (UNEP). Transboundary River Basins: Status and Trends. Summary for Policy Makers.
342. 2016. Available online: http://www.geftwap.org/publications/river-basins-spm (accessed on 19 August 2021).
10.
Gruel, C.R. Sédimentation et érosion dans le delta de l’Ayeyarwady, Focus sur trois archipels situés sur le cours principal du
fleuve. In Technical Report for IRD; Deltas of South East Asia: Villeneuve-les-Avignon, France, 2018; p. 221.
11.
Latrubesse, E.; Park, E.; Kastner, K. The Ayeyarwadi River (Myanmar): Whasload transport and its global role among rivers in
the Anthropocene. PLoS ONE 2021,16, e0251156. [CrossRef] [PubMed]
12.
Syvitski, J.P.M.; Kettner, A.J.; Overeem, I.; Hutton, E.W.H.; Hannon, M.T.; Brakenridge, G.R.; Day, J.; Vörösmarty, C.; Saito, Y.;
Giosan, L.; et al. Sinking deltas due to human activities. Nat. Geosci. 2009,2, 681–686. [CrossRef]
13.
Chen, D.; Li, X.; Saito, Y.; Liu, J.P.; Duan, Y.; Liu, S.; Zhang, L. Recent evolution of the Irrawaddy (Ayeyarwady) Delta and the
impacts of anthropogenic activities: A review and remote sensing survey. Geomorphology 2020,365, 107–231. [CrossRef]
14.
Robinson, R.A.J.; Bird, M.I.; Oo, N.W.; Hoey, T.B.; Aye, M.M.; Higgitt, D.L.; Lu, X.X.; Swe, A.; Tun, T.; Win, S.W. The Irrawaddy
river sediment flux to the Indian ocean: The original nineteenth-century data revisited. J. Geol. 2007,115, 629–640. [CrossRef]
15.
Garzanti, E.; Wang, J.G.; Vezzoli, G.; Limonta, M. Tracing provenance and sediment fluxes in the Irrawaddy River basin
(Myanmar). Chem. Geol. 2016,440, 73–90. [CrossRef]
16.
WWF Greater Mekong. Sediments and geomorphology of the Ayeyarwady. In Ayeyarwady State Of the Basin Assessment (SOBA);
WWF Greater Mekong: Bangkok, Thailand, 2017.
17.
Kadoe, B. Exploring Sand Mining in Yangon, Myanmar: Status, Regulations and Impacts, Practitioner Report. Ph.D. Thesis, Clark
University, Worcester, MA, USA, 2018.
18.
Kurekin, A.A.; Loveday, B.R.; Clements, O.; Quartly, G.D.; Miller, P.I.; Wiafe, G.; Agyekum, K.A. Operational monitoring of illegal
fishing in Ghana through exploitation of satellite earth observation and AIS data. Remote Sens. 2019,11, 293. [CrossRef]
19. Oliver, C.; Quegan, S. Understanding Synthetic Aperture Radar Images; SciTech Publishing: Chennai, India, 2004.
20.
Wang, Y.; Wang, C.; Zhang, H.; Dong, Y.; Wei, S. A SAR Dataset of ship detection for deep learning under complex backgrounds.
Remote Sens. 2019,11, 765. [CrossRef]
21. Crisp, D.J. The state-of-the-art in ship detection in Synthetic Aperture Radar imagery. Org. Lett. 2004,35, 2165–2168.
22.
Huo, W.; Huang, Y.; Pei, J.; Zhang, Q.; Yang, J. Ship detection from Ocean SAR Image based on local contrast variance Weighted
Information Entropy. Sensors 2018,18, 1196. [CrossRef] [PubMed]
23.
Roux, C.; Alber, A.; Bertrand, M.; Vaudor, L.; Piégay, H. “FluvialCorridor”: A new ArcGIS toolbox package for multiscale
riverscape exploration. Geomorphology 2015,242, 29–37. [CrossRef]
24.
Bendixen, M.; Iversen, L.L.; Best, J.; Franks, D.M.; Hackney, C.R.; Latrubesse, E.M.; Tusting, L.S. Sand, gravel, and UN Sustainable
Development Goals: Conflicts, synergies, and pathways forward. One Earth 2021,4, 1095–1111. [CrossRef]
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