ArticlePDF Available

Abstract and Figures

Asia dominates the world’s aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture expanded rapidly in coastal regions in Asia since the early 1990s and was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income, employment and contributes to food security, has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental-scale. We present a multi-sensor approach which utilizes Earth Observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as of the onshore area up to 200km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at single pond level based on temporal features derived from high spatial resolution SAR and optical satellite acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth observation derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units from regional, national, and sub-national scale.
Content may be subject to copyright.


Citation: Ottinger, M.; Bachofer, F.;
Huth, J.; Kuenzer, C. Mapping
Aquaculture Ponds for the Coastal
Zone of Asia with Sentinel-1 and
Sentinel-2 Time Series. Remote Sens.
2022,14, 153. https://doi.org/
10.3390/rs14010153
Academic Editor: Junshi Xia
Received: 11 November 2021
Accepted: 23 December 2021
Published: 30 December 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Article
Mapping Aquaculture Ponds for the Coastal Zone of Asia with
Sentinel-1 and Sentinel-2 Time Series
Marco Ottinger 1, * , Felix Bachofer 1, Juliane Huth 1and Claudia Kuenzer 1,2
1German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany;
Felix.Bachofer@dlr.de (F.B.); Juliane.Huth@dlr.de (J.H.); Claudia.Kuenzer@dlr.de (C.K.)
2Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg,
97074 Wuerzburg, Germany
*Correspondence: marco.ottinger@dlr.de; Tel.: +49-8153-281510
Abstract:
Asia dominates the world’s aquaculture sector, generating almost 90 percent of its total
annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond
aquaculture systems and serve as a primary protein source for millions of people. The total production
and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early
1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood
from a growing world population. The aquaculture sector generates income and employment,
contributes to food security, and has become a billion-dollar industry with high socio-economic
value, but has also led to severe environmental degradation. In this regard, geospatial information on
aquaculture can support the management of this growing food sector for the sustainable development
of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing
volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and
cloud computing services, we extracted coastal aquaculture at a continental scale. We present a
multi-sensor approach that utilizes Earth observation time series data for the mapping of pond
aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the
coastline. In this research, we developed an object-based framework to detect and extract aquaculture
at a single-pond level based on temporal features derived from high-spatial-resolution SAR and
optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we
performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset
to investigate spatial distribution and identify production hotspots at various administrative units at
regional, national, and sub-national scale.
Keywords: aquaculture; Asia; Earth observation; ponds; coastal zone; Sentinel-1; SAR; time series
1. Introduction
1.1. Asia’s Role in Global Aquaculture Production
The global increase in demand for protein-rich fish, seafood, and other fish products
has led to severe overfishing of the world’s oceans and stagnating catches from marine
fisheries in recent years. At the same time, aquaculture has recorded a rapid increase in
production and become the fastest growing sector in the global food industry, with an
average annual growth rate of 6.9 percent over the past three decades [
1
]. Aquaculture
production has increased more than six-fold since 1990: more than 85 million tons are
valued at 275 billion US dollars worldwide (according to the latest FAO statistics for the
year 2019), and the aquaculture sector is becoming increasingly important as a source of
income and nutrition [
2
,
3
]. Half of all edible fish are produced in offshore and onshore
aquaculture systems, and demand continues to rise. Aquaculture is a major protein supplier
and plays an important role in food security and the achievement of the United Nations
(UN) Sustainable Development Goals (SDGs) for a growing world population of up to
10 billion people for the year 2050—as projected by the UN [47].
Remote Sens. 2022,14, 153. https://doi.org/10.3390/rs14010153 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2022,14, 153 2 of 25
With a share of 88 percent (75 million tons) of the global total volume [
1
], Asia is the
world’s largest regional aquaculture producer and has been the major driver of global
aquaculture growth in recent years (see Figure 1). Land-based pond culture of fish and
shrimp in fresh water and brackish water has a long tradition in Asia and has contributed
most to Asia’s dominant role in aquaculture production. In Asia, aquaculture has expanded
rapidly since the 1990s along the coasts, especially those of Southeast Asia and East Asia [
8
],
and become a major protein source for millions of people. Aquaculture has become a
billion-dollar industry with high socio-economic value, generates income and employment,
and contributes to food security. However, the expansion of aquaculture in low-lying,
flat-topography coastal regions has also led to large-scale land use changes with various
negative socio-economic and environmental effects: the enormous reclamation of land for
fish and shrimp ponds, increasing use of antibiotics and pesticides, water pollution, the
loss of valuable coastal wetlands, and the degradation of important ecosystems (due to
increased salinization of soils and freshwater, wastewater discharge, and eutrophication
of coastal waters), among other effects, result from rapidly growing aquaculture produc-
tion
[911]
. Moreover, large amounts of nutrient-rich feed [
12
,
13
] and medication are
applied in intensive aquaculture to achieve high stocking densities and increase production
quantities, but they lead to increased nutrient input, eutrophication, and water pollution
of the aquatic coastal ecosystems [1315]. Consequently, sustainable aquaculture manage-
ment is essential to reduce the growing ecological footprint of Asian aquaculture and a
global challenge for future food security. In addition, the coastal regions are particularly
vulnerable to the consequences of climate change: global warming, rising sea levels, rising
water temperatures, salinization, acidification, and oxygen depletion in coastal waters
can lead to yield losses with far-reaching economic consequences for the aquaculture in-
dustry [
16
]. With regard to the global relevance of Asian aquaculture, up-to-date spatial
information and products for the quantitative assessment of the spatial extent, distribution,
and dynamics of aquaculture are of great importance for productive management, climate
change adaption measures, food security, and the protection of natural water resources.
Figure 1.
(
A
) Aquaculture production among continents from 1980–2019, (
B
) Global aquaculture
production and capture fisheries production from 1980–2019, and (
C
) Map of aquaculture production
in 2019. Data source: [1].
Remote Sens. 2022,14, 153 3 of 25
At the continental and national levels, there are very large regional differences in
aquaculture production. The five largest aquaculture-producing countries contribute to
more than 80 percent of the global volume and all come from Asia: China (
48.2 million t
),
India (7.8 million t), Indonesia (6.0 million t), Vietnam (4.4 million t), and Bangladesh
(2.5 million t) (see Figure 2). In 2019, China accounted for more than half (56 percent) of the
global aquaculture production, underlining its dominant role in the aquaculture sector in
recent years. In Asia, aquaculture mainly takes place in closed, land-based pond systems
in inland waters (see Figure 2) and ranges from extensive smallholder pond management
to commercial, industrialized pond farms with advanced feeding technologies [
17
,
18
].
Satellite remote sensing enables large-scale, area-wide, and routine monitoring of bodies
of water and anthropogenic activities in coastal regions [
19
,
20
] and is a suitable tool for
developing geographic information products on a national, regional, or global level. As a
result of the rapid global growth of aquaculture in recent years, the spatial recording and
mapping of aquaculture in the coastal regions and the collection of relevant environmental
data on the effects of aquaculture on natural ecosystems and their spatial and temporal
changes represent a focus in coastal research. In this regard, geospatial information on
aquaculture can support the management of this growing food sector for the sustainable
development of coastal ecosystems, resources, and human health.
Figure 2.
Bar chart: Aquaculture production in inland waters and marine areas among Asian
countries in 2019; Donut chart: Share of aquaculture production among continents and the top
5 global producers in 2019. Data source: [1].
1.2. Extracting Aquaculture Using Earth Observation
Only high-resolution imagery enables the detection of even fine linear structural
details of small-scale pond aquaculture objects with a size of less than 1 hectare. On the one
hand, a large number of Earth observation satellites orbiting our planet with passive optical
and active radar instruments with high spatial resolution are suitable for the detection and
mapping of aquaculture areas, but many have limited temporal and spatial coverage. On
Remote Sens. 2022,14, 153 4 of 25
the other hand, the object detection of pond aquaculture requires temporarily dense time
series in order to distinguish ponds from temporarily flooded areas (e.g., wetlands); this
cannot be clearly differentiated with mono-temporal acquisitions. Access to data from high-
resolution, continuous-monitoring missions such as Landsat or the European Sentinel-1
and -2 enables the processing and analysis of long-term time series [
21
]. However, cloud
cover limits seamless data acquisition and data availability with passive satellite sensors
and makes it difficult to identify aquacultures with multispectral optical sensors [
22
26
]
as a result of the lack of available data in cloud-prone regions. Active radar sensors emit
electromagnetic waves in the longer microwave ranges, which are hardly influenced by
the atmosphere and thus enable weather-independent mapping of the Earth’s surface [
19
].
Therefore, satellite-based SAR data has relevant advantages for mapping aquaculture in
coastal regions regularly covered by clouds. The Sentinel-1 mission of the Copernicus
program with the high-resolution Sentinel-1A and -1B satellites (including its successors
Sentinel-1C/D, scheduled to be launched in 2022) is definitely a game changer: it is the first
SAR-C-Band mission with open archive, providing free access to a global and temporally
dense data coverage that is growing continuously (Approx. 10 terabytes per day). The
access to vast amounts of freely available satellite remote sensing data and the development
of high-performance and cloud computing infrastructures in recent years have significantly
improved the efficiency of data processing and analysis, bringing Earth observation into a
new era. This has led to the generation of new data, technologies, and products for different
global-scale applications at a rapid pace [27,28].
Promising regional approaches exist for the automated extraction of land-based pond
aquaculture with Landsat [
29
,
30
], Sentinel-2 [
31
], and Sentinel-1 data [
32
36
]. However,
there is no satellite-derived mapping for land-based pond aquaculture on a continental or
global scale. A satellite-based quantitative assessment of relevant geoinformation on the
spatial extent, distribution, and dynamics of pond aquaculture is essential for sustainable
development, and management of aquaculture in coastal Asia is of utmost importance for
global food security. Therefore, it is of particular interest to monitor and collect aquaculture
and provide appropriate information that can be used for research in the fields of coastal
environmental change, hydrology, and oceanography, among others. The main goals of this
study are therefore the development of a methodology for the automatic, continental-scale
mapping of coastal pond aquaculture for the entirety of Asia based on satellite remote
sensing data, as well as the assessment and analysis of aquaculture areas at national and
subnational level.
2. Study Region
In this study, we focus on the coastal zone of South Asia, Southeast Asia, and
East Asia, which extends over 22 Asian countries and comprises a total coastline of
300,000 km (see Figure 3and Table 1). We defined a coastal zone of Asia (CZA) by
calculating a 200-km buffer—landwards and seawards—from a detailed coastline of Asia.
Efficient coastal sections were designed in this study in order to enable processing of
large satellite data time series for the entire coastal zone of Asia. For this purpose, the
200-km-wide coastal buffer was subdivided into 261 coastal parcels (see Figure 3) by calcu-
lating and digitizing lateral sections with a distance interval of 200 km coastline length.
The final coastal zone stretches from Iran in the west to Western New Guinea in the
southeast and Japan in the northeast and is inhabited by more than 1.7 billion people
(see Table 1), which corresponds to almost a quarter (22 percent) of the current world
population. The zone represents environments with different climates, different terrains,
and a variety of terrestrial and aquatic ecosystems. Rapid socio-economic development,
increased urbanization, and coast-ward migration [
37
], however, dramatically changed the
coastal regions in Asia during the last two decades. Land use changes were closely related
to the expanding farming of crops (e.g., paddy rice), fish and shrimp, which are the main
food source for millions of people. For a more general spatial comparison in Section 4, we
also defined five macro-regions (see Figure 3for coverage and Table 1for country codes):
Remote Sens. 2022,14, 153 5 of 25
South Asia (IRN, PAK, IND, LKA, BGD), Southeast Asia Mainland (MYR, THA, KHM,
VNM, West-MYS, SGP), Southeast Asia Maritime (East-MYS, IDN, PHL, BRN, TLS), East
Asia (CHN, MAC, HKG, TWN), and Northeast Asia (PRK, KOR, JPN).
Table 1. Overview of all countries that belong to the study region: coastal zone of Asia (CZA).
Country
ISO 3166-1
Alpha-3
Code
Coastline
Length a
[km]
Land Area b
[km2]
Coastline a/
Land Area a
Ratio
[m/km2]
Total
Population
(TP) c
[in Mio]
Population in
the Coastal
Zone (PCZA) d
[in Mio]
Share of
PCZA in
Relation to
TP [in %]
Indonesia IDN 97,680 1,878,758 51.99 267.67 250.32 93.5
Philippines PHL 34,541 293,319 117.76 106.65 96.78 90.7
China CHN 34,362 9,371,977 3.67 1459.38 429.65 29.4
Japan JPN 30,725 372,424 82.50 127.20 121.38 95.4
Myanmar MMR 18,008 662,913 27.16 53.71 28.52 53.1
Republic of Korea KOR 14,930 96,857 154.14 51.17 48.40 94.6
India IND 13,166 3,150,820 4.18 1352.64 386.57 28.6
Malaysia MYS 12,465 327,849 38.02 31.53 29.73 94.3
Thailand THA 8182 514,480 15.90 69.43 39.09 56.3
Vietnam VNM 6217 328,898 18.90 95.55 87.09 91.1
Iran IRN 5858 1,622,136 3.61 81.80 10.48 12.8
Pakistan PAK 4936 872,877 5.65 212.23 24.27 11.4
Dem. People’s Rep. Korea PRK 4896 121,458 40.31 25.55 23.71 92.8
Sri Lanka LKA 3930 66,288 59.29 21.23 20.58 96.9
Taiwan TWN 2863 36,207 79.07 23.73 23.06 97.2
Bangladesh BGD 2735 137,208 19.93 161.38 82.94 51.4
Cambodia KHM 1430 181,058 7.90 16.25 10.91 67.1
Hong Kong SAR HKG 1101 1038 1060.56 7.37 5.93 80.5
Timor-Leste TLS 803 15,083 53.24 1.27 1.17 92.1
Singapore SGP 490 511 959.73 5.76 5.44 94.4
Brunei Darussalam BRN 298 5713 52.16 0.43 0.43 100.0
Macao SAR MAC 89 29 3068.01 0.63 0.40 63.5
ROI total (22 countries) 299,705 20,057,900 Ø 269.26 4173 1726 41.4
South Asia (SA) 30,625 5,849,329 Ø 18.53 1829 524.84 28.7
Southeast Asia Mainland (SEAL) 38,474 1,819,271 Ø 176.86 267 194.76 73.1
Southeast Asia Maritime (SEAM) 141,640 2,389,311 Ø 63.50 383 354.71 92.7
East Asia (EA) 38,415 9,409,251 Ø 1,052.83 1491 459.04 30.8
Northeast Asia (NEA) 50,551 590,739 Ø 92.32 204 193.49 94.9
ROI: Region of interest (ROI) refers to all Asian countries, which cover the coastal zone of Asia (CZA); see Section 2.
Ø
—Average value;
a
—Coastline length calculated from vector data on administrative areas from the GADM
[
38
] dataset;
b
—Land area calculated from the free Natural Earth dataset (accessible via naturalearthdata.com);
c
—Population data source: FAOSTAT;
d
—European Commission, Joint Research Centre (JRC) (2015): GHS-POP
R2015A-GHS population grid, derived from GPW4 [39].
The coastal zone in South, East, and Southeast Asia with its variety of lagoons, estuar-
ies, river deltas, and abundant water resources provides ideal conditions for breeding fish
and crustaceans. Above all, the low-lying, flat-topography areas along the coastal regions
fostered the rapid growth of pond aquaculture. As a result, the fertile and often densely
populated coastal regions in Asia have experienced large-scale changes of land use and
shifts in farming systems [
8
,
40
], primarily through the expansion and intensification of
agriculture and land-based aquaculture, which provide the staple foods for the majority of
the coastal population. At the same time, the coastal zone is one of the most threatened
regions in the context of global environmental changes and global climate change effects,
such as increased frequency of weather extremes (floods, droughts, and storms) and salin-
ization [
9
,
16
,
41
,
42
]. Table 1shows the main characteristics of the respective countries that
cover the coastal study region. The large island state Indonesia with its coasts on Borneo,
Sumatra, Java, Sulawesi and New Guinea has the longest coastline of all Asian countries,
with a total length of 97,680 km. The total population of the coastal zone defined in this
study makes up 41 percent of the total population of the countries that belong to this zone.
More than half of the coastal countries (12 out of 22) have from 90 to 100 percent of their
total population within 200 km of the coast (see Table 1).
Remote Sens. 2022,14, 153 6 of 25
Figure 3.
Map of the study region, including the five macro-regions and the coastline parcels that
encompass a coastline of ~200 km in length.
3. Data and Methods
Land-based pond aquaculture objects can be identified and distinguished from other
artificial or natural water bodies (such as rivers, lakes, reservoirs) with radar satellite
data on the basis of different backscatter intensities on their structural components (low
backscatter on smooth water surface, strong backscatter on surrounding dams, dikes) in
combination with size and shape features. The data and methodological workflow for
the mapping of coastal aquaculture in this study is based in parts on the processing steps
published in [
32
]. The method was adapted and further developed in order to enable
large-scale continental mapping: the processing of the satellite data and the segmentation
and vectorization of the aquaculture objects including temporal features were carried out
in the JavaScript API of the cloud computing platform Google Earth Engine (GEE). Further
post-processing workflows and statistical analysis were carried out with the free and open
source software QGIS and with the Python libraries pandas, geopandas, and geofeather
(for more details, please see Appendix A).
3.1. Satellite Data
3.1.1. Sentinel-1 Time Series Data
For each study site in the entire Asian coastal zone, we used all available Sentinel-1
dual-polarized (VV + VH) data in Interferometric Wide-Swath Mode (IW) and Ground
Range Detected High-Resolution (GRDH) format for the time period from 1 January 2019
to 31 December 2019 [
43
] (see Table 2and Figure 4). Sentinel-1 is a twin-satellite constel-
lation with a synthetic aperture radar instrument operating in the C-band at a frequency
of 5.4 GHz. Sentinel-1A and Sentinel-1B were launched in April 2014 and April 2016,
respectively, enabling continuous, high-resolution radar mapping at global coverage scale
with high repetition frequency (revisit time of up to 6 days with two satellites).
Remote Sens. 2022,14, 153 7 of 25
Table 2. Available Sentinel-1 IW GRDH scenes for the study region for the complete year 2019.
Sentinel-1A/B
IW GRDH
Number of Scenes
Ascending
Orbit
Descending
Orbit
Ascending +
Descending Orbit
Quarter I 2019 1 January–31 March 2959 3168 6127
Quarter II 2019 1 April–30 June 2940 3088 6028
Quarter III 2019 1 July–30 September 3160 3229 6389
Quarter IV 2019 1 October–31 December 3144 3746 6890
Total 12,203 13,231 25,434
IW is the default mode over land, which captures three sub-swaths using Terrain
Observation with Progressive Scans SAR (TOPSAR) recording data with a 250 km swath
at 5 m by 20 m spatial resolution (resampled to 10 m spacing for GRDH products). The
Sentinel-1 mission provides day, night, and all-weather observation capabilities [
44
,
45
]
and ensures data continuity along with the upcoming launches of the successor satellites
Sentinel-1C and 1D within the next few years.
Figure 4.
Coverage frequency of VH-polarized Sentinel-1 scenes (1 January 2019–31 December 2019)
for the coastal zone of Asia acquired in ascending and descending orbit.
3.1.2. Sentinel-2 Time Series Data
For the entire study region, we used all available Sentinel-2 L2A Surface Reflectance
data (more than 47,000), which have been acquired by its wide-swath, high-resolution
sensor—the multi-spectral Imager (MSI). The L2A data accessible via the Google Earth
Engine that was downloaded from the Copernicus Open Access Hub (https://scihub.
copernicus.eu, accessed on 10 November 2021) and atmospherically corrected using the
Sen2Cor processor [
46
]. The Sentinel-2 data contains 12 spectral bands ranging from the
visible to the near-infrared and spatial resolution of 10 m to 60 m.
Remote Sens. 2022,14, 153 8 of 25
3.2. Auxilliary Data
3.2.1. DEM Data
For the identification of potential aquaculture areas (low topography, flat regions), we
derived terrain information using the Shuttle Radar Topography Mission (SRTM) Digital
Elevation Model (DEM) in Version 3 published by NASA JPL [
47
,
48
], which provides
void-filled data with a high spatial resolution of 30 m. We used the elevation (height above
sea level in meters) and slope (in decimal degree) information, which were considered as
the most important parameters influencing the presence or absence of aquaculture ponds
in the low-lying coastal regions, and used for topographical masking.
3.2.2. Administrative Boundary Data
Administrative boundary data was collected and processed to match statistical data
and the pond aquaculture vector data (derived from Earth observation data) to geographical
units. The geodatabase released by GADM provides comprehensive and detailed spatial
data on administrative areas for all countries and their sub-divisions (ADM0–country level,
ADM1–province level, ADM2–district level). The downloaded data was extracted from the
GADM database in version 2.8 (April 2020).
3.2.3. Coastline Data
For the separation of land and ocean areas, we generated a land mask and a sea mask
for the entire coastal zone of Asia on the basis of a detailed coastline dataset that represents
the boundary between land and ocean areas. Accurate determination of the coastline is
critical to the separation of water and land, as it determines the zone to be masked as
containing potential aquaculture areas. For comparison, various global shoreline datasets
from multiple sources were searched, collected, and processed to cover the entire coastal
zone of Asia. The positional accuracy of all coastlines was examined for several coastal
samples along the investigation areas. The Open Street Map (OSM) dataset turned out to
be more detailed and up-to-date than other coastline vector datasets (see Figure 5).
Figure 5.
Comparison of coastline datasets for three coastal sections (
A
C
) along the shoreline in
Guangdong Province, China. Background image: Median image derived from Sentinel-1 data.
3.2.4. Statistical Data
The Food and Agriculture Organization of the United Nations (FAO) publishes na-
tional catch fisheries and aquaculture statistics in their annual Yearbook of Fishery and
Aquaculture Statistics [
49
], which are provided by national ministries and institutions. The
Fisheries and Aquaculture Department of the FAO compiles and disseminates time series
of fishery data, which are publicly available within its data collections on capture and aqua-
Remote Sens. 2022,14, 153 9 of 25
culture statistics. This FAO FishStat database provides these data, which are structured at a
national level, providing a comprehensive overview of this sector. Aquaculture production
statistics were harmonized and downloaded from the FAO Fishstat software [
1
], a global
database with national-level production data that are submitted by FAO member countries
(see Table 3).
Table 3.
Aquaculture production (excluding aquatic plants) for all countries in the study region and
respective shares of the main aquatic product groups in 2019. Data source: [1].
Country Total * Fishes Crustaceans Mollusks
tons World
Share (%) tons World
Share (%) tons World
Share (%) tons World
Share (%)
China 48,246,255 56.54 27,086,062 48.09 5,674,350 54.14 14,579,369 83.07
India 7,795,000 9.13 7,005,792 12.44 776,208 7.41 13,000 0.07
Indonesia 5,950,000 6.97 4,913,000 8.72 977,800 9.33 58,400 0.33
Vietnam 4,442,257 5.21 3,137,200 5.57 977,157 9.32 315,000 1.79
Bangladesh 2,488,600 2.92 2,342,768 4.16 145,832 1.39 / /
Myanmar 1,082,141 1.27 1,019,886 1.81 62,255 0.59 / /
Thailand 964,266 1.13 431,423 0.77 384,567 3.67 116,135 0.66
Philippines 858,277 1.01 709,317 1.26 87,345 0.83 61,615 0.35
Japan 598,229 0.70 278,429 0.49 1400 0.01 305,500 1.74
Republic of
Korea 593,586 0.70 112,124 0.20 7952 0.08 442,046 2.52
Iran 505,000 0.59 449,950 0.80 55,050 0.53 / /
Cambodia 305,408 0.36 291,738 0.52 1590 0.02 11,900 0.07
Taiwan 291,499 0.34 197,516 0.35 17,621 0.17 73,500 0.42
Malaysia 224,171 0.26 153,033 0.27 53,909 0.51 16,608 0.09
Pakistan 160,744 0.19 160,438 0.28 306 0.00 / /
Dem.
People’s
Rep. Korea
76,560 0.09 13,995 0.02 / / 62,400 0.36
Sri Lanka 33,841 0.04 27,448 0.05 6098 0.06 27 0.00
Singapore 5831 0.01 4708 0.01 195 0.00 15 0.00
Hong Kong
SAR 3787 0.00 3167 0.01 / / 620 0.00
Macao SAR
1500 0.00 1020 0.00 440 0.00 40 0.00
Brunei
Darus-
salam
933 0.00 341 0.00 592 0.01 2 0.00
Timor-
Leste 120 0.00 119 0.00 1 0.00 / /
ROI total
(22
countries)
74,628,006 87.45 48,339,474 85.82 9,230,669 88.07 16,056,177 91.49
Asia total 75,435,608 88.40 49,082,188 87.14 9,321,341 88.93 16,060,694 91.51
World total 85,335,990 100.00 56,327,079 100.00 10,481,319 100.00 17,550,576 100.00
ROI: Region of interest (ROI) refers to all Asian countries, which cover the coastal zone of Asia (CZA); see Section 2.
* excluding aquatic plants; Note: / represents lack of available data or the production volume is regarded as
negligibly low.
3.3. Satellite Data Processing
In this study, we utilized all available Sentinel-1 IW GRDH and Sentinel-2 L2A data
from 1 January 2019 to 31 December 2019, provided as an analysis-ready data (ARD) cube
within the satellite imagery archive by the Google Earth Engine team [
28
]. The global
datasets of the twin-satellite missions Sentinel-1 and Sentinel-2 have been available since
October 2014 (Sentinel-1A) and June 2015 (Sentinel-2A), respectively. Based on experience
from a previous study [
32
] on the use of Sentinel-1 SAR data for the mapping of aquaculture
for four river deltas in Asia, the multi-sensor approach presented in this study has been
Remote Sens. 2022,14, 153 10 of 25
further developed and adopted. The most significant advancement is the integration of
water coverage information derived from multispectral Sentinel-2 data. Figure 6shows all
relevant processing steps of the applied workflow in this study. Processing of the satellite
time series data comprises the following steps: (1) data selection and pre-processing,
(2) calculation of time series metrics, and (3) object segmentation with histogram-based
thresholding. These steps are described in detail in a previous publication [
32
] and are
presented here in summarized form.
Figure 6.
Workflow of the applied method for the mapping of coastal pond aquaculture for Asia, with
following main processing blocks: preprocessing of the SAR Sentinel-1 data and optical Sentinel-2
data, generation of time series metrics, image segmentation, masking by topographic features, and
object filtering based on geometrical and water features.
3.3.1. Preprocessing of Sentinel Time Series
For the time series calculation, we used analysis-ready archive data from the Google
Earth Engine, which provides pre-processed Sentinel-1 and Sentinel-2 data. The scenes
of the Sentinel-1 data cube are pre-processed to the backscatter coefficient
σ
0 (sigma0 or
sigma nought). Preprocessing of the radar time series comprises the following steps: (1)
the application of an orbit file, (2) thermal noise removal, (3) radiometric calibration, and
(4) orthorectification. In order to limit the effects of look angle and orbit direction for
each individual pixel, the Sentinel-1 data was filtered to the orbit direction (ascending or
descending) with most observations (majority filter on each pixel). The preprocessing of
the Sentinel-2 L2A data included the following steps: (1) cloud filtering (<10 percent cloudy
pixel per scene) and (2) cloud masking using the QA60 bitmask band.
3.3.2. Derivation of Temporal Metrics
The basis for the segmentation procedure, shown in Figure 6, consists of multi-
temporal metrics that have been calculated for all Sentinel-1 and Sentinel-2 scenes from
Remote Sens. 2022,14, 153 11 of 25
1 January to 31 December 2019. For the Sentinel-1 SAR data cube, we calculated the median;
standard deviation; 10th, 25th, 50th, 75th, and 90th percentile; and standard deviation of the
VH and VV polarized data. The pixel-wise median (50th percentile) is particularly suitable
for identifying permanent and stable high (dams, dikes) and permanent low scatterers
(smooth water surface) from the dense annual time series. A per-pixel median image was
generated and used for the object-based extraction of pond aquaculture on a single-pond-
unit basis. The median of all observations was selected rather than the mean to describe a
single and more typical backscatter value because of the strong influence of possible ex-
treme outliers of mean values [
32
,
50
]. By averaging over time, the temporal median image
improved the appearance of narrow, elongated line structures such as dams and levees
(surrounding ponds), which is very crucial for the detection and mapping of aquaculture
on a single-pond level. For the optical multispectral Sentinel-2 data cube, we calculated the
median of band 2 (blue), band 3 (green), band 4 (red), band 8 NIR), band 11 (SWIR), and
the Normalized Difference Vegetation Index (NDWI) by Gao [
51
] and McFeeters [
52
] and
the modified NDWI (MNDWI) [53].
3.3.3. Segmentation
An automatic segmentation of the median image was applied using a histogram-
based thresholding algorithm by OTSU [
54
] that is suited for the separation of bimodal
distributions [
32
]. OTSU’s method [
54
] is non-parametric, unsupervised, and used for
automatic threshold selection grey-level histograms. It calculates the optimal threshold
separating two classes by minimizing the weighted sum of within-class variances for an
assumed bimodal histogram (see Figure 7). In a following step, the binary output image of
the histogram-based thresholding of the median
σ
0 values at VH polarization (segments
based on pixel data) was converted from raster to vector features. The resulting objects
were then used to extract the following features for each pond object: temporal metric
features of the satellite data (SAR features and optical features of Sentinel-1 and Sentinel-2)
and topographic features (mean of the elevation and slope derived from the DEM data),
which we stacked into one combined feature data cube. The object-based pond dataset was
processed for each coastal parcel and downloaded in GeoJSON format automatically in
a script written in the GEE JavaScript API. The final results of the 261 parcels were then
merged to five macro-regions (see Section 2) instead of one single file in order to limit
memory limitations on local machines and enable further processing.
On the basis of DEM-derived terrain information and coastline data, potential pond
aquaculture areas were identified and extracted in order to exclude other water bodies
that are most likely to be confused with aquaculture ponds, such as small mountain
lakes or reservoirs located in higher latitudes. The topographical features of elevation
and slope derived from the high-resolution SRTM dataset were used to generate a static
mask to extract flat and low-lying coastal areas where pond aquaculture could potentially
occur (see Figure 6). For this purpose, the mean elevation and mean slope values were
determined from digitized pond aquaculture samples along the coastal zone of Asia to
derive representative threshold values for the static mask. We defined a height of less than
50 m above sea level and slope of less than 10 percent as threshold values for generating
the topographic masks (altitude mask and slope mask).
Remote Sens. 2022,14, 153 12 of 25
Figure 7.
Histogram-based thresholding (OTSU) on the temporally smoothed SAR time series
(median image).
3.4. Pond Objects Filtering (Post-Processing)
In a final step, we focused on the evaluation of multiple criteria in order to improve
the aquaculture mapping result and minimize the false positive rate. For this purpose, we
added geometrical parameters and overlap analysis based on water features derived from
the OSM dataset. All these processes are part of the post-processing workflow and have
been integrated into a customized model with QGIS for automatic post-processing of the
extracted aquaculture pond datasets.
3.4.1. Vector Attribute Filtering Using Geometric Features
The compact shapes (mainly rectangular/circular) of aquaculture ponds are a char-
acteristic and defining feature for the distinction between natural standing waters and
managed aquaculture ponds. Without such information, the aquaculture areas, which are
generally permanently covered by water, can hardly be distinguished from other (natural)
water bodies. For this reason, we calculated four different geometrical parameters for the
merged aquaculture pond outputs of the five Asian coastal macro-regions and added them
to the mapping results. During the segmentation process, we converted the mapping results
from raster to vector format. This resulted in self-contacts at the edges of some polygons,
e.g., two vertices of the same polygon have the same coordinate (see Figure 8A), leading to
invalid geometries. Therefore, a negative buffer at a distance of
10–6 m was calculated
and applied to the pond polygon file to eliminate invalid self-contacting polygons. At the
end of the post-processing, a positive buffer with the same distance was applied in order
to reconstruct the original geometry of the pond polygons. Holes inside the segmented
aquaculture ponds posed another challenge and influenced the perimeter length of the
polygon (see Figure 8(B1)). Since the perimeter is also used for the calculation of some
compactness features, existing holes needed to be filled (see Figure 8(B2)) for a proper
representation of the pond area as a polygon.
To resolve these issues, the following steps were required as a preliminary process:
(1) patching of holes, (2) reprojection to the global metric coordinate system (EPSG: 3857),
and (3) elimination of polygon self-contact. For all aquaculture polygons we calculated the
following geometric attributes, which indicate shape metrics of the pond polygon features
and can be used to eliminate classification errors. The compactness, convex hull, and
oriented minimum bounding box were calculated to describe the convexity, elongation,
Remote Sens. 2022,14, 153 13 of 25
and rectangularity of a pond polygon. The ratio between area and area of the convex hull
was found to be a good indicator to eliminate polygons with non-rectangular shapes. For
example, lakes with dendritic shapes (see Figure 8(C2)) or rivers with curved shapes could
be excluded after applying a filter of geometric attributes. We defined more than 10 test
sites distributed across the study region and empirically determined threshold values based
on the geometric parameters.
Figure 8.
(
A
) Self-contacting polygon edges; (
B1
) Holes in polygons and (
B2
) Patched holes; (
C1
,
C2
)
Compactness parameters: yellow—water polygon; grey—convex hull of the polygon; light blue—
oriented minimum bounding box; (
D
) Result of the minimum oriented bounding box calculated for
the aquaculture pond mapping result.
3.4.2. Filtering of Natural Water Bodies
As a final post-processing step, we applied an overlap analysis between the pond
aquaculture mapping result and natural water body dataset. The overlap analysis on the
basis of the globally available OSM dataset allows for the removal of even compact lakes
and rectangular river segments (e.g., river section in between two bridges) that cannot be
detected via geometrical parameters. For all countries in the study region, we acquired and
merged all OSM country datasets (accessed via http://download.geofabrik.de/, accessed
on 8 May 2020) and filtered for all relevant water data (OSM keys “natural = water” and
“landuse = reservoir”, waterway = *), which are available in polygon (water) and polyline
(waterway) format. The resulting OSM overlap percentage with the aquaculture pond
dataset was calculated, and all polygons with more than 20 percent coverage were excluded.
3.5. Accuracy Assessment
A reference dataset including two classes—aquaculture ponds (AP) and non-aquaculture
ponds (Non-AP)—was created for the entire coastal study region. We randomly distributed
a total of 7500 sample points per class along the coastal buffer using a stratified sampling
scheme [
55
] to adequately represent the amount of detected pond aquaculture area between
different coastal regions. In order to include a statistically sound data basis for the validation,
the proportion of the AP class for each of the 261 coastal parcels (see definition of parcels in
Section 2) was calculated. The 7500 AP sample points and 7500 Non-AP sample points were
distributed according to the parcels’ shares of the total aquaculture area. Reference data was
derived from several remote sensing experts who assigned a class to each sample point (AP
or Non-AP) for the entire coastal zone based on optical satellite imagery. The acquisition of
very high-resolution optical data is clearly limited, since continuous coverage of the large
areal extent of the continental-scale study region with a total coastline length of approximately
300,000 km (see Table 2) is too cost-intensive. For this reason, we used very high-resolution
Remote Sens. 2022,14, 153 14 of 25
optical data provided via Google Earth imagery. Since these data are globally available, a
comprehensive validation database could be provided in order to assess the accuracy of
the mapping results even for very remote coastal sites and areas. Finally, we calculated a
confusion matrix including performance measures such as the overall, producer’s, and user’s
accuracy for the entire study region as well as for the five macro-regions (see Table 4).
Table 4.
Accuracy assessment for the two classes of aquaculture and non-aquaculture for the entire
coastal study region. Producer’s accuracy (PA), user’s accuracy (UA), and overall accuracy (OA).
Classification
Reference
AP Non-AP Sum UA
Aquaculture Ponds (AP) 6483 1017 7500 86.44
Non-Aquaculture Ponds (Non-AP)
198 7302 7500 97.36
Sum 6681 8319 15,000
PA 97.04 87.77 91.90 OA
3.6. Geospatial and Statistical Data Analysis
For statistical analysis of the post-processed aquaculture pond objects, we calculated
summary statistics and spatial statistics using the pandas library in Python. The post-
processed pond vector files include more than 30 features and are large datasets with more
than 10 GB data. In order to bypass memory limitations and enable faster data processing,
we transferred the datasets for intermediate data storage into the geofeather format (https:
//pypi.org/project/geofeather/, accessed on 29 October 2021), which supports pandas
data frames and enables faster import and export of larger geospatial files. To characterize
national- and sub-national-level statistics of aquaculture pond objects such as amount, area,
shape, and size features, we merged the pond object vector file with the administrative
areas at three different administrative levels (GADM0, GADM1, GADM2, see Section 3.2.2).
4. Results
4.1. Aquaculture Mapping Results for Coastal Asia
The results of the object-based pond aquaculture area extraction for the entire coastal
zone and selected coastal study areas are presented in Figure 9. Due to the small object
size of the ponds, it is difficult to display the entire object-based mapping result for the
complete study area. Therefore, we illustrate some exemplary coastal spots located along
the shoreline of Asia, with the respective mapped aquaculture objects illustrated in blue
color (see Figure 9). We calculated the accuracies for the two classes (aquaculture pond
and non-aquaculture pond) for the entire coastal study region. In order to quantify the
agreement between the mapping result and the reference data set, the user and producer
(UA and PA) and the overall accuracy (OA) as defined in [
55
] were computed (see Table 4).
The aquaculture ponds in the coastal zone were mapped with a mean overall accuracy
of 91.90 percent. In more detail, the overall accuracies per macro-region range from
92.69 percent in East Asia to 84.31 percent in Northeast Asia.
The bar plot in Figure 10 shows that the highest amount of aquaculture pond area is
concentrated in close proximity along the coastline of Asia (15 km distance to the coastline)
of the respective study area. The total area of the mapped aquaculture ponds within the
200 km coastal buffer zone in Asia accounts for over 2 million ha (2,000,852 ha). More
than one-fifth (21 percent) of the total pond area is located within 1 km distance from
the coastline, and 45 percent can be found within 5 km distance from the coastline. This
indicates that pond aquaculture is clearly concentrated in close proximity to the shores.
Remote Sens. 2022,14, 153 15 of 25
Figure 9. Extracted aquaculture ponds for the entire coastal zone of Asia. Coastal spots: (A) Guang-
dong Province, China; (
B
) Hainan Province, China; (
C
) Irrawaddy Delta, Myanmar; (
D
) Andhra
Pradesh state, India; (
E
) Chachoengsao Province, Thailand; (
F
) Sumatra, Indonesia; (
G
) Java, Indone-
sia; (H) Red River Delta, Vietnam.
Remote Sens. 2022,14, 153 16 of 25
Figure 10.
Total area of Earth-observation-derived aquaculture ponds per kilometer distance to
the coastline.
4.2. National Statistical Analysis
For each country, we summarize the total area of the mapped coastal pond aquaculture
at national level (see Figure 11, GADM level 0), at state/province/equivalent level (GADM
level 1), and district/county/equivalent level (GADM level 2). Statistics on annual aqua-
culture production are provided by the FAO and are listed individually for all countries.
Sub-national production data are partially available for individual countries (e.g., China),
but these are not available in standardized form nor accessible via a central platform for
all countries at a global level. However, data at the national level may not be suitable nor
relevant enough to describe regional distribution of coastal aquaculture. For this reason,
we calculated coastal pond area distribution also at the sub-national administrative units to
derive statistical information at the regional level. In Figure 12, we illustrate the distribution
of pond area at district level for the island state of Indonesia.
The counting and measuring of the shapes of the mapped aquaculture pond objects
provides essential information to quantitatively describe the composition of shapes among
different regions and countries and to recognize and describe patterns. Therefore, we ana-
lyzed various pond-specific topographic and geometric features (Figure 13) and calculated,
for example, the average size and mean compactness (Figure 14) of the mapped aquaculture
for each country in the study region.
For the five countries with the highest shares of mapped aquaculture area, we com-
puted scatterplots of the elevation and distance of all ponds and found that there are
different spatial patterns (Figure 13A). In China, aquaculture areas seem to be well dis-
tributed within a 150 km distance to the shorelines, while in Indonesia (and to a lesser extent
also in Vietnam and India) the ponds are more concentrated within a 10–20 km distance
from the coast. The reason for this could be the specific topography of the main islands of
the archipelagic country Indonesia, which are characterized by volcanic mountains that
slope down to the coastal plains.
Remote Sens. 2022,14, 153 17 of 25
Figure 11.
Bar chart: Total area (ha) of the mapped pond aquaculture per country in the study; Pie
chart: Share of total mapped aquaculture area (ha) among the top 5 ranking countries (in percent).
Figure 12. Mapping result: total pond aquaculture area for Indonesia at district level.
Remote Sens. 2022,14, 153 18 of 25
Figure 13.
Scatterplots of elevation and distance of aquaculture categorized among different pond
sizes for the top 5 Asian countries (China, Indonesia, India, Vietnam, and Bangladesh) (
A
); Boxplots
of the area of the convex hull (
B
) and the ratio between the pond area and its convex hull (
C
) for the
mapped ponds from the five defined macro-regions East Asia (EA), Northeast Asia (NEA), South
Asia (SA), Southeast Asia Mainland (SEAL), and Southeast Asia Maritime (SEAM).
Remote Sens. 2022,14, 153 19 of 25
Figure 14.
Comparative boxplots of the calculated area in ha (top) and compactness (perimeter
2
area) of the mapped coastal aquaculture ponds for each country in the coastal study region.
4.3. Hotspot Analysis
To enable a viable representation of the results for the entire study region, the areas
of the single-object pond features were aggregated to a hexagonal grid. In Figure 15,
areas with high aquaculture density in the hexagon plots can be identified by blueish
colors and are mainly concentrated in the greater delta regions of the Krishna River (IND),
Godavari River (IND), Ganges River (BGD), Irrawaddy River (MMR), Chao Phraya River
(THA), Mekong River (VNM), Pearl River (CHN), Yangtze River (CHN), and Yellow River
(CHN). More hotspot regions are located in the southeast of Sumatra (IDN), along the
northern coast of Java (IDN). The highest densities of pond aquaculture are concentrated
in the largest aquaculture shares, which can be found along the shorelines of the coastal
Chinese provinces of Hainan, Guangdong, Fujian, Zhejiang, Jiangsu, Shandong, Hebei,
and Liaoning.
Remote Sens. 2022,14, 153 20 of 25
Figure 15.
Pond aquaculture summarized into a 5 km hexagon grid vector dataset (interlocking
hexagon cells with a side length of 5 km and an area of ~65 km
2
). Area of aquaculture ponds per
hexagon (area of ponds in ha) for the entire coastal zone of Asia. Top: Overview with three subregions
and corresponding coastal spots (blue boxes): (
A
) South Asia with a focus on the Krishna River Delta
and Godavari River Delta, India; (
B
) Southeast Asia with a focus on Lampung Province, Sumatra,
Indonesia; (
C
) East Asia with a focus on Qiongzhou Strait between Leizhou Peninsula and Hainan
Island, China.
Remote Sens. 2022,14, 153 21 of 25
5. Discussion
The C-Band SAR imaging constellation of the European Sentinel-1 mission, with
its continuously updated time series of all-weather high-resolution satellite imagery, is
unique in radar remote sensing, representing one of the most significant data sources in
Earth observation. The spatial and temporal resolution of the Sentinel-1 SAR sensor meets
the requirements to recognize small-scale surface structures and individual objects on the
Earth’s surface. The majority of studies on pond aquaculture detection published up to now
focus rather on local studies and are limited in spatial transferability to other regions. With
the availability of essential time series information derived from high-spatial-resolution
Sentinel-1 radar imagery in combination with Sentinel-2 optical time series information, as
well as topographical and geometric features, our study has shown that we can effectively
detect and map coastal aquaculture at the single-pond level for a continental scale. Since
the countries in our study region are also representative of almost 90 percent of the world’s
aquaculture production, this new data set provides important information in the context of
global food production and food security.
The object filtering of the automatically extracted pond aquaculture objects, which
were derived from satellite time series information with segmentation algorithm, has
significantly enhanced our mapping results. It has been shown that post-filtering based
on vector attributes in combination with natural water overlap analysis can effectively
eliminate non-aquaculture polygons. The free and open access to the water-related features
contained in the OSM database provided an essential data source for the overlap analysis
during post-processing in order to eliminate non-aquaculture polygons that were not
determined by geometrical parameters (especially rectangular river segments).
The rectangular and compact shape of aquaculture ponds is the defining feature for
the differentiation between aquaculture and other natural or artificial water bodies. Due to
errors in the segmentation process of aquaculture ponds and varying shapes of aquaculture
ponds, the determination of thresholds for the calculated shape-related features focused
rather on optimally balancing the tradeoff between mapping accuracy and rule simplicity.
The elimination of non-aquaculture polygons with individually adjusted threshold values
for each coastal parcel or different coastal regions is too complex and could lead to a flawed
distinction. Therefore, we applied global thresholds for the coastal Asian study region
in order to exclude as many false positives (non-aquaculture polygons) as possible while
preventing the exclusion of true aquaculture polygons at a minimum.
A key advantage of SAR imaging consists of its permanent observation capabilities,
since radar waves can operate independent from weather conditions, cloud coverage, and
time of day. Cloud cover is a prevailing issue in optical remote sensing in the context of
aquaculture mapping, which limits the ability to derive full-area coverages of aquaculture
from high-resolution commercial or non-commercial optical sensors. The sensor’s spatial
resolution is an important criterion, because it influences the appearance of the pond
objects’ shapes and is thus crucial for the selection of appropriate segmentation parameters.
Even at the relatively high resolution of Sentinel-1 with a pixel spacing of 10 m, artificial
elongated and narrow structures (such as dams) between individual aquaculture ponds
may sometimes not be visible in the satellite data, which can result in connected ponds after
the image segmentation. In these cases, (connected) pond agglomerations may have larger
perimeters, lowering the objects’ compactness and thus impacting other shape metrics such
as convex hull that rely on the perimeter. Aquaculture ponds generally have convex (e.g.,
rectangular) shapes, but sometimes they may be shaped to fit natural features (curves at
rivers or terrain), which leads to concave pond shapes.
Apart from the challenges of identifying single-pond aquaculture units in radar im-
agery, it must be made clear that SAR sensors have clear benefits over optical remote
sensing instruments for continuous mapping and monitoring of aquaculture in the coastal
zone. The Copernicus Sentinel satellite missions offer free access to continuous, timely,
and routine Earth observation data and thus offer enormous potential for large-scale and
even global assessment of coastal aquaculture. Therefore, on the one hand, our approach
Remote Sens. 2022,14, 153 22 of 25
for continental-scale mapping benefits from scalability through the use of the open data
archives of the Sentinel-1 and Sentinel-2 satellites. These provide dense and large volumes
of radar and optical imagery that can be accessed, for example, via the Google Earth Engine
in preprocessed and analysis-ready data format. In our study, we make use of the analysis
capabilities of Google’s cloud-based computing platform to process a full annual time series
of wide-swath, high-spatial-resolution SAR and optical satellite data for coastal Asia. On
the other hand, the post-processing, including the statistical analysis in our study, is based
on open software, tools (e.g., python packages, QGIS, GDAL libraries), and auxiliary data
(e.g., FAO fishstats, Open Street Map). As a result, the workflow presented could also be
adopted and integrated into other cloud computing platforms or frameworks. For the first
time, satellite remote sensing was used for the comprehensive assessment of coastal pond
aquaculture at continental scale for the entire coastal zone of Asia. The method presented
in this study has three major advantages:
(1)
a key feature of the novel, continental-scale mapping approach is the use of free
and open remote sensing data; the launches of Sentinel-1C and 1D will provide
data continuity and satellite-based high-resolution SAR data and enable continuous
monitoring over the next decade;
(2)
the application of a simple and fast segmentation algorithm and scalable processing
capabilities within the cloud platform provide the capacity and ability to upscale the
framework and enable global mapping of coastal pond aquaculture;
(3)
the use of open source tools and software creates the possibility of integrating other
or updated pre- and post-processing toolboxes, image processing, computer vision
models, or algorithms into the framework.
Information on the spatial distribution of aquaculture is necessary for spatial planners,
policy makers, and ecologists in order to enable sustainable development in the coastal
zone and ensure food security. Aquaculture is in conflict with other existing or foreseeable
land uses, e.g., urban development, tourism, and traffic infrastructure, among others. The
coastal zone provides large resources for millions of people, and sustainable management
is essential for future development. Operating aquaculture facilities and the construction
of new ponds for fish and shrimp farming are closely linked to freshwater management
(groundwater withdrawal for pond water), waste water discharge, and other ecological
challenges, such as increasing coastal erosion, land subsidence, and salinization. For
this reason, there is a fundamental need for further research to investigate and analyze
aquaculture and its impact on natural ecosystems at continental and global scale.
6. Conclusions
A novel continental-scale mapping approach was developed to detect and assess pond
aquaculture for the entire coastal zone of Asia using multi-sensor Earth observation time
series. In this study, we used all available satellite data acquired by the high-resolution
C-band imaging radar mission Sentinel-1 and the multispectral Sentinel-2 mission, and we
calculated time series metrics to detect pond aquaculture objects. We processed more than
25,000 Sentinel-1 dual-polarized IW GRDH images, generated a temporal median image,
and applied image segmentation using histogram-based thresholding to automatically
detect and extract pond aquaculture on a single-pond-unit basis. The derived object-based
pond units were enriched with multispectral time series information (
Sentinel-2
), topo-
graphic terrain information, geometric features information, and supplementary overlap
analysis with global surface water data in order to extract aquaculture ponds in potential
low-lying coastal areas and separate them from other natural or artificial water bodies.
More than 3.4 million aquaculture ponds with a total area of more than 2 million ha were
detected and mapped for a 200-km-wide buffer zone along the shoreline of South Asia,
Southeast Asia, and East Asia. Spatial analyses show that aquaculture is clearly concen-
trated near the coast; 45 percent of the mapped ponds are located within a radius of 5 km
from the coast. For each country, we summarized the total area of the mapped coastal
pond aquaculture at national level and sub-national level. China accounts for the largest
Remote Sens. 2022,14, 153 23 of 25
share of coastal pond aquaculture: 41 percent of the total Earth-observation-derived pond
aquaculture area is in China, followed by Indonesia (13 percent), India (11 percent), Viet-
nam (7 percent), and Thailand (7 percent). The study region covers the coastal areas of
22 Asian countries, which altogether contribute more than 87 percent to the total global
annual aquaculture production, representing the world’s focus in terms of aquaculture
relevance. The results show that we mapped coastal aquaculture at single-pond-level with
a mean average overall accuracy of 91 percent. For the first time, satellite remote sensing
was used for the comprehensive assessment of coastal aquaculture at a continental scale,
covering a shoreline of more than 300,000 km.
Author Contributions:
Conceptualization, M.O., F.B., J.H., C.K.; methodology, M.O., C.K.; validation,
M.O.; data analysis, M.O.; writing—original draft preparation, M.O.; writing—review and editing,
M.O., F.B., J.H., C.K.; visualization, M.O. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments:
The authors would like to thank Julia Sagert for her support in the statistical
data analysis and accuracy assessment and Bruno Boehmke for his support in the object feature
generation and OSM analysis. Special thanks go to the Copernicus Satellite Program for providing
free access to the global Sentinel-1 and Sentinel-2 data; the NASA JPL for providing the free SRTM
version 3 dataset; the Open Street Map project for providing free data on waterbodies and shorelines;
and GADM for providing data on administrative areas. Furthermore, we would like to thank the
Google Earth Engine platform for generating and providing the satellite time series, and we would
also like to thank G. Donchyts for the thresholding script available in GEE. Finally, we would like to
thank the FAO for providing updated global aquaculture statistics.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Software packages used for reading the spatial data, processing it, and assessing
aquaculture ponds with an object-based method (segmentation): Google Earth Engine (JS
API), python 3.9.6, pandas 1.3.2, geopandas 0.9.0, geofeather 0.3, seaborn 0.11.2, matplotlib
3.4.3, numpy 1.21.2, QGIS 3.18, GDAL 3.0.4.
References
1.
FAO Fishery and Aquaculture Statistics. Global Aquaculture Production 1950–2019 (FishstatJ). In FAO Fisheries Division; FAO:
Rome, Italy, 2021.
2.
Gephart, J.A.; Golden, C.D.; Asche, F.; Belton, B.; Brugere, C.; Froehlich, H.E.; Fry, J.P.; Halpern, B.S.; Hicks, C.C.; Jones, R.C.; et al.
Scenarios for Global Aquaculture and Its Role in Human Nutrition. Rev. Fish. Sci. Aquac. 2021,29, 122–138. [CrossRef]
3.
Thilsted, S.H.; Thorne-Lyman, A.; Webb, P.; Bogard, J.R.; Subasinghe, R.; Phillips, M.J.; Allison, E.H. Sustaining Healthy Diets: The
Role of Capture Fisheries and Aquaculture for Improving Nutrition in the Post-2015 Era. Food Policy
2016
,61, 126–131. [CrossRef]
4.
Blanchard, J.L.; Watson, R.A.; Fulton, E.A.; Cottrell, R.S.; Nash, K.L.; Bryndum-Buchholz, A.; Büchner, M.; Carozza, D.A.; Cheung,
W.W.L.; Elliott, J.; et al. Linked Sustainability Challenges and Trade-Offs among Fisheries, Aquaculture and Agriculture. Nat.
Ecol. Evol. 2017,1, 1240–1249. [CrossRef]
5.
Belton, B.; Thilsted, S.H. Fisheries in Transition: Food and Nutrition Security Implications for the Global South. Glob. Food Secur.
2014,3, 59–66. [CrossRef]
6.
Fiedler, J.L.; Lividini, K.; Drummond, E.; Thilsted, S.H. Strengthening the Contribution of Aquaculture to Food and Nutrition
Security: The Potential of a Vitamin A-Rich, Small Fish in Bangladesh. Aquaculture 2016,452, 291–303. [CrossRef]
7.
Béné, C.; Barange, M.; Subasinghe, R.; Pinstrup-Andersen, P.; Merino, G.; Hemre, G.-I.; Williams, M. Feeding 9 Billion by
2050—Putting Fish Back on the Menu. Food Secur. 2015,7, 261–274. [CrossRef]
8.
Akber, M.A.; Aziz, A.A.; Lovelock, C. Major Drivers of Coastal Aquaculture Expansion in Southeast Asia. Ocean. Coast. Manag.
2020,198, 105364. [CrossRef]
9.
Ahmed, N.; Thompson, S.; Glaser, M. Global Aquaculture Productivity, Environmental Sustainability, and Climate Change
Adaptability. Environ. Manag. 2019,63, 159–172. [CrossRef] [PubMed]
Remote Sens. 2022,14, 153 24 of 25
10.
Dauda, A.B.; Ajadi, A.; Tola-Fabunmi, A.S.; Akinwole, A.O. Waste Production in Aquaculture: Sources, Components and
Managements in Different Culture Systems. Aquac. Fish. 2019,4, 81–88. [CrossRef]
11.
Ottinger, M.; Clauss, K.; Kuenzer, C. Aquaculture: Relevance, Distribution, Impacts and Spatial Assessments—A Review. Ocean.
Coast. Manag. 2016,119, 244–266. [CrossRef]
12.
Newton, R.; Zhang, W.; Xian, Z.; McAdam, B.; Little, D.C. Intensification, Regulation and Diversification: The Changing Face of
Inland Aquaculture in China. Ambio 2021,50, 1739–1756. [CrossRef]
13.
Hua, K.; Cobcroft, J.M.; Cole, A.; Condon, K.; Jerry, D.R.; Mangott, A.; Praeger, C.; Vucko, M.J.; Zeng, C.; Zenger, K.; et al. The
Future of Aquatic Protein: Implications for Protein Sources in Aquaculture Diets. One Earth 2019,1, 316–329. [CrossRef]
14.
Tacon, A.G.J. Trends in Global Aquaculture and Aquafeed Production: 2000–2017. Rev. Fish. Sci. Aquac.
2020
,28, 43–56. [CrossRef]
15.
Tacon, A.G.J.; Metian, M. Global Overview on the Use of Fish Meal and Fish Oil in Industrially Compounded Aquafeeds: Trends
and Future Prospects. Aquaculture 2008,285, 146–158. [CrossRef]
16.
Maulu, S.; Hasimuna, O.J.; Haambiya, L.H.; Monde, C.; Musuka, C.G.; Makorwa, T.H.; Munganga, B.P.; Phiri, K.J.; Nsekanabo,
J.D. Climate Change Effects on Aquaculture Production: Sustainability Implications, Mitigation, and Adaptations. Front. Sustain.
Food Syst. 2021,5, 70. [CrossRef]
17.
Bostock, J.; McAndrew, B.; Richards, R.; Jauncey, K.; Telfer, T.; Lorenzen, K.; Little, D.; Ross, L.; Handisyde, N.; Gatward, I.; et al.
Aquaculture: Global Status and Trends. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2010,365, 2897–2912. [CrossRef]
18.
Lazard, J.; Baruthio, A.; Mathé, S.; Rey-Valette, H.; Chia, E.; Clément, O.; Aubin, J.; Morissens, P.; Mikolasek, O.; Legendre, M.;
et al. Aquaculture System Diversity and Sustainable Development: Fish Farms and Their Representation. Aquat. Living Resour.
2010,23, 187–198. [CrossRef]
19.
Ottinger, M.; Kuenzer, C. Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land
Applications: A Review. Remote Sens. 2020,12, 2228. [CrossRef]
20.
Cherif, E.K.; Mozetiˇc, P.; Francé, J.; Flander-Putrle, V.; Faganeli-Pucer, J.; Vodopivec, M. Comparison of In-Situ Chlorophyll-a
Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea).
Water 2021,13, 1903. [CrossRef]
21.
Kuenzer, C.; Dech, S.; Wagner, W. Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway
Ahead. In Remote Sensing Time Series: Revealing Land Surface Dynamics; Remote Sensing and Digital Image Processing; Kuenzer, C.,
Dech, S., Wagner, W., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 1–24. ISBN 978-3-319-15967-6.
22.
Alexandridis, T.K.; Topaloglou, C.A.; Lazaridou, E.; Zalidis, G.C. The Performance of Satellite Images in Mapping Aquacultures.
Ocean. Coast. Manag. 2008,51, 638–644. [CrossRef]
23. Marini, Y.; Prayogo, T.; Hanzah, R.; Hasyim, B. Fishpond Aquaculture Inventory in Maros Regency of South Sulawesi Province.
Int. J. Remote Sens. Earth Sci. 2013,10, 25–35. [CrossRef]
24.
Sridhar, P.N.; Surendran, A.; Ramana, I.V.; Surendran, A.; Ramana, I.V. Auto-Extraction Technique-Based Digital Classification of
Saltpans and Aquaculture Plots Using Satellite Data. Int. J. Remote Sens. 2008,29, 313–323. [CrossRef]
25.
Virdis, S.G.P. An Object-Based Image Analysis Approach for Aquaculture Ponds Precise Mapping and Monitoring: A Case Study
of Tam Giang-Cau Hai Lagoon, Vietnam. Environ. Monit. Assess. 2014,186, 117–133. [CrossRef]
26.
Zhang, T.; Li, Q.; Yang, X.; Zhou, C.; Su, F. Automatic Mapping Aquaculture in Coastal Zone from TM Imagery with OBIA
Approach. In Proceedings of the 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010; pp. 1–4.
27.
Chi, M.; Plaza, A.; Benediktsson, J.A.; Sun, Z.; Shen, J.; Zhu, Y. Big Data for Remote Sensing: Challenges and Opportunities. Proc.
IEEE 2016,104, 2207–2219. [CrossRef]
28.
Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-Scale Geospatial
Analysis for Everyone. Remote Sens. Environ. 2017,202, 18–27. [CrossRef]
29.
Duan, Y.; Li, X.; Zhang, L.; Chen, D.; Liu, S.; Ji, H. Mapping National-Scale Aquaculture Ponds Based on the Google Earth Engine
in the Chinese Coastal Zone. Aquaculture 2020,520, 734666. [CrossRef]
30.
Ren, C.; Wang, Z.; Zhang, Y.; Zhang, B.; Chen, L.; Xi, Y.; Xiao, X.; Doughty, R.B.; Liu, M.; Jia, M.; et al. Rapid Expansion of Coastal
Aquaculture Ponds in China from Landsat Observations during 1984–2016. Int. J. Appl. Earth Obs. Geoinf.
2019
,82, 101902.
[CrossRef]
31.
Xia, Z.; Guo, X.; Chen, R. Automatic Extraction of Aquaculture Ponds Based on Google Earth Engine. Ocean. Coast. Manag.
2020
,
198, 105348. [CrossRef]
32.
Ottinger, M.; Clauss, K.; Kuenzer, C. Large-Scale Assessment of Coastal Aquaculture Ponds with Sentinel-1 Time Series Data.
Remote Sens. 2017,9, 440. [CrossRef]
33.
Ottinger, M.; Clauss, K.; Kuenzer, C. Opportunities and Challenges for the Estimation of Aquaculture Production Based on Earth
Observation Data. Remote Sens. 2018,10, 1076. [CrossRef]
34.
Prasad, K.A.; Ottinger, M.; Wei, C.; Leinenkugel, P. Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series.
Remote Sens. 2019,11, 357. [CrossRef]
35.
Stiller, D.; Ottinger, M.; Leinenkugel, P. Spatio-Temporal Patterns of Coastal Aquaculture Derived from Sentinel-1 Time Series
Data and the Full Landsat Archive. Remote Sens. 2019,11, 1707. [CrossRef]
36.
Sun, Z.; Luo, J.; Yang, J.; Yu, Q.; Zhang, L.; Xue, K.; Lu, L. Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1
SAR Data Using Google Earth Engine. Remote Sens. 2020,12, 3086. [CrossRef]
Remote Sens. 2022,14, 153 25 of 25
37.
Neumann, B.; Vafeidis, A.T.; Zimmermann, J.; Nicholls, R.J. Future Coastal Population Growth and Exposure to Sea-Level Rise
and Coastal Flooding—A Global Assessment. PLoS ONE 2015,10, e0118571. [CrossRef]
38.
GADM Global Administrative Areas. GADM Database of Global Administrative Areas, Version 2.0. 2012. Available online:
https://gadm.org (accessed on 10 November 2021).
39.
European Commission. Joint Research Centre GHS-POP R2015A—GHS Population Grid; Derived from GPW4; European Commis-
sion: Brussels, Belgium, 2015.
40.
Nguyen, M.T.; Renaud, F.G.; Sebesvari, Z. Drivers of Change and Adaptation Pathways of Agricultural Systems Facing Increased
Salinity Intrusion in Coastal Areas of the Mekong and Red River Deltas in Vietnam. Environ. Sci. Policy
2019
,92, 331–348.
[CrossRef]
41.
Lebel, L.; Lebel, P.; Soe, K.M.; Phuong, N.T.; Navy, H.; Phousavanh, P.; Jutagate, T.; Akester, M.; Lebel, B. Aquaculture Farmers’
Perceptions of Climate-Related Risks in the Mekong Region. Reg. Environ. Chang. 2020,20, 95. [CrossRef]
42.
McGranahan, G.; Balk, D.; Anderson, B. The Rising Tide: Assessing the Risks of Climate Change and Human Settlements in Low
Elevation Coastal Zones. Environ. Urban. 2007,19, 17–37. [CrossRef]
43. ESA. Sentinel-1 User Handbook; ESA: Paris, France, 2013.
44.
Torres, R.; Snoeij, P.; Geudtner, D.; Bibby, D.; Davidson, M.; Attema, E.; Potin, P.; Rommen, B.; Floury, N.; Brown, M.; et al. Remote
Sensing of Environment GMES Sentinel-1 Mission. Remote Sens. Environ. 2012,120, 9–24. [CrossRef]
45. Schubert, A.; Small, D.; Miranda, N.; Geudtner, D.; Meier, E. Sentinel-1A Product Geolocation Accuracy: Commissioning Phase
Results. Remote Sens. 2015,7, 9431–9449. [CrossRef]
46.
Louis, J.; Pflug, B.; Main-Knorn, M.; Debaecker, V.; Mueller-Wilm, U.; Iannone, R.Q.; Giuseppe Cadau, E.; Boccia, V.; Gascon, F.
Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor. In Proceedings of the IGARSS 2019—2019 IEEE
International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 8522–8525.
47.
NASA Jet Propulsion Laboratory (JPL) NASA Shuttle Radar Topography Mission 1 Arc Second. Version 3.0; 2013. Available
online: https://cmr.earthdata.nasa.gov/search/concepts/C1000000240-LPDAAC_ECS.html (accessed on 10 November 2021).
48.
Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The
Shuttle Radar Topography Mission. Rev. Geophys. 2007,45, 25–36. [CrossRef]
49.
Garibaldi, L. The FAO Global Capture Production Database: A Six-Decade Effort to Catch the Trend. Mar. Policy
2012
,36, 760–768.
[CrossRef]
50. Younos, T.M.A. Advances in Water Monitoring Research; Water Resources Publication: Littleton, CO, USA, 2002; p. 244.
51.
Gao, B. NDWI—A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens.
Environ. 1996,58, 257–266. [CrossRef]
52.
McFeeters, S.K. The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. Int. J.
Remote Sens. 1996,17, 1425–1432. [CrossRef]
53.
Xu, H. Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery.
Int. J. Remote Sens. 2006,27, 3025–3033. [CrossRef]
54. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern. 1979,9, 62–66. [CrossRef]
55.
Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed.; CRC Press: Boca Raton,
FL, USA, 2008; ISBN 978-0-429-14397-7.
... At present, the primary remote sensing data sources used to identify the mariculture area are multispectral satellite remote sensing images and microwave remote sensing images [7]. The multispectral satellite remote sensing images mainly include Spot, GF-1, GF-2, and Landsat [4,[8][9][10][11]; while microwave remote sensing images mainly include Radarsat-2, GF-3, Sentinel-1, and Sentinel-2 [12][13][14][15][16][17]. The spatial resolution of diverse remote sensing data sources is quite different, and the prices and effects they can achieve are naturally different. ...
Article
Full-text available
Wide-scale automatic monitoring based on the Normalized Difference Water Index (NDWI) and Mask Region-based Convolutional Neural Network (Mask R-CNN) with remote sensing images is of great significance for the management of aquaculture areas. However, different spatial resolutions brought different cost and model performance. To find more suitable image spatial resolutions for automatic monitoring offshore aquaculture areas, seven different resolution remote sensing images in the Sandu’ao area of China, from 2 m, 4 m, to 50 m, were compared. Results showed that the remote sensing images with a resolution of 15 m and above can achieve the corresponding recognition effect when no financial issues were considered, with the F1 score of over 0.75. By establishing a cost-effectiveness evaluation formula that comprehensively considers image price and recognition effect, the best image resolution in different scenes can be found, thus providing the most appropriate data scheme for the automatic monitoring of offshore aquaculture areas.
... In this context, objected-based methods could increase classification accuracy, as they additionally consider the relative location and the shape of surfaces. This improvement has already proven to be advantageous for the distinctive rectangular shape of aquaculture ponds (e.g., [123]) and could also facilitate the identification of building blocks and agricultural patches. However, object-based methods are still mainly applied when investigating areas less than 300 ha using high-resolution data [124], which significantly limits the effective use in the study area of the Ayeyarwady Delta, covering about 40,000 km 2 . ...
Article
Full-text available
Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world’s most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure or vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request.
... Recent studies mapping aquaculture ponds in coastal areas of Asia showed that most of the aquaculture ponds are located within 15 km of the coastline [60], with obvious natural gradient characteristics. This study used DeepLabv3+ architecture to extract ponds and analyse the urban-rural gradient characteristics of the pond landscape and found that 57.84% of the ponds are distributed in the peri-urban zone in the PRD. ...
Article
Full-text available
The impact of ponds by urbanization has been widely discussed on the landscape scale. However, relatively few studies have explored the spatial differentiation of pond landscapes across urban-rural gradients in rapidly urbanising areas. In this study, we applied the DeepLabv3+ network to perform a semantic segmentation on Google Map images to extract ponds in the Pearl River Delta (PRD) region, China; then we employed geographic information systems to analyse the ponds changes in 665 towns along an urban-rural gradient in the PRD. Results indicate that there are clear differences in landscapes between the urban core, urban, peri-urban, agricultural, and forested zone in terms of pond area and size. In total, 57.84% and 31.33% of the ponds are distributed in the peri-urban and agricultural zone, respectively; fewer ponds are present at either end of the urban-rural gradient. Owing to the legacy effects of historical land use and river systems, urban and peri-urban zone in the central and western parts of the PRD are still rich in ponds. We propose that management measures should be implemented according to the characteristics of different pond landscapes.
... Medium-resolution remote sensing images, whose spatial resolution ranges from 10 m to 39.9 m [16], including the Landsat series and Sentinel-2, are observed at a larger scene size and at high temporal repetition rates, and have great potential for mapping SWBs. In general, most studies use pixel-based hard classification to map SWBs, but are limited to mapping mixed water-land pixels [17][18][19][20]. Research has shown that 30 m Landsat images are limited to mapping SWBs of a 0.1-5 ha area [4,5], while Sentinel-2 images with a resolution of 10-20 m have difficulty in mapping water bodies smaller than 0.035 ha [21]. ...
Article
Full-text available
Accurately mapping surface water fractions is essential to understanding the distribution and area of small water bodies (SWBs), which are numerous and widespread. Traditional spectral unmixings based on the linear mixture model require high-quality prior endmember information, and are not appropriate in situations such as dealing with multiple scattering effects. To overcome difficulties with unknown mixing mechanisms and parameters, a novel automated and hierarchical surface water fraction mapping (AHSWFM) for mapping SWBs from Sentinel-2 images was proposed. AHSWFM is automated, requires no endmember prior knowledge and uses self-trained regression using scalable algorithms and random forest to construct relationships between the multispectral data and water fractions. AHSWFM uses a hierarchical structure that divides pixels into pure water, pure land and mixed water-land pixels, and predicts their water fractions separately to avoid overestimating water fractions for pure land pixels and underestimating water fractions for pure water pixels. Results show that using the hierarchical strategy can increase the accuracy in estimating SWB areas. AHSWFM predicted SWB areas with a root mean square error of approximately 0.045 ha in a region using more than 1200 SWB samples that were mostly smaller than 0.75 ha.
Article
The incidence and size of aquaculture ponds are related to a variety of economic, social, environmental, and policy factors, but there is scarce publicly available information. Satellite based aquaculture pond mapping has typically been undertaken by segmentation of single-date images or of temporal composites derived from image time series. In Asia, both aquaculture and rice farming can be undertaken in the same locality and, combined with frequent cloud cover, mean that segmentations should be derived throughout the year to be able to differentiate aquaculture ponds from rice paddies and to provide spatially-complete pond mapping results. In this paper, a new approach is presented to extract individual aquaculture ponds from seasonal Sentinel-2 10 m images and to combine different sets of pond objects extracted from different images into a single set of pond objects. The approach is demonstrated for the province of Nakhon Pathom (216,800 ha) in central Thailand that is a major shrimp farming area. Aquaculture ponds were extracted from near-cloud-free Sentinel-2 images acquired in January and June 2019 to reduce confusion with rice paddies that are typically vegetation covered in these months. The ponds extracted at different times were combined using a multi-temporal object combination strategy. Surveys undertaken in 2019 to elucidate farmers’ attitudes and land use practices were used to contextualize the extraction results. Across the province, 22,833 aquaculture ponds were extracted with a total area of 18,066 ha. The mean and median pond sizes were 0.79 ha and 0.60 ha, respectively, which is close to the 0.65 ha mean shrimp pond size in Thailand reported by other researchers via independent surveys. Current methods to map aquaculture ponds have not typically reported object-based validation results and so it is unknown whether object-level information (e.g., number of ponds and pond sizes) are reliable. Therefore, the extraction results were evaluated quantitatively using object-based accuracy metrics. A total of 1733 aquaculture ponds at three validation sites were manually digitized using multi-date GoogleEarth high-resolution images, and compared with the extracted ponds. The evaluation results indicated a robust pond extraction performance, with 87.7% object-based overall accuracy considering all the reference data and the extracted ponds at the three sites. 0.9% of the extracted ponds were over-extracted (commission error), 0.9% were under-split, 2.6% were over-split, and 6.9% of the of the reference ponds were not extracted (omission error). In addition, the mean over-segmentation error of 0.154, mean under-segmentation error of 0.111, and mean F-score (dice coefficient) of 0.858 were obtained. The causes of these errors were examined and discussed with recommended potential research using other sensor data.
Article
Full-text available
While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.
Article
Full-text available
Aquaculture continues to significantly expand its production, making it the fastest-growing food production sector globally. However, the sustainability of the sector is at stake due to the predicted effects of climate change that are not only a future but also a present reality. In this paper, we review the potential effects of climate change on aquaculture production and its implications on the sector's sustainability. Various elements of a changing climate, such as rising temperatures, sea-level rise, diseases and harmful algal blooms, changes in rainfall patterns, the uncertainty of external inputs supplies, changes in sea surface salinity, and severe climatic events have been discussed. Furthermore, several adaptation options have been presented as well as some gaps in existing knowledge that require further investigations. Overall, climate change effects and implications on aquaculture production sustainability are expected to be both negative and positive although, the negative effects outweigh the positive ones. Adapting to the predicted changes in the short-term while taking mitigation measures in the long-term could be the only way toward sustaining the sector's production. However, successful adaptation will depend on the adaptive capacity of the producers in different regions of the world.
Article
Full-text available
Trends in aquatic food consumption were matched against farm production surveys within Hubei province and compared to official production data and statistics. Surveys showed that consumer tastes were changing to a much broader aquatic food menu as their spending power increased. Traditional aquaculture species were becoming less profitable due to reduced profit margins as input costs increased and consumption preferences changed. Consequently, many producers were diversifying their production to meet local demand. Some farmers were also de-intensifying by reducing commercial aquafeed inputs and reverting to more traditional methods of dyke-crop culture to optimise trade-offs between input costs and labour, and manage their risk more effectively. In addition, analysis of local data showed that wholesale changes were occurring to aquaculture production as environmental protection legislation took effect which reduced the growing area for carps considerably.
Article
Full-text available
Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.
Article
Full-text available
Local studies have shown that aquaculture is impacted by extreme weather events, the risks of which vary seasonally and among years. Less is known about the effects of geographical differences in climate or social organization for how climate-related risks are perceived, as there have been few larger scale studies. This study documents key findings from a large-scale survey of levels of concern and experience with significant losses of over 3300 aquaculture farmers in Cambodia, Laos, Myanmar, Thailand, and Vietnam. Important climate-related risks included rapid changes in temperature, intense rainfall events, and floods. Farmers in Laos were less concerned with risks to profits than those in other countries. Farmers who had experienced significant losses from any climate-related source were more concerned with risks. Women and higher educated farmers perceived higher risks. Those with intensified production systems perceived lower risks and observed fewer climate changes. Perceptions of historical changes in risks and expectations for future risks were often strongly associated with each other and levels of concern, as well as experiences of losses, underlining that risk perception has multiple, inter-related dimensions. Geographical differences in risk perception were related to differences in both physical climate and social factors.
Article
Full-text available
The coastal zone offers among the world's most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land-and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and subtropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.
Article
Full-text available
Global demand for freshwater and marine foods (i.e., seafood) is rising and an increasing proportion is farmed. Aquaculture encompasses a range of species and cultivation methods, resulting in diverse social, economic, nutritional, and environmental outcomes. As a result, how aquaculture develops will influence human wellbeing and environmental health outcomes. Recognition of this has spurred a push for nutrition-sensitive aquaculture, which aims to benefit public health through the production of diverse, nutrient-rich seafood and enabling equitable access. This article explores plausible aquaculture futures and their role in nutrition security using a qualitative scenario approach. Two dimensions of economic development – the degree of globalization and the predominant economic development philosophy – bound four scenarios representing systems that are either localized or globalized, and orientated toward maximizing sectoral economic growth or to meeting environmental and equity dimensions of sustainability. The potential contribution of aquaculture in improving nutrition security is then evaluated within each scenario. While aquaculture could be “nutrition-sensitive” under any of the scenarios, its contribution to addressing health inequities is more likely in the economic and political context of a more globally harmonized trade environment and where economic policies are oriented toward social equity and environmental sustainability.
Article
Full-text available
Approximately 70% of the aquatic-based production of animals is fed aquaculture, whereby animals are provided with high-protein aquafeeds. Currently, aquafeeds are reliant on fish meal and fish oil sourced from wild-captured forage fish. However, increasing use of forage fish is unsustainable and, because an additional 37.4 million tons of aquafeeds will be required by 2025, alternative protein sources are needed. Beyond plant-based ingredients, fishery and aquaculture byproducts and insect meals have the greatest potential to supply the protein required by aquafeeds over the next 10–20 years. Food waste also has potential through the biotransformation and/or bioconversion of raw waste materials, whereas microbial and macroalgal biomass have limitations regarding their scalability and protein content, respectively. In this review, we describe the considerable scope for improved efficiency in fed aquaculture and discuss the development and optimization of alternative protein sources for aquafeeds to ensure a socially and environmentally sustainable future for the aquaculture industry.
Article
Aquaculture is one of China's fastest-growing animal food production sectors. It accounts for the largest share in the world, mainly distributed in coastal areas. Due to the depletion of offshore resources and increasing domestic demand for aquatic products, more and more land, including newly reclaimed land, is gradually being used to build aquaculture ponds. Understanding the location, spatial pattern, scale, and other properties is critical for China's food and protein security. However, until recently, how to detect, monitor and map the aquaculture ponds with remote sensing is still a problem, which hinders the understanding of its magnitude and value, and interferes sustainable management of coastal ecosystems. Here we proposed a framework for extracting aquaculture ponds by integrating existing multi-source remote sensing data on the Google Earth Engine platform. Taking Shanghai as a study area, the Multi-threshold Connected Component Segmentation and Random Forest algorithm method were used to extract aquaculture ponds automatically. The results show that this method can effectively generate the maps of Shanghai's aquaculture ponds from 2016 to 2019, and the overall accuracy of the classification results in 2018 can reach 91.8%. This method can greatly improve the efficiency of extracting aquaculture ponds, and has a good performance in detecting non-intensive aquaculture pond areas. It can also be easily used and has high spatio-temporal transferability with the help of the Google Earth Engine platform.
Article
Coastal aquaculture is socially, culturally and economically important in Southeast Asia, but its expansion in the last fifty years has had negative environmental and social consequences. Through analyses of detailed accounts of the expansion of coastal aquaculture, we aimed to increase knowledge of the factors underlying this expansion to guide future policies for sustainable management of the coastal zone. Using a systematic review of ninety studies, we found that increased aquaculture development and economic opportunities were the most frequently identified proximate drivers of coastal aquaculture expansion, while factors associated with location (e.g. availability of mangrove land), institutional policies and economy (e.g. capital investment and market demand) had lesser influence. However, complex combinations of the underlying factors were also important. For example, the influence of policies that stimulated aquaculture development was often associated with combinations of locational, institutional, economic, technological and social factors. Strategies aimed at enhancing expansion of sustainable coastal aquaculture and mangrove conservation should focus on government policy interventions, including those that support mangrove silvo-aquaculture.