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Emerging pressure on mangrove forest environments as a result of shrimp farming expansion - A remote sensing based analyses for an exemplary coastal site at the Pacific coast in South America

Ottinger, Marco and Bachofer, Felix and Üreyen, Soner and Huth, Juliane (2020) Emerging pressure on mangrove forest environments as a result of shrimp farming expansion - A remote sensing based analyses for an exemplary coastal site at the Pacific coast in South America. EGU 2020, 04.-08.05.2020, Wien / EGU2020: Sharing Geoscience Online.

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Abstract

Given the growing world population and rising demand for fish and seafood, aquaculture is becoming the main source of aquatic food in human consumption and a primary protein source for millions of people. Since 1990, the world aquaculture production increased from 13 to over 80 million tonnes and is currently valued at USD 231 billion. The cultivation of shrimp species in land-based ponds is one of the fastest growing food production economies and became an important industry in coastal regions, generates income and employment and contributes to food security. Shrimp farms are mainly found in low-lying coastal regions such as estuaries, bays and river deltas along the shorelines of Asia and America. Shrimp farming expanded rapidly in recent years and led to environmental degradation and conversion of valuable wetlands such as mangroves and other coastal forests. The loss of mangroves poses a major threat to coastal ecosystems and population, as mangroves provide valuable flood and coastal protection, as well as risk reduction benefits with regard to global climate change induced effects. In this research, we use image segmentation for temporal features derived from space-borne, high-resolution synthetic aperture radar (SAR) data to extract shrimp farming ponds in coastal mangrove forest areas in Ecuador, South America. An automatic object-based image processing approach aims for the detection of rectangular shaped pond objects utilizing per-pixel median images calculated from C-band Sentinel-1 and L-band ALOS-Palsar SAR time series data. An open source connected component segmentation algorithm was used to extract and locate rectangular shrimp farms in coastal areas based on backscatter intensity and shape features. This study illustrates the opportunities by earth observation for area-wide assessments of shrimp farming activities in mangrove areas to gain more knowledge on land use dynamics with regard to global change and food security. Earth observation can effectively support the planning and management of aquaculture practices and support stakeholders, politicians, and conservationists in implementing appropriate measures in order to protect coastal environments and foster sustainable development in the coastal zone.

Item URL in elib:https://elib.dlr.de/134857/
Document Type:Conference or Workshop Item (Speech)
Title:Emerging pressure on mangrove forest environments as a result of shrimp farming expansion - A remote sensing based analyses for an exemplary coastal site at the Pacific coast in South America
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ottinger, MarcoMarco.Ottinger (at) dlr.dehttps://orcid.org/0000-0002-7336-1283
Bachofer, FelixFelix.Bachofer (at) dlr.dehttps://orcid.org/0000-0001-6181-0187
Üreyen, SonerSoner.Uereyen (at) dlr.deUNSPECIFIED
Huth, JulianeJuliane.Huth (at) dlr.deUNSPECIFIED
Date:8 May 2020
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Aquaculture, Shrimp farming, Coast, Mangrove, SAR, Object-based, Sentinel-1, ALOS-PALSAR
Event Title:EGU 2020
Event Location:Wien / EGU2020: Sharing Geoscience Online
Event Type:international Conference
Event Dates:04.-08.05.2020
Organizer:EGU General Assembly
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Ottinger, Marco
Deposited On:19 May 2020 17:47
Last Modified:19 May 2020 17:47

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