Sustainability Nexus Analytics, Informatics, and Data (AID) Programme
WETLANDS
WHY DOES WETLANDS
MATTER?
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Wetlands are globally recognized as an invaluable natural resource that provides a wide range of ecological and socioeconomic benefits [link].
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Wetlands serve as critical habitats for diverse fish, wildlife, and plant communities, supporting biodiversity and fostering the health of ecosystems. Furthermore, wetlands play a vital role in flood control by storing floodwater and reducing peak runoff, thereby mitigating the impacts of destructive floods. They also contribute to groundwater recharge, ensuring the availability of freshwater resources [link]. Another key ecosystem service that wetlands provide is carbon sequestration and storage [link].
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Alarming rates of wetland loss have been observed worldwide. It was estimated that 3.4 million km2 of inland wetlands have been lost since 1700, primarily for conversion to croplands. This accounts for a net loss of 21% of global wetland area [link].
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The rate of wetland loss varies across countries, with the United States estimated to have lost 53% of its original wetlands between the 1780s and 1980s [link]. Similarly, China experienced a 33% decline in wetland area between 1978 and 2008 [link].
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These statistics underscore the urgent need for conservation and restoration efforts to safeguard remaining wetlands. By recognizing the multifaceted value of wetlands and implementing sustainable management practices, we can ensure the preservation of these critical ecosystems for future generations.
WHY A NEXUS APPROACH?
Wetlands play a significant role in achieving several United Nations Sustainable Development Goals (SDGs) due to their ecological, social, and economic importance. A nexus approach is particularly valuable when studying wetland loss and degradation due to its ability to integrate various disciplines and perspectives. Wetlands are complex ecosystems that interact with multiple environmental, social, economic, and governance factors. By adopting a nexus approach, researchers, policymakers, and practitioners can effectively address the multifaceted challenges associated with wetland conservation and restoration.
One key aspect of wetland management is understanding the distribution and extent of wetlands, and monitoring their dynamic changes. However, determining the global extent of wetlands is not a straightforward task. The diversity of wetland types, varying ecological characteristics, and regional differences make it challenging to establish a single, indisputable, universally accepted definition of wetlands.
An interdisciplinary nexus approach allows experts from different fields to collaborate and contribute their knowledge and expertise to defining and delineating wetlands. Ecologists, hydrologists, botanists, and other relevant scientists can provide insights into the ecological characteristics and criteria that help identify wetlands. Environmental scientists, geographers, and remote sensing specialists can utilize advanced technologies and data to map and monitor wetland distribution and changes over time.
Moreover, a nexus approach recognizes the social and economic aspects intertwined with wetlands. Economists, sociologists, and policy experts can analyze the value of wetland ecosystem services, such as water purification, flood mitigation, and recreational opportunities, and assess the trade-offs and benefits associated with wetland conservation and restoration. This interdisciplinary collaboration ensures that decision-making processes consider not only ecological factors but also the needs and perspectives of local communities, government agencies, NGOs, and industry stakeholders.
Additionally, the nexus approach acknowledges the importance of governance and policy frameworks in wetland management. Environmental lawyers, public policy specialists, and political scientists can contribute to the development of effective policies, regulations, and legal frameworks that support wetland conservation and restoration efforts. By considering governance mechanisms and engaging stakeholders in decision-making processes, the nexus approach promotes inclusive and sustainable wetland management practices.
By integrating ecological, environmental, social, economic, and governance perspectives, the nexus approach enables a comprehensive understanding of wetlands and facilitates effective strategies for their conservation and restoration. This multidisciplinary collaboration ensures that all relevant factors are considered, leading to more sustainable and resilient wetland management practices.
AID TOOLS
Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Wetland maps and inventories provide crucial information for wetland conservation, restoration, and management. Since the first multispectral satellite data (i.e., Landsat MSS) became publicly available in the 1970s, significant efforts have been made to develop remote sensing technology. The technological advances have led to the increasing availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. In the meantime, image analysis and processing methods have been improving, which enables us to map wetlands and monitor changes with unprecedented accuracy. In particular, the availability of high-resolution light detection and ranging (LiDAR), synthetic aperture radar (SAR), hyperspectral, and multispectral data, and the introduction of multisensor and multiscale data fusion techniques hold great potential for improving large-scale wetland mapping and monitoring. Some notable wetland inventories and mapping tools are shown below.
Global and national wetland inventories
Canadian Wetland Inventory
The Canadian Wetland Inventory (CWI) was established in 2002 to map and monitor wetlands across Canada, aiding in conservation, restoration, and monitoring efforts. The CWI uses advanced technologies like machine learning, high-resolution satellite imagery, and LiDAR data for mapping, and follows a national framework based on the Canadian Wetland Classification System to categorize wetlands.
Multi-source global wetland maps
Multi-source global wetland maps combine data from various sources, such as national surveys, satellite imagery, and groundwater modeling, to provide a comprehensive and consistent global view of wetlands. These maps integrate different wetland types, including those influenced by regular flooding (RFWs) and shallow groundwater (GDWs), to improve accuracy and overcome gaps or inconsistencies in individual data sources. They are valuable for research, environmental planning, and large-scale modeling.
Global non-floodplain wetlands
Global_NonFloodplain_Wetlands refers to a global geodatabase that maps wetlands not influenced by periodic flooding from rivers or floodplains. This dataset is the first of its kind, integrating several data sources to identify and quantify non-floodplain wetlands worldwide. It combines a high-resolution global floodplain mapping algorithm, satellite-based inundation data, modeled groundwater-driven wetland extent, and land cover data. The geodatabase is tested in 21 large watersheds in the continental United States (CONUS) and provides valuable information on wetlands that play key hydrological, biogeochemical, and ecological roles. This data fills a research gap and supports informed decision-making and management of natural resources.
The Boreal-Arctic Wetland and Lake Dataset (BAWLD)
The Boreal and Arctic Wetland and Lake Dataset (BAWLD) provides estimates of fractional land cover of 19 land cover classes within 0.5° ×0.5° grid cells. The total area of the BAWLD domain is 25 500 000 kilometers squared (km2), i.e. 17% of the global land surface. The domain includes the boreal and tundra biomes, as well as areas of rocks and glaciers at greater than 50° North (N). The dataset consists of 23,469 0.5° ×0.5° grid cells. Each grid cell includes information on the fractional cover of five wetland classes, seven lake classes, three river classes, along with glacier, rockland, tundra, and boreal forest classes.
Global land cover products including wetlands
JRC Global Surface Water Explorer
The JRC Global Surface Water Explorer is a tool developed by the Joint Research Centre of the European Commission. It provides satellite-based, global-scale data on surface water dynamics over the past 3+ decades. This tool helps in understanding the changes in water bodies over time, aiding in water management, policy formulation, and environmental research. By accessing this online platform, users can explore how surface water bodies have been altered globally, which is crucial for addressing water-related challenges.
Esri Global Land Cover
The Esri Global Land Cover dataset provides an annual 10-meter resolution map of Earth's land surface from 2017-2022. Existing artificial intelligence (AI) land classification models were enhanced by bringing together a massive training dataset of billions of human-labeled image pixels. The output provides a 9-class map of the surface, including water and flooded vegetation, which are related to wetlands.
ESA WorldCover
ESA WorldCover provides global land cover products for 2020 and 2021 at a 10m resolution, utilizing Sentinel-1 and Sentinel-2 data. It aims to benefit various user communities and foster the development of new services. The project is a collaborative effort among experienced European service providers and research organizations, contributing to worldwide land cover mapping
Dynamic World
Dynamic World is a near-real time 10m resolution global land use land cover dataset, produced using deep learning, and is freely available and openly licensed. It is produced using the Google Earth Engine and AI Platform. Over 5000 Dynamic World images are produced every day based on Sentinel-2 Top of Atmosphere. The output includes 9 land cover classes, such as water and flooded vegetation, which are related to wetlands.
Wetland mapping tools
Arc Hydro: Wetland Identification Model
The Arc Hydro: Wetland Identification Model (AH-WIM) is a GIS-based tool that identifies potential wetland areas using digital elevation models (DEMs), hydrological data, and environmental variables like soil and land cover. It analyzes terrain to detect low-lying areas prone to water accumulation and integrates hydrological and ecological data to map and classify wetlands. This model supports wetland conservation, environmental planning, and land management by providing scalable and systematic wetland identification.
Mangrove Mapping Methodology (GEEMMM)
The Google Earth Engine Mangrove Mapping Methodology (GEM) provides an intuitive, accessible and replicable tool which caters to a wide audience of non-specialist coastal managers and decision makers. This tool reflects a thorough review and incorporation of relevant mangrove remote sensing literature, and harnesses the power of cloud computing, including a simplified image-based tidal calibration approach. The GEM is freely accessible for non-profit use and runs on comprehensive and thoroughly commented code within GEE.
Wetland Hydrology Analyst
The Wetland-Hydrology-Analyst-Toolbox is an ArcGIS toolbox complemented by Python scripts, designed to support hydrological analysis for wetland-related research. The toolbox streamlines the processing and analysis of geospatial data to examine wetland hydrology, aiding researchers in modeling and understanding wetland dynamics.
GEE-based Wetland Mapping
GEE-based Wetland Mapping refers to a tool hosted on GitHub that utilizes Google Earth Engine (GEE) for wetland mapping and analysis. This repository, provides scripts and workflows for leveraging GEE's powerful cloud-computing platform to process large-scale geospatial datasets for wetland hydrology studies. It enables users to analyze satellite imagery and other geospatial data efficiently, supporting wetland identification, monitoring, and hydrological assessments.
Wetland Extent Tool 2.0 (WET 2.0)
The Wetland Extent Tool 2.0 utilizes Landsat 8, Sentinel 2, Sentinel 1 C-SAR to automate wetland classification in the entire Great Lakes Basin at 10 m resolution. The tool allows users to select date ranges and areas for analysis, outputs map visualizations of the classification, generates time series graphs of optical indices, and exports images to the user's Google Drive.
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You can consult with the AID group leader or any members for your regional, national, and global datasets, tools, and analytics projects and questions.
