WETLAND MAPPING IN NEW BRUNSWICK, CANADA WITH LANDSAT5-TM, ALOS-PALSAR, AND RADARSAT-2 IMAGERY

Several maps of wetland areas in central New Brunswick, Canada, were produced by applying the Random Forests classifier to different combinations of optical Landsat-5 TM images, dual-polarized (HH, HV) Radarsat-2 C-band and Alos-1 PalSAR L-band Synthetic Aperture Radar (SAR) images and digital elevation data. The resulting maps were compared to 199 GPS wetland sites that were visited between 2012 and 2018 as well as to a combination of two wetland maps currently used by the Province of New Brunswick. The number of correctly identified GPS wetland sites was the highest when both the Alos-PalSAR and Radarsat-2 images are used (97.9%). This percentage of correctly identified sites were well above the accuracy of the official New Brunswick wetland maps (44.7 %). With the best-classified image, the misidentifications were due to wetlands not being classified in the right wetland class, and just one case was a wetland site being classified in a non-wetland class. For the NB wetland map, about a quarter of the wetland validation sites were classified in a non-wetland class, and about the same number of sites were classified in the wrong wetland class.

Multi-source global wetland maps combining surface water imagery and groundwater constraints

Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national and regional wetland surveys, (ii) identifying inundated areas via satellite imagery and (iii) delineating wetlands as shallow water table areas based on groundwater modeling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modeling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFWs) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDWs) derived from groundwater modeling (either direct or simplified using several variants of the topographic index). Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater, disregarding most human alterations (potential wetlands). Seven CW maps were generated at 15 arcsec resolution (ca. 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess compared to the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27.5 and 29 million km2, i.e., 21.1 % and 21.6 % of the global land area, which are among the highest values in the literature and are in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9.7 % for RFW (with an overlap of ca. 3.4 %), including wetlands under canopy and/or cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modeling (hydrological, ecological and biogeochemical modeling) and environmental planning. The dataset consisting of the two selected CW maps and the contributing GDW and RFW maps is available from PANGAEA at https://doi.org/10.1594/PANGAEA.892657 (Tootchi et al., 2018).

Worth of wetlands: revised global monetary values of coastal and inland wetland ecosystem services

In this study, we have re-estimated the 2011 global monetary values of natural wetland ecosystem services using new information on the areas of different coastal and inland wetland classes, and included estimates for forested wetlands. The 2011 global monetary value of natural wetland ecosystem services is now estimated at Int$47.4 trillion per year, 43.5% of the value of all natural biomes. Despite forming only ~15% of global natural wetland area, coastal wetlands are estimated to deliver 43.1% (Int$20.4 trillion per year) of the total global ecosystem services monetary value of all natural wetland classes. There is a need to further refine these value estimates by factoring in other determinants of wetland ecosystem service monetary value, by disaggregating unit monetary values to each wetland class and by updating unit monetary values with more recent sources, especially for ecosystem services with no, or few, value estimates.

Multi-source global wetland maps combining surface water imagery and groundwater constraints

Many maps of open water and wetlands have been developed based on three main methods: (i) compiling national/regional wetland surveys; (ii) identifying inundated areas via satellite imagery; and (iii) delineating wetlands as shallow water table areas based on groundwater modelling. However, the resulting global wetland extents vary from 3 % to 21 % of the land surface area because of inconsistencies in wetland definitions and limitations in observation or modelling systems. To reconcile these differences, we propose composite wetland (CW) maps, combining two classes of wetlands: (1) regularly flooded wetlands (RFW) obtained by overlapping selected open-water and inundation datasets; and (2) groundwater-driven wetlands (GDW) derived from groundwater modelling (either direct or simplified using several variants of the topographic index). Wetlands are statically defined as areas with persistent near-saturated soil surfaces because of regular flooding or shallow groundwater. Seven CW maps were generated at the 15 arc-sec resolution (ca 500 m at the Equator) using geographic information system (GIS) tools and by combining one RFW and different GDW maps. To validate this approach, these CW maps were compared with existing wetland datasets at the global and regional scales. The spatial patterns were decently captured, but the wetland extents were difficult to assess against the dispersion of the validation datasets. Compared with the only regional dataset encompassing both GDWs and RFWs, over France, the CW maps performed well and better than all other considered global wetland datasets. Two CW maps, showing the best overall match with the available evaluation datasets, were eventually selected. These maps provided global wetland extents of 27.5 and 29 million km², i.e., 21.1 % and 21.6 % of global land area, which are among the highest values in the literature and in line with recent estimates also recognizing the contribution of GDWs. This wetland class covers 15 % of the global land area compared with 9.7 % for RFW (with an overlap of ca. 3.4 %), including wetlands under canopy/cloud cover, leading to high wetland densities in the tropics and small scattered wetlands that cover less than 5 % of land but are highly important for hydrological and ecological functioning in temperate to arid areas. By distinguishing the RFWs and GDWs based globally on uniform principles, the proposed dataset might be useful for large-scale land surface modelling (hydrological, ecological and biogeochemical modelling) and environmental planning. The dataset consisting of the two selected CW maps and the contributing GDW and RFW maps is available from PANGAEA at https://doi.pangaea.de/10.1594/PANGAEA.892657