A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> At present, monitoring, conservation and wise use of wetlands has become an international trend. Using Landst-7 ETM+ data acquired on Oct. 2000, we conducted a digital and rapid estimation of wetland vegetation biomass in the Poyang wetland natural conservation area, which is one of the seven international importance wetlands in China. Firstly, using the ETM band 4, 3 and 2 false color composite as one of the main references, we planned a reasonable sampling biomass field route. Based on geometric co-registration of both the field GPS positioning data and the ETM data, the linear relationships among the field biomass data and some transformed data derived from the ETM data were analyzed statistically. The results show that the sampling biomass data have the best positive correlation to DVI vegetation index data with a coefficient of 0.8546. Therefore, a linear regression model established between the sampling data and DVI data, was applied to estimate the total biomass of the whole Poyang Lake natural conservation area. The resultant total biomass is 10.98/spl times/10/sup 4/t. <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> Mangrove wetlands have been rapidly diminishing because of human pressures worldwide. The Guangdong Province in South China, which has the largest area of mangrove wetlands in the nation, is under severe threat as a result of rapid urbanization and economic development. In this paper, comparisons were made between optical Landsat TM images and Radarsat fine-mode images for estimating wetland biomass. Regression and analytical models were used to establish the relationships between remote sensing data and wetland biomass. The optimal parameter values for the analytical model were determined using genetic algorithms. Experiments indicate that the models using Radarsat fine-mode images have significant accuracy improvement in terms of Root Mean-Square Error (RMSE) whereas the use of the single Normalized Difference Vegetation Index (NDVI) may produce serious errors in biomass estimation. The Radarsat images can obtain more accurate trunk information about mangrove forests because of higher resolution and side-looking geometry. The use of genetic algorithms can help to decompose backscatter into vegetation and soil backscattering, which is very useful for ecological modelling. <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> [1] We used available field survey and literature data to produce inventory maps of wetland biomass and net primary production (NPP) for western Siberia. Field survey data were obtained for major types of wetland microlandscapes within the boreal (taiga) region. We developed a multiscale approach based on using a regional wetland typology map (1:2,500,000 scale), further refined by satellite image classifications (LANDSAT-7, SPOT, RESURS, 1:200,000 scale). Satellite images on test areas designated in the boreal region of western Siberia were classified by 30 landscape classes. We used aerial photography (1:25,000 scale) to evaluate the fraction of the area occupied by microlandscape elements within patterned wetlands. As a result, we were able to produce a GIS map-based inventory of ecosystem phytomass and NPP in west Siberian wetlands. Using the GIS map, the average and total net primary production and biomass were estimated by ecosystem type, the number of vegetation layers, and climatic gradient. The annual NPP to live biomass ratio increases southward from 0.27 in the tundra to 0.65 in the steppe region. Live biomass of wetlands amounts to only 10–30% of the average biomass of upland forests in the same climatic region, although wetland NPP was found to be equal or higher then that of upland forests. Mosses and the belowground fraction of grasses are the major contributors to NPP. Average live biomass and NPP in wetlands were estimated to be 1600 g/m2 and 790 g/m2/yr, respectively. Total wetland NPP amounts to 530 Tg/yr, and live biomass amounts to 1070 Tg. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> [1] Mangroves are salt tolerant plants that grow within the intertidal zone along tropical and subtropical coasts. They are important barriers for mitigating coastal disturbances, provide habitat for over 1300 animal species and are one of the most productive ecosystems. Mozambique's mangroves extend along 2700 km and cover one of the largest areas in Africa. The purpose of this study was to determine the countrywide mean tree height spatial distribution and biomass of Mozambique's mangrove forests using Landsat ETM+ and Shuttle Radar Topography Mission (SRTM) data. The SRTM data were calibrated using the Landsat derived land-cover map and height calibration equations. Stand-specific canopy height-biomass allometric equations developed from field measurements and published height-biomass equations were used to calculate aboveground biomass of the mangrove forests on a landscape scale. The results showed that mangrove forests covered a total of 2909 km 2 in Mozambique, a 27% smaller area than previously estimated. The SRTM calibration indicated that average tree heights changed with geographical settings. Even though the coast of Mozambique spans across 16 degrees latitude, we did not find a relationship between latitude and biomass. These results confirm that geological setting has a greater influence than latitude alone on mangrove production. The total mangrove dry aboveground biomass in Mozambique was 23.6 million tons and the total carbon was 11.8 million tons. <s> BIB004 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> Land use/cover change is the main drive factor for the change of ecosystem carbon storage. To understand and appraise ::: the carbon source/sink function of terrestrial ecosystem accurately, it needs to evaluate the impact of land use changes on ::: carbon storage firstly. Based on data from remote sensing TM imageries in 1980, 1990 and 2005 in the region around ::: Taihu Lake in China, the process of land use/cover change which divides to cropland, forest land, wetlands, grassland, ::: settlements and other land had been explained, then the conversion matrix of land use change had been analyzed by GIS ::: technology. Under the above, the paper calculates the change value of vegetation carbon storage in this area which ::: caused by land use change with the method supplied by the 2006 IPCC Guidelines for National Greenhouse Gas ::: Inventories. The main results as follows: (1) The area of cropland, forestland and grassland had decreased by 223.26km 2 , ::: 1,182.50km 2 and 178.90km 2 during 1980-1990, and the cropland had translated out to forest land and settlements ::: mainly, the land types transferred into cropland includes forest land, grassland and other land, among which 1,463.90km 2 ::: of forest land was transferred into cropland. The area of grassland had increased by 1,270.47km 2 , adversely, the cropland ::: and forest land had decreased by 5,929.51km 2 and 1381.94km 2 during 1990-2005, and the transferring-out object of ::: cropland includes wetlands, forest land, grassland, settlements and other land, with only 59.95% of the original cropland ::: remain unchanged. (2) The vegetation carbon storage had decreased by 642,171.27Mg because of land use change ::: during 1980-1990, and the transferring from forest land to cropland has caused a decrease of 775,867Mg in vegetation ::: carbon storage, that from cropland to forest land has caused a increase of 113,261.43Mg, that from grassland to cropland ::: has caused a increase of 8,129.36Mg, and that from grassland to forest land has caused a increase of 12,304.94Mg; The ::: vegetation carbon storage had decreased by 683,125.18Mg during 1990-2005, and the transferring from forest land to ::: cropland has caused a decrease of 285,034Mg, that from forest land to grassland has caused a decrease of 417,617.73Mg, ::: that from cropland to grassland has caused a decrease of 40,938.65Mg, while that from cropland and grassland to forest ::: land has caused a respective increase of 52,856.61Mg and 7,608.59Mg. (3) The results of vegetation carbon storage ::: change which calculated by the IPCC method had some errors in number value to other domestic research results, so the ::: paper suggests the government of China should compile the greenhouse gas inventories which accord with it's own ::: situation. <s> BIB005 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> Matang Mangrove Forest Reserve or known as Matang Mangroves is the largest single mangroves in Peninsular Malaysia. Covering an area of about 41,000 ha, majority of this area is forest reserve. Mangroves have long been known as extremely productive ecosystem that cycle carbon (C) rapidly, but studies related to carbon in this ecosystem are limited. This study was carried out to assess the carbon stock and quantify their changes following deforestation, wood extraction and forest degradation. Landsat-TM and SPOT-5 satellite images for 1991 and 2011 respectively were utilised to identify mangroves. Vegetation index generated from the images was used as a variable to indicate carbon stock and it was correlated to forest inventory information through regression. The study showed that carbon stocks of Matang Mangroves ranged from 1.03 to 263.65 t C ha-1 and 1.01 to 259.68 t C ha-1 for the years 1991 and 2011 respectively. Total carbon stock in Matang Mangroves was estimated at about 3.04 mil t C in year 1991 and 2.15 mil t C in 2011. The study suggested that the traditional use of vegetation index from optical imagery systems is still relevant and viable in vegetative studies. <s> BIB006 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> article i nfo There isaneedtoquantify large-scale plantproductivityincoastalmarshestounderstandmarshresiliencetosea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral re- flectance. Toaddress these challenges,we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species,Typhaspp. andSchoenoplectus acutus, at two restored marshes in the Sacra- mento-San Joaquin River Delta, California, USA. We usedfield spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data (R 2 <s> BIB007 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> Peatlands in Amazonian Peru are known to store large quantities of carbon, but there is high uncertainty in the spatial extent and total carbon stocks of these ecosystems. Here, we use a multi-sensor (Landsat, ALOS PALSAR and SRTM) remote sensing approach, together with field data including 24 forest census plots and 218 peat thickness measurements, to map the distribution of peatland vegetation types and calculate the combined above- and below-ground carbon stock of peatland ecosystems in the Pastaza-Maranon foreland basin in Peru. We find that peatlands cover 35 600±2133 km 2 and contain 3.14 (0.44–8.15) Pg C. Variation in peat thickness and bulk density are the most important sources of uncertainty in these values. One particular ecosystem type, peatland pole forest, is found to be the most carbon-dense ecosystem yet identified in Amazonia (1391±710 Mg C ha �1 ). The novel approach of combining optical and radar remote sensing with above- and below-ground carbon inventories is recommended for developing regional carbon estimates for tropical peatlands globally. Finally, we suggest that Amazonian peatlands should be a priority for research and conservation before the developing regional infrastructure causes an acceleration in the exploitation and degradation of these ecosystems. S Online supplementary data available from stacks.iop.org/ERL/9/124017/mmedia <s> BIB008 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> Mangroves are found throughout the tropics, providing critical ecosystem goods and services to coastal communities and supporting rich biodiversity. Despite their value, world-wide, mangroves are being rapidly degraded and deforested. Madagascar contains approximately 2% of the world's mangroves, >20% of which has been deforested since 1990 from increased extraction for charcoal and timber and conversion to small to large-scale agriculture and aquaculture. Loss is particularly prominent in the northwestern Ambaro and Ambanja bays. Here, we focus on Ambaro and Ambanja bays, presenting dynamics calculated using United States Geological Survey (USGS) national-level mangrove maps and the first localized satellite imagery derived map of dominant land-cover types. The analysis of USGS data indicated a loss of 7659 ha (23.7%) and a gain of 995 ha (3.1%) from 1990-2010. Contemporary mapping results were 93.4% accurate overall (Kappa 0.9), with producer's and user's accuracies ≥85%. Classification results allowed partitioning mangroves in to ecologically meaningful, spectrally distinct strata, wherein field measurements facilitated estimating the first total carbon stocks for mangroves in Madagascar. Estimates suggest that higher stature closed-canopy mangroves have average total vegetation carbon values of 146.8 Mg/ha (±10.2) and soil organic <s> BIB009 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> Broad-scale estimates of belowground biomass are needed to understand wetland resiliency and C and N cycling, but these estimates are difficult to obtain because root:shoot ratios vary considerably both within and between species. We used remotely-sensed estimates of two aboveground plant characteristics, aboveground biomass and % foliar N to explore biomass allocation in low diversity freshwater impounded peatlands (Sacramento-San Joaquin River Delta, CA, USA). We developed a hybrid modeling approach to relate remotely-sensed estimates of % foliar N (a surrogate for environmental N and plant available nutrients) and aboveground biomass to field-measured belowground biomass for species specific and mixed species models. We estimated up to 90% of variation in foliar N concentration using partial least squares (PLS) regression of full-spectrum field spectrometer reflectance data. Landsat 7 reflectance data explained up to 70% of % foliar N and 67% of aboveground biomass. Spectrally estimated foliar N or aboveground biomass had negative relationships with belowground biomass and root:shoot ratio in both Schoenoplectus acutus and Typha, consistent with a balanced growth model, which suggests plants only allocate growth belowground when additional nutrients are necessary to support shoot development. Hybrid models explained up to 76% of variation in belowground biomass and 86% of variation in root:shoot ratio. Our modeling approach provides a method for developing maps of spatial variation in wetland belowground biomass. <s> BIB010 </s> A Review of Wetland Remote Sensing <s> Biomass/Carbon Stock <s> We demonstrate the potential of using freely available satellite data from the Landsat ETM+ sensor for generating carbon balance estimates for lowland peatlands. We used a lowland ombrotrophic peatland in the UK as our test site representing a range of peatland conditions. A literature survey was undertaken to identify the simplest classification schema that could be used to distinguish ecohydrological classes for carbon sequestration on the peatland surface. These were defined as: active raised bog, Eriophorum-dominated bog, milled unvegetated peat and drained or degraded bog, with bracken and Carr woodland to define the bog edges. A maximum likelihood classifier (MLC) was used to map the spatial distribution of the six classes on the peatland surface. A Landsat ETM+ band-5 derived brightness-texture layer created using geostatistical methods greatly improved classification accuracies. The results showed the best accuracy of the MLC, when compared to finer scale methods, with Landsat ETM+ bands alone was 74%, which increased to 93% when including the brightness-texture layer. An estimate of carbon sequestration status of the site was performed that showed good agreement with the results of a finer-scale-based estimate. The coarse-scale map estimating ?12?000?kg carbon and fine scale map estimating +23?000?kg carbon per annum. We conclude that with further development of our tool, if textural measures are used alongside optical data in MLC, it is possible to achieve good quality estimates of carbon balance status for peatland landscapes. This represents a potentially powerful operational toolkit for land managers and policy makers who require spatially distributed information on carbon storage and release for carbon pricing and effective land management. <s> BIB011
Wetland biomass can be used to reflect the status of the wetland ecosystem and biomass estimates can provide basic information about wetland planning and utilization. Peregon et al. BIB003 classified Western Siberia into 30 landscapes based on a field survey and remote sensing data (Landsat ETM+, SPOT) and then used aerial photography to evaluate the ratio of micro-landscapes in wetlands. They evaluated the biomass of wetland in Western Siberia. Tan et al. BIB001 calculated the biomass of the Poyang Wetland in China using a linear regression model between the biomass derived from a field survey and the vegetation index obtained from Landsat ETM+ data. Partial least squares regression was used to select the appropriate spectral features of field survey and satellite reflectance to model the aboveground biomass of some marsh species. It was found that Landsat ETM+ and Hyperion infrared bands have slight advantages for the biomass estimation of wetlands BIB007 . The underground biomass of wetlands was also estimated for peatland BIB010 . A hybrid model based on the aboveground biomass and N content of leaves was proposed to estimate the underground biomass; 76% of the variation below the ground could be explained. Mangrove forest biomass has been focused on in many studies because of the higher biomass content and the challenges estimating the biomass. To estimate the biomass of mangroves, the vertical information is important because trunks and branches contain the vast majority of biomass. Radar remote sensing has potential for the estimate because of the penetration capability. Fatoyinbo et al. BIB004 used Landsat ETM+ and Shuttle Radar Topography Mission (SRTM) data to estimate the biomass of mangroves in Mozambique. Fatoyinbo and Simard  added LiDAR canopy height estimates and also derived three-dimensional (3-D) structure and biomass maps of mangroves in Africa. Some researchers argued that Radarsat images acquire the mangrove biomass with higher accuracy than Landsat TM images because of the higher resolution and side-looking geometry BIB002 . Peatland plays a key role in carbon balancing and it acts as source or sink based on the status of near-surface water tables. The accurate assessment of the carbon stock of peatland has significant effects on understanding the carbon cycle. Draper et al. BIB008 used remote sensing images together with field survey data to evaluate the carbon stock of peatland in Amazonia. Crichton et al. BIB011 used Landsat data to estimate the carbon balance in peatland of the UK and reported results that are in good agreement with the finer-scale estimates. Many scientists also estimated the carbon storage in mangroves BIB005 BIB006 BIB009 . All these studies were significant for land cover management and even for carbon cycle.