Areal Extent Of Wetlands Research Articles

Long-term spatio-temporal changes of wetlands in Tibetan Plateau and their response to climate change

Understanding the changes of wetlands on the Tibetan Plateau (TP) is important for action to ensure ecosystem resilience in Asia. However, mapping long-term changes of wetlands at high resolutions remains challenging. Here, we quantify the spatio-temporal changes of TP wetlands from 1990 to 2019, by combining Landsat imagery with deep learning to map TP wetlands. The deep learning model combined with transfer learning strategies achieves high classification performance using a few class samples. The validation results show that the user’s accuracy is 95.5% and the producer's accuracy is 90.1% for wetland extraction, satisfying with subsequent analysis of wetland spatio-temporal changes. Based on the wetland extraction model, we have created annual wetland map in the TP for the first time. We find that the areal extent of TP wetlands has increased by 31.2 ± 6.6 % over the past 30 years. The growth is particularly noticeable (by 22.5 ± 6.2 %) during 2015–2019. Spatially, the wetland areal extent on the Qiangtang Plateau (in the inner part of TP and as habitats of various birds and rare wild animals) and the source region of Yangtze River show the largest expansions by 55.3 ± 9.3 % and 44.0 ± 8.9 %, respectively. Such rapid wetland expansions are associated with increasing rainfall and temperature which have heterogeneous influences on wetland changes across the TP. Our findings provide evidence for the impact of climate change on wetland area. The marked wetland changes highlight that climate mitigation is a priority for high-latitude ecosystems.

River Basin Simulations Reveal Wide-Ranging Wetland-Mediated Nitrate Reductions.

River basin-scale wetland restoration and creation is a primary management option for mitigating nitrogen-based water quality challenges. However, the magnitude of nitrogen reduction that will result from adding wetlands across large river basins is uncertain, partly because the areal extent, location, and physical and functional characteristics of the wetlands are unknown. We simulated over 3600 wetland restoration scenarios across the ∼450,000 km2 Upper Mississippi River Basin (UMRB) depicting varied assumptions for wetland areal extent, physical and functional characteristics, and placement strategy. These simulations indicated that restoring wetlands will reduce local nitrate yields and nitrate loads at the UMRB outlet. However, the projected magnitude of nitrate reduction varied widely across disparate scenario assumptions─e.g., restoring 4500 km2 of wetlands (i.e., 1% of UMRB area) decreased mean annual nitrate loads at the UMRB outlet between 3 and 42%. Higher magnitude nitrate reductions correlated with best-case assumptions, particularly for characteristics controlling nitrate loading rates to the wetlands. These results show that simplified claims about basin-scale wetland-mediated water quality improvements discount the breadth of possible wetland impacts across disparate wetland physical and functional conditions and highlight a need for greater clarity regarding the likelihood of these conditions at river basin scales.

Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks

In the past two decades, global manufacturing of per- and polyfluoroalkyl substances (PFAS) has shifted from long-chain compounds to short-chain alternatives in response to evidence of the health hazards of long-chain formulations. However, accumulating data indicate that short-chain PFAS also pose health risks and are highly mobile and persistent in the environment. Because short-chain PFAS are relatively new chemicals, comprehensive knowledge needed to predict their environmental fate is lacking. This study evaluated the capacity of machine-learned Bayesian networks (BNs) to predict risks of exposure to short-chain PFAS in a Minnesota region affected by PFAS releases from the 3M Cottage Grove facility. Models were trained using long-term monitoring data provided by the Minnesota Department of Health (n = 12,406), which we coupled to a comprehensive dataset created by curating 88 other variables that describe potential PFAS sources, soil and hydrogeologic characteristics, and land use. Model performance was assessed using the area under the receiver-operating characteristic curve (AUC), a common measure of the accuracy of machine-learned classification algorithms. In addition, exposure risks were visualized spatially by coupling model predictions to a geographic information system. We found that machine-learned BN models had robust predictive performance, with AUCs above 0.96 in cross-validation. Significant risk factors identified by the BNs include distance to the 3M factory, distance to a former landfill, and areal extent of wetlands and developed land. We also found that risks of exposure to and the areal extent of perfluorosulfonic acids were greater than for perfluorocarboxylic acids with the same carbon number. The results suggest that machine-learned BNs could provide a promising screening tool for assessing short-chain PFAS exposure risks in groundwater.

The Third Generation of Pan-Canadian Wetland Map at 10 m Resolution Using Multisource Earth Observation Data on Cloud Computing Platform

Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the importance of multisource data and adequate test and train data for wetland classification at a countrywide scale.

2010–2016 methane trends over Canada, the United States, and Mexico observed by the GOSAT satellite: contributions from different source sectors

Abstract. We use 7 years (2010–2016) of methane column observations from the Greenhouse Gases Observing Satellite (GOSAT) to examine trends in atmospheric methane concentrations over North America and infer trends in emissions. Local methane enhancements above background are diagnosed in the GOSAT data on a 0.5∘×0.5∘ grid by estimating the local background as the low (10th–25th) percentiles of the deseasonalized frequency distributions of the data for individual years. Trends in methane enhancements on the 0.5∘×0.5∘ grid are then aggregated nationally and for individual source sectors, using information from state-of-science bottom-up inventories. We find that US methane emissions increased by 2.5±1.4 % a−1 (mean ± 1 standard deviation) over the 7-year period, with contributions from both oil–gas systems (possibly unconventional oil–gas production) and from livestock in the Midwest (possibly swine manure management). Mexican emissions show a decrease that can be attributed to a decreasing cattle population. Canadian emissions show year-to-year variability driven by wetland emissions and correlated with wetland areal extent. The US emission trends inferred from the GOSAT data account for about 20 % of the observed increase in global methane over the 2010–2016 period.

High-resolution inversion of methane emissions in the Southeast US using SEAC<sup>4</sup>RS aircraft observations of atmospheric methane: anthropogenic and wetland sources

Abstract. We use observations of boundary layer methane from the SEAC4RS aircraft campaign over the Southeast US in August–September 2013 to estimate methane emissions in that region through an inverse analysis with up to 0.25∘×0.3125∘ (25×25 km2) resolution and with full error characterization. The Southeast US is a major source region for methane including large contributions from oil and gas production and wetlands. Our inversion uses state-of-the-art emission inventories as prior estimates, including a gridded version of the anthropogenic EPA Greenhouse Gas Inventory and the mean of the WetCHARTs ensemble for wetlands. Inversion results are independently verified by comparison with surface (NOAA∕ESRL) and column (TCCON) methane observations. Our posterior estimates for the Southeast US are 12.8±0.9 Tg a−1 for anthropogenic sources (no significant change from the gridded EPA inventory) and 9.4±0.8 Tg a−1 for wetlands (27 % decrease from the mean in the WetCHARTs ensemble). The largest source of error in the WetCHARTs wetlands ensemble is the land cover map specification of wetland areal extent. Our results support the accuracy of the EPA anthropogenic inventory on a regional scale but there are significant local discrepancies for oil and gas production fields, suggesting that emission factors are more variable than assumed in the EPA inventory.

Remotely sensed MODIS wetland components for assessing the variability of methane emissions in Indian tropical/subtropical wetlands

Temperature and area fluctuations in wetlands greatly influence its various physico-chemical characteristics, nutrients dynamic, rates of biomass generation and decomposition, floral and faunal composition which in turn influence methane (CH4) emission rates. In view of this, the present study attempts to up-scale point CH4 flux from the wetlands of Uttar Pradesh (UP) by modifying two-factor empirical process based CH4 emission model for tropical wetlands by incorporating MODIS derived wetland components viz. wetland areal extent and corresponding temperature factors (Ft). This study further focuses on the utility of remotely sensed temperature response of CH4 emission in terms of Ft. Ft is generated using MODIS land surface temperature products and provides an important semi-empirical input for up-scaling CH4 emissions in wetlands. Results reveal that annual mean Ft values for UP wetlands vary from 0.69 (2010–2011) to 0.71(2011–2012). The total estimated area-wise CH4 emissions from the wetlands of UP varies from 66.47Ggyr−1with wetland areal extent and Ft value of 2564.04km2 and 0.69 respectively in 2010–2011 to 88.39Ggyr−1with wetland areal extent and Ft value of 2720.16km2 and 0.71 respectively in 2011–2012. Temporal analysis of estimated CH4 emissions showed that in monsoon season estimated CH4 emissions are more sensitive to wetland areal extent while in summer season sensitivity of estimated CH4 emissions is chiefly controlled by augmented methanogenic activities at high wetland surface temperatures.

A novel strategy for wetland area extraction using multispectral MODIS data

MODIS (Moderate Resolution Imaging Spectro-radiometer) is an imaging sensor onboard Terra/Aqua platform which provides information in 36 spectral bands. Spectral mixing of features due to high data dimensionality and high inter-band correlations constitutes the biggest challenge in the analysis of MODIS data. In view of this, present study attempts to develop a novel strategy involving Principal Component Analysis (PCA), Band to Band Correlation (BTBC) analysis, Stepwise Discriminant Analysis (SDA), and separability analysis to reduce the data dimensionality for extracting reliable and maximum information for wetland areal extent using coarse resolution MODIS (1km) data. The PCA explains variability in data and removes data redundant information; BTBC analysis eradicates the high correlated bands providing best bands suitable for wetland mapping; SDA evaluates the discriminatory power of different MODIS bands to discriminate the wetlands from other class types. Further, Normalized Difference Vegetation Index (NDVI) and Wetland Model Index (WMI) were also incorporated into the study to improve the classification accuracy. Finally, separability analysis was conducted to optimize the selected MODIS bands and indices.Results of rigorous data mining reveal that out of 24 input layers of MODIS (1km) data (22 optical MODIS bands, NDVI and WMI), only 4 input layers (WMI, NDVI, MODIS bands – NIR band 2 and SWIR band 6) are best suited for delineation and mapping of wetlands. This study also corroborates the usage of WMI, a newly developed index with combination of visible and short wavelength infra red (SWIR) MODIS bands, as the most optimal input layer to separate wetlands from the other land use class types (barren land, agricultural land, rivers/canals/streams, forest, wasteland/gullied or riverine land). It was observed that the magnitude of mapped wetland areal extent using MODIS (1km) data varied from 105,053ha in 2010–2011 to 111,479ha in 2011–2012 accounting for 0.44% (2010–2011)–0.46% (2011–2012) of the total geographical area of Uttar Pradesh, India. Seasonally, monsoon season displayed maximum wetland area with overall accuracy of 92% whereas summer season exhibited minimum wetland area with overall accuracies varying from 85 to 87% during both the sampling years. The output of the present research work will not only facilitate to improve the wetland area estimates but also provide an important input for climate change predictions in wetlands over large areas.

SVM and OBIA based Comparative Analysis on LANDSAT Multi Temporal Data for Wetland Mapping

Objectives: The Major objective of this study is to delineate the wetlands using the advanced image processing techniques and study the likelihood of these techniques for the mapping the real extent of wetlands on multi-temporal Lands at data. Methods/Statistical Analysis: The object based image analysis is based on the information of image objects rather than individual pixels. eCognition software is used for object oriented image analysis. The development of the objects and subsequent classification is achieved by multi resolution dissection using fuzzy logic approach1. The support vector machine is one of the supervised classification method is achieved by providing training sets. Findings: The study has been done from the year 1997 to 2014 for the changes in wetlands and their corresponding changes have been observed. The decrease in the areal extent of wetlands has been observed which is due to declining in the annual rainfall,population growth, rapid urbanization and industrialization over decades. It is observed that the classification by Object Based Image Analysis has outperformed the classification performed by SVM. The overall accuracy by this techniques is 76.01%, 75.02%, 77.80%, 76.83% for the data 1997, 2006, 2009, 2014 respectively while the accuracy is 94.2%, 93.2% ,93.8%, 93.1% respectively when classification is performed by object based image analysis. The areal extent of wetlands extracted by Support vector machine in Sq.km is 401.87, 381.96, 263.02, and 147.89 while they are 469.77, 406.46, 309.74 and 155.79 when extracted by object based image analysis for the year 1997, 2006, 2009 and 2014 respectively. Application/Improvements: The changes detected in wetlands over years can be used for the analysis of groundwater recharge, ecosystem & species growth etc.

Attribution of changes in global wetland methane emissions from pre-industrial to present using CLM4.5-BGC

An understanding of potential factors controlling methane emissions from natural wetlands is important to accurately project future atmospheric methane concentrations. Here, we examine the relative contributions of climatic and environmental factors, such as precipitation, temperature, atmospheric CO2 concentration, nitrogen deposition, wetland inundation extent, and land-use and land-cover change, on changes in wetland methane emissions from preindustrial to present day (i.e., 1850–2005). We apply a mechanistic methane biogeochemical model integrated in the Community Land Model version 4.5 (CLM4.5), the land component of the Community Earth System Model. The methane model explicitly simulates methane production, oxidation, ebullition, transport through aerenchyma of plants, and aqueous and gaseous diffusion. We conduct a suite of model simulations from 1850 to 2005, with all changes in environmental factors included, and sensitivity studies isolating each factor. Globally, we estimate that preindustrial methane emissions were higher by 10% than present-day emissions from natural wetlands, with emissions changes from preindustrial to the present of +15%, −41%, and −11% for the high latitudes, temperate regions, and tropics, respectively. The most important change is due to the estimated change in wetland extent, due to the conversion of wetland areas to drylands by humans. This effect alone leads to higher preindustrial global methane fluxes by 33% relative to the present, with the largest change in temperate regions (+80%). These increases were partially offset by lower preindustrial emissions due to lower CO2 levels (10%), shifts in precipitation (7%), lower nitrogen deposition (3%), and changes in land-use and land-cover (2%). Cooler temperatures in the preindustrial regions resulted in our simulations in an increase in global methane emissions of 6% relative to present day. Much of the sensitivity to these perturbations is mediated in the model by changes in methane substrate production and the areal extent of wetlands. The detrended interannual variability of high-latitude methane emissions is explained by the variation in substrate production and wetland inundation extent, whereas the tropical emission variability is explained by both of those variables and precipitation.

New York City Panel on Climate Change 2015 Report. Chapter 6: Indicators and monitoring.

William Solecki,1,a Cynthia Rosenzweig,2,a Reginald Blake,3,a Alex de Sherbinin,4 Tom Matte,5 Fred Moshary,6 Bernice Rosenzweig,7 Mark Arend,6 Stuart Gaffin,8 Elie Bou-Zeid,9 Keith Rule,10 Geraldine Sweeny,11 and Wendy Dessy11 1City University of New York, CUNY Institute for Sustainable Cities, New York, NY. 2Climate Impacts Group, NASA Goddard Institute for Space Studies, Center for Climate Systems Research, Columbia University Earth Institute, New York, NY. 3Physics Department, New York City College of Technology, CUNY, Brooklyn, NY; Climate Impacts Group, NASA Goddard Institute for Space Studies. 4 Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY. 5New York City Department of Health and Mental Hygiene, New York, NY. 6NOAA CREST, City College of New York, CUNY, New York, NY. 7CUNY Environmental Crossroads, City College of New York, CUNY, New York, NY. 8Center for Climate Systems Research, Columbia University Earth Institute, New York, NY. 9Department of Civil & Environmental Engineering, Princeton University, Princeton, NJ. 10Princeton Plasma Physics Laboratory, Princeton, NJ. 11New York City Mayor’s Office of Operation, New York, NY

Runoff from tropical alpine grasslands increases with areal extent of wetlands

Tropical alpine grasslands of the northern Andes, commonly known as the páramo, provide abundant high-quality water for downstream populations as well as a variety of other environmental services. Yet, very little is known about the role that landscape characteristics play in the hydrologic functioning of these ecosystems. To help fill this knowledge gap, we investigated the relationships between various landscape attributes and hydrology in a wet páramo ecosystem of southern Ecuador. Using linear regression analysis, we examined the influence of soil type, vegetation cover, catchment area, geology, and topography on runoff coefficient, streamflow rates, and evapotranspiration. Our study site is located at the Zhurucay River experimental catchment, which is composed of seven nested microcatchments ranging in size from 0.20 to 7.53km2 and in elevation from 3200 to 3900ma.s.l. We found that (1) water yield accounts for a high percentage of the water budget; (2) runoff coefficient and specific discharge are strongly correlated with the extent of Andean páramo wetlands (Histosol soils and cushion plants), and also increase with catchment size; (3) conversely, inferred evapotranspiration is the highest in catchments having the greatest percentages of upland terrain (Andosol soils and tussock grasses); and (4) low flows are highly positively correlated with steep slopes. These results suggest that in the high-elevation tropical grasslands of the Andes, runoff coefficient and specific discharge increase with catchment size because as catchment size increases, so does the relative area of permanently near-saturation zones (Andean páramo wetlands); likely because of reduced available storage capacity of the Andean páramo wetlands, and their hydrologic connectivity to the stream network.

Impact of climate variability on the hydrology of the Sudd wetland: signals derived from long term (1900–2000) water balance computations

The Sudd wetland is a huge swampy area (30,000–40,000 km2), with vegetation composed mainly of papyrus, water hyacinth and grasslands. It is located in South Sudan, and is of vital importance for livelihoods, ecosystem services and water resources. Half of the White Nile flow evaporates when passing through the Sudd (~16,000 106 m3/year). Historically, this phenomenon triggered several water conservation projects in the Nile region (the Jonglei Canal Project). However, the available information on the hydrology of the Sudd is very limited, and mostly outdated. This paper investigates the long term dynamics of the Sudd hydroclimatology (water balance components), and how it affects the wetland areal extent. The water balance results show that the extent of the Sudd wetland area is determined both by the regional climate (outflow from Lake Victoria) and the local climate, represented by precipitation and evaporation over the wetland itself. The long term trends of the Sudd hydrology from 1900 to 2000 have been analyzed with the Mann–Kendall test statistics. The water flows into and out of the Sudd demonstrate a statistically significant increasing trends during the last 100 years. This can be attributed to increasing rainfall over Lake Victoria in the early 1960s. The daily maximum and minimum temperature in the Sudd shows an increasing trend of 0.6 and 1.5 °C, respectively, over the last 100 years. However, this has not caused any change of the wetland actual evaporation (open water evaporation plus plant transpiration). The impact of the temperature rise has likely been compensated by a reduction of the relative humidity and solar radiation over the wetland. The statistical test shows that both relative humidity and solar radiation (sunshine hours) has significantly decreased, each reduced by 10 % over the last 50 year. The precipitation over the Sudd showed no significant change during the last 100 years. On the other hand, the areal extent of the Sudd wetland increased by 19,000 km2 (80 %) during the last 100 years, as a result of the increased inflows. The long term variability of the Sudd areal extent provides new results and deeper insights of the two-way land surface climate feedbacks, and informs wetland conservation and water resources management for this important tropical wetland.

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Abstract. Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.

Assessment of wetland dynamics in Sirohi district of Rajasthan, India using remote sensing and GIS techniques

Wetlands are very dynamic ecosystems in the world. The present study provides scenario of wetlands of Sirohi district of Rajasthan, India. Landsat TM data of October, 1992, IRS-P6 LISS-III data of October 2005 and IRS-P6 AWiFS data of January, February, March, April and May months (2005) have been used in the study. Visual interpretation technique was adopted to map the areal extent of wetlands on 1:50,000 scale. Wetlands were classified following the Ramsar classification. The total extent of wetlands for the Sirohi district as a whole is estimated to 11,961 ha with representation 189 wetlands during 1992 which later decreased 11,307 ha in 2005 with a loss of 654.2 ha during 13 years. Of the total wetland area of the district, rivers accounted for 61.9% area, followed by reservoirs (15.1%), seasonal lakes (15%), perennial lakes (5.6%), and ponds (2.5%). Further it is also observed that drastic reduction in the water spread area from February to May months. For conservation of wetlands it is necessary to assess the changes which take place in the water bodies from time to time. Key words: GIS; Rajasthan; Remote sensing; Sirohi district; Wetlands DOI: http://dx.doi.org/10.3126/jowe.v5i0.4996 J Wet Eco 2011 (5): 66-72

Assessment of Wetlands in Kuala Terengganu District Using Landsat^TM

Wetland cover mapping is very important in identifying its areal extent and the rate of change over time. This studyaims to map the areal extent and its rate of change in Kuala Terengganu district which covers approximately 4,690.65hectares. Three LandsatTM images, which dated on 15th October 1998, 14th July 2002 and 15th August 2005 were used indigital image processing by using a RGB band combination of 4, 5, and 2. The overall classification accuracies for the1998, 2002 and 2005 images were 74.55, 82.42 and 90.91 percent, respectively. The United State Geology Survey(USGS) Classification Scheme was used to determine the wetland and the images were independently classified andtotal areas of wetland cover were compared between different dates of imageries. Surprisingly, there was an unexpectedsignificant increase (from 102.35 to 381.35 ha) in the areal extent of wetlands in a seven year period of 1998 to 2005with a rate of change of 0.84% increase per year. This study implies that the integration of remote sensing andGeographical Information System (GIS) may provide a useful tool for temporal studies in wetland cover and its rate ofchange in Kuala Trengganu district

Sustainable Development Planning of Wetlands in Kuala Trengganu District Using Satellite Imagery

Wetland cover mapping is very important in the sustainable planning, identifying its areal extent and the rate of change over time. This study aims to map the areal extent and its rate of change in Kuala Terengganu district for its future sustainable develoment. Three LandsatTM images, which dated on 15th October 1998, 14th July 2002 and 15th August 2005 were used in digital image processing by using a RGB band combination of 4, 5, and 2. The overall classification accuracies for the 1998, 2002 and 2005 images were 74.55, 82.42 and 90.91 percent, respectively. The United State Geology Survey (USGS) Classification Scheme was used to determine the wetland and the images were independently classified and total areas of wetland cover were compared between different dates of imageries. Surprisingly, there was an unexpected significant increase (from 102.35 to 381.35 ha) in the areal extent of wetlands in a seven year period of 1998 to 2005 with a rate of change of 0.84% increase per year. This study implies that the integration of remote sensing and Geographical Information System (GIS) may provide a useful tool for temporal studies in wetland cover and its rate of change in Kuala Trengganu district.

Seasonal TOC export from seven boreal catchments in northern Sweden

Total organic carbon (TOC) concentrations from seven boreal catchments in northern Sweden were monitored between June 1996 and May 1998 to examine spatial and temporal variations in streamwater TOC export and its relationship with catchment characteristics. The annual average export of TOC ranged between 36 and 76 kg ha–1 yr–1 and correlated positively with the areal extent of wetlands (r2 = 0.72; p = 0.03). The daily output of TOC was 5–11 times higher during the spring than during any other season. In total, the four week long spring period contributed between 50% and 68% of the annual TOC export from the seven catchments. The relative importance of the spring snow melt period for the annual TOC export, however, correlated negatively with the percentage of wetlands (r2 = 0.83; p<0.01). We suggest that the smaller relative importance of the spring runoff period for the annual TOC export from wetland dominated catchments is a result of the hydrological flow paths associated with the snow melt period. While a large fraction of the spring runoff from forested areas reaches the stream via subsurface flow paths across riparian soils rich in TOC, the flow paths through wetland dominated systems include a much larger component of low-TOC snow melt water via surface flow over ice and frozen peat.

Ecological significance and classification of Chinese wetlands

China supports a great variety of wetlands, including some of the most important in the world. However, an appropriate classification system applicable to all wetlands is not available. Based on a preliminary inventory, a new classification system for Chinese wetlands is proposed. This system classifies natural wetlands into three categories on the basis of their natural features and distribution: peatlands, coastal and estuarine wetlands, and riverine and lacustrine wetlands. Each category is divided into several sub-classes. The areal extent of wetlands in each Province has been estimated and their ecological importance assessed.