A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales. <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Abstract To aid in the environmental planning and management of Los Haitises National Park, a neotropical park in the Dominican Republic, a land cover change analysis was performed on the lower Yuna River watershed, within which a portion of the park exists and which contains a diversity of agricultural practices. Separate image classifications were performed on a 1973 Landsat MSS image and a 1985 Landsat TM image with resulting overall classification accuracies of 77.3 per cent and 81.3 per cent, respectively. In both classifications, spectral similarities between the various growth stages of rice, mangrove, orchard, and permanent grassland made separation and delineation of these classes difficult. The implications of land cover trends within the watershed for ecologic and economic management issues which affect both the watershed and the park were discussed. <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> To evaluate the accounts of local fishermen, Landsat TM images (1986, 1993, 1999) were examined to assess potential losses in the mangrove forests of the Teacapan-Agua Brava lagoon system, Mexico. A binary change mask derived from image differencing of a band 4/3 ratio was employed to calculate any changes within this for- ested wetland. The results indicate that by 1986 approxi- mately 18% (or 86 km 2 ) of the mangrove area under study was either dead or in poor condition. The majority of this dam- age had occurred in the eastern section of the Agua Brava basin, which coincides, with the reports of the elderly fisher- men. Examination of aerial photographs from 1970 revealed no adverse impacts in this area and would suggest, as postu- lated by the fishermen and other scientists, that modifications in environmental conditions following the opening of a canal, Cuautla canal, in 1972 may have initiated the large-scale mor- tality. Although these areas of impact are still developing, the results from the satellite data indicate that the majority of the more recent changes are occurring elsewhere in the system. Obvious in the 1999 satellite data, but not so in the 1993, are large areas of mangrove degradation in the northern section of the Teacapan region. In the Agua Brava basin, the more re- cent transformations are appearing on the western side of the basin. Since long-term records of environmental conditions are absent, it is difficult to determine why these latest changes are occurring or even if the earlier losses were the result of the canal. Potential agents of change that have recently been ob- served include a hurricane, a second canal, and the uncon- trolled expansion of the Cuautla canal since 1994. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> A multitemporal post-classification study with data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) was made to detect changes in the landscape of the Majahual coastal system, along the Mexican Pacific. Six land-use classes were used as direct indicators of the landscape condition. Mangrove, lagoon, saltmarsh, dry forest, secondary succession, and agriculture were the categories selected to evaluate the changes by comparing four thematic maps (from 1973 to 19971. The accuracy of the classification (only in the 1997 scene) was calculated from an error matrix, using the overall accuracy assessment (70%) and the Kappa coefficient (0.61). Both values indicate that the agreement in the classification was moderate, but better than one obtained by chance. The analytical comparison of data sets (1973 vs. 1986, 1986 vs. 1990, and 1990 vs. 1997) was done by using a change detection matrix and the Kappa coefficient. Agreement between data sets varied from 61% to 68%, all moderate but enough to determine the general trends of change in the system. These are mainly typified as loss of natural cover (especially dry forest) and the fragmentation of the landscape, with agricultural activities and their subsequent effects (secondary succession, modification of drainage patterns) the main transforming agents. <s> BIB004 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Wetland mapping derived from remotely sensed images is subject to error and uncertainty. Fuzzy classification techniques can deal with the spectral and spatial vagueness, and can be used to model the uncertainty in remote sensing classification. In this paper, we present a modified Fuzzy C-Means (FCM) classifier that allows the local texture information to regularize the classification result iteratively, and we propose a threshold defuzzification method to extract the potential wetland areas from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) imagery. By introducing the transition classes during the defuzzification process, the modified classifier reduces commission-errors and improves the mapping accuracy when compared to the standard FCM classifier. The accuracy assessment and a test of statistical significance indicate that the modified FCM classifier shows a significantly better mapping result than the standard FCM classifier because of the incorporation of this spatial information. <s> BIB005 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> The extent of wetland in New Zealand has decreased by approximately 90% since European settlement began in 1840. Remaining wetlands continue to be threatened by drainage, weeds, and pest invasion. This article presents a rapid method for broad-scale mapping and prioritising palustrine and estuarine wetlands for conservation. Classes of wetland (lacustrine, estuarine, riverine, marine, and palustrine) were mapped using Landsat ETM+ imagery and centre-points of palustrine and estuarine sites as ancillary data. The results shown are for the Manawatu–Wanganui region, which was found to have 3060 ha of palustrine and 250 ha of estuarine wetlands. To set conservation priorities, landscape indicators were computed from a land-cover map and a digital terrain model. Four global indicators were used (representativeness, area, surrounding naturalness, and connectivity), and each was assigned a value to score wetland sites in the region. The final score is an additive function that weights the relative importance of each indicator (i.e., multicriteria decision analysis). The whole process of mapping and ranking wetlands in the Manawatu–Wanganui region took only 6 weeks. The rapid methodology means that consistent wetland inventories and ranking can now actually be produced at reasonable cost, and conservation resources may therefore be better targeted. With complete inventories and priority lists of wetlands, managers will be able to plan for conservation without having to wait for the collection of detailed biologic information, which may now also be prioritised. <s> BIB006 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Reliable information on the extent and spatial distribution of mangroves has not been available for China. To create a map assessment of the mangroves for this region, an object-oriented classification technique was applied to Landsat-5/7 imagery at 30 m spatial resolution and verified using ground-truthing. Areal statistics for the mapped mangroves revealed that there were 20778 ha of mangroves located along the southeast coast of China. Extensive tracts of mangrove were found in Guangdong, Guangxi, Hainan, and Fujian Province (9289, 5813, 3576, and 1023 ha, respectively). Based on ground-truthing, the overall accuracy of our mangrove map was 92.6 % and the Kappa confidence was 0.85. Knowledge of the status and distribution of mangroves is important for advancing their management and conservation in China. <s> BIB007 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> ABSTRACT Long, J.; Napton, D.; Giri, C., and Graesser, J., 2014. A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010. Information on the present condition and spatiotemporal dynamics of mangrove forests is needed for land-change studies and integrated natural resources planning and management. Although several national mangrove estimates for the Philippines exist, information is unavailable at sufficient spatial and thematic detail for change analysis. Historical and contemporary mangrove distribution maps of the Philippines for 1990 and 2010 were prepared at nominal 30-m spatial resolution using Landsat satellite data. Image classification was performed using a supervised decision tree classification approach. Additionally, decadal land-cover change maps from 1990 to 2010 were prepared to depict changes in mangrove area. Total mangrove area decreased 10.5% from 1990 to 2010. Comparison of estimates produced from this study with selected historical mangrove area esti... <s> BIB008 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Summary We propose a methodology to identify and map shallow lakes (SL) in the Parana River floodplain, the largest freshwater wetland ecosystem in temperate South America. The presence and number of SL offer various ecosystem services and habitats for wildlife biodiversity. Our approach involved a frequency analysis over a 1987–2010 time series of the Normalized Difference Vegetation Index (NDVI), derived from Landsat 5 and 7 TM/ETM data. Through descriptive statistics of samples of pixels and field work in different types of SL, we established an NDVI threshold of 0.34 below which we assumed the presence of water in each pixel. The standard deviation of the estimated SL area decreases with the number of images in the analysis, being less than 10% when at least 30 images are used. The mean SL area for the whole period was 112,691 ha (10.9% of the study area). The influence of the hydrological conditions on the resulting SL map was evaluated by analyzing twelve sets of images, which were selected to span the whole period and different time frames according to multiannual dry and wet periods and to relative water level within each period. The Kappa index was then calculated between pairs of resulting SL maps. We compared our maps with the available national and international cartographic documents and with other published maps that used one or a few Landsat images. Landsat images time series provide an accurate spatial and temporal resolution for SL identification in floodplains, particularly in temperate zones with a good provision of cloud free images. The method evaluated in this paper considers the dynamics of SL and reduces the uncertainties of the fuzzy boundaries. Thus, it provides a robust database of SL and its temporal behavior to establish future monitoring programs based on the recent launch of Landsat 8 satellite. <s> BIB009 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Mangrove forests provide important ecosystem goods and services for human society. Extensive coastal development in many developing countries has converted mangrove forests to other land uses without regard to their ecosystem service values; thus, the ecosystem state of mangrove forests is critical for officials to evaluate sustainable coastal management strategies. The objective of this study is to investigate the multidecadal change in mangrove forests in Ca Mau peninsula, South Vietnam, based on Landsat data from 1979 to 2013. The data were processed through four main steps: 1) data preprocessing; 2) image processing using the object-based image analysis (OBIA); 3) accuracy assessment; and 4) multitemporal change detection and spatial analysis of mangrove forests. The classification maps compared with the ground reference data showed the satisfactory agreement with the overall accuracy higher than 82%. From 1979 to 2013, the area of mangrove forests in the study region had decreased by 74%, mainly due to the boom of local aquaculture industry in the study region. Given that mangrove reforestation and afforestation only contributed about 13.2% during the last three decades, advanced mangrove management strategies are in an acute need for promoting environmental sustainability in the future. <s> BIB010 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Abstract The Matang Mangrove Forest is the largest single mangrove forest in Peninsular Malaysia, covering an area of more than 40,000 ha. As a national treasure, it lies under the jurisdiction of both the federal and state authorities. Monitoring temporal changes of mangrove area on a large scale requires a more efficient tool. Remote sensing is often a reliable alternative to ground-survey methods that provides useful source of information and coverage that is timely and complete especially in mangroves areas where accessibility is difficult. In this study, status and changes of land use and land cover in the Matang Mangrove Forest during the past 18 years (1993–1999, 1999 to 2011 and 1993 to 2011) were determined using multi temporal satellites, and threats to the ecosystems were also identified. Classification of land use and land cover approach was implemented using the maximum likelihood classifier (MLC) along with vegetation index differencing (NDVI) technique. Classification accuracy at 85.7%, 90%, and 88.9% with Kappa statistics of 0.82, 0.88, and 0.94 respectively for 1993, 1999 and 2011 image were obtained. The overall change in the area during the 18-year period indicated the loss of mangrove area at 8017.3 ha, while 2998 ha was newly planted or rehabilitated. The present study revealed that Rhizophora apiculata and Rhizophora mucronata are still being preserved, accounting to more than 80% of the total species. Temporal changes of the mangrove for the 18-year period showed that the mangroves were converted to water bodies at 31.1%, dry land forest at 30.6%, oil palm plantation at 18.9%, aquaculture at 11.1%, paddy plantation at 4.9%, horticulture at 3.1%, and urban settlement area at 0.3%. Threats towards the mangrove area were due to erosion, tree harvesting rotation, transitional forest, shrimp ponds, illegal agricultural activities and trespassing. The findings indicated an approach in collecting regular and appropriate mangrove forest area database and detection of activities that violate regulations. This will provide adequate information to the stakeholders in enhancing the management practise and for legislative purposes. Hence, combinations of these approaches has been proven to be adequate in detecting changes in the mangrove area and indicating the nature of the changes which will promote and enhance the management planning process towards sustainability of the Matang Mangrove Forest. <s> BIB011 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Coastal marshlands may provide ecosystem services but their vegetation and related services may be impacted by environmental changes. Habitat mapping is a key step to monitor the spatio-temporal dynamics of vegetation and detect on-going changes. However, it is still a challenge to produce reliable vegetation maps at the regional scale. This study aims to evaluate the potential of new Landsat-8 imageries (acquired in September and December 2013) for mapping fine-grained plant communities in coastal marshlands. Field-based vegetation maps were collected for 270 km of marshlands along the French Atlantic coast. In order to be identifiable on the satellite image, fine-grained vegetation units were aggregated into fewer plant community combinations. The classification accuracy was assessed by comparison with field-based vegetation data and compared between the supervised methods used, including Minimum Distance, Mahalanobis, Maximum Likelihood, Random Forest and Support Vector Machine. The best result was obtained with the Maximum Likelihood classifier and by combining the two Landsat-8 images (85.9 % accuracy overall). Three main habitat types dominated the coastal Atlantic marshlands: croplands, Trifolio maritimae-Oenantheto silaifoliae geosigmetum and Puccinellio maritimae-Arthrocnemeto fruticosi geosigmetum. The reliability of the vegetation map produced will provide a good basis for monitoring the conservation status of the various habitats. <s> BIB012 </s> A Review of Wetland Remote Sensing <s> Mapping/Classification of Wetland <s> Abstract Wetlands are among Earth's most dynamic, diverse and varied habitats as the balance between land and water surfaces provide shelter to a unique mixture of plant and animal species. This study explores the changes in two Mediterranean wetland delta environments formed by the Axios and Aliakmonas rivers located in Greece, over a 25-year period (1984–2009). Direct photo-interpretation of four Landsat TM images acquired during the study period was performed. Furthermore, a sophisticated, semi-automatic image classification method based on support vector machines (SVMs) was developed to streamline the mapping process. Deposition and erosion magnitudes at different temporal scales during the study period were quantified using both approaches based on coastline surface area changes. Analysis using both methods was conducted in a geographical information systems (GIS) environment. Direct photo-interpretation, which formed our reference dataset, showed noticeable changes in the coastline deltas of both study areas, with erosion occurring mostly in the earlier periods (1990–2003) in both river deltas followed by deposition in more recent years (2003–2009), but at different magnitudes. Spatial patterns of coastline changes predicted from the SVMs showed similar trends. In absolute terms SVMs predictions of sediment erosion and deposition in the studied area were different in the order of 5–20% in comparison to photo-interpretation, evidencing the potential capability of this method in coastline changes monitoring. One of the main contributions of our work lies to the use of the SVMs classifier in coastal mapping of changes, since to our knowledge use of this technique has been under-explored in this application domain. Furthermore, this study provides important contribution to the understanding of Mediterranean river delta dynamics and their behaviours, and corroborates the usefulness of EO technology and GIS as an effective tool in policy decision making and successful landscape management. The latter is of considerable scientific and practical value to the wider community of interested users, given the continued open access to observations from this satellite radiometer globally. <s> BIB013
Wetlands have been threatened globally by human activities or climate change. These changes are reflected in wetland area shrinkage or vegetation cover or land cover changes. Understanding these changes could help to evaluate wetland ecosystems and provide information for environmental protection departments. Long-term change detection will enable researchers to better understand the trends and sudden changes of wetlands, analyze the dynamics, and protect wetlands. Mapping wetland areas, classifying the vegetation, and change detection are the most common research themes using Landsat data on a regional scale, not only because of the advantages in terms of spatial and temporal resolution but also because of the free data. Johnston and Barson BIB001 argued that the mid-infrared band of Landsat TM was useful for mapping wetland locations or ranges. Wetlands mapping or classification were mostly performed with the following aspects: Contribution of Landsat data to wetland vegetation classification. Florenzano  used different Landsat TM and SPOT HRV data to map the geomorphology of a section of Taquari River in Brazil and concluded that TM can be used to create geomorphologic maps with a 1:100,000 scale. The author also emphasized that the precise relief units defined by TM and field survey data are quite important in mapping the geomorphology. Son et al. BIB010 used Landsat data from 1979 to 2013 to monitor the mangrove forest changes of Ca Mau Peninsula in South Vietnam. They found that the mangrove forest decreased by 16% over the past 34 years in this region and suggested that advanced management strategies should be proposed to protect mangrove ecosystem. Laba et al. BIB002 used MSS and TM data and assessed the dynamics of land cover changes in Yuna River watershed of the Dominican Republic to aid environmental planning and management. Gao  argued that Landsat TM (with high spectral resolution) obtained higher accuracies than SPOT High Resolution Visible (HRV) (with higher spatial resolution) when mapping mangrove because the higher spatial resolution increased the confusion of mangrove and non-mangrove. Multitemporal Landsat data can also be used to evaluate the magnitude of mangrove forest decline when scientific records are absent. Kovacs et al. BIB003 concluded that the mangrove forest of Teacapán-Agua Brava lagoon system in Mexico degraded notably, which coincides with reports of local elderly fishermen. Berlangarobles and Ruizluna BIB004 used six land cover types (mangrove, lagoon, salt marsh, dry  Wetlands have been threatened globally by human activities or climate change. These changes are reflected in wetland area shrinkage or vegetation cover or land cover changes. Understanding these changes could help to evaluate wetland ecosystems and provide information for environmental protection departments. Long-term change detection will enable researchers to better understand the trends and sudden changes of wetlands, analyze the dynamics, and protect wetlands. Mapping wetland areas, classifying the vegetation, and change detection are the most common research themes using Landsat data on a regional scale, not only because of the advantages in terms of spatial and temporal resolution but also because of the free data. Johnston and Barson BIB001 argued that the mid-infrared band of Landsat TM was useful for mapping wetland locations or ranges. Wetlands mapping or classification were mostly performed with the following aspects: Contribution of Landsat data to wetland vegetation classification. Florenzano  used different Landsat TM and SPOT HRV data to map the geomorphology of a section of Taquari River in Brazil and concluded that TM can be used to create geomorphologic maps with a 1:100,000 scale. The author also emphasized that the precise relief units defined by TM and field survey data are quite important in mapping the geomorphology. Son et al. BIB010 used Landsat data from 1979 to 2013 to monitor the mangrove forest changes of Ca Mau Peninsula in South Vietnam. They found that the mangrove forest decreased by 16% over the past 34 years in this region and suggested that advanced management strategies should be proposed to protect mangrove ecosystem. Laba et al. BIB002 used MSS and TM data and assessed the dynamics of land cover changes in Yuna River watershed of the Dominican Republic to aid environmental planning and management. Gao  argued that Landsat TM (with high spectral resolution) obtained higher accuracies than SPOT High Resolution Visible (HRV) (with higher spatial resolution) when mapping mangrove because the higher spatial resolution increased the confusion of mangrove and non-mangrove. Multitemporal Landsat data can also be used to evaluate the magnitude of mangrove forest decline when scientific records are absent. Kovacs et al. BIB003 concluded that the mangrove forest of Teacapán-Agua Brava lagoon system in Mexico degraded notably, which coincides with reports of local elderly fishermen. Berlangarobles and Ruizluna BIB004 used six land cover types (mangrove, lagoon, salt marsh, dry forest, secondary succession, and agriculture) from Landsat data as direct indicators to detect landscape changes in the Majahual coastal system of Mexico. They found that farming activities were the main reason for landscape change. Ibharim et al. BIB011 argued that erosion, tree harvest, and farming activities are the main threats related to mangrove degradation. Jia et al. BIB007 and Long et al. BIB008 used Landsat data to map the distribution of mangrove forests in China and Philippines, respectively, advance the management, and protect mangroves. Qin et al.  used Landsat ETM+ and OLI data to map paddy rice planting area and obtained precise results with 97.3% accuracy. Rapinel et al. BIB012 found that Landsat 8 OLI images can be used to map plant communities in coastal marshlands with higher accuracy. Development of a new method to map and monitor wetlands. Johnston and Barson BIB001 compared different classification methods and found that density slicing of the mid-infrared band of Landsat TM is a suitable method for mapping the location and range of wetlands. Jensen et al.  used an image with adequate field survey information as standard data; images from other years were normalized to the radiometric characteristics of the standard image to monitor the aquatic macrophyte change within Florida Everglades Water Conservation Area. Petropoulos et al. BIB013 proposed a semi-automatic classification method based on support vector machines (SVMs) to quantify the deposition and erosion in two Mediterranean wetland deltas using Landsat TM data. NDVI and Land Surface Water Index (LSWI) derived from Landsat images were used by Dong et al.  to map wetland areas and provided satisfactory results. Borro et al. BIB009 proposed a new method for mapping shallow lakes using NDVI derived from Landsat data. After comparing their results with other studies, they found that the new method can increase the accuracy of lake boundary and also considers the dynamics of shallow lakes. Jia et al. BIB007 mapped mangrove areas in China using an object-oriented classification method applied to Landsat data and obtained accurate results. Ausseil et al. BIB006 presented a rapid method for wetland mapping on an extended scale using Landsat ETM+ images. It just took one and a half months to map and rank the wetland of Manawatu-Wanganui region in New Zealand. Based on Landsat ETM+ images, Chiu and Couloigner BIB005 proposed a modified Fuzzy C-Means (FCM) classifier to identify potential wetland areas and obtained better results than the standard FCM classifier.