Detection of Small Wetlands with Multi Sensor Data in East Africa

Mwita, Emiliana; Menz, Gunter; Misana, Salome; Nienkemper, Pamela

doi:10.4236/ars.2012.13007

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…Clustering represents a common method to perform image segmentation under consideration of multi-spectral and multi-temporal data [63]. Cluster algorithms have already been used as segmentation methods for the derivation of TFV [64][65][66]. The grouping of pixels into segments was implemented for the study area using a k-means cluster algorithm.…”

Section: Clustering Approach For Segment Generationmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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Abstract:The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

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Section: Clustering Approach For Segment Generationmentioning

confidence: 99%

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

et al. 2018

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“…Both automated and semi-automated techniques were used to map the wetland [12], where Landsat TM bands 3, 4, and 5 were used for image enhancement [8]. Using July 2015 image data, unsupervised image clustering was conducted using the iterative self-organizing data analysis (ISODATA) algorithm [23]; after which, a number of natural clusters were generated following 100 iterations in a self-organizing way [18].…”

Section: Image Processing and Analysismentioning

confidence: 99%

“…It has been shown that satellite remote sensing has the potential to effectively assist in monitoring and inventorying wetlands due to repeated coverages, allowing both seasonal and annual assessments [8,10,11]. Optical remote sensing works effectively in the detection of wetlands [12] and has been used to map large wetland ecosystems, including marshes and swamps on Sango Bay in Uganda [13]. Satellite remote sensing has the capability to identify areas where changes are occurring and where more detailed information must be gathered.…”

Section: Introductionmentioning

confidence: 99%

Mapping and Monitoring the Akagera Wetland in Rwanda

et al. 2017

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Wetland maps are a prerequisite for wetland development planning, protection, and restoration. The present study aimed at mapping and monitoring Rwanda's Akagera Complex Wetland by means of remote sensing and geographic information systems (GIS). Landsat data, spanning from 1987 to 2015, were acquired from different sensor instruments, considering a 5-year interval during the dry season and the shuttle radar topographic mission (SRTM) digital elevation model (30-m resolution) was used to delineate the wetland. The mapping and delineation results showed that the wetland narrowly extends along the Rwanda-Tanzania border from north to south, following the course of Akagera River and the total area can be estimated at 100,229.76 ha. After waterbodies that occupy 30% of the wetland's surface area, hippo grass and Cyperus papyrus are also predominant, representing 29.8% and 29%, respectively. Floodplain and swamp forest have also been inventoried in smaller proportions. While the wetland extent has apparently remained stable, the inhabiting waterbodies have been subject to enormous instability due to invasive species. Lakes, such as Mihindi, Ihema, Hago and Kivumba have been shrinking in extent, while Lake Rwanyakizinga has experienced a certain degree of expansion. This study represents a consistent decision support tool for Akagera wetland management in Rwanda.

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“…Although wetland extraction has been moderately successful in some applications, automatic mapping remains a challenge, largely due to the ambiguous spectral characteristics and complex shapes, sizes, and forms of wetlands [10,47,48]. In order to assess the change characteristics and dynamics of wetlands in the Yellow River Delta over the past four decades, we proposed a per-parcel level classification approach based on the knowledge transfer classification and machine learning for automatic classification of wetlands.…”

Section: Study Areamentioning

confidence: 99%

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

Zhu

Zhang

Huang

2018

Water

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Yellow River Delta wetlands are essential for the migration of endangered birds and breeding. The wetlands, however, have been severely damaged during recent decades, partly due to the lack of wetland ecosystem protection by authorities. To have a better historical understanding of the spatio-temporal dynamics of the wetlands, this study aims to map and characterize patterns of the loss and degradation of wetlands in the Yellow River Delta using a time series of remotely sensed images (at nine points in time) based on object-based image analysis and knowledge transfer learning technology. Spatio-temporal analysis was conducted to document the long-term changes taking place in different wetlands over the four decades. The results showed that the Yellow River Delta wetlands have experienced significant changes between 1973 and 2013. The total area of wetlands has been reduced by 683.12 km 2 during the overall period and the trend of loss continues. However, the rates and trends of change for the different types of wetlands were not the same. The natural wetlands showed a statistically significant decrease in area during the overall period (36.04 km 2 ·year −1 ). Meanwhile, the artificial wetlands had the opposite trend and showed a statistically significant increase in area during the past four decades (18.96 km 2 ·year −1 ). According to the change characteristics revealed by the time series wetland classification maps, the evolution process of the Yellow River Delta wetlands could be divided into three stages: (1) From 1973From -1984, basically stable, but with little increase; (2) from 1984-1995, rapid loss; and (3) from 1995-2013, slow loss. The area of the wetlands reached a low point around 1995, and then with a little improvement, the regional wetlands entered a slow loss stage. It is believed that interference by human activities (e.g., urban construction, cropland creation, and oil exploitation) was the main reason for wetland degradation in the Yellow River Delta over the past four decades. Climate change also has long-term impacts on regional wetlands. In addition, due to the special geographical environment, the hydrological and sediment conditions and the location of the Yellow River mouth also have a significant influence on the evolution process of the wetlands.

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Detection of Small Wetlands with Multi Sensor Data in East Africa

Cited by 17 publications

References 23 publications

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Mapping and Monitoring the Akagera Wetland in Rwanda

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

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