Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing data | ||
| پژوهش های جغرافیای طبیعی | ||
| Volume 53, Issue 3, December 2021, Pages 365-380 PDF (833.19 K) | ||
| Document Type: Full length article | ||
| DOI: 10.22059/jphgr.2021.322649.1007610 | ||
| Authors | ||
| Mohammad Hossein Hajarian; Sara Atarchi* ; Saeid Hamzeh | ||
| Department of Remote sensing and GIS, Faculty of Geography, University of Tehran | ||
| Abstract | ||
| Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing images Abstract The aim of this study is to monitor the seasonal changes of Meighan wetland located in Markazi province in Iran. This is a multi-sensor approach; Sentinel-1 and Landsat 8 images were captured from May 2019 to January 2020. Modified Normalized Difference Water Index (MNDWI) and Land surface temperature were computed based on spectral bands of Landsat 8. Backscattering values in VH and VV polarimetric bands of Sentinel 1 images were also considered. Different wetland land cover classes were extracted based on these three measures. The results of each season were further compared with the classification output with support vector machines. The wetland main water body reaches its maximum extent in May 2019 (61.18 square kilometers) and its minimum extent is reported in August 2019 with an extent of 19.25 square kilometers. The outputs of the support vector machine classification were more compatible with MNDWI index. The results of this study show that the multi-sensor approach can efficiently be used in monitoring seasonal changes of wetland. Introduction Wetlands are one of the natural ecosystems that play an important role in plant and animal diversity conservation. Wetlands are very sensitive to environmental changes because they are located in an intermediate zone between land and marine ecosystems. Their constant monitoring is of great importance especially in wetlands with seasonal changes pattern. The Wetland ecosystems are influenced by anthropogenic and natural factors. Drought, reduced rainfall, unsustainable management of water resources, overexploitation, and dam construction threaten wetlands. Field surveying and mapping of natural resources are generally not cost-effective because these methods are expensive and time-consuming. Also, it is not possible to repeat it periodically with a constant interval. Therefore, the use of remote sensing data such as optics and radar data is necessary in the study of natural resources. However, natural landscapes are complex and composed of various land cover types. Optical multispectral images are not always able to classify such a landscape, perfectly. This source of data is also affected by atmospheric conditions; the presence of clouds or fog block capturing these images. SAR sensors unlike optics sensors are capable of capturing images in all weather conditions. In fact, the use of each satellite image has advantages and disadvantages and in many applications they complement each other. Multi-sensor approaches beneficiate from the capabilities of different satellite images. Researches have shown that a multi-sensor approach in natural resources studies, especially wetlands is of great value. The multi-source approach and the seasonal variations discussed in this study have not been followed in any research on Meighan wetland. The benefits of Sentinel-1 characteristics; such as suitable spatial and radiometric resolutions and free access highlight the finding of this research. Materials and methods Meighan wetland is located in the center of Iran in Markazi province. This wetland has ecological and economical importance in the region. In the last two decades, one road is constructed on it and divided it into two parts; this changes the wetland into a calm environment and subsequently the evaporation has been increased. In this study, the seasonal changes of Meighan wetland were investigated using Landsat 8 and Sentinel-1 images. The images in each season were selected in such a way that the minimum possible difference exist between their acquisition date. The preprocessing steps were done independently on each optic and SAR image. Sentinel-1 SAR images have been calibrated and the digital numbers were converted into the corresponding backscattering values (in decibel) in each polarimetric band. Although, from spectral reflectance values in different Landsat bands, Modified Normalized Difference Water Index (MNDWI) were calculated in each season. Land surface temperatures were also calculated from thermal bands. Five different land cover classes are observed in the wetland and its surroundings; main water body of the wetland, shallow water zone, saline soil, surrounding area and remaining land covers (known as others). These areas were also extracted based on MNDWI index, land surface temperature (LST) and backscattering values in VH and VV sentinel-1 polarimetric bands. Then, the whole area is classified by the support vector machine classifier. In the last step, the extracted regions from different methods were compared with the land cover classification results in each season. The differences and similarities of the extracted areas were discussed further. Results and discussion The findings of this study show that the main wetland body reaches its maximum extent in May 2019 based on the SVM classification results. In this month, MNDWI index-based results were closer to the one obtained with the support vector machine classification. The support vector machine classification results and MNDWI index achieved similar results in the delineation of the wetland water zone, the shallow water zone and saline soil. In August 2019, the wetland water area was reduced based on the support vector machine classification. In May 2019 and January 2020, when the wetland water area was larger in comparison to other months, the results of the MNDWI index are close to the results of the support vector machine classification. The extracted area of shallow water class and saline soil class show the highest difference between classification results and MNDWI results. The same results have been obtained by comparison of extracted area based on the backscattering values of VH and VV polarimetric bands and MNDWI index; the maximum differences are observed in shallow water and saline soil classes. This could be related to the sensitivity of SAR backscattering values to moisture content. Over the year, the moisture content varies in response to temperature, rainfall, and evapotranspiration. The changes in moisture content affect the dielectric constant of the material. The dielectric constant governs the magnitude of backscattering values. The moisture changes cause variation in SAR backscattering values over the year. Conclusion Long-term wetland change detection is frequently studied with optical remote sensing images. Although, wetlands show the seasonal pattern in response to temperature and rainfall changes over the year, however, wetland seasonal variations are not fully explored. In this study, Sentinel 1 and Landsat8 images covering the study area were captured over the year. The results of the present study showed that the seasonal variation of wetland can be monitored based on a multi-sensor approach. In May 2019, the Meighan main water body reached the highest extent and the smallest area was observed in August 2019. In addition, in January 2020, the wetland water area increased again. Also some differences are observed between the extracted areas based on the MNDWI index, VH and VV polarizations, and the support vector machine classification results in different seasons. These differences are observed more in the spring. The performance of MNDWI index in wetland water area extraction in most seasons is very close to the classification results of the support vector machine. This shows the high capabilities of MNDWI spectral index in monitoring wetlands. In addition, the main water body of the wetland can be well separated by backscattering values of VH and VV Sentinel 1 polarimetric bands. Keywords Land surface temperature, Remote Sensing, Spectral index, Synthetic Aperture Radar images, Wetland | ||
| Keywords | ||
| Land surface temperature; Remote Sensing; Spectral index; Synthetic Aperture Radar images; Wetland | ||
| References | ||
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