Geographic Data
Shahin Jafari; Saeid Hamzeh; Hadi Abdolazimi; Sara Attarchi
Abstract
Extended AbstractIntroductionHuman activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential ...
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Extended AbstractIntroductionHuman activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential for taking measures and making decisions based on the goals of sustainable water and soil resources management. Over the past decade, many researchers around the world have been attracted to remote sensing and especially satellite remote sensing and used this technology to detect such changes over time. The present study has used Landsat (monitoring the area of water body), TRMM (monitoring rainfall), MODIS (monitoring vegetation and evapotranspiration), Grace (monitoring groundwater) satellite images available in Google Earth Engine to study last two decades changes (from 2000 to 2019) in Maharloo wetland, Goshnegan catchment and their surroundings. Materials & MethodsMaharloo wetland is located in Fars province and Goshnegan catchment (426 square kilometers). The present study has used Landsat 7 and 8 images to extract the area of water body, TRMM images to obtain precipitation values, MODIS products to calculate NDVI and evapotranspiration, and data received from Grace to extract changes in groundwater level. These satellite images were available in Google Earth Engine. Mann-Kendall test was also used to assess the overall trend of the aforementioned factors. Results & DiscussionThe automated water extraction index was used in the present study to identify and estimate the area covered by water bodies in the study area. The largest area belonged to 2006 (216.76 square kilometers) and the smallest belonged to 2018 (66 square kilometers). In 2000 (the beginning of the reference period), an area of 216.52 square kilometers was covered by this wetland which is close to what was observed in 2006. In 2018, this has reduced to 66 square kilometers. Thus, there is about 150.72 square kilometers (69.54 percent) difference between these two years. In 2009, the total area has reduced to 66.67 square kilometers. A numerical comparison between 2000 and 2019 also indicates a reduction of 91.17 square kilometers (42% decrease) in the total area covered by this wetland. Also, a 53.72 square kilometers (29.60%) difference was observed between the average area covered by the water body in the first and second ten years. Since calculated p-value value (< 0.00001) is less than the alpha level (0.05), so a significant trend was observed in the average annual data of the area covered by this wetland. Kendall's tau also indicated declining trend of the collected data. Groundwater level was calculated using data received from Grace Satellite to investigate the role of groundwater level in reducing the area covered by the water body. Results indicated that since 2008, groundwater level have always showed a negative value (a decreasing trend). For an instance, a groundwater level of -10.86 cm in 2019 indicates a decrease in the water level in the study area. As the calculated p-value (< 0.0001) is less than the alpha level (0.05), so a significant decreasing trend was observed in the groundwater level. Results of Mann-Kendall test (-0.6) also indicated that changes in water bodies, vegetation, rainfall and groundwater level had a decreasing, increasing, increasing and decreasing trend, respectively. No significant trend was observed in evapotranspiration. It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. ConclusionWetlands provide many ecological services including water treatment, natural hazard prevention, soil and water protection, and coastline management (Amani et al., 2019). Therefore, understanding the importance of wetlands and their management need to be seriously considered by relevant organizations in different countries of the world, and Iran is no exception. Satellite data and remote sensing methods and techniques are considered to be one of the most important and cost-effective methods of monitoring wetlands. The present study used satellite data collected by Landsat, MODIS, Grace, and TRMM to monitor water bodies, vegetation, groundwater level, and rainfall in Goshnegan catchment in which Maharloo wetland is located. The results of Mann-Kendall test showed a decreasing annual trend for changes in the average area of this wetland. This decreasing trend is considered to be a serious threat to human settlements around the wetland which can intensify over time. It will also affect the thermal islands of Shiraz and Sarvestan in near future. Obviously, management of agricultural and forest land uses with the aim of stopping their increasing trend can improve water balance in catchment areas. A 132.2 ha (approximately 36.16%) difference was observed between the average vegetation cover in this catchment area over the first and second ten years (233.4 vs. 365.6 ha). It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. Due to the proximity of this wetland to the city of Shiraz and its importance as an ecological and tourist attraction, it is suggested that related authorities (Department of Environment and Water Organization) demarcate lake bed and riparian zone with the help of remote sensing researchers to improve the management of this wetland and prevent it from drying up. Also, it is suggested that the Organization of Agriculture Jihad review and improve water consumption methods and cultivation patterns in the areas surrounding this wetland.
Sara Attarchi; Najmeh Poorakbar
Abstract
Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational ...
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Extended Abstract
Introduction
Free access to the Landsat dataset and Sentinel 2 images has provided a great opportunity for long-term monitoring of resources. Landsat 8 was launched in 2013 to continue the mission of the previous Earth observation satellites. Landsat 8multi-spectral sensor, Operational Land Imager (OLI), provides multi-spectral images with 30-meter resolution. Sentinel 2 was launched in 2015 with a multispectral sensor called MSI which captures images with different spatial resolutions (10m to 60m). The secret mission of Landsat satellites started in the 1970s and they have the longest archive of satellite images collected from the Earth. Sentinel 2 offers higher spatial, spectral and temporal resolutions and therefore it is important to compare the compatibility of Sentinel 2 and Landsat 8 images. OLI and MSI sensors both operate in the optical region, thus weather conditions can impose some limitations on their data acquisition. In such circumstances, data collected by a compatible and similar sensor can replace the cloud-covered images.
Generally, spectral features of new sensors are designed in such a way toconform to the corresponding bands of the previous sensors. The present study compares the corresponding bands of MSI and OLI sensors. The efficiency of both sensors in the classification of a heterogeneous and complex region has also been investigated.
Materials & Methods
Three near-simultaneous pairs of Landsat 8 and Sentinel-2 scenes were obtained to conduct a comparative study. Images were acquired in August 2017, November 2017, and July 2018.Minudasht - in northern Iran- was selected as the study area because of the presence of different land cover classes including rainfed agricultural lands, irrigated agricultural lands, forests, residential areas, and bare lands.Thescenes were processed for further analysis. First, the scenes were atmospherically corrected. In the next step, spatial resolution of MSI bands was resampled to 30 m, and each pair of mages were geometrically co-registered. To do so, 10 tie points were selected, and scenes were co-registered usingthe first-degree polynomial method. RMSE values were reported 2.5 m, 2.4 m, and 2.8 m for August 2017, November 2017, and July 2018, respectively. To investigate the similarities and differences of the sensors’ spectral content, the correlation between corresponding bands of the two sensors was estimated.
Then, images were classified using the support vector machine (SVM) algorithm. Five distinct land cover classes were found in the region including rainfed agricultural land, gardens and irrigated agricultural land, forests, residential areas, and bare lands. The training samples were selectedfromthe land use map and high-resolution Google Earth images. Approximately 300 training samples were selected for each land cover class. The accuracy of classification results was compared to verify the efficiency of two sensors in land cover mapping. Independent validation samples were selected for each class. Overall accuracy, commission error, and omission error were calculatedbased on the confusion matrices.
Results & Discussion
The reported correlation coefficientfor all corresponding bands was higher than 0.8. Results indicate a high level of similarity between the two sensors. Similar findings were reported by previous studies. Overall classification accuracy ofOLIimagescollected in August 2017, November 2017, and July 2018 was 91. 35 %, 89.60 %, and 93.12%, respectively. Overall classification accuracy ofMSI images collected inAugust 2017, November 2017, and July 2018 was 94.76 %, 95.55 %, and 94.07%, respectively. As it is obvious, Sentinel 2showed a higher performance in comparison to Landsat’s, because of its higher spatial resolution. A medium spatial resolution image collected from a complex landscape is often composed of mixed pixels, since different land cover types exist in one pixel. As the image’s spatial resolution improves, the dimensions of each pixeldecrease. Therefore, the number of mixed pixels will decrease and a higher classification accuracy will be expected.
Conclusion
Results confirm the similarity of two sensors in land cover classification. However, the findings could not be extended to other applications. MSI sensorslacka thermal bandand thus are not applicable when such a feature is needed (for an instance inthe retrieval of land surface temperature). In such applications, MSI cannot substitute OLI. For further studies, it is necessary to compare the performance of these sensors in different regions, since different land cover types may impactclassification results. Findings of the present study may raise attention to the differences between Landsat 8- OLI and Sentinel 2 MSI. Further studies can be conducted to investigate the differences between these two sensors. The possible similarities of othersimilar sensors can also be a topic for further investigations.