Mapping and Monitoring Wetland Dynamics Using Thermal, Optical, and SAR Remote Sensing Data

Wetlands are transition zone where the flow of water, the cycling of carbon and nutrients, and the energy to form a unique ecosystem are characterized by its hydrology, soils, and vegetation, between dryland and water. Over the years, remote sensing techniques have proven to be a successful tool for monitoring wetlands. Both optical and microwave earth observation sensors can be used for monitoring wetlands. Land surface temperature (LST), as one of the most important variables in physical processes of the Earth, is one of the unexplored parameters for studying wetland dynamics. In this chapter, seasonal LST, SAR data values (dual polarization VV + VH), as well as the seasonal normalized difference water index will be explored, and the relation between them will be analyzed. For this purpose, satellite images from Landsat 8 and Sentinel-1, over a wetland area, were downloaded, preprocessed, and analyzed. As a study case, Seyfe Lake located in the central Anatolian part of Turkey has been selected. The results show Seyfe Lake’s seasonal dynamics and the relation between the investigated parameters. The results helped in understanding the wetland seasonal dynamics which can be used in better managing and monitoring wetlands using remote sensing data

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

Spaceborne Nitrogen Dioxide Observations from the Sentinel-5P TROPOMI over Turkey

With rapid population growth, both urbanization and transportation affect air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the biggest air pollutants, nitrogen dioxide (NO2), is mainly caused by the combustion of fossil fuels, especially from traffic exhaust gases. Over the years, air pollution has been monitored using satellite remote sensing data. In this study, we investigate the relationship of the tropospheric NO2 retrieved from the recently launched Sentinel-5 Precursor, a low-earth-orbit atmosphere mission dedicated to monitoring air pollution equipped with the spectrometer Tropomoi (Tropospheric Monitoring Instrument), and the population density over Turkey. For this purpose, we use the mean value of the NO2 collected from July 2018 to January 2019 and the statistic population data from 2017. The results showed a significant correlation of higher than 0.72 between the population density and the maximum NO2 values. For future studies, we recommend investigating the correlation of different air pollutants with population and other factors contributing to air and environmental pollution

Monitoring the Water Quality of Small Water Bodies Using High-Resolution Remote Sensing Data

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas

Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data

As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time

Object-based water body extraction model using Sentinel-2 satellite imagery

Water body extraction is an important part of water resource management and has been the topic of a number of research works related to remote sensing for over two decades. Extracting water bodies from satellite images with a pixel-based method or indexes cannot eliminate other objects that have a low albedo, such as shadows and built-up areas. Since their spectral differences cannot be separated, in this paper a method that combines a pixel-based index and object-based method has been used on a Sentinel-2 satellite image with a resolution of 10 m. The method uses image segmentation on a multispectral image containing 13 bands. It also uses indexes used for extracting water bodies, such as the Normalized Difference Water Index (NDWI). Two study areas with different characteristics have been chosen, one mountainous and one urban region, both of them located in Macedonia. Using object-based techniques and pixel-based indexes, such as NDWI, the results from the NDWI have been improved by a kappa value of more than 0.5

Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, for the first case, the standard deviation was 2.4°C, and for the second case, it was 2.7°C. For future studies, the tool should be refined with in situ measurements of land surface temperature

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface’s processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration’s (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively

Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps from Landsat: A PyQGIS Plugin

This paper presents a Python QGIS (PyQGIS) plugin, which has been developed for the purpose of producing Land Surface Temperature (LST) maps from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS, Thermal Infrared (TIR) imagery. The plugin has been developed purposely to ease the process of LST extraction from Landsat Visible, Near Infrared (VNIR) and TIR imagery. It has the ability to estimate Land Surface Emissivity (LSE), calculating at-sensor radiance, calculating brightness temperature and performing correction of brightness temperature against atmospheric interference though the Plank function, Mono Window Algorithm (MWA), Single Channel Algorithm (SCA) and the Radiative Transfer Equation (RTE). Using the plugin, LST maps of Moncton, New Brunswick, Canada have been produced for Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 TIRS. The study put much more emphasis on the examination of LST derived from the different algorithms of LST extraction from VNIR and TIR satellite imagery. In this study, the best LST values derived from Landsat 5 TM were obtained from the RTE and the Planck function with RMSE of 2.64 °C and 1.58 °C, respectively. While the RTE and the Planck function produced the best results for Landsat 7 ETM+ with RMSE of 3.75 °C and 3.58 °C respectively and for Landsat 8 TIRS LST retrieval, the best results were obtained from the Planck function and the SCA with RMSE of 2.07 °C and 3.06 °C, respectively