scholarly journals SHARPENING OF THERMAL SATELLITE IMAGERY FROM KLANG INDUSTRIAL AREA IN PENINSULAR MALAYSIA USING THE TSHARP APPROACH

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 87-102
Author(s):  
M. Z. Dahiru ◽  
M. Hashim ◽  
N. Hassan
Keyword(s):  
Industrial Area ◽  
Peninsular Malaysia ◽  
Landsat 8 ◽  
Large Area ◽  
Related Factors ◽  
Strong Negative Correlation ◽  
Area Index ◽  
Thermal Images ◽  
Modis Data ◽  
Long Term Study

Abstract. Measuring high spatial/temporal industrial heat emission (IHE) is an important step in industrial climate studies. The availability of MODIS data products provides up endless possibilities for both large-area and long-term study. nevertheless, inadequate for monitoring industrial areas. Thus, Thermal sharpening is a common method for obtaining thermal images with higher spatial resolution regularly. In this study, the efficiency of the TsHARP technique for improving the low resolution of the MODIS data product was investigated using Landsat-8 TIR images over the Klang Industrial area in Peninsular Malaysia (PM). When compared to UAV TIR fine thermal images, sharpening resulted in mean absolute differences of about 25 °C, with discrepancies increasing as the difference between the ambient and target resolutions increased. To estimate IHE, the related factors (normalized) industrial area index as NDBI, NDSI, and NDVI were examined. The results indicate that IHE has a substantial positive correlation with NDBI and NDSI (R2 = 0.88 and 0.95, respectively), but IHE and NDVI have a strong negative correlation (R2 = 0.87). The results showed that MODIS LST at 1000 m resolution can be improved to 100 m with a significant correlation R2 = 0.84 and RMSE of 2.38 °C using Landsat 8 TIR images at 30 m, and MODIS LST at 1000 m resolution can still be improved to 100 m with significant correlation R2 = 0.89 and RMSE of 2.06 °C using aggregated Landsat-8 TIR at 100 m resolution. Similarly, Landsat-8 TIR at 100 m resolution was still improved to 30 m and used with aggregate UAV TIR at 5 m resolution with a significant correlation R2 = 0.92 and RMSE of 1.38 °C. Variation has been proven to have a significant impact on the accuracy of the model used. This result is consistent with earlier studies that utilized NDBI as a downscaling factor in addition to NDVI and other spectral indices and achieved lower RMSE than techniques that simply used NDVI. As a result, it is suggested that the derived IHE map is suitable for analyzing industrial thermal environments at 1:10,000 50,000 scales, and may therefore be used to assess the environmental effect.

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

scholarly journals Estimation of turbulent heat flux over leads using satellite thermal images

2019 ◽  
Vol 13 (6) ◽  
pp. 1565-1582 ◽  
Author(s):  
Meng Qu ◽  
Xiaoping Pang ◽  
Xi Zhao ◽  
Jinlun Zhang ◽  
Qing Ji ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Surface Temperature ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Landsat 8 ◽  
Turbulent Heat ◽  
Thermal Images ◽  
Modis Data ◽  
Ice Leads

Abstract. Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as prime windows for heat exchange between the atmosphere and the ocean, especially in the winter. Lead geometry and distribution in the Arctic have been studied using optical and microwave remote sensing data, but turbulent heat flux over leads has only been measured on-site during a few special expeditions. In this study, we derive turbulent heat flux through leads at different scales using a combination of surface temperature and lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. First, ice surface temperature (IST) was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images using a split-window algorithm; then, lead pixels were segmented from colder ice. Heat flux over leads was estimated using two empirical models: bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that even though the lead area from MODIS is a little larger, the length of leads is underestimated by 72.9 % in MODIS data compared to TIRS data due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 56.70 % larger than that from MODIS data. When the fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 32.34 % higher than that from bulk formulae. In both cases, small leads accounted for more than a quarter of total heat flux over leads, mainly due to the large area, though the heat flux estimated using the fetch-limited model is 41.39 % larger. A greater contribution from small leads can be expected with larger air–ocean temperature differences and stronger winds.

scholarly journals Estimation of turbulent heat flux over leads using satellite thermal images

2019 ◽  
Author(s):  
Meng Qu ◽  
Xiaoping Pang ◽  
Xi Zhao ◽  
Jinlun Zhang ◽  
Qing Ji ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Microwave Remote Sensing ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Landsat 8 ◽  
Turbulent Heat ◽  
Thermal Images ◽  
Modis Data ◽  
Ice Leads

Abstract. Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as the prime window for heat exchange between the atmosphere and the ocean, especially in winter seasons. Lead geometry and distribution in the Arctic have been studied in previous studies using optical or microwave remote sensing data. But turbulent heat flux over lead area has only been measured on site during a few special expeditions. In this study, we derive turbulent 10 heat flux through leads at different scale using a combination of lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. Firstly, ice surface temperature was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and MODIS thermal images using split-window algorithm at 30 m and 1 km scales, respectively, then lead pixels are segmented from colder ice. Heat flux over lead area is calculated using two empirical models, including bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that, even though lead area 15 from MODIS is generally a little higher, the length of leads is underestimated by 72.9 % in MODIS data compared to that from TIRS due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 42.33 % larger than that from MODIS data. When fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 31.87 % higher than that from bulk formulae. In both cases, small leads account for more than a quarter of total heat flux over lead, mainly due to its large area, though the heat flux estimated using fetch-limited model is 42.26 % larger. More contribution 20 from small leads can be expected at larger air-ocean temperature difference and stronger winds.

scholarly journals An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2730
Author(s):  
Animesh Chandra Das ◽  
Ryozo Noguchi ◽  
Tofael Ahamed
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
Land Surface ◽  
Climatic Factors ◽  
Standardized Precipitation Index ◽  
Landsat 8 ◽  
Drought Frequency ◽  
Moisture Index ◽  
Area Index ◽  
Drought Period

Drought is one of the detrimental climatic factors that affects the productivity and quality of tea by limiting the growth and development of the plants. The aim of this research was to determine drought stress in tea estates using a remote sensing technique with the standardized precipitation index (SPI). Landsat 8 OLI/TIRS images were processed to measure the land surface temperature (LST) and soil moisture index (SMI). Maps for the normalized difference moisture index (NDMI), normalized difference vegetation index (NDVI), and leaf area index (LAI), as well as yield maps, were developed from Sentinel-2 satellite images. The drought frequency was calculated from the classification of droughts utilizing the SPI. The results of this study show that the drought frequency for the Sylhet station was 38.46% for near-normal, 35.90% for normal, and 25.64% for moderately dry months. In contrast, the Sreemangal station demonstrated frequencies of 28.21%, 41.02%, and 30.77% for near-normal, normal, and moderately dry months, respectively. The correlation coefficients between the SMI and NDMI were 0.84, 0.77, and 0.79 for the drought periods of 2018–2019, 2019–2020 and 2020–2021, respectively, indicating a strong relationship between soil and plant canopy moisture. The results of yield prediction with respect to drought stress in tea estates demonstrate that 61%, 60%, and 60% of estates in the study area had lower yields than the actual yield during the drought period, which accounted for 7.72%, 11.92%, and 12.52% yield losses in 2018, 2019, and 2020, respectively. This research suggests that satellite remote sensing with the SPI could be a valuable tool for land use planners, policy makers, and scientists to measure drought stress in tea estates.

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

scholarly journals Rapid large-area mapping of ice flow using Landsat 8

2016 ◽  
Vol 185 ◽  
pp. 84-94 ◽  
Author(s):  
Mark Fahnestock ◽  
Ted Scambos ◽  
Twila Moon ◽  
Alex Gardner ◽  
Terry Haran ◽  
...  
Keyword(s):  
Landsat 8 ◽  
Large Area ◽  
Ice Flow

Comparative analysis of three NDVI resolutions in different urban typologies. The case of Granollers in the Metropolitan Area of Barcelona.

2021 ◽  
Author(s):  
Alan García-Haro ◽  
Josep Roca
Keyword(s):  
Climate Change ◽  
Metropolitan Area ◽  
Urban Studies ◽  
Rural Areas ◽  
Industrial Area ◽  
Landsat 8 ◽  
Urban Context ◽  
Urban Core ◽  
First Results ◽  
Definition Of

&lt;p&gt;In recent years, the use of remote sensed NDVI has become recurrent in urban studies regarding the adaptation of cities to climate change. However, due to the physical diversity within cities and the different resolution offered by the sensors, the territorial interpretation of what the NDVI values really mean becomes difficult. Where the larger the size of the cells of the image, the greater the number of elements of the built environment within it, and the more complex the interpretation becomes.&lt;/p&gt;&lt;p&gt;In this work, the relationship between the NDVI of three sensors with different cell resolution for the same location and date is studied. In particular, the city of Granollers in the Metropolitan Area of Barcelona is analyzed. First, the NDVI images were obtained from Landsat-8 with 30m resolution, Sentinel-2 with 10m and from the Ministry of Agriculture, Livestock, Fisheries and Food of Catalonia (DARP) with 0.125m resolution. Then, the comparison was performed with a sample of five different typologies of the territory: dense urban core, suburban, industrial, area of highway and rural.&lt;/p&gt;&lt;p&gt;As first results, a supervised classification of the DARP image allowed the definition of 0.30 as the precise minimum value of NDVI that indicates the actual presence of vegetation. On the other hand, the comparison indicates that, in the urban context, the larger the cell size, the presence of vegetation quality is overestimated, where the higher percentage of cells is concentrated in higher NDVI values than in those with lower resolution. However, this behavior is not appreciated in rural areas, where higher percentages of cells of different resolutions were concentrated in the same NDVI ranges.&lt;/p&gt;&lt;p&gt;In such a way, it is corroborated that it is in the urban context where this indicator has a greater difficulty of territorial interpretation. Statements that are analyzed in greater depth in this study, where its implications in the use of NDVI in urban studies for the adaptation of cities to climate change are discussed.&lt;/p&gt;

scholarly journals Comparison of LAI Estimates from High Resolution Satellite Observations Using Different Biophysical Processors

2021 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Earth Observation ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Biophysical Parameters ◽  
Area Index ◽  
Satellite Sensors

Earth observation provides timely and spatially explicit information about crop phenology and vegetation dynamics that can support decision making and sustainable agricultural land management. Vegetation spectral indices calculated from optical multispectral satellite sensors have been largely used to monitor vegetation status. In addition, techniques to retrieve biophysical parameters from satellite acquisitions, such as the Leaf Area Index (LAI), have allowed to assimilate Earth observation time series in numerical modeling for the analysis of several land surface processes related to agroecosystem dynamics. More recently, biophysical processors used to estimate biophysical parameters from satellite acquisitions have been calibrated for retrieval from sensors with different high spatial resolution and spectral characteristics. Virtual constellations of satellite sensors allow the generation of denser LAI time series, contributing to improve vegetation phenology estimation accuracy and, consequently, enhancing agroecosystems monitoring capacity. This research study compares LAI estimates over croplands using different biophysical processors from Sentinel-2 MSI and Landsat-8 OLI satellite sensors. The results are used to demonstrate the capacity of virtual satellite constellation to strengthen LAI time series to derive important cropland use information over large areas.

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