Geo-spatial Modeling for Automated Demarcation of Snow Avalanche Hazard Areas Using Landsat-8 Satellite Images and In Situ Data

2019 ◽  
Vol 47 (3) ◽  
pp. 513-526 ◽  
Author(s):  
Dhiraj Kumar Singh ◽  
Varunendra Dutta Mishra ◽  
Hemendra Singh Gusain ◽  
Neena Gupta ◽  
Arun Kumar Singh
Keyword(s):  
Spatial Modeling ◽  
Satellite Images ◽  
Snow Avalanche ◽  
Landsat 8 ◽  
Avalanche Hazard ◽  
In Situ Data

scholarly journals Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

2020 ◽  
Vol 12 (15) ◽  
pp. 2437 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Chlorophyll A ◽  
Satellite Images ◽  
Landsat 8 ◽  
Lake Chad ◽  
Chl A ◽  
Estimation Algorithms ◽  
In Situ Data ◽  
Fluorescence Line ◽  
Sentinel 2

Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such water bodies. We compared four established satellite reflectance algorithms—the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)—to estimate Chl a concentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chl a estimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chl a concentrations in Lake Chad. With an averaged R2 of 0.8, the 3BDA and NDCI Chl a algorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals Discrimination of Soil Types in Southern Kuwait Using Remote Sensing Classification Techniques on Landsat 8 ETM+ Satellite Images

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Waleed Khalid Al -Azemi
Keyword(s):  
Remote Sensing ◽  
Spatial Modeling ◽  
Satellite Images ◽  
Soil Classification ◽  
Oil Fields ◽  
Landsat 8 ◽  
Remote Sensing Classification ◽  
Desert Areas ◽  
Remote Sensing Techniques ◽  
And Performance

This study focuses on satellite data Landsat 8 ETM+ and application of remote sensing techniques to delineate various kinds of soils using digital image processing in the southern part of Kuwait. Southern part of Kuwait comprises desert plains, oil fields, sabkha and wafra farms. In this study, satellite imagery is used to achieve rapidly to the actual soil classification. Considering the inaccessibility of the desert areas, RS is essential to accommodate spatial variability and information. Spatial modeling involves the use of GIS for representation of the conceptual model and performance of simple mathematical computations on the stored GIS object attributes for displaying the results spatially.

scholarly journals Small scale spatial variability of bare-ice albedo at Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Full Range ◽  
Temporal Variations ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
In Situ Data ◽  
Depth Analysis ◽  
The Difference

Abstract. As Alpine glaciers recede, they are quickly becoming snow free in summer and, accordingly, spatial and temporal variations in ice albedo increasingly affect the melt regime. To accurately model future developments, such as deglaciation patterns, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, little in situ data of ice albedo exits. As a contribution to this knowledge gap, we present spectral reflectance data from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel 2 and Landsat 8 overpass and are compared to the respective ground reflectance products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 at dark spectra where liquid water and/or fine grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks, although transitions between types are fluid. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We wish to highlight the need for further, systematic measurements of in situ spectral albedo, its variability in time and space, and in- depth analysis of time-synchronous satellite data.

scholarly journals RETRIEVING COASTAL SEA SURFACE TEMPERATURE FROM LANDSAT-8 TIRS FOR WANGI-WANGI ISLAND, WAKATOBI, SOUTHEAST SULAWESI, INDONESIA

2019 ◽  
Vol 16 (1) ◽  
pp. 13
Author(s):  
Eko Susilo ◽  
Rizki Hanintyo ◽  
Adi Wijaya
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Zone ◽  
Single Band ◽  
Sea Surface ◽  
Superior Performance ◽  
Landsat 8 ◽  
Infrared Sensors ◽  
In Situ Data

The new Landsat generation, Landsat-8, is equipped with two bands of thermal infrared sensors (TIRS). The presence of two bands provides for improved determination of sea surface temperature (SST) compared to existing products. Due to its high spatial resolution, it is suitable for coastal zone monitoring. However, there are still significant challenges in converting radiance measurements to SST, resulting from the limitations of in-situ measurements. Several studies into developing SST algorithms in Indonesia waters have provided good performance. Unfortunately, however, they have used a single-band windows approach, and a split-windows approach has yet to be reported. In this study, we investigate both single-band and split-window algorithms for retrieving SST maps in the coastal zone of Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia. Landsat-8 imagery was acquired on February 26, 2016 (01: 51: 44.14UTC) at position path 111 and and row 64. On the same day, in-situ SST was measured by using Portable Multiparameter Water Quality Checker – 24. We used the coefficient of correlation (r) and root mean square error (RMSE) to determine the best algorithm performance by incorporating in-situ data and the estimated SST map. The results showed that there were differences in brightness temperature retrieved from TIRS band10 and band 11. The single-band algorithm based on band 10 for Poteran Island clearly showed superior performance (r = 69.28% and RMSE = 0.7690°C). This study shows that the split-window algorithm has not yet produced a accurate result for the study area.

scholarly journals ANALYSIS OF IN-SITU SPECTRAL REFLECTANCE OF SAGO AND OTHER PALMS: IMPLICATIONS FOR THEIR DETECTION IN OPTICAL SATELLITE IMAGES

2018 ◽  
Vol IV-3 ◽  
pp. 185-191
Author(s):  
Jojene Rendon Santillan ◽  
Meriam Makinano-Santillan
Keyword(s):  
Optical Sensors ◽  
Spectral Reflectance ◽  
Satellite Images ◽  
Spectral Response ◽  
Coconut Oil ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Palm Species ◽  
Optical Satellite Images

We present a characterization, comparison and analysis of in-situ spectral reflectance of Sago and other palms (coconut, oil palm and nipa) to ascertain on which part of the electromagnetic spectrum these palms are distinguishable from each other. The analysis also aims to reveal information that will assist in selecting which band to use when mapping Sago palms using the images acquired by these sensors. The datasets used in the analysis consisted of averaged spectral reflectance curves of each palm species measured within the 345&amp;ndash;1045&amp;thinsp;nm wavelength range using an Ocean Optics USB4000-VIS-NIR Miniature Fiber Optic Spectrometer. This in-situ reflectance data was also resampled to match the spectral response of the 4 bands of ALOS AVNIR-2, 3 bands of ASTER VNIR, 4 bands of Landsat 7 ETM+, 5 bands of Landsat 8, and 8 bands of Worldview-2 (WV2). Examination of the spectral reflectance curves showed that the near infra-red region, specifically at 770, 800 and 875 nm, provides the best wavelengths where Sago palms can be distinguished from other palms. The resampling of the in-situ reflectance spectra to match the spectral response of optical sensors made possible the analysis of the differences in reflectance values of Sago and other palms in different bands of the sensors. Overall, the knowledge learned from the analysis can be useful in the actual analysis of optical satellite images, specifically in determining which band to include or to exclude, or whether to use all bands of a sensor in discriminating and mapping Sago palms.

scholarly journals Remote Sensing Satellite Imagery and In-Situ Data for Identifying Geothermal Potential Sites: Jaboi, Indonesia

2020 ◽  
Vol 9 (2) ◽  
pp. 237-245
Author(s):  
Muhammad Isa ◽  
Dwiky Pobri Cesarian ◽  
Ismail Ahmad Abir ◽  
Elin Yusibani ◽  
Muhammad Syukri Surbakti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drainage Density ◽  
Ground Temperature ◽  
Landsat 8 ◽  
Large Area ◽  
In Situ Data ◽  
Geothermal Potential ◽  
Electrical Sounding

Remote sensing makes it possible to map potential geothermal site for a large area effectively using thermal infrared. The purpose of the present research is to overlay ground temperature, resistivity and satellite retrieved temperature in identifying geothermal potential site in Jaboi, Sabang-Indonesia. The data of acquisition of the DEM imagery was January 3rd, 2009 and the Landsat 8 imagery is July 18th, 2017. The satellite data were applied to extract the land surface temperature and land classification across. Two supporting data in situ were used to validate the results from remote sensing. First dataset was ground temperature measurements with total 114 points and second dataset was vertical electrical sounding (VES) with total of 51 points. Satellite, VES and ground temperature data were processed and analysed using the Envi 5.3, PCI Geomatica 2016 and ArcMap 10.4. The results from each data were integrated to produce a map shows geothermal potential. Its integration produced four areas which were considered to have high geothermal potential. However, these areas vary in term of the clustering of the features of interest, for example lineament and drainage density of the area, high temperature in the surface area, fault existence and low resistivity subsurface. All the features must take into consideration to rank potential area which has higher potential. Finally, a map of geothermal potential across were successfully created as an insight for future reference. ©2020. CBIORE-IJRED. All rights reserved

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