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 345–1045 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 POTENTIAL OF RESAMPLED MULTISPECTRAL DATA FOR DETECTING DESMODIUM-BRACHIARIA INTERCROPPED WITH MAIZE IN A ‘PUSH-PULL’ SYSTEM

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1017-1022 ◽  
Author(s):  
B. T. Mudereri ◽  
E. M. Abdel-Rahman ◽  
T. Dube ◽  
T. Landmann ◽  
S. Niassy ◽  
...  
Keyword(s):  
Crop Yields ◽  
Spectral Response ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Maize Stover ◽  
Pull System ◽  
Multispectral Sensors ◽  
Sentinel 2

Abstract. Poor crop yields remain one of the main causes of chronic food insecurity in Africa. This is largely caused by insect pests, weeds, unfavourable climatic conditions and degraded soils. Weed and pest control, based on the climate-adapted ‘push-pull’ system, has become an important target for sustainable intensification of food production adopted by many small-holder farmers. However, essential baseline information using remotely sensed data is missing, specifically for the ‘push-pull’ companion crops. In this study, we investigated the spectral uniqueness of two of the most commonly used ‘companion’ crops (i.e. greenleaf Desmodium (Desmodium intortum) and Brachiaria (Brachiaria cv Mulato) with co-occurring soil, green maize, and maize stover. We used FieldSpec® Handheld 2™ analytical spectral device to collect in situ hyperspectral data in the visible and near-infrared region of the electromagnetic spectrum. Random forest was then used to discriminate among the different companion crops, green maize, maize stover and the background soil. Experimental ‘push-pull’ plots at the International Centre of Insect Physiology and Ecology (icipe) in Kenya were used as test sites. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of four multispectral sensors (i.e. Landsat-8, Quickbird, Sentinel-2, and WorldView-2) using spectral response functions. The performance of the four sensors to detect the ‘push-pull’ companion crops, maize and soil was compared. We were able to positively discriminate the two companion crops from the three potential background endmembers i.e. soil, green maize, and maize stover. Sentinel-2 and WorldView-2 outperformed (> 98% overall accuracy) Landsat-8 and Quickbird (96% overall accuracy), because of their added advantage of the strategically located red-edge bands. Our results demonstrated the unique potential of the relatively new multispectral sensors’ and machine learning algorithms as a tool to accurately discern companion crops from co-occurring maize in ‘push-pull’ plots.

scholarly journals The impact of sensors for satellite derived bathymetry within the Canadian Arctic

GEOMATICA ◽  
2020 ◽  
Vol 74 (2) ◽  
pp. 46-64
Author(s):  
Ryan Ahola ◽  
René Chénier ◽  
Mesha Sagram ◽  
Bradley Horner
Keyword(s):  
Optical Sensors ◽  
Landsat 8 ◽  
Sea Floor ◽  
Arctic Regions ◽  
Canadian Context ◽  
Multiple Band ◽  
Survey Accuracy ◽  
The Impact ◽  
Sentinel 2

Canada’s coastline presents challenges for charting. Within Arctic regions, in situ surveying presents risks to surveyors, is time consuming and costly. To better meet its mandate, the Canadian Hydrographic Service (CHS) has been investigating the potential of remote sensing to complement traditional charting techniques. Much of this work has focused on evaluating the effectiveness of empirical satellite derived bathymetry (SDB) techniques within the Canadian context. With greater knowledge of applying SDB techniques within Canadian waters, CHS is now interested in understanding how characteristics of optical sensors can impact SDB results. For example, how does the availability of different optical bands improve or hinder SDB estimates? What is the impact of spatial resolution on SDB accuracy? Do commercial satellites offer advantages over freely available data? Through application of a multiple band modelling technique to WorldView-2, Pléiades, PlanetScope, SPOT, Sentinel-2, and Landsat-8 imagery obtained over Cambridge Bay, Nunavut, this paper provides insight into these questions via comparisons with in situ survey data. Result highlights in the context of these questions include the following: Similarities between sensors: Overall linear error at 90% (LE90) results for each sensor ranged from 0.88 to 1.91 m relative to in situ depths, indicating consistency in the accuracy of SDB estimates from the examined satellites. Most estimates achieved Category of Zone of Confidence level C accuracy, the suggested minimum survey accuracy level for incorporating SDB information into navigational charts. SDB coverage: Between sensors, differences in the area of the sea floor that could be measured by SDB were apparent, as were differences in the ability of each sensor to properly represent spatial bathymetry characteristics. Sensor importance: Though relationships between SDB accuracy and sensor resolution were found, significant advantages or disadvantages for particular sensors were not identified, suggesting that other factors may play a more important role for SDB image selection (e.g., sea floor visibility, sediments, waves). Findings from this work will help inform SBD planning activities for hydrographic offices and SDB researchers alike.

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 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 Monitoring of the landslide area state on Bureya river in 2018-2019 according to radar and optical satellite images

2019 ◽  
pp. 26-35
Author(s):  
V. G. Bondur ◽  
L. N. Zakharova ◽  
A. I. Zakharov
Keyword(s):  
Optical Sensors ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite System ◽  
Small Scale ◽  
Landslide Area ◽  
Interferometric Technique ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Synthetic Aperture Radars

The monitoring results of the current state of landslide area on the Bureya River in 20182019 are given using images from synthetic aperture radars and optical sensors of Sentinel multi-satellite system. Differential radar interferometry technique allowed to reveal the stability of the landslide surface in the first four months after the landslide and since the end of July 2019. Small-scale dynamics of the surface within the landslide circus was detected. It is shown that the interferometric technique is inapplicable for the observation of the large-scale modifications of the shoreline unlike the optical images where the effects of the collapse of the shoreline fragments and shoreline flooding were clearly observed compared also with radar amplitude images. The ongoing landslide activity within the landslide circus and the coastline collapse area was detected using satellite images. It requires the establishment of continuous monitoring of this and other dangerous landslide zones on Bureya River.

scholarly journals Satellite Pre-Failure Detection and In Situ Monitoring of the Landslide of the Tunnel du Chambon, French Alps

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 313 ◽  
Author(s):  
Mathilde Desrues ◽  
Pascal Lacroix ◽  
Ombeline Brenguier
Keyword(s):  
Satellite Images ◽  
Failure Detection ◽  
Satellite Observation ◽  
In Situ Monitoring ◽  
Tertiary Creep ◽  
Strong Activity ◽  
French Alps ◽  
Optical Images ◽  
Optical Satellite Images

Recent studies using satellite data have shown a growing interest in detecting and anticipating landslide failures. However, their value for an actual landslide prediction has shown variable results. Therefore, the use of satellite images for that purpose still requires additional attention. Here, we study the landslide of the Tunnel du Chambon in the French Alps that ruptured in July 2015, generating major impacts on economic activity and infrastructures. To evaluate the contribution of very high-resolution optical satellite images to characterize and potentially anticipate the landslide failure, we conduct here a retro analysis of its evolution. Two time periods are analyzed: September 2012 to September 2014, and May to July 2015. We combine Pléiades optical images analysis and geodetic measurements from in situ topographic monitoring. Satellite images were correlated to detect pre-failure motions, showing 1.4-m of displacement between September 2012 and September 2014. In situ geodetic measures were used to analyze motions during the main activity of the landslide in June and July 2015. Topographic measurements highlight different areas of deformations and two periods of strong activity, related to the last stage of the tertiary creep and to anthropic massive purges of unstable masses. The law of acceleration toward the rupture observed in June and July 2015 over the topographic targets also fits well the satellite observation between 2012 and 2014, showing that the landslide probably already entered into tertiary creep 2.5 years before its failure.

scholarly journals ALGORITMA DUA DIMENSI UNTUK ESTIMASI MUATAN PADATAN TERSUSPENSI MENGGUNAKAN DATA SATELIT LANDSAT-8, STUDI KASUS: TELUK LAMPUNG

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Muchlisin Arief ◽  
Syifa Wismayati Adawiah ◽  
Maryani Hartuti ◽  
Ety Parwati
Keyword(s):  
Linear Equation ◽  
Optical Sensors ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Quality Parameters ◽  
Landsat 8 ◽  
The West ◽  
Remote Sensing Technique ◽  
Measurement Results

Remote sensing technique is a powerful tool for monitoring the coastal zone. Optical sensors can be used to measure water quality parameters Total Suspended Matter (MPT). In order to be able to extract information MPT, the satellite data need to be validated with in situ measurements that make the relationship between the reflectance band with concentration MPT measurement results. In this model, do the correlation between the measurement results with the reflectance values band 3 and band 4. then obtained a linear equation, then calculated using the argument of a ratio of 60:75 to each of the correlation coefficient, the obtained linear equation two Dimension T (X3, X4) = 2313.77 X3 + 4741.11 X4 + 314.95. Based on the concentration MPT of dated June 3, 2015 was lower than in the west to the east. this is because the east is already contaminated with the plant, effluent solids by humans, while the west for still many floating net fish, and mangrove. Based on the results of measurement and calculation results , is still far from perfect (accuracy 60%), one factor is the value thresholding, when determining the boundary between: clouds, sea, and land. Generally indicates that the model is still in need for repair. Abstrak Penginderaan jauh adalah alat yang ampuh untuk memantau zona pesisir. Sensor optik dapat digunakan untuk mengukur parameter kualitas air Total Suspended Solid/Muatan Padatan Tersuspensi (MPT). Agar supaya dapat mengekstraksi informasi MPT, maka, data satelit perlu divalidasi dengan pengukuran in situ yaitu membuat hubungan antara reflektansi band dengan konsentrasi MPT hasil pengukuran. Pada model ini, dilakukan korelasi antara hasil pengukuran dengan nilai reflektansi band3 dan band4, maka diperoleh persamaan linier, kemudian dihitung dengan menggunakan dalil perbandingan 60 : 75, untuk masing-masing koefisien korelasinya, maka diperoleh persamaan linier dua dimensi T(X3,X4) = 2313.77 X3 + 4741,11 X4 + 314.95.  Berdasarkan konsentrasi MPT  pada 3 Juni 2015 di sebelah baratlebih rendah dibandingkan sebelah timur. Hal ini dikarenakan sebelah timur sudah terkontaminasi dengan pabrik, buangan benda padat oleh manusia, sedangkan sebelah barat karena masih banyak keramba jaring apung ikan dan mangrove. Berdasarkan hasilnya antara pengukuran dan hasil perhitungan, masih jauh dari sempuna (ketelitiannya 60 %), salah satu faktornya adalah dalam menentukan nilai thresholding, pada saat  menentukan batas antara: awan, laut dan darat. Secara umum menunjukkan bahwa model yang masih membutuhkan perbaikan.

scholarly journals HYBRID CANONICAL CORRELATION ANALYSIS AND REGRESSION FOR RADIOMETRIC NORMALIZATION OF CROSS-SENSOR SATELLITE IMAGES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 181-183
Author(s):  
L. G. Denaro ◽  
C. H. Lin
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Satellite Images ◽  
Polynomial Regression ◽  
Experimental Result ◽  
Landsat 8 ◽  
Acquisition Condition ◽  
Landsat 7 ◽  
Optical Satellite Images

Abstract. Relative radiometric normalization (RRN) minimizes radiometric differences among images caused by inconsistencies of acquisition condition. In this study, a cross-sensor RRN method is proposed for optical satellite images from Landsat 8 OLI (L8) and Landsat 7 ETM+ (L7) sensors. The data from these two sensors have different pixel depths. Therefore, a rescaling on the radiometry resolution is performed in the preprocessing. Then, multivariate alteration detection (MAD) based on kernel canonical correlation analysis (KCCA) is adopted, which is called KCCA-based MAD, to select pseudo-invariant features (PIFs). The process of RRN is performed by using polynomial regression with Gaussian weighted regression. In experiments, qualitative and quantitative analyses on images from different sensors are conducted. The experimental result demonstrates the superiority of the proposed nonlinear transformation, in terms of regression quality and radiometric consistency, compared with RRN using linear regression.

Spectral reflectivity variations of Brassica napus depending on degree of soil moisture

2021 ◽  
Author(s):  
Yury Davidovich
Keyword(s):  
Optical Properties ◽  
Soil Moisture ◽  
Brassica Napus ◽  
Spectral Reflectance ◽  
Multispectral Image ◽  
Anthropogenic Factors ◽  
Landsat 8 ◽  
Reflectance Function ◽  
Spectrometric Measurements

<p>Studying of the optical properties of agricultural vegetation is one of the methods for plants condition estimation, prediction of their development and changes influenced by natural and anthropogenic factors.</p><p>The work is dedicated to the investigation of spectral reflectance function of agricultural <em>Brassica napus</em> taking into account the degree of soil moisture. When most of the agricultural lands in Belarus are covered with vegetation in summer, employing the optical properties of agricultural vegetation for deciphering the soil depends on the degree of soil moisture. Insufficient numbers of days in year when the soil is not covered by vegetation or is in a plowed state requires in-situ optical measurements, because there are more than 50 % cloudy conditions in the year, especially in spring and autumn time.</p><p>The study has been carried out near the Minsk 11.06.2020 (53.837004º N, 27.487597º E) in clear, cloudless day. The relief for investigated field is hilly-ridge, characterized by a predominance of elevation marks from 250 to 300 m and it is actively sown field. During the spectrometric measurements, the field has been sown with <em>Brassica napus</em> in the phenological phase of pod formation.</p><p>When studying the spectral reflectance of <em>Brassica napus</em>, in-situ spectrometric measurements and analysis of a multispectral image have been carried out. Spectrometric measurements have been carried out by FSR-02 spectrometer (spectral range 400-900 nm, spectral resolution 4.3 nm) aiming to retrieve spectral reflectance function.</p><p>The normalized vegetation index NDVI has been used for analyzing the multispectral image from Landsat 8 OLI system with a spatial resolution of 30 m. The results of a study of the correlation between the reflection coefficient of <em>Brassica napus</em><span> and the area of observed soils will be presented. In addition, the results of the analysis of quasi-synchronous values of the NDVI index and in-situ measurements of the spectral reflectance of <em>Brassica napus</em> will be discussed.</span></p>

scholarly journals Detection of Liquefaction Phenomena from the 2017 Pohang (Korea) Earthquake Using Remote Sensing Data

2019 ◽  
Vol 11 (18) ◽  
pp. 2184 ◽  
Author(s):  
Baik ◽  
Son ◽  
Kim
Keyword(s):  
Satellite Images ◽  
Remote Sensing Data ◽  
Short Wave ◽  
Landsat 8 ◽  
Content Increase ◽  
Infrared Band ◽  
Water Index ◽  
Before And After ◽  
Optical Satellite Images ◽  
Sentinel 2

On 15 November 2017, liquefaction phenomena were observed around the epicenter after a 5.4 magnitude earthquake occurred in Pohang in southeast Korea. In this study, we attempted to detect areas of sudden water content increase by using SAR (synthetic aperture radar) and optical satellite images. We analyzed coherence changes using Sentinel-1 SAR coseismic image pairs and analyzed NDWI (normalized difference water index) changes using Landsat 8 and Sentinel-2 optical satellite images from before and after the earthquake. Coherence analysis showed no liquefaction-induced surface changes. The NDWI time series analysis models using Landsat 8 and Sentinel-2 optical images confirmed liquefaction phenomena close to the epicenter but could not detect liquefaction phenomena far from the epicenter. We proposed and evaluated the TDLI (temporal difference liquefaction index), which uses only one SWIR (short-wave infrared) band at 2200 nm, which is sensitive to soil moisture content. The Sentinel-2 TDLI was most consistent with field observations where sand blow from liquefaction was confirmed. We found that Sentinel-2, with its relatively shorter revisit period compared to that of Landsat 8 (5 days vs. 16 days), was more effective for detecting traces of short-lived liquefaction phenomena on the surface. The Sentinel-2 TDLI could help facilitate rapid investigations and responses to liquefaction damage.

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