Observing the tail of reionization: neutral islands in the z = 5.5 Lyman-α forest

ABSTRACT Previous studies have noted difficulties in modelling the highest opacities of the z > 5.5 Ly α forest, epitomized by the extreme Lyα trough observed towards quasar ULAS J0148 + 0600. One possibility is that the most opaque regions at these redshifts contain significant amounts of neutral hydrogen. This explanation, which abandons the common assumption that reionization ended before z = 6, also reconciles evidence from independent observations of a significantly neutral Universe at z = 7.5. Here, we explore a model in which the neutral fraction is still ${\approx }10{{\ \rm per\ cent}}$ at z = 5.5. We confirm that this model can account for the observed scatter in Ly α forest opacities, as well as the observed Ly β transmission in the J0148 trough. We contrast the model with a competing ‘earlier’ reionization scenario characterized by a short mean free path and large fluctuations in the post-reionization ionizing background. We consider Ly α and Ly β effective optical depths, their correlations, trough size distributions, dark pixel fractions, the IGM thermal history, and spatial distributions of Lyman-α emitters around forest sightlines. We find that the models are broadly similar in almost all of these statistics, suggesting that it may be difficult to distinguish between them definitively. We argue that improved constraints on the mean free path and the thermal history at z > 5 could go a long way towards diagnosing the origin of the z > 5.5 opacity fluctuations.

A Method of Restoring Fuzzy Remote Sensing Image Based on Dark Pixel Prior

Remote sensing image deblurring is a long-term and challenging inverse problem. Among them, the ability to find the correct image prior is the key to recovering high-quality and clear images. Therefore, in order to recover high-quality clear images, this paper has found a new and effective image prior: The dark pixel a priori in remote sensing images and a fuzzy remote sensing image restoration method based on dark pixel prior is proposed. Since the dark pixels in the clear remote sensing image will increase the pixel value of the dark pixels in the blurred remote sensing image due to the weighted balance with the bright pixels around it, the sparsity of the dark pixels in the blurred remote sensing image is reduced. Therefore, by using this nonsparse feature of dark pixels in fuzzy remote sensing images, fuzzy remote sensing images and clear remote sensing images can be effectively distinguished, thus realizing the restoration of fuzzy remote sensing images. The experimental results show that the proposed method has obvious effects on the restoration effect and time.

Model-independent constraints on the hydrogen-ionizing emissivity at z > 6

ABSTRACT Modelling reionization often requires significant assumptions about the properties of ionizing sources. Here, we infer the total output of hydrogen-ionizing photons (the ionizing emissivity, $\dot{N}_\textrm {ion}$) at z = 4–14 from current reionization constraints, being maximally agnostic to the properties of ionizing sources. We use a Bayesian analysis to fit for a non-parametric form of $\dot{N}_\textrm {ion}$, allowing us to flexibly explore the entire prior volume. We infer a declining $\dot{N}_\textrm {ion}$ with redshift at z > 6, which can be used as a benchmark for reionization models. Model-independent reionization constraints from the cosmic microwave background (CMB) optical depth and Ly α and Ly β forest dark pixel fraction produce $\dot{N}_\textrm {ion}$ evolution ($\mathrm{ d}\log _{10}\dot{\mathbf {N}}_{\bf ion}/\mathrm{ d}z|_{z=6\rightarrow 8} = -0.31\pm 0.35$ dex) consistent with the declining UV luminosity density of galaxies, assuming constant ionizing photon escape fraction and efficiency. Including measurements from Ly α damping of galaxies and quasars produces a more rapid decline: $\mathrm{ d}\log _{10}\dot{\mathbf {N}}_{\bf ion}/\mathrm{ d}z|_{z=6\rightarrow 8} =-0.44\pm 0.22$ dex, steeper than the declining galaxy luminosity density (if extrapolated beyond $M_\rm{\small UV}\gtrsim -13$), and constrains the mid-point of reionization to z = 6.93 ± 0.14.

Assessment of chlorophyll and water quality using remote sensing and GIS imagery in the Cauvery watershed of Karnataka, India

India has vast inland water resources having immense potential for aquaculture potential. Assessment of water quality parameters is a pre-requisite to any scientific intervention as they are of prime importance in fisheries perspective. However, monitoring water quality parameters of such vast area is not an easy task with the conventional tools and methods. In the present study, water quality parameters and chlorophyll pigment concentration were assessed using IRS P-6 remote sensing imagery in the Cauvery watershed of Karnataka State, India. Images captured by optical satellite sensors are often obscured by atmospheric effects. Hence, the images were rectified by Dark pixel subtraction method before analysing data in order to extract useful information from the imagery. The study revealed that there was significant correlation between spectral reflectance and in-situ water quality parameters. Near infra-red band (0.77-0.86 µm), was useful to assess the water quality parameters like depth, specific conductivity, total alkalinity, chlorinity, salinity and turbidity. Similarly, short wave infrared band (1.55-1.70 µm) was useful for assessing chlorophyll-a. However, the models were found to be region specific and they appear to have potential for monitoring water quality of large water bodies at regular intervals.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

METODE DUAL KANAL UNTUK ESTIMASI KEDALAMAN DI PERAIRAN DANGKAL MENGGUNAKAN DATA SPOT 6 STUDI KASUS : TELUK LAMPUNG (DUAL BAND METHOD FOR BATHYMETRY ESTIMATION IN SHALLOW WATERS DEPTH USING SPOT 6 DATA CASE STUDY: LAMPUNG BAY)

Depth data can be used to produce seabed profile, oceanography, biology, and sea level rise. Remote sensing technology can be used to estimate the depth of shallow marine waters characterized by the ability of light to penetrate water bodies. One image that can estimate the depth is SPOT 6 which has three visible canals and one NIR channel with 6 meter spatial resolution. This study used SPOT 6 image on March 22, 2015. The image was first being dark pixel atmospheric corrected by making 30 polygons. The originality of this method was to build a correlation between the dark pixel value of red and green channels with the depth of the field measurement results, made on June 3 to 9, 2015. The algorithm derived experimentally consisted of: thresholding which served to separate the land by the sea and the correlation function. The correlation function was obtained: first correlating the observation value with each band, then calculating the difference of minimum pixel darkness value and minimum for red and green channel was 0.056 and 0.0692. The model was then constructed by using the comparison proportions, so that the linear equations were obtained in two channels: Z (X1, X2) = 406.26 X1 + 327.21 X2 - 28.48. Depth estimation results were for a 5-meter scale, the most efficient estimation with the smallest error relative mean occured in shallow water depth from 20 to 25 meters, while the result of 10 meters scale from 20 to 30 meters and the estimated depth hadsimilar patterns or could be said close to reality. This method was able to detect sea depths up to 25 meters and had a small RMS error of 0.653246 meters. Thus the two-channel method coukd offer a fast, flexible, efficient, and economical solution to map topography of the ocean floor.AbstrakData kedalaman dapat digunakan untuk menghasilkan profil dasar laut, oseanografi, biologi, dan kenaikan muka air laut. Teknologi penginderaan jauh dapat digunakan untuk mengestimasi kedalaman perairan laut dangkal yang ditandai dengan kemampuan cahaya untuk menembus badan air. Salah satu citra yang mampu mengestimasi kedalaman tersebut adalah SPOT 6 yang memiliki tiga kanal visible dan satu kanal NIR dengan resolusi spasial 6 meter. Pada penelitian ini, Citra SPOT-6 yang digunakan adalah 22 Maret 2015. Citra terlebih dahulu dilakukan koreksi atmosferik dark pixel dengan membuat 30 poligon. Originalitas dari metode ini adalah membangun suatu korelasi antara nilai dark pixel kanal merah dan hijau dengan nilai kedalaman hasil pengukuran lapangan yang dilakukan pada 3 sampai dengan 9 Juni 2015. Algoritma diturunkan secara eksperimen yang terdiri dari thresholding yang berfungsi untuk memisahkan daratan dengan lautan dan fungsi korelasi. Fungsi korelasi diperoleh pertama-tama mengkorelasikan nilai pengamatan dengan masing-masing band, kemudian menghitung selisih nilai dark pixel maksimum dan minimum untuk kanal merah dan hijau yaitu 0,056 dan 0,0692. Selanjutnya, dibangun model dengan menggunakan dalil perbandingan sehingga diperoleh persamaan linier dalam dua kanal yaitu: Z(X1,X2) = 406,26 X1 + 327,21 X2 – 28,48. Hasil estimasi kedalaman, untuk skala 5 meter, estimasi yang paling efisien dengan Mean relatif error terkecil terjadi pada kedalaman perairan dangkal dari 20 sampai dengan 25 meter, sedangkan untuk skala 10 meter dari 20 sampai dengan 30 meter dan juga hasil estimasi kedalaman yang diperoleh mempunyai pola kemiripan atau dapat dikatakan mendekati kenyataan. Metode ini mampu mendeteksi kedalaman laut hingga 25 meter dan mempunyai RMS error yang kecil yaitu 0,653246 meter. Dengan demikian, metode dua kanal ini dapat menawarkan solusi cepat, fleksibel, efisien, dan ekonomis untuk memetakan topografi dasar laut.

OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.

OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.