Cloud Detection and Cloud Removal of Satellite Image—A Case Study

According to UNCLOS, Indonesian marine territorial covers an area equal to around 2.8 million square kilometers inner archipelagic seas. Though the Indonesian water region is very wide, the resource within it is not yet been exploited optimally. Indonesia still has problems that have to be copped with, including identification of marine fishing ground areas. This report proposes a technology to make the fish-catching be more efficient and effective with the help of MODIS satellite image in term of Surface Temperature and chlorophyll-a computation. Data conversion from digital number to Water Brightness Temperature are performed. The determination of potential fishing ground area were conducted based on temperature and chlorophyll-a parameters which serve as an indicator of upwelling and observations were carried out on parameters which show this phenomenon. Based on the result, during May 2004 the upwelling process were not happened yet, and it seems to occur in June 2004. It showes by the decreasing of water temperature in South Coast of West Java particularly between the border of West Java and Central of Java. This phenomenon acts as an indicator for the raising of primer productivity and will takes about one month after upwelling to the bloom of phytoplankton.

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The Identification of Fishing Ground Area with MODIS Satellite Image (Case Study: South Coast of West Java)

ITB Journal of Engineering Science ◽

10.5614/itbj.eng.sci.2006.38.2.5 ◽

2006 ◽

Vol 38 (2) ◽

pp. 147-158 ◽

Cited By ~ 2

Author(s):

Dudung Muhally Hakim ◽

Ketut Wikantika ◽

Nengah Widiadnyana ◽

Asmi M. Napitu ◽

Soni Darmawan

Keyword(s):

Satellite Image ◽

South Coast ◽

Fishing Ground ◽

West Java

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Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

Forum Geografi ◽

10.23917/forgeo.v19i1.4567 ◽

2017 ◽

Vol 19 (1) ◽

pp. 1

Author(s):

Beny Harjadi

Keyword(s):

Remote Sensing ◽

Field Survey ◽

Critical Level ◽

Satellite Image ◽

Geographic Information ◽

Level Data ◽

The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

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USO DO SOLO E DEGRADAÇÃO AMBIENTAL: estudo de caso da bacia do Mata Fome em Belém, Pará

InterEspaço Revista de Geografia e Interdisciplinaridade ◽

10.18764/2446-6549.v2n7p162-178 ◽

2017 ◽

Vol 2 (7) ◽

pp. 162

Author(s):

Marcos Jonatas Damasceno da Silva ◽

Luziane Mesquita da Luz

Keyword(s):

Urban Space ◽

Environmental Degradation ◽

Health Risks ◽

Urban Areas ◽

Satellite Image ◽

Land Uses ◽

Vegetation Water ◽

The Relationship ◽

Soil Changes

São diversos os problemas presentes nos espaços das cidades brasileiras, principalmente nos grandes espaços urbanos. Um desses problemas é a degradação do meio ambiente decorrente de intervenções não planejadas nesses espaços. Nesse sentido, este trabalho tem o propósito de analisar a relação entre a produção do espaço urbano, que atribui diferentes usos ao solo e a degradação do meio ambiente na Bacia do Mata Fome em Belém, Pará. Além disso, foi realizado um mapeamento do uso do solo da área de estudo, onde foi utilizada a imagem do satélite Ikonos de 2006. Os resultados deste trabalho evidenciaram que a produção do espaço urbano na Bacia do Mata Fome e os diversos usos do solo, provocaram degradação ambiental, por desencadearem a destruição da cobertura vegetal, poluição da água e do solo, mudanças na topografia dos terrenos, inundações, riscos à saúde, entre outros danos.Palavras-chave: Meio ambiente; Urbanização; Bacia hidrográfica; Poluição.USE OF SOIL AND ENVIRONMENTAL DEGRADATION: a case study of Mata Fome basin in Belém, ParáABSTRACTThere are several problems present in the spaces of brazilian cities, especially in large urban areas. One such problem is the degradation of the environment due to unplanned interventions in these spaces. In this sense, this work aims to analyze the relationship between the production of urban space that assigns different uses to soil and environmental degradation in the Mata Fome Watershed in Belém, Pará. In addition, we carried out a mapping of the use of soil of the study area where the satellite image Ikonos 2006. The results of this study indicated that the production of urban space in Mata Fome Watershed and various land uses, caused environmental degradation was used to trigger the destruction of vegetation, water pollution and soil changes in the topography of the land, floods, health risks and other damage.Keywords: Environment; Urbanization; Hydrographic watershed; Pollution.USO DEL SUELO Y DEGRADACIÓN AMBIENTAL: estudio del caso de la cuenca del Mata Fome en Belém, ParáRESUMEN Hay varios problemas presentes en los espacios de las ciudades brasileñas, especialmente en las grandes áreas urbanas. Uno de estos problemas es la degradación del medio ambiente debido a las intervenciones no planificadas en estos espacios. En este sentido, este trabajo tiene como objetivo analizar la relación entre la producción del espacio urbano, que asigna a los diferentes usos del suelo y la degradación del medio ambiente en la Cuenca del Mata Fome en Belém, Pará. Además, se realizó un mapeo del uso del suelo de la zona de estudio, donde la imagen de satélite Ikonos 2006. Los resultados de este estudio indicaron que la producción del espacio urbano en la Cuenca del Mata Fome y diversos usos de la tierra causado la degradación ambiental se utilizó para desencadenar la destrucción de la vegetación, la contaminación del agua y los cambios de suelo en la topografía del terreno, inundaciones, riesgos para la salud, y otros daños.Palabras clave: Medio ambiente; Urbanización; Cuenca hidrográfica; Contaminación.

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CLOUD REMOVAL FROM SENTINEL-2 IMAGE TIME SERIES THROUGH SPARSE RECONSTRUCTION FROM RANDOM SAMPLES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-469-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 469-473

Author(s):

D. Cerra ◽

J. Bieniarz ◽

R. Müller ◽

P. Reinartz

Keyword(s):

Time Series ◽

Satellite Image ◽

Reconstruction Error ◽

Sparse Reconstruction ◽

Shadow Mask ◽

Spectral Evolution ◽

Random Samples ◽

Restoration Algorithm ◽

Cloud Removal ◽

Sentinel 2

In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

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Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal

10.5194/egusphere-egu21-14320 ◽

2021 ◽

Author(s):

Boli Yang ◽

Yan Feng ◽

Ruyin Cao

Keyword(s):

Quality Assessment ◽

Land Surface ◽

Missing Values ◽

Similarity Index ◽

Visual Assessment ◽

New Method ◽

Landsat Data ◽

Cloud Detection ◽

Cloud Removal ◽

Ultimate Objective

Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image&#160;requires the cloud and cloud shadow mask.&#160;In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (&#8220;QA_Auto-PCP&#8221;). Experimental results, from both&#160;actual and simulated Landsat cloud images, show that&#160;QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method&#160;substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover,&#160;Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that&#160;Auto-PCP can further popularize cloud removal and advance the application of Landsat data. &#160;&#160;&#160;&#160;&#160;Keywords: Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN

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Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey

Environmental Monitoring and Assessment ◽

10.1007/s10661-017-6254-2 ◽

2017 ◽

Vol 189 (11) ◽

Cited By ~ 5

Author(s):

Alkan Günlü ◽

Sedat Keleş ◽

İlker Ercanlı ◽

Muammer Şenyurt

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Satellite Image ◽

Area Index

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Development of a neural network model for cloud fraction detection using NASA-Aura OMI VIS radiance measurements

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-1649-2013 ◽

2013 ◽

Vol 6 (1) ◽

pp. 1649-1681

Author(s):

G. Saponaro ◽

P. Kolmonen ◽

J. Karhunen ◽

J. Tamminen ◽

G. de Leeuw

Keyword(s):

Neural Network ◽

Satellite Image ◽

Cloud Fraction ◽

Electromagnetic Spectrum ◽

Cloud Detection ◽

Ozone Monitoring Instrument ◽

Neural Network Approach ◽

Imaging Spectrometer ◽

Simultaneous Application ◽

The Neural Network

Abstract. The discrimination of cloudy pixels is required in almost any estimate of a parameter retrieved from a satellite image in the ultraviolet (UV), visual (VIS) or infra-red (IR) parts of the electromagnetic spectrum. Also, the distincion of clouds within satellite imagery and the distribution of their micro-physical properties is essential to the understanding of radiative transfer through the atmosphere. This paper reports the development of neural network algorithms for cloud detection for the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained by training mathematical neural networks with simultaneous application to OMI and Aqua-MODerate Resolution Imaging Spectrometer (MODIS) data. The neural network delivers cloud fraction estimates in a fast and automated way. The developed neural network approach performs generally well in the training. Highly reflective surfaces, such as ice, snow, sun glint and desert, or atmospheric dust mislead the neural network to a wrong predicted cloud fraction.

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