Studi tentang Agihan tutupan awan di atas wilayah daratan Indonesia berdasarkan data GMS=The Characteristics of Cloud Cover Distribution Over Indonesian Land Areas Based on GMS Data

ABSTRACT The aim of this study is to find out the characteristics of the spatio-temporal distribution of cloud cover over Indonesian land areas in relation to remote sensing activities. Data collection in this research has been carried out manually with the aid of a grid system being superimposed upon the GMS imageries, covering an area bounded by 10 N and 12.6 S latitudes and by 95 E and 1425E longitudes having 18 x 38 or 684 cells as subareas the size of which is about 1 15\u27 x 1 15\u27 or 139 sq.km/cell approximately. The original or main data source comprises four GMS imageries which have been taken at random every month during a four-year period (1981-1985). Data analysis has been performed iterative-interactively through a micro computer by applying techniques of factor analysis combined with the so-called \u27parallelepiped classifier". The results have been the identiruotion of 18 spatio-temporal cloud cover homogeneous areas for the entire Indonesian land areas with 0.7 "cell-class" correlation to limit the class number. The required supplementary data covering Landsat and SPOT imageries have been used to verify, calibrate and even improve the class profiles. This will lead to the forcasting of cloud cover probabilities, i.e. probabilities of remotely sensed data acquisition by considering predictive profiles/graphs, so that the planning of remote sensing activities/surveys will be more effective and efficient. Key words: spatio temporal distribution - GMS imageries - cell-class -- predictive profile

Development of an empirical model for chlorophyll-a and Secchi Disk Depth estimation for a Pampean shallow lake (Argentina)

Shallow Pampean lakes are located in the most productive plain of Argentina. They are highly variable in salinity, turbidity and surface area. Laguna Chascomús has been monitored as a representative example of them. We developed a linear model based on satellite images validated against field measurements (2001–2011 period). A vegetation index and Landsat Surface Reflectance (Band 4) produced the best correlations with chlorophyll-a (Chl-a) and Secchi Disk Depth (SDD), respectively. In a second instance, a retrospective analysis (1986–2013) was performed. As a result, significant positive trends were observed for SDD and Chl-a. In addition, both variables displayed trends related to rainfall and site depth.Fil: Bohn, Vanesa Yael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Carmona, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Tandil. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Tandil; ArgentinaFil: Rivas, Raúl Eduardo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Rectorado. Instituto de Hidrología de Llanuras - Sede Tandil. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto de Hidrología de Llanuras - Sede Tandil; ArgentinaFil: Lagomarsino, Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Diovisalvi, Nadia Rosalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Zagarese, Horacio Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin

Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"

Remote sensing images often suffer from cloud cover. Cloud removal is required in many applications of remote sensing images. Multitemporal-based methods are popular and effective to cope with thick clouds. This paper contributes to a summarization and experimental comparation of the existing multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion with poisson-adjustment method to fuse multi-sensor and multi-temporal images for cloud removal. The experimental results show that the proposed method has potential to address the problem of accuracy reduction of cloud removal in multi-temporal images with significant changes.Comment: This is a correction version of the accepted IGARSS 2017 conference pape