scholarly journals Indirect Assessment of Sedimentation in Hydropower Dams Using MODIS Remote Sensing Images

2019 ◽  
Vol 11 (3) ◽  
pp. 314 ◽  
Author(s):  
Rita Condé ◽  
Jean-Michel Martinez ◽  
Marco Pessotto ◽  
Raúl Villar ◽  
Gérard Cochonneau ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Trapping Efficiency ◽  
Retrieval Algorithm ◽  
Independent Field ◽  
Hydropower Dams ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sedimentation Processes

In this study, we used moderate resolution imaging spectroradiometer (MODIS) satellite images to quantify the sedimentation processes in a cascade of six hydropower dams along a 700-km transect in the Paranapanema River in Brazil. Turbidity field measurement acquired over 10 years were used to calibrate a turbidity retrieval algorithm based on MODIS surface reflectance products. An independent field dataset was used to validate the remote sensing estimates showing fine accuracy (RMSE of 9.5 NTU, r = 0.75, N = 138). By processing 13 years of MODIS images since 2000, we showed that satellite data can provide robust turbidity monitoring over the entire transect and can identify extreme sediment discharge events occurring on daily to annual scales. We retrieved the decrease in the water turbidity as a function of distance within each reservoir that is related to sedimentation processes. The remote sensing-retrieved turbidity decrease within the reservoirs ranged from 2 to 62% making possible to infer the reservoir type and operation (storage versus run-of-river reservoirs). The reduction in turbidity assessed from space presented a good relationship with conventional sediment trapping efficiency calculations, demonstrating the potential use of this technology for monitoring the intensity of sedimentation processes within reservoirs and at large scale.

scholarly journals Development of a remote sensing-based rice yield forecasting model

2016 ◽  
Vol 14 (3) ◽  
pp. e0907 ◽  
Author(s):  
Mostafa K. Mosleh ◽  
Quazi K. Hassan ◽  
Ehsan H. Chowdhury
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rice Yield ◽  
Decision Makers ◽  
Coefficient Of Determination ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

scholarly journals Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

2019 ◽  
Author(s):  
Juan Huo ◽  
Daren Lu ◽  
Shu Duan ◽  
Yongheng Bi ◽  
Bo Liu
Keyword(s):  
Satellite Data ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Ka Band ◽  
Retrieval Accuracy ◽  
Retrieval Algorithms ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Radar Measurements ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing were compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs were found to be underestimated by −1.10 ± 2.53 km and 49 % of CTH differences were within 1.0 km. Like the MODIS results, the AHI CTHs were underestimated by −1.10 ± 2.27 km and 42 % were within 1.0 km. Both the MODIS and AHI retrieval accuracy depended strongly on the cloud depth (CD). Large differences were mainly occurring for the retrieval of thin clouds of CD  1 km, the CTH difference decreased to −0.48 ± 1.70 km for MODIS and to −0.76 ± 1.63 km for AHI. MODIS CTHs greater than 6 km showed better agreement with the radar data than those less than 4 km. Statistical analysis showed that the average AHI CTHs were lower than the average MODIS CTHs by −0.64 ± 2.36 km. The monthly accuracy of both retrieval algorithms was studied and it was found that the AHI retrieval algorithm had the largest bias in winter while the MODIS retrieval algorithm had the lowest accuracy in spring.

Forest and Field Fire Search System Using MODIS Data

2007 ◽  
Vol 11 (8) ◽  
pp. 1043-1048 ◽  
Author(s):  
Eiji Nunohiro ◽  
◽  
Kei Katayama ◽  
Kenneth J. Mackin ◽  
Jong Geol Park ◽  
...  
Keyword(s):  
Large Scale ◽  
Image Database ◽  
System Configuration ◽  
Search System ◽  
Modis Data ◽  
Level Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Process Level

Tokyo University of Information Sciences receives MODIS (Moderate Resolution Imaging Spectroradiometer) data from NASA’s Terra and Aqua satellites, and provides the processed data to universities and research institutes as part of the academic frontier project. This paper considers the utilization of MODIS data for a system to search for fire regions in forests and fields. For the search system to be effective, the system must be able to extract the location, range and distribution of fires in forests and fields from a large scale image database quickly with high accuracy. In order to achieve high search response time and to improve the accuracy of the analysis, we propose a forest and field fire search system which implements a) a parallel distributed system configuration using multiple PC clusters, and b) MOD02, MOD03 and MOD09 process levels of MODIS data for input data which provide higher resolution and more accurate readings than the standard MOD14 process level data.

scholarly journals Volcanic Ash Retrieval Using a New Geostationary Satellite

2015 ◽  
Vol XL-7/W4 ◽  
pp. 67-74
Author(s):  
K. H. Lee ◽  
K. T. Lee
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Volcanic Ash ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
New Era ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aerosol Remote Sensing ◽  
Moderate Resolution Imaging Spectroradiometer

The paper presents currently developing method of volcanic ash detection and retrieval for the Geostationary Korea Multi-Purpose Satellite (GK-2A). With the launch of GK-2A, aerosol remote sensing including dust, smoke, will begin a new era of geostationary remote sensing. The Advanced Meteorological Imager (AMI) onboard GK-2A will offer capabilities for volcanic ash remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Based on the physical principles for the current polar and geostationary imagers are modified in the algorithm. Volcanic ash is estimated in detection processing from visible and infrared channel radiances, and the comparison of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every 15 min for volcanic ash for pixel sizes of 2 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously, both over water and land. The algorithm has been tested with proxy data generated from existing satellite observations and forward radiative transfer simulations. Operational assessment of the algorithm will be made after the launch of GK-2A scheduled in 2018.

scholarly journals ANALISIS TUTUPAN LAHAN DENGAN MENGGUNAKAN METODE BREAK FOR ADDITIVE SEASONAL AND TREND (BFAST) BERDASARKAN INDEKS VEGETASI

2017 ◽  
Author(s):  
Seminar Nasional Multidisiplin Ilmu 2017 ◽  
Ramos Lumban Tobing
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Trend Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Metode penginderaan jarak jauh (Remote Sensing) telah banyak digunakan dalam berbagai bidang termasuk diantaranya bidang tutupan lahan/vegetasi termasuk perkebunan. Produk dari penginderaan jauh tersebut banyak tersedia diantaranya NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Indeks) yang merupakan indikator proxy dari suatu lokasi atau kondisi tutupan lahan lokasi tersebut. Dari beberapa penilitian, NDVI telah banyak digunakan namun EVI masih belum banyak digunakan. Kami membandingkan pengaruh dari penggunaan NDVI dan EVI pada jumlah dan waktu perubahan yang terekam dengan menggunakan metode BFAST (Breaks For Additive Seasonal and Trend). Data yang digunakan adalah MODIS (Moderate Resolution Imaging Spectroradiometer)16 harian NDVI dan EVI berupa gambar komposit (06 April 2000 s.d. 16 November 2014) dari empat piksel (pixel 293,294,295 dan 296) disekitar menara fluks Aek Loba.Hasil penelitian menunjukkan bahwa EVI untuk pemantauan tutupan lahan di kawasan perkebunan tropis yang ditutupi oleh awan intens lebih baik dari NDVI itu. Meskipun demikian, penelitian lebih lanjut dengan meningkatkan resolusi spasial dari citra satelit untuk aplikasi NDVI sangat dianjurkan

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