scholarly journals Evaluating nonlinear maximum likelihood optimal estimation uncertainty in cloud and aerosol remote sensing

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
Luke M. Western ◽  
Jonathan C. Rougier ◽  
I. Matthew Watson ◽  
Peter N. Francis
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Optimal Estimation ◽  
Estimation Uncertainty ◽  
Aerosol Remote Sensing

Aerosol remote sensing over oceans

1995 ◽  
Vol 16 (10) ◽  
pp. 99-102
Author(s):  
Th. Heinemann ◽  
J. Fischer
Keyword(s):  
Remote Sensing ◽  
Aerosol Remote Sensing

scholarly journals Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing

2021 ◽  
Vol 13 (8) ◽  
pp. 1592
Author(s):  
Nikolai Knapp ◽  
Andreas Huth ◽  
Rico Fischer
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Canopy Height ◽  
Biomass Estimation ◽  
Inventory Data ◽  
Border Effects ◽  
Tree Crowns ◽  
Forest Inventory Data ◽  
Estimation Uncertainty ◽  
Voxel Space

The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.

Measuring and Monitoring Urban Sprawl of Jaipur City using Remote Sensing and GIS

2015 ◽  
Vol 6 (2) ◽  
pp. 46-65
Author(s):  
Pushpendra Singh Sisodia ◽  
Vivekanand Tiwari ◽  
Anil Kumar Dahiya
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Sustainable Development ◽  
Developing Countries ◽  
Maximum Likelihood ◽  
Natural Resources ◽  
Urban Sprawl ◽  
Remote Sensing And Gis ◽  
Direct Impact ◽  
Maximum Likelihood Classification

The world's population increased drastically and forced people to migrate from rural area to major cities in search of basic amenities. The majority of the World's population are already living in the major cities and it is continuously increasing. The increase in population forced the major cities to expand. Expansion of cities acclaimed more unplanned settlement that leads unplanned growth. This is a global phenomenon that has a direct impact on natural resources. It is the biggest challenge for urban planners to achieve sustainable development. Developing countries like India, where the population is increasing at an alarming pace, require more attention towards this problem. In this study, an attempt has been made to measure and monitor urban sprawl in Jaipur (Capital, State of Rajasthan, India). Built-up area with corresponding population has been analysed over a period of 41 years (1972-2013). Remotely sensed images of 1972-2013 (MSS, TM and ETM+) have been classified using Supervised Maximum Likelihood Classification (MLC) for digital image processing. Shannon's entropy has been used to quantify the degree of urban sprawl, and eight landscape metrics have also been used to quantify urban sprawl and its pattern.

Uncertainty and interpretation of aerosol remote sensing due to vertical inhomogeneity

2013 ◽  
Vol 114 ◽  
pp. 91-100 ◽  
Author(s):  
Peng-Wang Zhai ◽  
Yongxiang Hu ◽  
Chris A. Hostetler ◽  
Brian Cairns ◽  
Richard A. Ferrare ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aerosol Remote Sensing ◽  
Vertical Inhomogeneity

scholarly journals Microscopic Observations of Core‐Shell Particle Structure and Implications for Atmospheric Aerosol Remote Sensing

2018 ◽  
Vol 123 (24) ◽  
Author(s):  
Florin Unga ◽  
Marie Choël ◽  
Yevgeny Derimian ◽  
Karine Deboudt ◽  
Oleg Dubovik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Aerosol ◽  
Core Shell ◽  
Particle Structure ◽  
Shell Particle ◽  
Aerosol Remote Sensing

scholarly journals KLASIFIKASI HABITAT PERAIRAN DANGKAL MENGGUNAKAN LOGIKA FUZZY DAN MAXIMUM LIKELIHOOD PADA CITRA SATELIT MULTISPEKTRAL

2018 ◽  
Vol 10 (3) ◽  
pp. 667-681
Author(s):  
Muhammad Siddiq Sangadji ◽  
Vincentius Paulus Siregar ◽  
Henry Munandar Manik
Keyword(s):  
Remote Sensing ◽  
Fuzzy Logic ◽  
Maximum Likelihood ◽  
Satellite Imagery ◽  
Data Retrieval ◽  
Classification Algorithm ◽  
Benthic Habitat ◽  
Fuzzy Logic Algorithm ◽  
Accuracy Level ◽  
Sentinel 2A

ABSTRAKLogika fuzzy memiliki aplikasi di berbagai bidang, namun memiliki arti khusus untuk penginderaan jarak jauh. Logika fuzzy memungkinkan keanggotaan parsial, bagian yang sangat penting dibidang penginderaan jarak jauh, karena keanggotaan parsial diterjemahkan secara dekat dengan masalah piksel campuran. Penelitian ini bertujuan untuk menerapkan algoritma klasifikasi logika fuzzy untuk memetakan habitat dasar Perairan dangkal pada Citra Satelit SPOT 7 dan Sentinel 2A, menguji tingkat akurasinya dan membandingkan algoritma klasifikasi logika fuzzy dengan maximum likelihood. Pengambilan data lapang berlokasi di gusung Karang Lebar dan Karang Congkak, Kepuluan Seribu pada tanggal 6 Desember sampai dengan 10 Desember 2017. Keseluruhan hasil uji akurasi menunjukan bahwa algoritma logika fuzzy masih memiliki tingkat akurasi yang baik dibandingkan dengan algoritma maximum likelihood. Perbedaan ukuran pixel (resolusi spasial) dari citra satelit juga mempengaruhi hasil akurasi, dimana citra satelit SPOT 7 memiliki tingkat akurasi yang lebih besar dibandingkan dengan Sentinel 2A.ABSTRACTFuzzy logic has applications in various fields, but has special meaning for remote sensing. Fuzzy logic allows partial membership, a very important property in the field of remote sensing, since partial membership is translated closely to the problem of mixed pixels. The aim of this research is to apply fuzzy logic classification algorithm to map benthic habitat in SPOT 7 and Sentinel 2A satellite imagery, test its accuracy level and compare fuzzy logic classification algorithm with maximum likelihood. Field data retrieval located in Karang Lebar and Karang Congkak, Kepulauan Seribu on 6 December until 10 December 2017. The overall accuracy test results show that fuzzy logic algorithm still has a good accuracy level compared to the maximum likelihood algorithm. Differences in pixel size (spatial resolution) of satellite imagery also affect accuracy results, where SPOT 7 satellite imagery has greater accuracy then Sentinel 2A. 

Measuring and Monitoring Urban Sprawl of Jaipur City Using Remote Sensing and GIS

2019 ◽  
pp. 694-713
Author(s):  
Pushpendra Singh Sisodia ◽  
Vivekanand Tiwari ◽  
Anil Kumar Dahiya
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Sustainable Development ◽  
Developing Countries ◽  
Maximum Likelihood ◽  
Natural Resources ◽  
Urban Sprawl ◽  
Remote Sensing And Gis ◽  
Direct Impact ◽  
Maximum Likelihood Classification

The world's population increased drastically and forced people to migrate from rural area to major cities in search of basic amenities. The majority of the World's population are already living in the major cities and it is continuously increasing. The increase in population forced the major cities to expand. Expansion of cities acclaimed more unplanned settlement that leads unplanned growth. This is a global phenomenon that has a direct impact on natural resources. It is the biggest challenge for urban planners to achieve sustainable development. Developing countries like India, where the population is increasing at an alarming pace, require more attention towards this problem. In this study, an attempt has been made to measure and monitor urban sprawl in Jaipur (Capital, State of Rajasthan, India). Built-up area with corresponding population has been analysed over a period of 41 years (1972-2013). Remotely sensed images of 1972-2013 (MSS, TM and ETM+) have been classified using Supervised Maximum Likelihood Classification (MLC) for digital image processing. Shannon's entropy has been used to quantify the degree of urban sprawl, and eight landscape metrics have also been used to quantify urban sprawl and its pattern.

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