scholarly journals Spectral Nonlinearity of Silicon Photodiodes in Over-filled Illumination Condition

2021 ◽  
Author(s):  
Minoru Tanabe ◽  
Kenichi Kinoshita
Keyword(s):  
Illumination Condition

scholarly journals Modeling Rheotaxis based on Preference to Predict Fish Migration Behavior in A River

2016 ◽  
Vol 22 (1) ◽  
pp. 23
Author(s):  
Rina Febrina
Keyword(s):  
Information System ◽  
Laboratory Experiments ◽  
Fish Migration ◽  
Paired Comparisons ◽  
Migration Behavior ◽  
Lower Section ◽  
Illumination Condition ◽  
Plecoglossus Altivelis ◽  
Behavior Simulation ◽  
Gis Software

In this research, we attempt to determine preference of rheotaxis and estimated weight values in laboratory experiments using adult and juvenile ayu (Plecoglossus altivelis altivelis). We conducted paired comparisons of ayu distribution between the upper and lower sections of a test watercourse using several velocity conditions (10, 30, and 40 cm/s for juveniles; 20, 30, 50, 70, and 90 cm/s for adults). In upper watercourse sections, juvenile ayu preferred velocities of 30 cm/s and 40 cm/s, and adults preferred a velocity of 50 cm/s. Even when a highly preferred illumination condition of 4000 lux was present in the lower section, fish maintained a higher distribution in the upper section. We design a procedure to calculate rheotaxis preference and built it into our fish behavior simulation model on geographic information system (GIS) software. The model successfully predicted natural migration behavior of fish.

scholarly journals On The Estimation of Temporal Changes of Snow Water Equivalent by Spaceborne Sar Interferometry: A New Application for the Sentinel-1 Mission

2019 ◽  
Vol 67 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Vasco Conde ◽  
Giovanni Nico ◽  
Pedro Mateus ◽  
João Catalão ◽  
Anna Kontu ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Snow Water Equivalent ◽  
Temporal Variations ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Snow Layer ◽  
Illumination Condition ◽  
Interferometric Phase ◽  
Snow Water

Abstract In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms. The main advantage of the proposed methodology with respect to those based on the inversion of microwave SAR backscattering models is its simplicity and the reduced number of required in-situ SWE measurements. The maps, updated up to every 6 days, can attain a spatial resolution up to 20 m with sub-centimetre ΔSWE measurement accuracy in any weather and sun illumination condition. We present results obtained using the proposed methodology over a study area in Finland. These results are compared with in-situ measurements of ΔSWE, showing a reasonable match with a mean accuracy of about 6 mm.

scholarly journals The Effect of Topographic Correction on Forest Tree Species Classification Accuracy

2020 ◽  
Vol 12 (5) ◽  
pp. 787
Author(s):  
Chao Dong ◽  
Gengxing Zhao ◽  
Yan Meng ◽  
Baihong Li ◽  
Bo Peng
Keyword(s):  
Remote Sensing ◽  
Tree Species ◽  
Forest Tree ◽  
Species Classification ◽  
Illumination Condition ◽  
Topographic Correction ◽  
Forest Tree Species ◽  
Tree Species Classification ◽  
Inconsistency Rate ◽  
Rotation Model

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.

scholarly journals Evaluation and Normalization of Topographic Effects on Vegetation Indices

2020 ◽  
Vol 12 (14) ◽  
pp. 2290
Author(s):  
Rui Chen ◽  
Gaofei Yin ◽  
Guoxiang Liu ◽  
Jing Li ◽  
Aleixandre Verger
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Topographic Effects ◽  
Terrestrial Vegetation ◽  
Mountainous Areas ◽  
Near Infrared Reflectance ◽  
Illumination Condition ◽  
Vegetation Monitoring ◽  
Local Illumination

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

scholarly journals Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network

2020 ◽  
Vol 12 (21) ◽  
pp. 3493
Author(s):  
Dipanwita Dutta ◽  
Gang Chen ◽  
Chen Chen ◽  
Sara A. Gagné ◽  
Changlin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Invasive Plants ◽  
Plant Invasion ◽  
Urban Parks ◽  
Aerial Image ◽  
Illumination Condition ◽  
Contextual Variation ◽  
The Difference

Invasive plants are a major agent threatening biodiversity conservation and directly affecting our living environment. This study aims to evaluate the potential of deep learning, one of the fastest-growing trends in machine learning, to detect plant invasion in urban parks using high-resolution (0.1 m) aerial image time series. Capitalizing on a state-of-the-art, popular architecture residual neural network (ResNet), we examined key challenges applying deep learning to detect plant invasion: relatively limited training sample size (invasion often confirmed in the field) and high forest contextual variation in space (from one invaded park to another) and over time (caused by varying stages of invasion and the difference in illumination condition). To do so, our evaluations focused on a widespread exotic plant, autumn olive (Elaeagnus umbellate), that has invaded 20 urban parks across Mecklenburg County (1410 km2) in North Carolina, USA. The results demonstrate a promising spatial and temporal generalization capacity of deep learning to detect urban invasive plants. In particular, the performance of ResNet was consistently over 96.2% using training samples from 8 (out of 20) or more parks. The model trained by samples from only four parks still achieved an accuracy of 77.4%. ResNet was further found tolerant of high contextual variation caused by autumn olive’s progressive invasion and the difference in illumination condition over the years. Our findings shed light on prioritized mitigation actions for effectively managing urban invasive plants.

Illumination condition and mask bias for 0.15-μm pattern with KrF and ArF lithography

1999 ◽  
Author(s):  
Hiroki Tabuchi ◽  
Y. Shichijo ◽  
N. Oka ◽  
N. Takenaka ◽  
K. Iguchi
Keyword(s):  
Illumination Condition
Sign in / Sign up

Export Citation Format

Share Document