492 research outputs found
The proper conflict-free k-coloring problem and the odd k-coloring problem are NP-complete on bipartite graphs
No full textA proper coloring of a graph is proper conflict-free if every non-isolated vertex v has a neighbor whose color is unique in the neighborhood of v. A proper coloring of a graph is odd if for every non-isolated vertex v, there is a color appearing an odd number of times in the neighborhood of v. For an integer k, the PCF k-COLORING problem asks whether an input graph admits a proper conflict-free k-coloring and the ODD k-COLORING problem asks whether an input graph admits an odd k-coloring. We show that for every integer k >= 3, both problems are NP-complete, even if the input graph is bipartite. Furthermore, we show that the PCF 4-COLORING problem is NP-complete when the input graph is planar.
FOREST PARAMETER ESTIMATION FROM AIRBORNE LIDAR DATA IN RUGGED MOUNTAINOUS AREAS
No full textEnvironmental Science and EngineeringDuring the past decade, the procedure for quantification of forest parameters using LiDAR data has been rapidly improved. Among various forest parameters, biomass is the paramount in understanding the potentials productivity of forests. Various methods have been developed to estimate biomass at both plot and individual tree levels. In order to quantify biomass at the individual tree level, tree crown delineation must be conducted, which is sometimes challenging especially for multi-layer dense forests in rugged mountainous areas. In this study, Light Detection and Ranging (LiDAR) data were used to delineate tree crowns and estimate biomass in a mountainous forest. Firstly, a novel algorithm was proposed to identify individual tree crowns using the concept of live crown ratios based solely on LiDAR data. Then, above ground biomass (AGB) was estimated using machine learning approaches based on tree crowns delineated in the previous step. LiDAR-derived metrics related to forest parameters such as tree height and crown areas as well as topographic characteristics extracted based on the delineated tree crowns were used to estimate AGB. Three machine learning models— random forest, Cubist, and support vector regression—were evaluated for AGB estimation and
relative importance of input variables was examined.ope
Monitoring and Characterization of Arctic Sea Ice using Radar Altimetry
No full textDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)Launching CryoSat-2, which is a current radar altimeter mission for the monitoring of polar region
enables to produce monthly based sea ice thickness since April 2010. The Sea ice thickness cannot be
measured directly by satellite. Sea ice freeboard that is an elevation above sea level can be converted in to
sea ice thickness by assuming hydrostatic equilibrium. Sea ice leads (e.g., linear cracks in sea ices) are
regarded as sea surface tie points for the estimation of sea ice freeboard. Identifying the sea ice leads is
one of the core factors to retrieve sea ice thickness. The surface elevation is estimated by the use of
Threshold First maxima Retracker Algorithm (TFMRA) for a 40% threshold using CryoSat-2 L1b data
and the leads are detected by machine learning approaches such as decision trees and random forest. The
machine learning produces better accuracy for the sea ice thickness than previous simple thresholding
approach, validating EM-31, airborne sea ice thickness observations. A novel method to overcome
previous threshold based lead detection methods for identifying leads is developed, which is waveform
mixture algorithm that linear mixture analysis is applied in terms of waveforms. The waveform mixture
algorithm can distinguish leads without beam behavior parameters and backscatter sigma-0 but just use
waveforms, which is less affected by updating baseline for CryoSat-2. In addition to the development of
the algorithms, a scientific research is carried out. Causes for sea ice anomaly phenomenon in November
2016 is investigated. Eventually, sea ice the volume derived by thickness is used for the analysis of sea ice
extent minimum in November 2016 and suggest a new insight of sea ice minimum phenomenon. Unlike
sea ice extent, the sea ice volume is not a minimum in November 2016. However, since the base period
for sea ice volume is short, it is hard to mention climatology of sea ice volume.ope
??????????????? ????????? ??????????????? ?????? ?????? ??? ??????????????? ????????????
No full textDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)ope
Explainable deep learning-based characterization and forecasting of tropical cyclones through the synergistic fusion of satellite observations and numerical model data
No full textDepartment of Urban and Environmental Engineering (Environmental Science and Engineering)clos
All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning
No full textOpen AccessArticle
All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning
by Dongjin ChoORCID,Dukwon Bae,Cheolhee YooORCID,Jungho Im *ORCID,Yeonsu LeeORCID andSiwoo LeeORCID
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Anand Inamdar
Remote Sens. 2022, 14(8), 1815; https://doi.org/10.3390/rs14081815
Received: 9 February 2022 / Revised: 5 April 2022 / Accepted: 7 April 2022 / Published: 9 April 2022
(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)
Download PDF Browse Figures Citation Export
Abstract
A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model???s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R2) of 0.89???0.91 with a root mean square error (RMSE) of 1.11???1.39 ??C during the daytime, and an R2 of 0.96???0.97 with an RMSE of 0.59???0.60 ??C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R2 of 0.55???0.63 with an RMSE of 2.41???3.00 ??C during the daytime, and an R2 of 0.70???0.74 with an RMSE of 1.31???1.36 ??C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting
- …
