최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기대한원격탐사학회지 = Korean journal of remote sensing, v.33 no.5 pt.3, 2017년, pp.889 - 900
백원경 (서울시립대학교 공간정보공학과) , 박숭환 (서울시립대학교 공간정보공학과) , 정남기 (서울시립대학교 공간정보공학과) , 권수경 (서울시립대학교 공간정보공학과) , 진원지 (서울시립대학교 공간정보공학과) , 정형섭 (서울시립대학교 공간정보공학과)
In this study, we analyzed the paper about NIR (Near-Infrared) remote sensing data and systematically summarized the research and application fields of NIR. To do this, we conducted a case study on the use of NIR in domestic journals, and SCI journals in the field of technology development for the l...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
능동형 센서는 어떠한 한계가 존재하는가? | 이 때문에 구름에 의한 영향이 적어 위성 취득률은 높 다. 하지만 하나의 밴드를 활용하기 때문에 취득할 수 있는 지상의 정보에 한계가 존재한다. 한편 수동형 센서의 경우 구름에 의한 영향을 많이 받기는 하지만 사람이 색을 인식하는 방법과 동일하게 지상의 특징을 관측할 수 있기 때문에 분석이 용이한 장점이 있다. | |
원격탐사 기법이 가지는 가장 큰 장점은? | 위성체 혹은 비행체 기반의 원격탐사 기법은 비접근 지역에 대해서 관측이 가능할뿐더러 넓은 영역에 대하여 신뢰도 높은 자료를 제공할 수 있다. 이로써 얻을 수 있는 광역의 공간적 특성 분포는 원격탐사 기법이 가지고 있는 가장 큰 장점이며 이러한 장점에 의하여 다양한 분야에 활발하게 적용되어 왔다(Queensland Dept. of Science, Information Technology and the Arts and Dept. | |
원격탐사 기법은 어떠한 센서로 나뉘는가? | of Natural Resources and, Mines, 2014). 이러한 원격탐사 기법은 크게 능동형 센서(Active sensor)와 수동형 센서 (Passive sensor)로 나뉜다. 능동형 센서의 경우 마이크로파 대역의 전자기파를 활용하여 광선의 투과율이 높다. |
Agam, N., W.P. Kustas, M.C. Anderson, F. Li, and C.M. Neale, 2007. A vegetation index based technique for spatial sharpening of thermal imagery, Remote Sensing of Environment, 107(4): 545-558.
Bastarrika, A., E. Chuvieco, and M. P. Martin, 2011. Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors, Remote Sensing of Environment, 115(4): 1003-1012.
Brown, M. E., 2015. Satellite remote sensing in agriculture and food security assessment, Procedia Environmental Sciences, 29: 307.
Galvao, L. S., J. R. dos Santos, D. A. Roberts, F. M. Breunig, M. Toomey, and Y. M. de Moura, 2011. On intra-annual EVI variability in the dry season of tropical forest: A case study with MODIS and hyperspectral data, Remote Sensing of Environment, 115(9) : 2350-2359.
Gamon, J. A., K. F. Huemmrich, R. S. Stone, and C. E. Tweedie, 2013. Spatial and temporal variation in primary productivity (NDVI) of coastal Alaskan tundra: Decreased vegetation growth following earlier snowmelt, Remote sensing of environment, 129: 144-153.
Gholizadeh, M. H., A. M. Melesse, and L. Reddi, 2016. A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques, Sensors, 16(8): 1298.
Gitelson, A.A., Y. Peng, J.G. Masek, D.C. Rundquist, S. Verma, A. Suyker, J.M. Baker, J.L. Hatfield, and T. Meyers, 2012. Remote estimation of crop gross primary production with Landsat data, Remote Sensing of Environment, 121: 404-414.
Gomez, C., R. Oltra-Carrio, S. Bacha, P. Lagacherie, and X. Briottet, 2015. Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery, Remote Sensing of Environment, 164: 1-15.
Hwang, Y. S. and J.-S. Um, 2015. Monitoring the Desiccation of Inland Wetland by Combining MNDWI and NDVI: A case study of Upo Wetland in South Korea, Journal of Korea Spatial Information Society, 23(6): 31-41 (in Korean with English abstract).
Jiang, Y. and Q. Weng, 2013. Estimating LST using a vegetation-cover-based thermal sharpening technique, IEEE Geoscience and Remote Sensing Letters, 10(5): 1249-1252.
Jung, G.S., S. Koo, and H.H. Yoo, 2011. Temperature Change Analysis for Land use Zoning Using Landsat Satellite Imagery, Journal of the Korean Society for Geo-Spatial Information System, 19(2): 55-61. (in Korean with English abstract).
Karami, J., A. Alimohammadi, and S. Modabberi, 2012. Analysis of the spatio-temporal patterns of water pollution and source contribution using the MODIS sensor products and multivariate statistical techniques, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4): 1243-1255.
Kim, K.T., J.W. Cho, and H.H. Yoo, 2011. Carbon storage Estimation of Urban Area Using KOMPSAT-2 Imagery, Journal of the Korean Society for Geo-Spatial Information System, 19(2): 49-54. (in Korean with English abstract).
Kumar, L., P. Sinha, S. Taylor, and A. F. Alqurashi, 2015. Review of the use of remote sensing for biomass estimation to support renewable energy generation, Journal of Applied Remote Sensing, 9(1): 097696.
Lagomasino, D., R. M. Price, D. Whitman, P. K. Campbell, and A. Melesse, 2014. Estimating major ion and nutrient concentrations in mangrove estuaries in Everglades National Park using leaf and satellite reflectance, Remote Sensing of Environment, 154: 202-218.
Lee, H., 2006. Investigation of SAR systems, Technologies and application fields by a Statical Analysis of SAR-related Journal Paler, Korean Journal of Remote Sensing, 22(2): 153-174 (in Korean with English abstract).
Liu, Q., S. Liang, Z. Xiao, and H. Fang, 2014. Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data, Remote Sensing of Environment, 145: 25-37.
Lobo, F.L., M. P. Costa, and E. M. Novo, 2015. Timeseries analysis of Landsat-MSS/TM/OLI images over Amazonian waters impacted by gold mining activities, Remote Sensing of Environment, 157: 170-184.
Meng, X., S. Lu, Y. Gao, and J. Guo, 2015. Simulated effects of soil moisture on oasis self-maintenance in a surrounding desert environment in Northwest China, International Journal of Climatology, 35(14): 4116-4125.
Peng, Y. and A.A. Gitelson, 2012. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content, Remote Sensing of Environment, 117: 440-448.
Queensland Department of Science, Information Technology and the Arts and Department of Natural Resources and Mines, 2014. Review of Remote Sensing Applications for Natural Resource Management, Eco Logical Australia, Australia.
Singh, R.K. and P. Shanmugam, 2014. A novel method for estimation of aerosol radiance and its extrapolation in the atmospheric correction of satellite data over optically complex oceanic waters, Remote Sensing of Environment, 142: 188-206.
Van Trung, N., J. H. Choi, and J. S. Won, 2013. A land cover variation model of water level for the floodplain of Tonle Sap, Cambodia, derived from ALOS PALSAR and MODIS data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(5): 2238-2253.
Viedma, O., I. Torres, B. Perez, and J. M. Moreno, 2012. Modeling plant species richness using reflectance and texture data derived from QuickBird in a recently burned area of Central Spain, Remote Sensing of Environment, 119: 208-221.
Wang, M., W. Yang, P. Shi, C. Xu, and L. Liu, 2014. Diagnosis of vegetation recovery in mountainous regions after the Wenchuan earthquake, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7): 3029-3037.
Wojtowicz, M., A. Wojtowicz, and J. Piekarczyk, 2016. Application of remote sensing methods in agriculture, Communications in Biometry and Crop Science, 11: 31-50.
Xie, Y., Z. Sha, and M. Yu, 2008. Remote sensing imagery in vegetation mapping: a review, Journal of Plant Ecology, 1(1): 9-23.
Yang, W., M. Wang, and P. Shi, 2013. Using MODIS NDVI time series to identify geographic patterns of landslides in vegetated regions, IEEE Geoscience and Remote Sensing Letters, 10(4): 707-710.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.